GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Authors: Lakshya A Agrawal
Shangyin Tan
Dilara Soylu
Noah Ziems
Rishi Khare
Krista Opsahl-Ong
Arnav Singhvi
Herumb Shandilya
Michael J Ryan
Meng Jiang
Christopher Potts
Koushik Sen
Alex Dimakis
Ion Stoica
Dan Klein
Matei Zaharia
Omar Khattab
Venue: ICLR 2026 Oral (2026)
Keywords: prompt optimization
natural language
reflection
large language models
agent design
agent discovery
code optimization
compound AI systems
genetic
language based learning
evolutionary algorithms
Primary area: foundation or frontier models, including LLMs
TL;DR: GEPA uses natural language reflection to optimize prompts, outperforming GRPO and MIPROv2 while needing far fewer rollouts.

Abstract: Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language often provides a much richer learning medium for LLMs, compared to policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error.
    Given any AI system containing one or more LLM prompts, GEPA samples trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain.
    Across six tasks, GEPA outperforms GRPO by 6 percentage points on average and by up to 19pp, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10 percentage points (e.g., +12pp on AIME-2025), and demonstrates promising results as an inference-time search strategy for code optimization. We release our code at https://github.com/gepa-ai/gepa.

Published as conference paper ICLR 2026
GEPA: REFLECTIVE PROMPT EVOLUTION CAN OUTPER- FORM REINFORCEMENT LEARNING
Lakshya A Agrawal', Shangyin Tan', Dilara Soylu?, Noah Ziems*, Rishi Khare', Krista Opsahl-Ong', Arnav Singhvi Herumb Shandilya', Michael J Ryan', Meng Jiang'. Christopher Potts", Koushik Sen', Alexandros G. Dimakis 1. lon Stoica', Dan Klein', Matei Zaharia Omar Khattab
UC Berkeley
Stanford BespokelLabs.ai
Notre Dame
Databricks
MIIT
ABSTRACT
Large language models (LLMs) are increasingly adapted to downstream tasks via rein- forcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable na- ture of language often provides much richer learning medium for LLMs, compared to policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto) prompt optimizer that thoroughly incorporates natural language re- flection to learn high-level rules from trial and eгror. Given any AI system containing one or more LLM prompts, GEPA samples trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As result of GEPA's design, it can often turn even just a few rollouts into large quality gain. Across six tasks, GEPA outperforms GRPO by percentage points on average and by up to 19pp. while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10 percentage points (c.g.. +12pp on AIME-2025), and demonstrates promising results code optimization. We release our code at https: thwbbbb..ttt....//// com/ gepa- ai /gepa
inference-time search strategy for
HotpotQA, Qwen3
IFBench, Qwen3
60.0
62.5
Optimization Methed
Suselin
Vi PR02
80
Optinitaation Methed
Saseline
MIPROv2
38,5
ORFO
GEPO
38,0
75
GEPA
55.0
Score
37.5
70
50.0
37.0
45.0
42.5
36,0
0
5000
1.0000
15000
20000
25000
0
5000
10000
15000
20000
25000
Number Rollouts
Number Rollouts
(a) HotpotQA, Qwen3 8B
(b) IFBench, Qwen3 8B
Figure 1: A comparison of learning behavior of the GEPA prompt optimizer against state-of-the-an prompt optimizer (MIPROv2) and GRPO (24,000 rollouts). As more rollouts are sampled, the prompt optimizers can learn much more quickly than GRPO. GEPA substantially outperforms both GRPO and MIPROv2 in final score. The Test-set star markers demonstrate the performance gap in a held-out set of questions.

---

Published as a conference paper at ICLR 2026
1 INTRODUCTION
Large language models (LLMs) have enabled development of agents and systems that combine fuzzy natural-language specifications with tools like retrieval and code execution. This raises the question of how LLMs should be optimized for downstream performance. One popular approach is Reinforcement Learning with Verifiable Rewards (RLVR), e.g. with Group Relative Policy Optimization (GRPO) (Shao et al. al.| 2002), which treats success metries as end-of-rollout scalar rewards used estimate policy gradients (Lambert 2025). While these RL approaches are effective, they typically require tens of thousands of rollouts in prac- tice to fit new tasks. For example, recent works leveraging GRPO typically use up to hundreds of thousands
of rollouts for training (Chen et al. 2025b Wu et al. 2025b Zhang et al. 2025 Jin et al.] [025 Si et al.)
2025 Wang et al., 2025aj [Cen et al. 2025a Sha et al.) 2025 Lin et al. 2025a; Peng et al.) 2025 Song
et al. 2025). This sample inefficiency can quickly become serioss bottleneck: many downstream LLM
applications invoke expensive tool calls, have limited inference budget for sampling from the LLM itself, or simply cannot finetune the weights of the largest or best-performing L.Mss.
We observe that rollouts sampled from even highly sophisticated LLM systems can be serialized into traces of natural language: they contain nothing but the instructions of each LLM module, the resulting LLM reasoning chains, tool calls, and potentially the internal workings of the reward function (e.g.. compiler error messages, before they are collapsed into scalar rewards). Because such serialized trajectories are readily understood by modern LLMs, we argue that algorithms that learn deliberately in natural language by reflecting on these trajectories can make more effective use of the strong language priors that LLMs have, compared with standard RL approaches.
We introduce GEPA (Genetic-Pareto), reflective prompt optimizer for compound AI systems that merges textual reflection with multi-objective evolutionary search. GEPA iteratively mutates prompts using natural language feedback drawn from new rollouts. In each mutation, the candidate prompt is derived from an ancestor, accumulating high-level lessons derived from observations and LLM feedback. To avoid local optima that afflict greedy prompt optimization, GEPA maintains a Pareto front: instead of evolving only the global best prompt, it stochastically explores the top-performing prompts for each problem instance. This diversification enables robust generalization and mitigates getting stuck in local minima.
We evaluate GEPA across multi-hop reasoning (HotpotQA: Yang al. 2018), Math (AIME, LiveBench- Math; Balunović et al. )2025); While et al. (2025)) instruction following (IFBench; Pyatkin et al. 20256). privacy-aware delegation (PUPA; Li et al. 2025a), and serrival--uummeter verification (HoVer; Jiang al. 2020), using both open (Qwen3 8B: Yang et al. 2025 Team 2023) and proprietary (GPT-4.1 Mini; OpenAl
2025 models. We find that GEPA generalizes well and is highly sample-efficient: on Qwen3 8B, it outper- forms GRPO (24k rollouts) by up to 19pp while using up to 35x fewer rollouts, with an average gain of +6pp across six tasks, GEPA also surpasses the prior ofate-of-the-art, MIPROv2 (Opsahl-Ong al. 2024 on all benchmarks and models, achieving +13pp aggregate gains, over double MIPROv2's 45.6pp.
Qualitatively, GEPA-learned prompts are quite rich. Figure 2 shows excerpts from prompt crafted for the query- ceeettcreation module of a multi-hop question answering system used in HotpotQA, and Figure 26 shows that even a single reflective update often yields large gains. These results highlight that reflective prompt evolution with language feedback enables improved sample efficiency and robust generalization, offering a practical approach to optimizing complex Al workflows in data- or budget-constrained environments. We also demonstrate GEPA as an inference-time search strategy for code optimization on NPUEval (Kalade & Schelle 2025) & KernelBench (Ouyang et al. (025)) in Sec and for adversarial prompt search in See F
2 PROBLEM STATEMENT
We follow related work in defining a compound Al system as any modular system composed of one or more language model (LLM) invocations, potentially interleaved with external tool calls, orchestrated through ar-
2

---

Published as conference paper ICLR 2026
Seed Prompt for Second-Hop Multi-Hop QA System
Given the leeds question, summary_ 1. produce the Belds query.
GEPA's Optimized Prompt for Second- -Hop of Multi-Hop QA System, GPT-4.1 Mini
You will given two input fields: ques tion and summary_ Your task: Generate a new search query (query) optimized for the second hop of multi hop retrieval system.
original user question typically complex and requires information multiple
answer.
The first hop query is the original question (used retrieve initial documents).
Your goal: generate query to retrieve documents found in first hop but necessary answer the question completely.
Input Understanding: question is the original multi question posed by the sumary-
summary of information from
document retrieved in the first hop, which partially addresses the question.
Purpose and Context:
Your generated query aims lind missing of information needed fully the question....
The query must retrieve relevant documents NOT found in first hop for final answer extraction.
Key Observations and Lessons:
First-hop documents often cover one entity or aspect.
Remaining documents often involve connected concepts mentioned sumnary_1 not explicitly question.
query should target these missing, but linked, documents. Avoid merely paraphrasing the original question restating known facts from sunnary_..
broader or related entitiessloncopts might provide the crucial missing information. For example:
total population wider region, your query should larget that wider region (egg. "Madeira archipelago population in 2011").
sunnary_ describes population small civil parish.
question
summary_ covers song and the question asks for the album, target album level documents.
How to Build the Query:
Identify entities or topics mentioned summary. 11 that are related but different from first-hop documents.
Reframe the query to explicitly mention these broader or related entities connected the original question.
Include relevant key context question maintain specificityy
missing piece.
goal
or complement was retrieved initially.
Practical Strategy:
Read the sumnary.1 carefully to references bigger contexts or other entities not covered in the first hop.
Ask: "What entity aspect does this summary hint that could answer the original question but was not found yet?"
Formulate precise, focused factual query largeting that entity or concept to retrieve the missing documents.
Output:
query clear, question keyword phrase designed eflicient retrieval second hup documents.
Ensure the query relates logically the original question while targeting the bronder complementary knowledge identified sumnary.1.
Do not include the original question simply rephrase Do not duplicate information already well-covered by the first hop retrieval.
Figure This figure shows an example prompt generated by GEPA for the second-hop document retrieval to be performed in multi-hou hopp question-anse answer system, along with the seed prompt it started with. Appendix compares GEPA's prompts for tasks with prompts generated by MIPROv2.
bitrary control flow. This definition subsumes a broad class of real-world LLM- based Al systems, including agents, multi-agent systems, and general-purpose scaffolding techniques like ReAct (Yao et al. 2023 Ar-
chon (Saad-Fall Falcon et al. 2025), etc. Following Soylu et al. (2024); Khattab et al. (2024: Opsahl -0n et al.
y Tan al. (2025) formalize such system + (M, C. )), where M (M,
M
3

---

Published as a conference paper ICLR 2026
denotes language modules, C' specifies control flow logic, and X, J' are global input/output schemas. Each module M, = , 8,, X; 34) is an LLM subcomponent: T, is its (system) prompt including instructions and few-shot demonstrations; 0, the underlying model weights; X, 3 are input/output schemas. At runtime, C orchestrates the sequencing and invocation of modules e.g.. passing outputs from one module to another, invoking modules conditionally, or leveraging tool APIs. This way, C' can invoke different modules in any order multiples of times.
Given +, let 4 = (TI..... M ) denote the collection of all module prompts and O. = (01...... Mwi
the set of module weights. The learnable parameters are thus (TI, O) o For task instance (x, m)mmm)mw) where x maps to the input schema X' and m contains evaluator metadata (e.g., gold answers, evaluation rubries, code unit tee system induces an output y = (a;; (II, O) A metric μ y x M - [0, 1] then measures the output quality of with respect to metadata (for example by calculating, exact match, Fl, pass rate, etc.). The optimization problem is thus defined as follows, where T is a task distribution.:
III* O = arg max F(,,m)~T (a(;(a; 0)4,, m)] (II,))
We adopt this general problem formulation, allowing updates to both prompts and weights of language modules, to enable comparisons between optimization algorithms that operate different parameter spaces (e.g., GEPA vs. GRPO).
Sample-Efficient Optimization. In many real-world scenarios, cellouss-concrette invocations of plus evaluation by дı-are often computationally, monetarily, timewise expensive. The optimizer is thus limited to at most B rollouts on a dataset Dirain = {2, ) with full access to μ. The goal to identify parameters (II", O*) that maximize held-out performance, subject not exceeding the rollout budget B:
III", @²) = arg max ((z,m)-T [u( (1; (II, 9) m)] s.t. #rollouts S B. (11,9)
(2)
The core challenge, then, is: How do we extract maximal learning signal from every expensive rollout to enable effective adaptation of complex, modular Al systems in low-data or budget-constrained settings?
3 GEPA: REFLECTIVE PROMPT EVOLUTION
We introduce GEPA (Genetic-Parcee Pareti), sample-efficient optimizer for compound AI systems motivated by three core principles: genetic prompt evolution (Section B) reflection using natural language feedback (Sec- tion (3). and Pareto-based candidate selection (Section B.I. Figure gives overview of GEPA and the full GEPA algorithm is formalized in Figure 5 GEPA receives the following inputs: A system Φ instan- tiated with simple prompts be optimized, training dataset Dtrain (consisting of task instances (ar, m) as described in Section 2). the standard evaluation metric μ for the task, feedback function μ (introduced in Section 3 and the total rollout budget B. Note that GEPA evolves only the set of prompts, denoted as ILg. whereas the underlying LLM weights, denoted by O₂ remains fixed.
Genetic Optimization Loop: Given an Al system क. the goal is to identify parameters HL that maximize task performance. GEPA begins with a candidate pool P containing only the base system, where each can- didate is concrete instantiation of (II, θ frozen) 4. then enters an optimization loop, repeatedly proposing new candidates until the evaluation budget is exhausted. Candidates are derived from existing ones via re- flective mutation or crossover, guided by feedback from rollouts, with each inheriting learning signals from its parents and its own rollout so that GEPA accumulates knowledge along the genetic tree. In each iteration, GEPA (i) selects promising candidates, (ii) proposes and evaluates a variant on a minibatch of tasks, and (iii) if it outperforms its parent(s), adds it to P with ancestry records and evaluate D Dparetos the validation set used for selection. After the budget is exhausted, GEPA returns the candidate with the best aggregate performance on D perrtoo
4

---

Published as a conference paper ICLR 2026
Intialiee
Discard
Performance
Perform
White Budget
Eval on all banks
Yeur
Propose New Candidate
Prews
Prompi
g
System
Reflect and Prompt
Stralopicalyy each rosdule, efterr
Candidotc
Otnain sedbassee using
madiate has select from Py and vise- venta.
Rollouts: Exccuto P. on
Dash
Sample
Gest cancidate Jur
Fittered Pool (Pantoo Prantier)
X
X
X
X
Sample candidate
Sample merge
Docc
X
X
X
X
X
Scores Matrix
Figure 3: GEPA proposes a new candidate in every iteration by improving existing candidates using one of the two strategies (Reflective Prompt Mutation (Section B] or System Aware Merge (Appendix [DII), first evaluating them on a minibatch, and improved, evaluating on a larger dataset. Instead of selecting the best performing candidate mutate always, which can lead local-optimum, GEPA introduces Pareto- based candidate sampling (Section 3.T.. which filters and samples from the list of best candidates per task, ensuring sufficient diversity. Overall, these design decisions allow GEPA be highly sample-efficien while demonstrating strong generalization.
Reflective Prompt Mutation: Natural language traces generated during the execution of compound AI system offer rich visibility into the behavior and responsibilities of cach module, as they capture the intermediate inferences and underlying reasoning steps. When these traces paired with the final outcome of the system (e.g., success or failure), they provide substantial diagnostic value, allowing practitioners to trace errors successes back to specific decisions made at the module level. LLMs leverage these traces via reflection to perform implicit credit assignment, attributing responsibility for the final outcome the relevant modules. This process of reflection modules, making large and effective updates to the whole system's 's behavior.
then be used make targeted updates individual
Given candidate mutate in the current iteration of the optimization loop (stochastically selected from the Pareto-frontier, see Section 3.1 below), GEPA executes the selected candidate on a stochastically sampled minibatch of input queries from the trainset, tracing the program's 's execution. From the execution traces, GEPA extracts the module's 's inputs, outputs, and reasoning, and calls the functicn
function μt, which returns a numeric score and text feedback including details about the evaluation (like compiler error messages, failed rubrics, etc.). GEPA selects the module (among the JM| modules that the language program contains) to be updated based on policy (round-robin), and a reflection LM is then shown the (current prompt, language program trajectory, score, feedback) with the task to reflectively attribute successes failures to prompt elements and propose revised instructions. The updated module, with the rest of the language program, evaluated again the minibatch, and if the score improves, then the program is added the candidate pool. The meta meta-prommp for reflective prompt updates shown Appendix C and the full algorithm presented in Algorithm
5

---

Published as a conference paper at ICLR 2026
Evaluation traces as diagnostic signals: The text that LLMs produce the execution trace of the Al system. The text that the environment produces to compute the reward (e.g. compiler error messages before giving reward 0) is the evaluation trace. Beyond reflection on execution traces, we identify a second valuable source of diagnostic information in the evaluation traces. Many evaluation metrics apply rich strategies (e.g., code evaluation may involve compilation, execution, and profiling), producing natural language traces before computing scalar reward. We propose leveraging these evaluation traces for reflective credit assignment and targeted prompt updates. GEPA achieves this by extending rewards j into a feedback function μ that extracts textual traces during evaluation and returns them with the final score as feedback_ text. When available, such feedback can even be module-specific (e.g. in multi-hop systems the evaluator may provide feedback after each hop). In practice, there are domains where human-graders are able rate the Al system's responses, along with providing detailed feedback justifying their scalar ratings. When available, Dtrain can be augmented with such numan--rittee explanations for each instance; during reflection, and GEPA can consume these explanations as auxiliary feedback_ted text to guide targeted prompt updates, even when natural- language feedback from rollouts is limited or unavailable.
3.1 PARETO-BASED CANDIDATE SELECTION
GEPA is a highly modular algorithm that supports various strategies for candidate selection, with the choice of strategy governing the exploration-explor exploitationn tradcoff. A naive approach is to always select the best- performing candidate, but this often traps the optimizer in a local optimum: once a dominant strategy is found, it becomes difficult to surpass, and the optimizer exhausts its budget without learning new, potentially better strategies. Figure Ba illustrates this behavior: after finding one new strategy (the first child node), the search repeatedly attempts refine it, fails improve, and ultimately depletes the budget.
To address this, GEPA employs a Pareto-based "illumination" strategy (Mouret & Clune 2015). shown in Algorithm 2 For each training instance, GEPA records the highest score across all candidates, forming a Pareto frontier. Candidates that achieve the best score on at least one task are retained, while strictly dominated ones are pruned. From this pruned set, GEPA stochastically samples candidate, weighting probabilities by how many tasks each candidate leads. This strategy helps GEPA escape local optima without inflating the search, efficiently balancing exploration and exploitation by focusing resources on candidates that embody "winning" strategies within the optimization budget.
4 EVALUATION
We adopt a standard train/validation/test split. Optimizers have full access to the train split, including text and labels, for program tuning. Although optimizers may monitor the performance of candidate parameters (like model checkpoints) by tracking scores on the validation set (to implement early stopping, for example), direct access to the content of validation instances is restricted. We evaluate on six benchmarks-
2025 (Balunović et al., (022)), LiveBench-Matt (White et al.) 2025), HotpotQA (Yang et al.) 2018). IF- Bench (Pyatkin et al.) 20255). HoVer (Jiang et al., 2020), and PUPA (Li al.) (025a) eachh paired with existing compound AI systems and feedback functions. Experiments use Qwen3 8B (Yang et al. (025) and GPT-4.1 Mini (OpenAl (025) with standardized inference settings, and compare against state-of art optimizers MIPROv2 (Opsahl-Ong et al., 2024), Trace (with its OptoPrime optimizer) (Cheng al. 2024). TextGrad (Yuksekgonul et al. 2025), and GRPC (Shao et al. 2024). Appendix G provides further
details on benchmarks, systems, and feedback functions (Subsection G.I models and inference settings
(Subsection G.2): 2 monetary cost to run the experiments (Subsection
G.3 and optimizer configurations
We use LoRA for GRPO due to its low cost and succesful adoption with GRPO (Wang et al. 2025b
Xu ct al.
2025b Li et al. 2025b Yue et al. 22255) Sun et al. 2025 Hayou et al. [2025] Zhao et al. 2025 Teknium ct al. 2024
Zhao et al. [2024] (Sidahmed al. 2024) Additionally,
explore full- parameerrrtrr finetuning. Figure 10 shows similar
result comparing GEPA to GRPO with full finetuning.
6

---

Published as conference paper ICLR 2026
(Subsection G.4 Table
[
Table
2 and Figure 9 summarize our main results, from which we derive the
following observations:
Table I: Benchmark results for different optimizers with Qwen3 8B. GEPA and GEPA+Merge achieve better performance than GRPO with far fewer rollouts on all benchmarks except AIME. For example, for IFBench, GEPA found optimal prompts after just 678 rollouts achieving 38.61 66,,,, outperforming GRPO's test set score of 35.88% with 24,000 rollouts.
Qwen3 8B
HotpotQA
IF FeBecch
Hover
PUPA
AIME-2025
LiveBench-Math
Aggregate
Improvement
Bascline
42.33
36.90
35.33
80.82
27.33
48.70
45.23
GRPO
43.33
35.88
38.67
86.66
38,00
51.26
48.91
+3.68
MIPROv2
55.33
36.22
47.33
81.55
20.00
46.60
47.84
+2.61
GEPA
62.33
38.61
52.33
91.85
32.00
51.95
54.85
+9.62
Merge
64.33
28.23
51.67
86.26
32,00
51.95
52.40
+7.17
Total optimization budget (# rollouts)
GEPA (+Merge)
6871
3593
7051
2426
1839
1839
3936
GRPO
24000
24000
24000
24000
24000
24000
24000
Table 2: Benchmark results for different optimizers evaluated GPT-4.1 Mini. As prompt-optimization system, GEPA works off-the-shelf on closed-source models as well, outperforming state-of-the-ard prompt optimizers including MIPROv2 (in 2 settings: Instruction-only optimization ("MIPROv2-No- Demos") as well as joint instruction and few-shot optimization ( ("MIPROv2")), Trace (with its OptoPrime optimizer), and TextGrad. Additionally, GEPA-optimized prompts demonstrate strong cross- cross-model generalization: "GEPA- Qwen-Opt opttimzed entirely for (and using) the weaker Qwen3-8B achieves a +9% gain when eval- uated on GPT-4.1-Min without modification, notably outperforming all baselines (MIPROv2, TextGrad, Trace) that optimized directly for (and using) 1PT-4.1-Mini.
GPT-411 Mini
HotpotQA
IFBench
Hover
PUPA
AIME-2025
LiveBench-MMtth
Aggregate
Improvement
Baseline
38.00
47,79
46.33
78.57
49.33
58,20
53.03
Trace (OptoPrime)
60.33
51.19
46.00
74.18
45,33
60.74
56,30
+3.27
MIPROV2-No-Demos
38.00
52.04
51.33
91.85
48.67
60.97
57.14
+4.11
MIPROv2
58,00
49.15
48.33
83.37
51.33
61.84
58.67
+5.64
TextGrad
62.33
48.64
47.67
46.67
63.84
59.14
GEPA
69.00
52.72
51.67
94.47
59.33
64.13
65.22
+12.19
GEege
65.67
55.95
56.67
96.46
59,33
64.13
66.36
+13.33
Optimized Qwen3-88R, evaluated Mini
GEPA-Qwen-Opt
65.67
49.83
54.67
90.05
52.67
59.31
62.03
+9.00
Observation Reflective Prompt Evolution highly sample-efficient and can outperform weight- space reinforcement learning: Across six benchmarks, GEPA adapts rapidly and generalizes robustly in compound AI systems- bystems-beattng GRPO (24,000 rollouts) by up 19 percentage points while using up 35x fewer rollouts. It reaches optimal test performance with 4-35 x fewer rollouts and exceeds GRPO on 5 out tasks by 19.0, 2.73, 13.66, 5.,9, and 0.7 percentage points. GEPA matches GRPO's best validation after only 243, 402, 330, 1143, 1179, and 306 rollouts up to 78x greater sample efficiency. GEPA+Merge widens the gap, outperforming GRPO by 21 percentage points a comparable rollout budget to GEPA.
The majority of GEPA's rollout budget is spent validation, where scores are utilized solely for candidate selection and not for producing learning signals. we restrict the analysis to train set rollouts, GEPA requires only 79 to 737 rollouts to reach optimal performance. To match GRPO's best validation scores, GEPA achieves this with only 102, 32, 6, and 179 train rollouts for four tasks, respectively, underscoring the high sample efficiency of learning based reflective prompt evolution.
Since tracking candidates' validation performance accounts for majority GEPA's rollout budget, sample efficiency can be further improved by evaluating smaller validation set by tracking scores dynam-
7

---

Published as a conference paper at ICLR 2026
ically selected validation subsets instead of the full set-both of which we propose as directions for future work. Figures Ta
14c show the full performance-vs-rollour curve for all optimizers over benchmarks HotpotQA, IFBench, Ho and PUPA, respectively.
lb 33c
and
Observation 2: Reflective prompt evolution enables custruction-optiiizatoo alone to outperform joint instruction and few-shot optimization: We compare GEPA with MIPROv2, a state-of-the-arr instruction and few-shot optimizer, using two leading models across six diverse tasks, and observe that GEPA consis- tently outperforms MIPROv2 in all settings, achieving margins as high as ppp for GPT-4.1 mini and ppp for Qwen3 8B. Further, GEPA and GEPA+Merge more than double the aggregate gains over baseline seen with MIPROv2 across all benchmarks and models (+13.33pp and + +12. 19p vs +5.64pp for MIPROv2).
While prior works such as Opsahl-Ong et al. (2024) and Wan et al. 2024) have provided compelling ev- idence for the effectiveness of few-shot example optimization often outperforming instruction- basiio ap- proaches-one our findings suggest an exciting shift in this trend. We attribute this primarily to recent advances the instruction-folloving and self-reflective abilities of LLMs, as well as the design choices in GEPA that capitalize on these improved capabilities. To further contextualize our findings, we redo the study on generalization gap (the difference between validation and test set performance for optimized prompts) as proposed by Wann al. (2024). The results presented in Figure [5 reinforce these observations: reflectively evolved instructions now demonstrate a lower generalization gap, underscoring both advancements in model capabilities and the benefits of GEPA's design. We see this as a reflection of the continuous evolution of LLMs and GEPA's ability to effectively leverage these improvements.
We provide the full-length optimized prompts produced by GEPA for all systems, benchmarks, and mod- els in Appendix N alongside MIPROv2 prompts. Notably, in contrast to prior findings where instruction optimization yielded improvements primarily through quasi-exemplars (Wan et al. 2024), GEPA's prompts frequently contain detailed declarative instructions for completing the task, as illustrated in Figure 2
Observation 3: The next-candidate selection strategy strongly influences the optimization trajectory and final performance, with Pareto-based sampling providing a distinct advantage. GEPA refines prompts iteratively with rollout feedback; to test our Pareto Pareto-based selection, we compare against baseline that always picks the best pest-performing candidate in the SelectBestCandidate strategy (which is similar to the strategy used by TextGrad Yuksekgonul et al. 2025)), and (aanSeearc((N:4)) (used by APO Pryzant et al. (2023)). As shown in Table 3 these baselines often yield suboptimal exploration of the prompt search space, leading to poor performance. GEPA with Pareto-based sampling outperforms the BeamSearch strat- egy by upto 11.33pp, and SelectBestCandide +7.33pp and +6.4pp across all benchmarks, respectively. Figure 4 highlights the difference in optimization trajectories: always choosing the current best candidate gives immediate improvement but quickly stalls, wasting rollouts on single candidate. In contrast, our pareto-based method expands the search by con- sidering all Pareto-optimal candidates (all "winning" strategies found so far), balancing exploration and exploitation and converging to a higher-performing solution within the same rollout budget.
strategy by up to 8. 17pp, with an aggregate margin of
We also find that instruetion-optimized prompts are more efficient and generalize better than few- shot demonstrations (Observation 4). Further, System-aware crossover strategies such Merge can improve performance, but their effectiveness depends on budget allocation between mutation and crossover and on timing (Observation 5). Finally, GEPA-optimized prompts demonstrate mross generalization (Observation 6). See Appendix for further discussion.
5 EXTENDED APPLICATIONS OF GEPA
GEPA For Inference- Time Search GEPA can be used an inference-time search technique by setting the validation set equal to the training set (Dval = Daran), under which GEPA iteratively proposes better so- lutions for each target task. We evaluate this approach hardware-specifie code generation: writing AMD
8

---

Published as a conference paper ICLR 2026
Table 3: Comparing candidate selection strategies across different tasks with Qwen3 8B while keeping the evolution harness fixed. At each step, CalecttestCandddaee (used by TextGrad Yuksekgonul et al. (2025) evolves only from the top-scoring candidate. BeamSearch maintains a pool of the top-N candidates (used by APO Pryzant al. (2023)) but is still prone to local optima. In comparison, GEPA's Pareto-based selection yields a +12.44% improvement, significantly outperforming the +6.05% and +5.11% gains of greedy and beam-search strategies respectively.
Qwen3 8B
HotpotQA
IFBench
Hover
PUPA
Aggregate
Improvement
Baseline Selecteestandidda
42.33
36,90
35.33
80.82
48.84
58.33
30.44
45.33
85.45
54.89
+6.05
BeamSearch GEPA
57.33
36.39
41.00
81.08
53.95
+5.11
62.33
38.61
52.33
91.85
61.28
+12.44
122.88
186.320
178.000
186.220
15
192.321
(80-02)
12
20
23
2B
(a) SelnciBestaandidate Strategy
(h) Pareto-based candidate sampling.
Figure 4: Comparing the impact of different candidate selection strategies. (Left) As can be seen, selecting the best-performing candidate in every iteration led to a local-optima after one iteration, leading to subopti- mal search performance. (Right) On the other hand, using pareto-based candidate selection strategy, GEPA was able to generate a balanced search tree, finding a better performing program within the same budget.
NPU kernels (NPUEval (Kalade & Schelle 2025 and generating NVIDIA GPU's CUDA kernels (Kernel- Bench (Ouyang et al. 2025)) using GPT-4o OpenAl (2024). GEPA raises mean kerell vector utilization on NPUEval from 4.25% % (sequential refinement baseline) to 30.52%, and increases the fraction of KemelBench
tasks with CUDA code surpassing baseline-Po Py Torch speed from close to 0% to over 20%. Full experimental details are provided Appendix E
GEPA for Adversarial Prompt Search GEPA can discover adversarial prompts by inverting the re- ward-finding instructions that minimize task performance. On AIME-2025 with GPT-5 Mini, starting from a simple instruction, GEPA evolved a trivia-style distractor that reduced pass from 76% to 10%. Adver- sarial search drew AIME 2022-200 2022-0024 problems, with evaluation AIME-2025 using 5 runs per problem (30 problems; 150 generations). This robustness probe surfaces brittle mnstruction-folloving interactions (e.g., trivia with striet formatting) and yields reusable stress tests and regression suites for deployment and show that reflective prompt evolution is also effective for stress-testing them by locating universal, query- agnostic perturbations that sharply degrade accuracy. These findings parallel recent results query- aggossic
9

---

Published as conference paper ICLR 2026
adversarial triggers, which report two- to seven-fold increases in enror rates across model families; in our AIME-2025 setting, GEPA's learned universal instruction yields a comparable 3.8x increase (errors from 24% to 90%) (Rajeev et al. 2025). Full experimental details are provided in Appendix
6 RELATED WORK
Prompt optimization improves LLMs but often needs manual expertise; for instance, chain-of-thought prompting Wei et (2023). To scale this approach, recent methods use LLMs to optimize prompts auto- matically (Zhou et al. 2022 Yang et al. [024] Agarwal et al. [024] Fernando et al. 2024). GEPA leverages
L.LMs, but differs by incorporating textual environment feedback, Pareto-aware search over candidates, and evolution strategies per submodule within an Al system.
Evolutionary algorithms have been used to optimize prompts, e.g., EvoPrompt (Guo al. 2024). which
evolves prompt populations. Rainbow Teaming (Samvelyan al. 2024) applies quality-diversity evolution to generate diverse adversarial prompts. GEPA additionally uses domain-specific feedback for targeted mu- tations achieving higher sample efficiency. AlphaEvolve (Novikov et al. 2025) and OpenEvolve (Sharmal 2025) apply evolutionary search directly to code rewriting, excelling when problem solution can codi- fied. While AlphaEvolve targets single hard problem, GEPA brings evolution prompts across domains, combining Pareto-frontier optimization and prompt evolution to transfer tactics from related problems.
Feedback-driven improvement often reinforcement learning, such majority voting signals (Zuo jet al.] 2025), but can be sample-inefficien when rewards are slow to compute. An alternative is learning in the language space: in-context bandit/self- bandit/self-bootstrapiin bootstrappitn (Shinn et al. 20023 Madaan et al., (0233) (Monea
jet al.
2025 Xu et al. 2025a: Feng et al., 2025 Cheng et al. 2024), workflow memory and skills (Wang
al.
2024 2025c). and test-time strategy synthesis via Dynamic Cheatsheet (Suzgun et al. 2025), reasoning
cache (Chen et al. 2025c). GEPA instead uses examples to propose new instructions, yiclding task-specific
rules.
To optimize compound AI systems and agents (Lin et al.
2025b). DSPy
(Khattab et al.) 2022 2024)
searches/bootstraps few-shot examples, TextGrad (Yuksekgonul al. 2025 backpropagates textual feed- back, and MIPROv2 (Opsahl-Ong et al., 2024) jointly aligns instructions and examples via Bayesian opti- mization; these largely rely on global rewards. Agent-Pro (Zhang et 2024) evolves agent policies through dynamic belief generation and reflection on interactive experiences. Optimas (Wu et al. (025a) introduces globally aligned local rewards per module. GEPA combines global rewards with environment textual feed- back per module and maintains Pareto frontier individual data instances, matching prompts/agent design to specific examples. The Pareto-guided evolution lets GEPA explore diverse prompt/code/agent design strategies before converging robust, generalizable set.
7 CONCLUSION
We introduced GEPA, a prompt optimizer for arbitrary LLM agents and workflows that leverages explicit re- flection and Pareto-based selection, showing superior sample efficiency compared to reinforcement learning (GRPO), while outperforming leading prompt optimizers (MIPROv2). By explicitly incorporating natural language feedback and maintaining diverse pool of Pareto-optimal candidates, GEPA rapidly adapts AI systems to new tasks. Our results across benchmarks and models suggest that language- reflection can offer scalable strategy for optimizing complex real-world AI workflows, especially in resource-constrained settings. GEPA also shows promise as an inference-time search strategy, showing the ability to write code in challenging domains.
10

---

Published as a conference paper ICLR 2026
ACKNOWLEDGMENTS
This research supported in part by gifts from Accenture, Amazon, AMD, Anyseale, Broadcom, Google, IBM, Intel, Intesa Sanpaolo, Lambda, Lightspeed, Mibura. NVIDIA, Samsung SDS, SAP, by the U.S. De- partment of Energy. Office of Science, Office of Advanced Scientific Computing Research through the X-STACK: Programming Environments for Scientific Computing program (DESC0021982.) and the De- fense Advanced Research Projects Agency (DARPA) under Agreement No. HR001 12590134. Lakshya A Agrawal is supported by Laude Slingshot grant provided by the Laude Institute and an Amazon Al PhD Fellowship. We are grateful to the AMD University Program for provisioning AMD NPU Mini PCs, and to Dr. Graham Schelle and Dr. Sarunas Kalade for their support during development. Finally, we thank Dr. Nicholas Tomlin and Prof. Joseph E. Gonzalez for their valuable comments on this manuscript.
REFERENCES
Advanced Micro Devices. Amd XM architecture. http:://www...ucccccm/e//eeeeees html#xdna2 2025. Accessed on: 2025-07-23.
Eshaan Agarwal, Joykirat Singh, Vivek Dani, Raghav Magazine, Tanuja Ganu, and Akshay Nambi.
Promptwizard: Task-aware prompt optimization framework, 2024. URL https://arxiv.. org/bbs/ 2405. 18369
Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S Gulavani, and Ramachandran Ramjee. Sarathi: Efficient 11m inference by piggybacking decodes with chunked prefills. arXiv preprint arXiv:2308. 16369, 2023.
Meta AI. Ilama- hama-prompt-ops. An open-source tool for seamless migration from other ilms to llama, and for general prompt optimization. https:///ithht hwt.gtttt.....mettaaaae.eoo - 2025. Accessed: 2025-07-11.
Mislav Balunović, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev. Aime-2025, Febru- ary 2025. URL https: / https::////gggggaceeeeee co/ wwtaastss//aatteea/aaaaaaaa aatpa//aammm222222 Dataset from Math- Arena: Evaluating LLMs on Uncontaminated Math Competitions.
Shiyi Cao, Sumanth Hegde, Dacheng Li, Tyler Griggs, Shu Liu, Eric Tang, Jiayi Pan, Xingyao Wang, Akshay Malik, Graham Neubig, Kourosh Hakhamaneshi, Richard Liaw, Philipp Moritz, Matei Zaharia, Joseph E. Gonzalez, and Ion Stoica. Skyrl-vO: Train real-world long-horizon agents via reinforcement learning, 2025.
Mingyang Chen, Tianpeng Li, Haoze Sun, Yijie Zhou, Chenzheng Zhu, Haofen Wang. Jeff Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou, and Weipeng Chen. ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning, March 2025a. URL htRp:t/t////xvv.or//attttt 19470 arXiv:2503. 19470 [es].
Peter Chen, Xiaopeng Li, Ziniu Li, Xi Chen, and Tianyi Lin. correct Reasoning in GRPO, May 2025b. URL WRKLhtpp://axxii.or//////////// 11595 arXiv:2505. 11595 [cs].
Lin. Spectral Policy Polica Optimization Coloring your In-
Peter Baile Chen, Yi Zhang, Dan Roth, Samuel Madden, Jacob Andreas, and Michael Cafarella. Log- augmented generation: Scaling test-time reasoning with reusable computation, 2025c. URL https: //arxiv.org/abs//500. 14398
Ching-An Cheng, Allen Nie, and Adith Swaminathan. Trace is the next autodiff: Generative optimization with rich feedback, execution traces, and llms, 2024. http://pps///aai.oor//////a////s 16218

---

Published as a conference paper ICLR 2026
Xidong Feng, Bo Liu, Yan Song. Haotian Fu, Ziyu Wan, Girish A. Koushik, Zhiyuan Hu, Mengyue Yang, Ying Wen, and Jun Wang. Natural language reinforcement learning, 2025. URL https: abs/2411. 14251
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rockt""aschell Promptbreeder: self-referential self-improvement via prompt evolution. In Proceedings of the 41st In- ternational Conference on Machine Learning, ICML'24 24. JMLR.org, 2024.
Tyler Griggs, Sumanth Hegde, Eric Tang, Shu Liu, Shiyi Cao, Dacheng Li, Charlie Ruan, Philipp Moritz, Kourosh Hakhamaneshi, Richard Liaw, Akshay Malik, Matei Zaharia, Joseph H. Gonzalez, and lon Stoica. Evolving skyrl into a highly-modular rl framework, 2025. Notion Blog.
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song. Xu Tan, Guoqing Liu, Jiang Bian, and Yu- jiu Yang. Connecting large language models with evolutionary algorithms yields powerful prompt op- timizers. In The Twelfth International Conference on Learning Representations, 2024. URL https: //openreview. www..n//oorummddizeaa...o.
Soufiane Hayou, Nikhil Ghosh, and Bin Yu. PLoP: Precise LoRA Placement for Efficient Finetuning of httttp://arxiv.orr//bba/
Large Models, June 2025.
20629 ar arXiv:2506220629 [cs].
Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal. HoVer: A dataset for many-hop fact extraction and claim verification. Trevor Cohn, Yulan He, and Yang Liu (eds.), Findings of the Association for Compulational Linguistics: EMNLP 2020, pp. 3441-3460, Online, November 2020. Association for Computational Linguistics. doi: 10. (6665/////222.ffnnngeeemm/////// URL htt.httss/acaaattooog..oooo
Lindings-emnlp. 309/ 309/
Bowen Jin, Hansi Zeng. Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang. Hamed Zamani, and Jiawei Han. Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning, July 2025. URL
09516 arXiv:2503.0951 [cs].
Sarunas Kalade and Graham Schelle. Npueval: Optimizing npu kernels with Ilms and open source compilers, 2025. URL httpp:://rrvv..or///b/////////// 14403
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. Bemmonstaate-scarch--rredc Composing retrieval and language models for knowledge-intensive NLP. arXiv preprint 22t.::22222.1222.. 2022.
Omar Khattab, Arnay Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan A, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts. DSPy: Compiling declarative language model calls into state-of-the-art pipelines. In The Twelfth International Conference on Learning Representations, 2024. h httphhtsss///ppeiiieeeee net/Forum?id=syYNozz Y50d
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonza- lez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems principles, pp. 611-626, 2023.
Nathan Lambert. Policy gradient algorithms. In RLHF Book: Reinforcement Learning from Human Feed- back chapter 11. RLHF Book, 2025. URL httths::///hth/ba Accessed July 16, 2025.
http://////-plllyyyerieeetttsss gradients.....oo.oo html
12

---

Published as a conference paper ICLR 2026
Siyan Li, Vethavikashini Chithrra Raghuram, Omar Khattab, Julia Hirschberg, and Zhou Yu. PAPILLON:
Privacy preservation from Internet-based and local language model ensembles. In Luis Chiruzzo, Alan Ritter, and Lu Wang (eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguisties: Human Language Technologies ( Volume I: Long Papers), pp. 3371 Albuquerque, New Mexico, April 2025a. Association for Computational Linguistics. ISBN 979 7779-8889176 188-899766-18488 189-6. doi: 10. 018533/112225..m/m 173. URL http::h/aalaaattolllll..o org/ 2025. loon.llloog.111 173/
Xianming Li, Aamir Shakir, Rui Huang, Julius Lipp, and Jing Li. ProRank: Prompt Warmup via Rein- forcement Learning for Small Language Models Reranking, June 2025b. httlhttt///rxxw.orr////// 2506. 03487 arXiv:2506.0338" [cs].
Chenyu Lin, Yilin Wen, Du Su, Fei Sun, Muhan Chen, Chenfu Bao, and Zhonghou Lv. Knowledgeable-rl. Policy Optimization for Knowledge Exploration in Setrieval-Augmenter Generation, June 2025a. URL http://arxiv www.aaiwwwo.oooooeooooooo hbrgg//bb/006666 05154 urXiv:2506.05154 [cs].
Matthieu Lin, Jenny Sheng, Andrew Zhao, Shenzhi Wang, Yang Yue, Victor Shea Jay Huang, Huan Liu, Jun Liu, Gao Huang, and Yong-Jin Liu. Training of scaffolded language models with language supervision: survey, 2025b. URL httt/h:tt///rxrtti org hts///bs/24111 2410. 16392
Shu Liu, Sumanth Hegde, Shiyi Cao, Alan Zhu, Dacheng Li, Tyler Griggs, Eric Tang, Akshay Malik, Kourosh Hakhamaneshi,, Richard Liaw, Philipp Moritz, Matei Zaharia, Joseph E. Gonzalez, and Ion Sto- ica. Skyrl-sql: Matching gpt-4o and 04-mini on text2sql with multi-turn rl, 2025.
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang. Shashank Gupta, Bodhisatwaa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refine- ment with self-feedback. In A. Oh, Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 46534 46594. Curran Associates, Inc., 2023. URL /tttps: http://ceeeeeeeeeeeeee.....ppppp 92eef17722h1555555eefffffff---pp
cc/
Giovanni Monea, Antoine Bosselut, Kianté Brantley, and Yoav Artzi. Lims are in-context bandit reinforce- ment learners, 2025. URL httpttss///axxi.orrraaaa//////// 05362
Jean-Baptiste Mouret and Jeff Clune. Illuminating search spaces by mapping elites, 2015. URL https:
//arxiv org wwtp://///abb//0000// 1544 04909
Alexander Novikov, Ngån Vū, Marvin Eisenherger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Ab- bas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli, and Matej Balog. Alphaevolve: A cod- ing agent for scientific and algorithmic discovery. Technical report, Google DeepMind, URL https:
2025.
httgg//ppppp..ooeeeeeeeemmmmmeea ctmp:iidddooooeeeeeeeeeeeeeeeeee com/Blog/ BBog/ thms/
alphaevolve- a
AlphaEvolve pdf] White paper.
OpenAl. Hello gpt-4o.
2024. Accessed: 2025-09-08.
OpenAL. GPT-4.1 series, 2025. Large language model series, released April 2025. https: /atpss:///ppaai.comm index/gpt-4-17
13

---

Published as a conference paper ICLR 2026
Krista Opsahl-Ong, Michael Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, and Omar Khattab. Optimizing instructions and demonstrations for multi-stage language model programs. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 2024. Association for Computational Linguistics. doi: 1/66/////222..mmmlpmmm //aclanthollggyorrg/// w-n..- 5257
Miami, Florida, USA, November
URL https:
Anne Ouyang, Simon Guo, Simran Arora, Alex L Zhang, William Hu, Christopher Re, and Azalia Mirho- seini. Kernelbench: Can LLMs write efficient GPU kemels? In Scaling Seif-Improving Foundation Models without Human Supervision, 2025. URL. https://openreview. wtt.://wwww....oooomooo/vo/////.
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, and Juanzi Li. VerlF: Verification Engineering for Reinforcement Learning in Instruction Following, June 2025. URL http://arxxw.orr/bbb///22 09942 arXiv:2506. 09942 [cs].
Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu and Michael Zeng. Automatic prompt optimization with "gradient descent" and beam search. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Pro- cessing, pp. 7957-7968, Singapore, December 2023. Association for Computational Linguistics. doi: 10.18653/v (01553/v1/222..err .01e2...mlpp1ae. caaull-main.499) m9in.494. URL/Intps://aclaattooooo
wow.orgg//22222 2023. amnlp-mainn 494/
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. IF-RIVR-Trrin, July 2025a. URL https://hugging: co/
Gatasets/allemaile __mumlllt ti wtt.iloonsstttttsssoott _ppto5
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison. Shengyi Huang, Pradeep Dasigi. Nathan Lambert, and Hanmaneh Hajishirzi. Generalizing verifiable instruction following, 2025b. URI. https:
//arxiv.org/allt wtw.o///ab////////// 02833
Meghana Rajeev, Rajkumar Ramamurthy, Prapti Trivedi, Vikas Yadav, Oluwanifemi Bamghose, Sathwik Te- jaswi Madhusudan, James Zou, and Nazneen Rajani. Cats confuse reasoning Ilm: Query agnostic adver- sarial triggers for reasoning models, 2025. URL httpps://arxiw.or/aahtt 250../////35881111111
Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan, Nahum Maru, Hristo Todorov, Etash Guha, E. Kelly Buchanan, Mayee Chen, Neel Guha, Christopher Ré, and Azalia Mirhoseini. Archon: An ar- chitecture search framework for inference-time techniques, 2025. URL http:/:://aaxii..o///aabbs/ 15254
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang. Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, and Roberta Raileanu. Rainbow teaming: open-ended generation of diverse adversarial prompts. In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS '24, Red Hook, NY, USA, 2024. Curran Associates Inc. ISBN 9798331314333
Zeyang Sha, Shiwen Cui, and Weiqiang Wang. SEM: Reinforcement Learning for Eefrch-Efficient Large Language Models, May 2025. URL hwt.:httpp//ee /tttxi////xvv...e/ http:://aw....og/////aaa// wttp://///bbs......./ 20750 07903 arXiv:2505.07903 [cs].
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y. K. Li, Y. Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL//tttsss//arii.orr////////// 03300
Asankhaya Sharma. Openevolve: Open-source implementation of alphaevolve. codelioon/openevollee 2025. GitHub.
com/
14

---

Published as a conference paper ICLR 2026
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: lan- guage agents with verbal reinforcement learning. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS 23,, Red Hook, NY, USA, 2023. Curran Associates Inc.
Shuzheng Si, Haozhe Zhao, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Bofei Gao, Kangyang Luo, Wenhao Li, Yufei Huang, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, and Maosong Sun. Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning, 16483 2iii::2005.1648 [cs].
May 2025. URL
Hakim Sidahmed, Samrat Phatale, Alex Hutcheson, Zhuonan Lin, Zhang Chen, Zac Yu, Jarvis Jin, Simral Chaudhary, Roman Komarytsia, Christiane Ahlheim, Yonghao Zhu, Bowen Li, Saravanan Ganesh, Bill Byrne, Jessica Hoffmann, Hassan Mansoor, Wei Li, Abhinav Rastogi, and Lucas Dixon. Parameter Effi- cient Reinforcement Learning from Human Feedback, September 2024. URL http:/ttt////./rrrrrrrr/// wrg.aaaiaioooogs//// 2403. 10704 arXiv:2403. 140. [cs].
Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen. R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning, March 2025.
05592 arXiv:2503.0552 [cs].
Dilara Soylu, Christopher Potts, and Omar Khattab. Fine-tuning and prompt optimization: Two great steps that work better together, 2024. URL https: or wrt..oggg//a....o..oo.oo.oooo..s hbsp/bb///4// 10930
Zhongxiang Sun, Qipeng Wang. Haoyu Wang. Xiao Zhang, and Jun Xu. Detection and Mitigation of Hallucination Large Reasoning Models: A Mechanistic Perspective, May 2025. URL http:
12886 arXiv:2505. 12886 [es].
Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic cheatsheet: Test-time learning with adaptive memory, 2025. TRL/https: //arxiv. org/ abs/2504 07952
Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, and Matei Zaharia. Langprobe: language programs benchmark, 2025. URL https:
//arxiv.org/abb/2255 20315
Qwen Team. Qwen/qwen3-8i -bb
https://huginngfac..o/// wwwp:/aeee..coooee//////eeea 2025. Accessed: 2025-07-11.
Ryan Teknium, Jeffrey Quesnelle, and Chen Guang. Hermes 3 Technical Report, August 2024, URL
http://arxiv.orgg www.oorgg//b///aeeeeo abaaaas//22// 2408. 11857 1rXiv:2408.11857 [cs].
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, and Sercan Arik. Teach better or show smarter? on instructions and exemplars in automatic prompt optimization. Advances in Neural Information Processing Systems, neurips....se. cc/ cccppapprraeee wwwp:ff/asssee/////////22 37:58174-58244, 2024. URL htt.:9///b.bbbbtaaat--t-ooerrrre html de031defd145bon h0
093
Hongru Wang, Cheng Qian, Wanjun Zhong, Xiusi Chen, Jiahao Qiu, Shijue Huang, Bowen Jin, Mengdi Wang, Kam-Fai Wong, and Heng Ji. Acting Less is Reasoning More! Teaching Model to Act Efficiently, 14877 arXiv:2504.14870 [cs].
May 2025a.
Shangshang Wang, Julian Asilis, Ömer Faruk Akgül, Enes Burak Bilgin, Ollie Liu, and Willie Neiswanger. Tina: Tiny Reasoning Models via LoRA, April 2025b. URL arXiv:2504.15777 [es].
15777
Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham Neubig. Agent workflow memory, 2024. URL https///aaxiv.org//a//222/ 07429
15

---

Published as a conference paper at ICLR 2026
Zora Zhiruo Wang, Apurva Gandhi, Graham Neubig, and Daniel Fried. Inducing programmatic skills for agentic tasks, 2025c. http::///arxiv.or//bb////// 06821.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoe Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2023. URL
wttpaa/www..rrrabb//2........... 11903
Colin White, Samuel Dooley, Manley Roberts, Arka Pal. Ben Feuer, Siddhartha Jain, Ravid Shwartz-ZZ Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, and Micah Goldblum. Livebench: A challenging, contamination-limitee IIm benchmark, 2025. URL http:///aaxxvv.ooaa/////e// 19314
Shirley Wu, Parth Sarthi, Shiyu Zhao, Aaron Lee, Herumb Shandilya, Adrian Mladenic Grobelnik, Nurendra Choudhary, Eddie Huang, Karthik Subbian, Linjun Zhang, Diyi Yang, James Zou, and Jure Leskovec. Optimas: Optimizing compound ai systems with globally aligned local rewards, 2025a.
URL https:
//arxiv. org/ 250gg//bb////..... 03041
Yihong Wu, Liheng Ma, Muzhi Li, Jiaming Zhou, Jianye Hao, Ho-fung Leung, Irwin King, Yingxue Zhang, and Jian- Yun Nie. Reinforcing Question Answering Agents with Minimalist Policy Gradient Optimiza- tion, July 2025b. URL http:://aaxxiv.ogabb/b/////s
17886 arXiv:2505.17086 [cs].
Wanqiao Xu, Allen Nie, Ruijie Zheng, Aditya Modi, Adith Swaminathan, and Ching-An Cheng. Provably learning from language feedback, 2025a.
/tttp:://arxiv.rrggg
10341
Yixuan Even Xu, Yash Savani, Fei Fang, and Zico Kolter. Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning, June 2025b. URL arXiv:2504.138 [cs].
13818.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang. Jiaxi Yang. Jing Zhou, Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingfeng Xue. Mingze Li. Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao. Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yugiong Liu, Zekun Wang. Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025. URL https: httrxiiv.rgg/bbs/2500 09388
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoe V. Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers, 2024. HRLL/httss///arxt http:///aaxxvvv.ggaabbae wwrgg/bbs/3300 03409
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. Re- act: Synergizing reasoning and acting in language models, 2023. URI https:/ts:/// http://///aaxivoooggabb///////// www.//////22/ 03629
Lingfan Yu, Jinkun Lin, and Jinyang Li. Stateful large language model serving with pensieve. In Proceedings of the Twentieth European Conference Computer Systems, EuroSys '25, pp. 144-158, New York, NY, USA, 2025. Association for Computing Machinery. ISBN 9798400711961. doi: 10.1145/3689031. 369686991111113186666 3696086
3696086. URL wRthhttss:/ http::::/tttiiorrateeee http:///oo.orgg////// 01w0://00..1111999011111
16

---

Published as conference paper ICLR 2026
Zhenrui Yue, Bowen Jin, Huimin Zeng, Honglei Zhuang, Zhen Qin, Jinsung Yoon, Lanyu Shang, Jiawei
Han, and Dong Wang. Hybrid Latent Reasoning via Reinforcement Learning, May 2025. URL http: 7/arxiv. abs/ 2505. 18454 htt.:2505.18454 [cs].
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang, Carlos Guestrin, and James Zou. Optimizing generative ai by backpropagating language model feedback. Nature, 639:609-
616, 2025.
Qi Zhang. Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang. Sheng Guo, via Process-and-
Bo Zheng, Haobo Wang, and Junbo Zhao. LeTS: Learning to Think- Reward Hybridization, May 2025, URL http::t/ta/iii/ httpaaawwww....ab/////aaaaaaaa// httg/abs/25055 1...17744 17447 EeXiv:2505.1744 [cs].
Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang. and Weiming Lu. Agent-pro: Learning to evolve via policy-level reflection and optimization. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2348-5375, Bangkok, Thailand, August 2024, Association for Computational Linguistics. doi: 10. 10.1865//1/00244ccll 292 URL https://aclanthologg/ http://ntttoooooooog2222........ acl- 2222
Siyan Zhao, John Dang, and Aditya Grover. Group Preference Optimization: Few-Shot Alignment of Large Language Models, October 2024. URL httphttp///aaxi..orr//bb//// 11523 arXiv:2310.11523 [cs].
Siyan Zhao, Devaansh Gupta, Qinqing Zheng, and Aditya Grover. dl: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning, June 2025. URL
12216
arXiv:2504. 12216 [cs].
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Livia Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E Gonzalez, et al. Sglang: Efficient execution of structured lan- guage model programs. Advances in neural information processing systems, 37:62557-62583, 2024.
Yongchao Zhou, Andrei loan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-lewe prompt engineers. The eleventh international conference on learning representations, 2022.
Noah Ziems, Dilara Soylu, Lakshya A Agrawal, Isaac Miller, Liheng Lai, Chen Qian, Kaiqiang Song, Meng Jiang, Dan Klein, Matei Zaharia, Karel D' D'Oosterlinck, Christopher Potts, and Omar Khattab. Multi module grpo: Composing policy gradients and prompt optimization for language model programs, 2025.
URL
org/ http//bbb///..... 2e0///20888000880
Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, Biqing Qi, Youbang Sun, Zhiyuan Ma, Lifan Yuan, Ning Ding, and Bowen Zhou, Ttrl: Test-time reinforcement learning, 2025. URL https://arr...or///a///////// 2504. 16084
17

---

Published as conference paper ICLR 2026
A
APPENDIX OUTLINE
Usage of Large Language Models
GEPA's Reflection and Prompt Update Meta Prompt
GEPA Algorithm and Methodology Details
GEPA For Inference- Time Search (Contd.)
GFPP for Adversarial Prompt Search (Contd.)
Evaluation Setup (Contd.)
Results and Analysis (Contd.)
Performance vs. Budget (Rollouts) Curves
Generalization Gapl
Cost Performance Analysis for optimized systems
GEPA Search Trees)
Visualizing the Iterative Refinement achieved by GEPA
Examples of best prompts for every benchmark
GEPA generated prompts for kernel generation
Number reflection LM calls made by GEPA during optimization
USAGE OF LARGE LANGUAGE MODELS
The authors used large language models (LLMs) only for polishing prose of text where the complete draft was fully written by the authors initially and polished later with the help of LLM-based assistants including ChatGPT, Gemini, and Perplexity. The authors' used code assistants including Cursor and Copilot imple- ment the authors' original design and ideas. The scientific contributions, technical methods, ideas and core results are entirely the original work of the authors.
C GEPA'S REFLECTION AND PROMPT UPDATE META PROMPT
GEPA's Meta Prompt
provided assistant with the following instructions perform task for me:
<current instruction
The following are examples different task inputs provided to the assistant along wi th the assistant's 's response for each of them, and some feedback on how the assistant's response could be better:
<Inputs, Outputs and Feedback for minibatch of examples>
Your task is write instruction for the assistant.
18

---

Published as conference paper ICLR 2026
Read the inputs carefully and identify the input format and infer detailed task description about the task I wish to solve with the assistant.
Read all the assistant responses and the corresponding feedback. Identify all niche and domain specific factual information about the task and include in the instruction, as lot of it may not be available to the assistant in the future. The assistant may have utilized generalizable strategy to solve the task, if so, include that in the instruction well.
Provide the new instructions within blocks.
Figure shows the meta-promp used by GEPA, which guides the LLM to reflectively refine its current instruction based on input-output examples and corresponding feedback from the environment.
D GEPA ALGORITHM AND METHODOLOGY DETAILS
Figure 5 presents the GEPA Algorithm, along with the algorithm for Pareto-based candidate selection.
D.1 MERGE: SYSTEM-AWARE CROSSOVER STRATEGY FOR COMPOUND AI OPTIMIZATION
ggoorthhm4Irrvicen the instantiation of the System aware Merge strategy used in GErA+ Intuitively, merge will be helpful when there are candidates in the pool that learn complementary strategies. Algorithm defines the selection criteria: candidates are merged only if they share a common ancestor but have optimized disjoint sets of prompts (complementary strategies), the aggregate performance of the ancestor. GEPA routinely checks if the pool has such candidates, invoking merge when identified. These strict lineage conditions mean merge occurs sparsely.
pareto-optimal, and both candidates improve upon
GEPA FOR INFERENCE- TIME SEARCH (CONTD.)
While the primary focus of this paper is sample-efficient adaptation of AI systems to new tasks, preliminary findings suggest that GEPA may also serve promising inference-time search technique. This can be achieved by passing the set of tasks to be solved (for example, a list of Pytorch modules to be converted CUDA) as the training set GEPA, ensuring that both Dtrain and D Pareto contain the full set of tasks. This way. GEPA can "overfit" the set of tasks, iteratively proposing better solutions to every problem. We also note that this allows GEPA to apply lessons and insights extracted from rollouts for one task other tasks. To explore this use case, we conduct preliminary experiments using GEPA as an inference-time search technique for code-generation tasks on two hardware platforms: writing kernels for AMD's recently introduced XDNA2 Architecture (Advanced Micro Devices (025) using early version of the NPUEval benchmark (Kalade & Schelle 2025), and generating CUDA code for NVIDIA-VION GPUs using Kernel- Bench (Ouyang al. 2025).
distinguishing aspect of these experiments is the use of the feedback function μyy to dynamically inject domain-specne specific knowledge into the optimization process. Specifically, kernel development expertise-ofte codified in technical manuals and documentation can be selectively surfaced by retrieving relevant man- ual sections based rollout failures (e.g., compiler error messages). By using error information make targetted retrieval queries, GEPA promotes integration of architectural best practices into prompt evolu- tion, exemplified by the detailed prompt for NPUEval shown in Figure 27
We also note that generation
19

---

Published as a conference paper ICLR 2026
Algorithm 1 GEPA: Reflective Evolutionary Prompt Opti- Algorithm 2 Pareto-base candidate selection
mizer
1:
function SELECTCANDIDATE(P S)
Require: Inputs: System t; dataset Dirinn eval metric J4, feed-
2:
// Build instance wise Pareto sets for each do
back function j4,, budget F3
3:
Require: Hyperparams: minibatch size b. Pareto set size Rgareta
8" [ +- max; Sp [1]
1: Split Dunin into Draathua. pueetoo s.I. pareetol = Ripareto
5:
p [] + [P[k] Spapp = s (1]}
2: Initialize candidates P <- (Φ), parents A <- [None]
6:
end for
3: for cach (z., m.) in puucto do
7:
C unique candidates U, [*]]]
4: [i + (2x)), m.)
D
5: end for
9:
while there exists &b E C D dominated by
6: while budget B not exhausted do
another in C D do
7:
& <- CELLECTCANDIDATE(?"
10:
D - DUD
8:
SELECTMODULL((;))
11:
end while
9:
.M < minibatch of size from Dicedtck
12:
Remove from each [*[d to get [*]
10:
Gather feedback, scores, traces for [ on M using jy
13:
Let f[0] = number of i for which E
11:
T + UPDATEPRRMPTT(T, feedbacks, traces(i])
12:
+ + Copy of 24 w/ module j updated by T
14:
Sample 4, from é with probability f(0x]
13:
a' < avg score M (before, after)
15:
return index & of db in P
14:
if o' improved then
16:
end function
15:
Add to Pi Add lt to A for each (æz, m4s in pumeto do
16:
17:
Sg [i] <- (1 (
18:
end for end if
19:
20:
end while
21:
return o' maximizing average score on Dpureto
Figure 5: (Left) GEPA's core algorithm for reflective prompt evolution. GEPA works iteratively, in each iteration, selecting some of the current candidates evolve (line 7), executing the identified candidate on a minibatch of rollouts, while utilizing a special feedback function μ&f to gather module specific feedback when available (lines 9-10, described in detail in Section (3,, using an LLM to reflectively update the prompt (line 11), and evaluating whether the system instantiated with the new prompt improved the performance on the minibatch (line 14). If improved, GEPA then proceeds evaluate the new system candidate on the full D pareto set, adding it to the list of candidates tracked and marking the new system's 's parent. (Right) The SelectCandidate subprocedure used by GEPA's core algorithm is tasked with identifying the best candidate evolve in the next optimization iteration. GEPA's chief candidate selection strategy is to find non-dominated candidates in the Pareto frontier (of all task instances), and stochastically select one of them based on their appearance frequency in the Pareto front.
stochasticity (temperature based sampling) is eliminated by operating under a cache; this ensures that ob- served improvements tie closely to inference scaling through prompt updates and GEPA's diverse prompt exploration, rather than stochasticity in the model's sampling process.
We create sequential refinement agent that iteratively generates kernels (up to 10 times) based on feedback like compiler errors and profiling results (Sequential10), and evaluate the Best-of-N generation. With GPT-4o alone, Sequential reaches only 4.25% mean vector utilization. Adding RAG, sourced from technical manuals, improves this to 16.33%, and integrating MIPROv2 further raises it to 19.03%. Notably, applying GEPA Sequential (without RAG) dramatically boosts kernel performance, with several generated kernels achieving up to 70% vector utilization and a mean of 30.52%. Furthermore, single prompt generated by GEPA enables Sequential10 (again without RAG) attain score of 26.85%.
NPU Kernels:
20

---

do Published as conference paper ICLR 2026
Algorithm 3 Check if module combination is de-
Algorithm 4 MERGE: Genetic Crossover for Modular Candidates
sirable
1: function DESIRABI.(Q: i. P)
1:
function MERGE(P, A,
2:
for module m to [M] do
2:
i, r.sample(2, (P]) // distinct i +
3: Na ancestor's prompt for module
3:
A; GETANCESTORSG. A). A, GETANCES-
4:
Ri descendent prompt module < descendent j's prompt for module m if (Ta = R, and * n, or(Ta ny and W. * σ,) then
TORSÚ, A)
5:
4:
if E Aj or AA; then
6:
5:
continue // skip direct ancestry
6: end if
7:
return True end
for a A, nA, do
8:
8:
if this merge (i, a) has been tried before then continue
9:
end for
9:
10: return False
10:
end if
11:
end function
11:
if S[a min(S[I,,S then continue
12:
13:
end
14:
if not DESIRABLE(a, i, P) then continue end if
15:
16:
17:
+- copy of Pla)
18:
for module m to [M]
19:
+- Pla..Mmm
20:
T PiI.Mm.
21:
T, + Mm...
22:
Ta = ni and TT # n. then
23:
24:
else if TT Tj and n, + Tú
then
25:
T T'.MMm..
ni
26:
else T. * T. # na then Choose d"
27:
arg
(break ties randomly)
28:
4' .'M... ma else
29:
30:
Φ D'..M.F + ni // default
31:
end if end for
32:
33:
return (0',i,j.0)
34: end for
35: return None
36:
end function
Figure 6: Details of System Aware Merge. represents a seeded stochastic sampler.
For tasks from the KernelBench "representative subset" (Ouyang et al. 2025). spanning three difficulty levels, we ran GEPA with GPT-4o. As depicted in Figure 8 GEPA boosts GPT-4o's 's close- to-0% fast, score to above 20% with increasing search budget. This task used an agent that could generate upto sequential refinements based environment feedback (Sequential5).
CUDA Kernels:
These experiments with GPT-4o also demonstrate GEPA's ability to leverage the abilities of frontier LLMs. However, these early results and warrant further systematic study. We believe that leveraging GEPA for inference-time search, particularly when coupled with domain specific textual feedback, could generalize to other code generation and domain adaptation tasks direction we leave for future work.
21

---

Published as conference paper ICLR 2026
NPUEval: Mean Vector Utilization (%) (GPT-4o)
NPUDval: Vector Utilization (S) for Punctionally Correct (GPP40)
30.52%
abn_me3 (m
76.45%
asd
Vector Utilization (%)
biwneeedd
reducemay,
14.02%
vactorsudtrac:
resucesum
36,3395
Kern!
15-
hardägmaid
redusamin
argmin
10
argmux
maxpoo 24
lealy
detprddct,
suggoo
Jegowol
Rotioed Sequentialzo RAG
ReqwidsUdd SequesBaZD SMyyRNSHO: MPPR012
Bap
OPN
GEPa Paneto
Utilicatlon
Figure 7: GEPA with GPT-4o is able to generate kernels for AMD NPUs that achieve vector utilization rates as high as 70%, with a mean utilization score of 30.52%. In comparison, GPT-4o, even after up to 10 sequential refinements with environment feedback, achieves aggregate score of only 4.25%. When enhanced with retrieval-augee -aagmeeeeed generation (RAG) and MIPRO, the sequential refinement agent improves to scores of 16.33% and 19.03%, respectively. Notably, the final prompt produced by GEPA enables the same agent to reach a utilization score of 26.85%, all without requiring any runtime RAG.
KernelBench: Fast, Score Budget with GEPA, GPT-4o
0.5
Fastp
fasta.5
fast;
0.4
Fast, Score
0.3
0.2
0.1
0.0
500
1000
1500
2000
2500
3000
Budget (Number rollouts)
Figure 8: GEPA with GPT-4o is able to iteratively refine and improve CUDA Kernel Code. The graphs shows fast, rollouts plot for p=(0.5. 1]. where the speedup calculated over Pytorch-eager. fast, is metric described in (Ouyang et al. 2025) that measures the fraction of tasks for which the method generated kernel executing faster than p times the baseline. As can be seen, GEPA with GPT-4o is able generate cuda kernels executing faster than Pytorch-eage eager for over 20% of the 35 representative tasks.
22

---

Published as a conference paper ICLR 2026
F GEPA FOR ADVERSARIAL PROMPT SEARCH (CONTD.)
We instantiate GEPA for adversarial prompt search by inverting the reward signal: the optimizer proposes prompt edits to include additional information like trivia that minimize task performance (pass@) 1), while requiring that prompts de not contradict the task and still contain all information needed to solve it. For AIME, GEPA's adversarial search used AIME 2022-2024 problems as the pool for prompt evolution. The learned prompt evaluated on AIME-2025 (30 problems), using GPT-5 Mini with 5 runs per problem (150 generations total). We started from a clean instruction prompt and evolved single universal adversarial instruction that prepended to each query.
The learned adversarial instruction reduced pass@1 on AIME-2025 from 76% (clean prompt) to 10% (ad- versarial prompt), despite preserving the nominal task description and the required answer-format directive.
Base (clean) prompt:
You are a helpful assistant. You are given question and you need to answer it. The answer should be given at the end of your response in exactly the format "### <final answer>'.
GEPA-generate adversarial prompt (abridged):
You are a helpful assistant. You are given a question and you need to answer it. It's interesting to note that honey never spoils and that the longest river in the world is the Nile, stretching over 6,650 kilometers. When providing your answer, be sure to format it at the end of your response exactly as '### <final answer>', For this task, remember that many mammals, including dolphins, sleep with one eye open. Proceed to answer the given question accordingly.
Manual inspection showed that the adversarial prompt caused GPT-5 Mini end most responses with the lit- eral placeholder ### <final answer>, indicating a systematic misinterpretation of the formatting rule when paired with the injected distractors. This suggests that the large drop arises from the interaction between traneous details and a strict, literal formatting constraint, rather than from the formatting requirement alone.
Adversarial prompt search systematically uncovers instruction-level perturbations that sharply degrade model performance, providing a principled, automated way to probe worst-case robustness beyond average- case metrics. By finding universal, task-preserving distractors (e.g., trivia plus strict formatting), it reveals brittle instruction-folloiing interactions and turns them into reusable stress tests and regression suites for continuous evaluation. The resulting adversarial prompts could be used to provide targeted data for fine- tuning or safety training. In practice, this could improve deployment reliability, enables red-teaming at scale, and help track robustness drift over time across models, versions, and domains.
G EVALUATION SETUP (CONTD.)
G.1 BENCHMARKS, REFERENCE COMPOUND SYSTEMS, AND FEEDBACK FUNCTIONS
To rigorously evaluate the performance of GEPA and and compare it against current state-of-the-art com- pound AI system optimizers, we assemble a diverse suite of benchmarks mostly obtained from Tan et al. (202)) each paired with available Compound AI Systems.
HotpotQA (Yang et al. 2018) is a large-scale question-answe answering dataset consisting of 113K Wikipedia- based question-answer pairs. It features questions that require reasoning over multiple supporting doeu- ments. We modify the last hop of the HoVerMultiHHp program (described below) to answer the question instead of generating another query, and the rest of the system remains unmodified. The textual feedback
23

---

Published as a conference paper at ICLR 2026
module identifies the set of relevant documents remaining to be retrieved at each stage of the program, and provides that as feedback to the modules at that stage. We use 150 examples for training, 300 for validation, and 300 for testing.
IFBench (Pyatkin et al. 2025b) introduced benchmark specifically designed to assess language models' ability to follow precise human instructions, especially output constraints ge.g.. "answer only with yes or no". or "mention word at least three times"). The IFBench test set consists of 58 new and out-of-distribution output constraints and instructions to test system's is ability to generalize to new task constraints. [Pyatknn et al.) (2025b) also release IFTrain and IF-RLVR Train data (Pyatkin et al.) 2025a) which are used for training. We split the IF-RLVR Train into our train/val sets, and IFBench as our test set in order to ensure that the optimizers do not access the new, unseen constraints being tested in IFBench. We design a 2-stage system, that first attempts to answer the user query, and then in the second stage, rewrites the answer following the constraints. The textual feedback module provides the descriptions of constraints satsified and failed-to-be- satisifed by the system's 's response. Our splits contain 150 training examples, 300 for validation, and 294 for testing.
AIME-2025 (Balunović et al., 2025) The AIME-2025 benchmark consists 2 problem sets of 15 questions each (total 30) obtained from the AIME examination conducted by Mathematical Association of America. We use prior years AIME questions (2022-2024 totalling 90 questions) split equally into training and vali- dation set, and use the AIME-2025 questions, repeating each question times, as the final test set. We use a single-step ChainOfThought as the Al system under optimization.
Livt White et al. (2025) LiveBench is a cross-domain benchmark consisting of regularly up- dated questions. We use the math subset of LiveBench questions retrieved on July 30, 2025. This set of questions (n=368) shuffled (with python random seed 0) and split equally into train/val/test questions. We use a single-step ChainOrThought the AI system under optimization.
Hover (Jiang et al. 2020) an open-domain multihop fact extraction and claim verification benchmark built a Wikipedia-based corpus requiring complex reasoning across multiple sentences and documents, typically involving multiple wikipedia articles. Following Tan et al. 2025). ) the systems are evaluated for their ability to write queries in multiple hops to retrieve all relevant wikipedia documents (gold documents) required to make the claim. We obtain the HoverMultiHHp program from Tan et al. 2005)). which performs up 3-hop retrievals using 2 query writer modules, and 2 document summary modules. The textual feed- back module simply identifies the set of correct documents retrieved, and the set of documents remaining to be retrieved, and returns them as feedback text. For the full-parameter finetuning results demonstrated in fig- ure [O] we instantiate a 2-hop program, where the first hop is performed with the initial claim, and the LLM is prompted in single turn with the claim and first-hop retrieved documents, provide the second-hop search query. For Ver, we use 150 examples for training, 300 for validation, and 300 for testing.
PUPA (Li et al., 2025a) propose the task of Crinciy- Delegation: addressing real-world user queries using an ensemble of trusted and untrusted models. The core challenges are maintaining high re- sponse quality while minimizing leakage of personally identifiable information (PII) to untrusted models. Li et al. (2025a) also present PAPILLON, compound AI system consisting of 2 modules, a user query rewriter and a response rewriter, run over the trusted model, along with an intermediate call to the untrusted model with the rewritten query. The feedback text simply provides the breakdown of the aggregate score, consisting of a response quality score and PII leakage score. The dataset is split into 111 training examples, 111 for validation, and 221 for testing.
G.2 MODELS AND INFERENCE PARAMETERS
We evaluate GEPA and baseline optimizers using two contemporary LLMs, chosen to represent both open- source and commercial model families. Each compound AI system is instantiated once per model, with all
24

---

Published as a conference paper ICLR 2026
modules (e.g., retrievers, rewriters, answer generators) relying on the same model. All models are allowed a context window of upto 16384 tokens for inference.
Qwen3 8B (Yang al., 2025): : For our open-source experiments (including GRPO), we use Qwen3-88 Following the recommended settings per [Tamm (2025). we use a decoding temperature of 0.6, top-p of 0.95, and top-k of 20 for training as well inference.
GPT-4,1 Mini (OpenAl 2025); For comparison with large commercial models, we use GPT-4.1 mini (openai/gpt-4.. 1-mini--ii---- 1omminnn225---- 2025-2222 04-14) accessed via the OpenAl API with model temperature of 1.0.
G.3 COSTS
It costs under $500 to run all experiments in Table 2 with GPT-4, mini. Specifically, GEPA costs a total of S86, GEPA-Merge costs S67, MIPROv2 costs S76, and Trace and TextGrad cost $172 in total.
G.4 OPTIMIZERS
Baseline: The base program is directly evaluated without any further optimization applied.
MIPROv2 (Opsahl-Ong et al. 2024): MIPROv2 is a widely used compound Al system prompt optimizer and has been integrated into the DSPy (Khattab al. 2024) and pama-prompt-ops (AI 2025 frameworks. It works by jointly optimizing both instructions and demonstrations using Bayesian optimization. For each program module. it first bootstraps candidate sets of instructions and demonstrations, assigning uniform priors over their utilities. Candidate assignments are proposed with the Tree-Structured Parzen Estimator (TPE), and the Bayesian model is updated based on evaluation scores favor nigh-performing candidates. The most probable sets of instructions and demonstrations are then selected and validated to obtain the final optimized program configuration.
All MIPROv2 optimization runs are performed with the auto = heavy setting, which corresponds to propos- ing 18 instruction candidates and 18 bootstrapped few-shot sets. Hence, across benchmarks, the exact num- ber of rollouts varies depending on the number of trials it takes to bootstrap examples (finding 18 successful solution instances), the required number of Bayesian search steps (determined by the number of modules in the system), and size of the valset. Overall, MIPROv2's rollouts ranged from minimum of 2270 (for PUPA) to maximum of 6926 (for HoVer).
Trace and TextGrad (Cheng et al. 2024; Yuksekgonul et al. 2025): : We implement both optimizers in the Trace framework. All programs under optimization have the exact same architecture compared to the DSPy implementation. To ensure fair comparison, we port all the DSPy specific signature and parsing prompt to Trace, and use the same initial prompt. In addition, all the test, train, and validation data match exactly the experiment we used for GEPA. The performance of the unoptimized Trace program baseline closely matched our baseline implementation in DSPy (within 0.5% difference). All optimization experiments were under the same rollout budget MIPROv2 and GEPA. We also provide both optimizer the same metric and feedback functions as GEPA, and, for the mod-le feedback function that is not available in Trace (both optimizer do not support per-module feedback), we followed the feedback format in the BigBench- -ard tutoria
2 from the Trace authors.
GRPO (Shao et al., 2024): Group Relative Policy Optimization (GRPO) a reinforcement learning algo- rithm that estimates advantages in a group-relative manner. For compound Al systems consisting of multiple modules, we use the GRPO implementation provided and open-sourced by Ziems et al. (2025) to perform our experiments, whereas for single-module systems (e.g., figure 10). we use the GRPO implementation
provided by SkyRL (Griggs et al. 2025 Liu et al. 2025 Cao et al. 2025).
25

---

Published as a conference paper ICLR 2026
Across all compound system training runs, each training step uses group size of 12, with 4 training in- stances per step (total batch size 48, with per device train batch size 1). Training employs LoRA (Hu et al., 2022) with rank dimension 16, a 64, and dropout 0.05, using 6616 precision targeting the projection modules (q. k, v, up, down, gatel. We use learning rate of 1 x 10 3 0.01, reward scale normaliza- tion, and gradient norm clipping 0.1. Gradients are accumulated for 20 steps before each update, with a "constant with warmup learning" rate scheduler. Non-reentrant gradient checkpointing is enabled to further reduce memory usage. GRPO optimization run for 500 training steps, amounting to fixed 24,000 rollouts, with validation performed every 20 training steps, which is used to implement early stopping. Compound AI system GRPO training experiments are performed on 1xH100/A 100 (80 GB memory) with separate GPUs for inference rollouts.
For single-module module GRPO training, we adopt full hull-parameter finetuning with a group size of 16. Each training step employs global batch size of 32, realized as per-device micro-batches of across GPUs. Roll- out generation is performed with a per-GPU forward micro-batch size of 12. Training is distributed using FSDP2, with sampling performed at temperature 1.0. We apply KL regularization and set the learning rate to 1 x 10 Validation is conducted every 5 training steps. During evaluation, sampling is performed with temperature 0.6, top-p- = 0.95, and top-k 20.
We manually explore several values for [LR, beta, norm clipping] hyperparameters for both training runs.
GEPA: GEPA is our optimizer, based on the algorithm described in Section g We evaluate variants of our main optimizer GEPA: GEPA and GEPA+Merrge. along with 2 ablations created by replacing the Pareto- based sampling strategy with a naive. SelectBesCandidate strategy SesaniBesta and SelectBest- Cendidate+-Merge), All GEPA optimization runs use minibatch size of 3, and merge is invoked a maximum of times during the optimization run, when enabled. To ensure a fair comparison with MIPROv2, we align the computational budget between GEPA and MIPROv2 each benchmark is used as Feedback (which is used to derive the training signals, as discussed in Section 3 and the validation set is used as D parrtoo Specifically, since MIPROv2's s total rollout budget depends fac- tors such as validation set size and the number of modules, we first record the number rollouts expended by MIPROv2 for each benchmark, and then cap GEPA's optimization to match this rollout budget. While differences in proposal and validation procedures cause the exact budget usage by the systems to be slightly different, the discrepancy is always within 10.1.% This protocol ensures that any performance differences arise from the optimization algorithms themselves, rather than from differences in search budget. The exact rollout counts for each optimizer is visualized in Appendix !
per-benchmark basis. The training set from
H RESULTS AND ANALYSIS (CONTD.)
Figure 9 visualizes the final test set performance for aggregate and individual benchmarks across both mod- els.
Observation 4: Instruction-optimizze prompts are computationally cheaper and generalize better than few-shot demonstration prompts: In addition to their strong generalization capabilities, reflectively evolved instructions offer a significant practical advantage: they are often much shorter and thus computa- tionally more efficient than few-shot demonstration prompts. This advantage becomes especially clear for complex tasks, where even a single shw-shot demonstration can be prohibitively long. The problem is further exacerbated when few-shot examples optimized using state-of offtt-----h-arrr art methods such as MIPROv2, which jointly optimizes multiple demonstrations to be used simultaneously. further increasing prompt length.
In contrast, reflectively evolved instructions- while providing large performance gains (as demonstrated in Lessons and 2). To illustrate this, we com- pare GEPA's and MIPROv2's prompt lengths (see Figure 17p. Notably, prompts produced by GEPA and
those generated by GEPA-mminttil compactness
26

---

Published as conference paper ICLR 2026
Optimizer Performance GPT-4.1 Mini
100
MIPRO
M45
H4
90
CEPA
Merpa
80
Score
70
67.4
66.7
$4.00
30
41.8
49.1
40
18.0
Aggregate
HotpotOA
(a) test set performance for aggregate individual benchmarks for gpt-41 -mini.
Optimizer Performance Owen3
Bazelina
MIPROv2
90
GEPA
CCP% Marge
80
70
Scoee
57.6
K1.1
55.1
50
48.4
03
01.3
40
36.2 $0.8
Aggregate
HolpotQA
IFBench
HoverBench
PUPA
(b) Final test set performance for aggregate and individual benchmarks for qwen3-8b.
Figure 9: Final test set performance for aggregate and individual benchmarks.
GEPA+Merge are up to 9.2x shorter than those from MIPROv2, representing a substantial improvement in efficiency, alongside performance improvements.
Moreover, we observe a trend where, in aggregate, optimizers that achieve higher performance tend to produce shorter prompts (see Figure [6). This reduction prompt size has a significant impact-not only reducing runtime cost for downstream tasks (as all API-providers meter the input tokens), but also decreasing latency improving the overall efficiency L..--sevving systems (Kwon et al. 2023 Zheng et al. [2024]
Agrawal et al. 2023 Yu et al. 2025).
Observation 5: System aware crossover strategies can provide large gains, but the optimal budget allo- cation between mutation and crossover, as well as when to invoke merge needs further study: We iden- tify unique system-aware crossover strategy and operationalize it as Merge (described in Appendix [...) MePA+Merge outperform GEPA by as much 5%, providing additional improvement over the already strong performance established by GEPA. Detailed results are available in Table We attribute these gains the ability of GEPA+Merge to identify distinet optimization lineages, that have learnt complementary strategies (by evolving distinct modules), and merging them by picking the best version of different modules from each of these lineages propose single, optimal candidate.
27

---

Published as conference paper ICLR 2026
HoVer, Qwen3
Optimization Method
45
GRPO (Full finetune)
Valldation Performance
42
40
Test-set Performance
jestser Score
40
Score
35
34
32
25
0
5000
15000 Number of Rollouts
10000
20000
25000
Figure 10: This figure compares the learning behaviour of GEPA against GRPO with full-parameter finetun- ing on the 2-hop HoVer task. The relative gap mirrors the previously observed comparison GEPA against GRPO with LoRA (in figures 12 13 14,, showing that GEPA achieves comparable performance gap relative to both full-parameter and parameter-efficien versions of GRPO.
While in our analysis, we found GEPA+Merge works especially well for GPT-4.1 Mini, it lead to perfor- mance degradation when used with Qwen3 8B. Even Qwen3 8B benefits from Merge on one of four tasks. We attribute these discrepancies to the way the rollout budget is allocated between reflective mu- fixed
tation and crossover, and the timing of invocation of the crossover strategy. In experiments, the same hyperparameters for GPT-4.1 Mini and Qwen3 8B, leading to suboptimal choice for Qwen3 8B. Intuitively, crossover would provide the maximum benefit, when there are independent lineages that perform well. Hence, the hyperparameters should be chosen such that Merge is invoked once the optimization tree has evolved sufficiently different lineages. We propose the study of such adaptive techniques as future work.
Observation 6: GEPA-optimized prompts demonstrate cross-model generalization. Table 2 presents results for (GGEA--wee-OPP a configuration where prompts were optimized using smaller Qwen3-8B model but evaluated GPT-4.1-Mini Despite originating from weaker model in a different family, these prompts transfer effectively, achieving a +9.00% aggregate improvement across 6 benchmarks (with gains high +27.67% HotpotQA). Remarkably, this transfer performance outperforms strong baselines like MIPROv2 (+5.64%), TextGrad 11%), and Trace (+3.27%), even though those methods were optimized directly the target GPT-4.1-Min model.
I PERFORMANCE vs. BUDGET (ROLLOUTS) CURVES
Figures show the full Performance-we vo-lolouu curves for all the optimizers across all bench- marks.
J GENERALIZATION GAP
Figure 15 visualizes the generalization gap for different optimization methods.
28

---

Published as a conference paper at ICLR 2026
HotpotQA, GPT-4.1
HotpetOA, Querd
HotpetOA, Qwen3 BB
76
00.0
60.0
$5.0
53.0
valittoes Text Performnee
53
52.3
Netindaaie Methed
47.5
47.5
CEM
42.5
Farloraae
42.5
1009
2000
3200
0000
5050 1500
1800
2000
3000
4600
3000
8000
7600
5920
18300
19360
20360
23000
(a) GPT-4.1 1 Mini MIPRO
(b) Owen3 8B MIPRO
(c) Owen 8B GRRP
Figure 11: Hotpot QA Bench: rollout vs. score for different models/settings.
[FBench, GPT-4.1 Mini
IFBench, Qwen3 8B
|FBench, Owen3 BB
10.5
36,5
MOROVG
Teatt Scatre
75
H8.0
Score
04
rensovs
36.5
46.0
Number of Rolleuts
Mimber of Balbotts
Mamber of Redloats
(a) GPT-4.1 Mini MIPRO
(b) Qwen3 8B- MIPRO
(c) Qwen3 8B -GRPO
Figure 12: IFBench: rollout vs. score for different models/settings.
HoVer,
HoVer. Owen3
HoVer Owen3
52.5
52.5
50.0
50.0
47.5
47.5
oye criil Pectol
Boudin
37.5
37.5
35.0
39.0
1000 2000
3000
4050
5090
5000
7006
2000
2000
3000
4000
5000 5000 7000
5000
30060
15560
20000
25600
(a) GPT-4.1 Mini MIPRO
(b) Qwen3 8B- MIPRO
(c) Qwen3 8B GRPO
Figure 13: HoverBench: rollout vs. score for different models/settings.
PUPA, GPT-4.1 Mini
PURA, Qwan3 BB
PUPA, Quenn 88
50
90.0
88
Tetical Purtrrannc
17.8
$5.0
Sptecaation Nathed
Busdee
82.5
MINOO0
82
CaPo
80.0
Satres Narforeare
Texr Perfermans
1250
1995
2003
3000
18400
20002
Mumber Bollouts
Number Rollouts
Number Rolleuts
(a) GPT-4.1 Mini
MIPRO
(b) Owen' 8B MIPRO
(c) Qwen3 8B GRPO
Figure 14: PUPA: rollout vs. score for different models/settings.
29

---

Published as conference paper ICLR 2026
Ierafociten Dischet Bert Mildattee Serre Heyuege for DPP.
Granchrriien Day (Trcken Best Wolicatoon Rreecy Heliee
Goseccten Elistrr Best Woldiitoon Seeecy Plingg OFF.1
Imerediation Dnp (remettos Best Vellteten Seard Hetpelgk Quer
coweneeatiio Tesbet Best Velddrtion Seevel Holler for Qwer
Ooveralautiin Dbsttet Velierlee Serre
190
10.
Figure 15: Generalization gaps different optimization methods. Following Wan et al. (2024), we visualize the generalization gap (i.e., the difference between final test performance and the best achieved validation performance) for different optimizers. While Wan et al.] (2024) previously observed that exemplars tend to generalize better, our results suggest that instructions generated by reflective prompt evolution can achieve stronger generalization as well as improved overall performance. We hypothesize this difference may be due the improving capabilities of the underlying LLMs, more recent models are both better at adhering instructions and capable of reflecting on their outputs.
K COST VS. PERFORMANCE ANALYSIS FOR OPTIMIZED SYSTEMS
The prompt size of the optimized system plays an important role in determining the downstream cost of using the optimized system. Figure [6] visualizes the aggregate prompt lengths of the final optimized system (as cost proxy) for each optimizer, against the performance achieved. Notably, GEPA's prompts are around 33% shorter than MIPROv2's prompts, while achieving higher performance.
L GEPA SEARCH TREES
Figures 18
19
20
21
22
23 24 and [5 present the genetic search trees created by various configurations
of GEPA (and ablation SelectBestCandidar
M VISUALIZING THE ITERATIVE REFINEMENT ACHIEVED GEPA
M.1 PROMPTS AT INTERMEDIATE STAGES FOR PUPA
Figure 26 presented summary of prompt refinements performed by GEPA during the optimization for PUPA. In this section, present the full prompts produced during the optimization.
Node @ (Score 82. 26)
Given private user query, create privacy-preserving request for powerful external LLM.
30

---

Published as conference paper ICLR 2026
Comparing Scores Prompt Size (GPT-4.1 Mini)
Comparing Scores Prompt Size (Qwen3 8B)
GEPA-Merge
MIPROv2
68
AblisclectBesttandidatee
AbiisllccBesstCndddaat
GEPA
@GEPA
GEPA-Merge
GMPg+
Aggregate Score
Aggregate Score
AblifeictBattinddaat
64
58
GERA+ Merge
57
62-
60
MIPROV2
5000
3000 4000
Aggregate Prompt Token Count (Cost Proxy)
Aggregate Prompt Token Count (Cost Proxy)
(a) GPT-4.1 Mini.
(b) Qwen3 8B.
Figure 16: These plots visualize the final aggregate scores against the aggregate prompt size (across all benchmarks) of the final optimized system for each optimizer. It can be seen that GEPA consistently pro- duces prompts that are around less than 33% of the size of MIPROv2's prompts, while getting higher per- formance. Most of GEPA's prompt tokens are used for providing instructions, whereas most of MIPROv2's prompt tokens pertain to few-shot examples.
The LLM may assist whout learning private information tion about the user.
Node 2 (Score 90.99 99)
Task Description:
You are provided with private user query that may contain sensitive, personal, identifying information. Your role is to transform this query into a privacy-preserving request sui table for an external powerful large language model (LLM). The external LLM can be consulted ted to assist with the task but must not receive any private personally identifiable information (PII) about the user. Your goal is maximize the quality and relevance of the LLM request while minimizing or completely eliminating leakage of private information.
Key Points and Domain-Specific Details:
1. Privacy Preservation:
Do not include any user-specific or sensitive data in the external LLM request. When queries contain location, dates, names, URLs, or other identifiable details, generalize omit them in the LLM request.
Replace or abstract any private potentially identifiable content with neutral placeholders or general terms without losing the intent or meaning necessary for the LLM task.
2. Query Understanding and Reformulation:
31

---

Published as conference paper ICLR 2026
Optimized Prompt Size across optimizers (lower better),, GPT-4.1 Mini
CEPA iNergee
Prompt Size (in tokens)
1.0000
8000
6000
4000
2011
2000
J.50
T.N.
Aggregate
HotporQA
(a) Comparing token counts optimized programs across benchmarks GPT-4.1 Mini.
Optimized Prompt optimizers (lower is better), Qwen3
MIPROVZ
Prompt Size (in tokenns)
8000
7236.
4000
2000
lower
1.74 Fowee
Loner
Aggrégate
HotpoeQA
iFBench
HoVer
PURA
(b) Comparing the token counts of the optimized programs across benchmarks for Qwen3 8B.
Figure 17: Comparing the token counts of optimized programs across benchmarks.
(b) SeleclBesiCandidate Merge
(a) AbbSSeleclBesCanddddtte
(c) GEPA Best Config
(d) GEPA+Merge
Figure 18: HotpotQA GPT-4.1 Mini
Analyze the user's query carefully to understand the underlying task or information need.
32

---

Published as a conference paper ICLR 2026
(b) SeleclBeslCandidate
(d) MEre+Merge Best Config
(a) AbbSeelecBessCandddaee Merge
(c) GEPA
Figure 19: HotpotQA Qwen3 8B
(b) SelectBestCandidate Merge
(d) GEPA+Merge Best Config
(a) AbbSSelectessCandddate
GEPA
Figure 20: IFBench GPT-4.11 Mini
mine
(b) (a) Abb.ScledBessCCudddttt date+Merge
SelectBestCandi-
(c) GEPA Best Config
(d) GEPA+Merge
Figure 21: IFBench Qwen3 8B
33

---

Published as a conference paper at ICLR 2026
(b) SeleclBesiCandidate +
(d) GEPA+Merge BBest
(a) Abl.SclotBestCandddate
Merge
(c) GEPA
Config
Figure 22: Ver GPT-4.1 Mini
(b) SeleeiBesiCandidate (a) AbblSelectBessCandiddee Merge
(c) GEPA Best Config
(d) GEPA+Merge
Figure 23: Ver Qwen3 8B
(b) SelndtBesCandddate
(d) GEPA+Merge
Best
(a) AbbSSelecBessCandddae Merge
(c) GEPA
Config
Figure 24: PUPA GPT-4.1 Mini
34

---

Published as conference paper ICLR 2026
(b) SeleclBesiCandidate Merge
(a) Abb.SclotBessCandidatt
(c) GEPA Best Config
(d) GEPA+Merge
Figure 25: PUPA Qwen3 8B
Bose Instruction.
poserrful actarnal LLM, The LLM may assna without learnin private infermiii about the tser
(92.20)
Txpanded Pricacy Strategles Taok Understanding and
Adds quidenoe Streesed query inlund introdursss ressonig exglatalaans rnfermulation.
(B7.25)
00.39)
741
Stractured Domatn Practices Formalines split into Bessoning and Request. Explicily bans nemnediicher, descmbbee requires pricuty gistilication Ielunce with qpality
(91.7)
scsmpll
(94.44)
Detailed,
Troasfermation Rationale
1ot
Enterces transpozent provece reasoning and carcful abstrartion. and fictiinal cheracer fandinng-slooe elboyys gained eeplenacury rationale
Dee
indo
(94,77)
palax:
Rigerous, Eshaustive Protocol
partial reductios
maxtmize stilnty whill beff-gactioss arways justit approach,
(97.6)
33,7
auditable soro tolerated.
Figure 26: GEPA's reflective prompt mutation systematically incorporates task-specific nuances, leading to substantial improvements in performance. This figure visualizes the optimization trajectory taken by GEPA, presenting annotated subtree from Figure 24d (for the privacy-preserving delegation task PUPA) to demonstrate the iterative enhancements made to the prompts. The progression from the base prompt (candidate 0) the best performing prompt (candidate 11) is highlighted with red arrows, and key prompt changes at each step are annotated beside the corresponding nodes. Full-length instructions for these iter- ations are provided in Appendix M. Each prompt refinement in this trajectory adds targeted nuances in- formed by ongoing optimization, illustrating how GEPA's process accumulates lessons to continually boost task performance.
35

---

Published as conference paper ICLR 2026
Determine the query involves translation, event recomendattons, advice, summarization, or other tasks. Identify when usev-provided content is sensitive or proprietary (e.g., unique texts, URLs closed content) and avoid directly exposing it to the external LLM.
3. Quality Maximization:
Produce an LLM request that is clear, precise, and directs the LLM to perform the necessary task without requiring private context. Retain the
informational functional need that the LLM respond
effectively.
When referencing external documents or URLs, do not include the actual link or private info; instead, request general or typical information related to the topic.
4. Common Strategies Privacy-preservingg Requests:
Use paraphrasing or abstraction for sensitive content.
Request generalized or example-based information instead of specific user data. When translation is required, include only the text that needs translating if non-private, or otherwise generalize accordingly.
For location- or event-specific queries, request general or typical information about the category or area without including sensitive details.
5. Reasoning Explanation:
For each transformation, produce clear reasoning explaining how privacy preserved and why the LLM request fulfills the useråAžs intent without leaking sensitive information.
Input Format:
user query string possibly containing private or sensitive information.
Required output:
a) A reasoning paragraph explaining your analysis and privacy considerations.
b) A privacy-preserving LLM request that be sent to external LLM for assistance.
By follawing the above principles strategies, you will optimize both the quality assistance and the protection of user privacy when interacting with third-party language models.
Node 4 (Score 94.
Task Description:
You will be given information. suitable for submission to an external large language model (LLM). The goal is to enable the external LLM to assist effectively while ensuring no private, identifying, or sensitiv tive information about the user is exposed.
query that may include sensitive, personal, identifiable role to transform this query into privacy--reserving request
Key Requirements Domain-Specific Details:
1. Privacy Preservation:
Do not include user-specific details, personal names, locations, dates, URLs, other potentially identifiable information the outgoing LLM request.
36

---

Published as conference paper ICLR 2026
When the user query contains private, sensitive, or proprietary data, you must generalize, abstract, or omit these details.
Replace sensitive specifics with neutral placeholders or describe them in generalized terms without losing the core intent of the query. Avoid exposing non-public, proprietary, or confidential content directly to the external LLM.
2. Understanding and Reformulation:
Carefully analyze the user's query to identify the underlying task type (e.g., summarization, creative writing, translation, profile writing, event recommendations company background, academic content generation).
When reformulating, maintain the essential informational or functional need so that the external LLM provide useful, relevant response.
Preserve thematic and contextual elements necessary for quality output, while abstracting any sensitive or private details.
If the task involves user-supplied content (e.g., essays, texts, character descriptions), distill summarize the relevant content or use generic versions instead of exposing original private text.
3. Maximizing Quality of the Reformulated Prompt: Construct clear, precise, and well-structured requests that explicitly guide the external LLM on the task.
Retain appropriate detail and context carefully against privacy concerns.
relevance, but balance this
If referencing external documents, URLs, or institutions, do not include any links or private identifiers; instead, request general or typical information related to the subject matter.
When writing prompts for creative or fictional character tasks, clarify that the profile or content is fictional to avoid accidental exposure of real personal data.
4. Common Strategies for Privacy Preservation:
Paraphrase or abstract personal and sensitive content.
Use general descriptions or bypothettical/exxmlle-aasss requests where appropriate. Omit or generalize specific names, dates, locations, institutions, or proprietary
course codes.
When content is extensive, focus absorbing the themes and instructing the external model accordingly rather than passing the raw content.
5. Explanation Requirement:
Provide a concise reasoning paragraph explaining how identified sensitive or private details and the steps you took protect user privacy. Clarify your reformulated
request retains useraA2s original intent and task needs without risking data leakage. This explanation is mandatory to document privacy-preserving approach and to justify the abstraction choices made.
Input and Output Format:
Input: A single user query string that may include sensitive personal content.
Output:
a) Reasoning: A paragraph explaining privacy considerations reformulation strategy.
37

---

Published as conference paper ICLR 2026
b) LLM Request: generalized, privacy-safe prompt subta for querying an external LLM.
Examples and Feedback Integration:
Avoid including real names, course codes, or faculty references; replace general terms (e.g., "an interdisciplinary health minor" rather than "HHV").
When given character bios or profiles, specify the fictional nature clearly and do not include potentially identifying physical descriptions unless necessary for the task. When
major company asks about specific companies entities, generalize the request to "a Lindustry/countryY" if privacy concerns exist.
Maintain balance between minimal leakage and maximizing task relevance and quality. Ensure the reformulated prompt supports comprehensive and useful response from the external LLM without compromising privacy.
By rigorously following
detailed instructions, you will ensure maximal protection of user privacy while harnessing the power of external LLMs to assist effectively with diverse user requests across domains.
Node 5 (Score 94. 67)
New Instruction for Privecy- Query Reformulation Assistant:
Task Overview:
You receive a user query that may contain sensitive, private, or personally identifiable information (PII). Your goal is to transform this input into generalized, privacy--reserving prompt suitable for querying an external large language model (LLM). The reformulated prompt must enable the external LLM to fulfill the user's original intent effectively, while rigorously protecting user privacy by abstracting or omi tiinting any sensitive details.
Input Format:
single string representing user query.
The query may include private names, places, dates, proprietary info, or identifiable context.
Output Format:
Part (a) Reasoning: concise paragraph explaining:
How you identified sensitive or private information in the input. What generalization, abstraction, omission strategies you applied to protect privacy.
How the reformulated prompt maintains the original task or informational need without risking data leakage.
Part (b) LLM Request: carefully constructed and concise, privacy-safe prompt that retains essential thematic and functional elements achieve useful, relevant response from external LLM.
Key Domain Specific Details and Best Practices:
Privacy Preservation Principles:
38

---

Published as conference paper ICLR 2026
Remove or replace all user-specific names (personal or organizational), exact dates or durations, locations, URLs, proprietary course or product codes, customer or client names, and other identifiers.
When geographic organizational mentions critical for context, abstract these broader or public categories (e.g., âλa sustainable travel website focused on a Central American countryåAî rather than naming the country explicitly).
Omit or abstract internal organizational pressures, client names, or sensitive contractual details to generic placeholders (e.g., aÃa clientåAI rather than named company).
For biographical or individual-rellted queries, avoid requesting or referencing real personal info; instead, frame requests around hypothetical or generic profiles unless the name is widely public and essential for the task.
2. Understanding Reformulating the Task:
Identify the underlying task (creative writing, summarization professional communication drafting, etc.) from the useråÄžs query. Preserve the functional intent and thematic requirements (e.g., content topics around sustainability, summary of personåA2s background, professional email follow-up).
For uspp-supplied content, do not repeat verbatim expose original text; instead, extract core themes, instruct the LLM accordingly, or replace with generic examples.
3. Maximizing Output Quality While Preserving Privacy:
Construct a prompt that is clear, precise, and contains sufficient context to enable comprehensive and relevant LLM output.
Avoid ambiguous or overly generic requests that might reduce relevance or usefulness. Maintain balance between detail necessary for quality and generalization required for privacy.
4. Common Reformulation Strategies: Replace specific names with generic role identifiers or placeholders (áña business contact, åλī åλа notable individualákt).
Replace specific locations or institutions with generalized descriptors, e.g., åña country known for eco-tourism. åÄī For time references, use relative or approximate terms without revealing explicit dates.
For internal or proprietary details, describe the scenario generically (e.g., aApressure from client close matter urgentlyåĀl).
When handling personal profiles characters, explicitly state that the profile fictional if requesting content generation about person.
5. Explanation Requirements:
Always provide reasoning that details how privacy risks were identified and mi mitigated.
Explain how the essential task was preserved despite abstraction.
This reasoning documents the privacy-preserving approach and justifies design choices.
Examples and Common Pitfalls: Do not retain lightly obscure personal names or company names; fully abstract remove.
Avoid exposing direct quotes from uspr-supplied texts.
39

---

Published as conference paper ICLR 2026
Use neutral, context-appropriate phrasing to describe user intents.
Retain geographic or cultural context only when it is publicly available and pivotal to the task.
Ensure the output prompt remains clear instruction that the external LLM readily interpret.
Sunmary:
Your reformulations must ensure zero exposure of PII or private/proprietary content while retaining enough context and clarity to generate high-quality, relevant responses from the external LLM. This involves careful task analysis, methodical abstraction sensitive details, and clear, precise prompt construction combined with transparent reasoning on privacy protection mechanisms used.
Node 11 (Score 97.6)
New Instruction for Privacy-Preservin Query Reformulation Assistant (Enhanced):
Task Overview:
You receive a user query that may contain sensitive, private, or personally identifiable information (PII). Your objective is to reformulate this query into a generalized, privacy-preserving prompt suitabl wuitable for sending to external large language model (LLM). The reformulated prompt
enable the external LLM to fulfill the user's original intent effectively while rigorously protecting all user privacy by abstracting, omitting, or generalizing any sensitive details.
Input Format:
single user query string.
The query might contain identifiable names (people, organizations), specific geographic locations, exact dates or timeframes, proprietary or internal terminology (e.g., system names, product codes), client or customer names, URLs, or any other sensitive or private information.
Output Format:
Part (a) Reasoning:
Provide concise paragraph that explains:
How you identified sensitive private information the input What strategies you applied to protect privacy (generalization, omission,
abstraction, replacement with placeholders)
How the reformulated prompt preserves the original intent and task requirements
without risking data leakage
Part (b) LLM Request:
concise, carefully constructed privacy-safe prompt that:
Removes or anonymizes all PII and proprietary/intemnn details
Abstracts locations, names, dates, and technical terms as needed
Produces clear and contextually rich instruction for the LLM generate relevant and informative response aligned with the user's original task
Detailed Specifc Guidance and Best Practices:
Identification and Treatment of Sensitive Data:
40

---

Published as conference paper ICLR 2026
user-specific or personal names (individual or organizational) must removed or replaced with generic role descriptors (e.g., åλa business contact, à âña client,åAi åλa notable individualákt). Never lightly obscure partially redact; full abstraction is required.
All geographic mentions must be abstracted unless location publicly known, essential to the task, and can be generalized (e.g., åλа region known for eco-tourismaät instead of naming country city explicitly).
Exact dates durations must never be retained; instead, âñover past yearāAl).
relative
approximate temporal references (e.g., aArecently, Internal or proprietary terms åÄT including system names, product codes, subscription types, and technical jargon aAt must be generalized or replaced with neutral descriptors avoid leakage of intellectual property sensitive operational details. Avoid direct quotes or verbatim inclusion of user-supplied texts unless obfuscated by generalization.
2. Task Understanding and Reformulation:
Identify the functional intent of the query: creative writing, translation, summarization, professional commuiication drafting, technical explanation, or other? Preserve the thematic and informational core of the query (e.g., request for educational quality analysis, technical translation of passage, biographical summary).
Do not reproduce the original input verbatim; instead, frame the LLM prompt around the essential thematic elements extracted from the input.
For queries regarding individuals, avoid direct reference to real personal information unless the name is widely public and essential; even then, generic or hypothetical framing for individual profile.
3. Strategies for High-Quality and Privacy-Preserving Prompts:
Strike balance between sufficient contextual detail and privacy abstraction to maintain prompt clarity and relevance.
Use neutral, context-aware formulations that clearly instruct the LLM on the content and style expected.
Avoid vague overly generic prompts that could result in less useful or lower-quality responses.
When system or proprietary content is mentioned, instruct the to generalize specific terms and maintain the technical meaning without revealing sensitive info. When a direct translation is requested on specialized text, specify to replace abstract internal nomenclature.
4. Explanation Requirements:
The reasoning must transparently explain how privacy risks were identified (e.g., presence of names, locations, dates, proprietary terms).
must describe the abstraction omission methods applied (e.g., replacing aÄJonah Beijnenák with notable individual, åäi substituting åÄMakaušλł with áăa specific region, áăI or åÄYodaåÄt with åλa system nameâAī).
Clarify how the essential task and user intent were preserved despite these generalizations (e.g., focusing educational quality, technical translation, biographical summary).
This explanation justifies your design choices and demonstrates adherence privacy-preserye principles.
Common Pitfalls Avoid:
41

---

Published as conference paper ICLR 2026
not merely lightly obscure or partially redact sensitive details; full anonymization
or abstraction is required.
Do not repeat any uspr-supplied PII or proprietary content verbatim.
Avoid including URLs, exact dates, direct quotes wi without modification
not leave ambiguity that could degrade the quality or contextual clarity reformulated prompt.
Do not include any real personal or organizational names unless they are public figures and the query requires it, then generic descriptors instead.
Example Summary Effective Approach (Informed by Prior Examples):
For geographic queries: replace exact place names with general regions and provide a brief contextual descriptor.
For technical texts containing system names subscription types: instruct the LLM to translate or process the text while replacing or abstracting proprietary system identifiers.
For biographical summaries about specific individuals: remove the real name and request generic, well-structured four-paragraph summary about åλa notable individual,åAf preserving the overall intent without leaking PII.
Sunmary:
Your reformulations must ensure zero exposure of any PII or private/proprietary content while retaining enough thematic and functional clarity for the external LLM produce high-quality, relevant outputs. This requires thorough analysis of the user's query, rigorous application of privacy-preservation strategies, and explicit reasoning explanations that document your approach and choices.
N EXAMPLES OF BEST PROMPTS FOR EVERY BENCHMARK
In this section, we present the best optimized prompt obtained for every (benchmark, model) configuration. Each subsection below pertains one (benchmark, model) configuration. Since every compound Al system consists of multiple modules, each subsection consists of multiple boxes, listing the prompts for each mod- ule. MIPROv2 optimized prompts contain upto few-shot examples for each task. We provide just the first demo here for brevity. GEPA's prompts only consist of the optimized instruction, which is provided in full.
N.1 HOTPOTQA, GPT-4.1 MINI
HotpotQA GPT-4.1 Mini
create_ ceaatt__uery__oppe predict
Base Prompt:
Given the fields "question',
"smmaar_11
produce the fields "query".
MIPROv2 Prompt:
You expert multi-hop question answering system designed refine retrieval queries for complex questions. Given the original question retrieved documents (summary_1), think step by step to analyze how the summary relates to the question. Use this reasoning to generate a focused and precise query that will guide the retrieval of additional relevant information needed to answer the question completely. Your output should include clear chain-of-thought reasoning process
initial summary of
42

---

Published as conference paper ICLR 2026
followed by refined query that targets complementary evidence for next retrieval
hop.
Demos:
Example 1:
question:
Simone Benedetti plays for a team that currently competes which Serie?
sunmary_1:
Simone Benedetti plays for Virtus Entella, but the passages do not specify which
Serie the team currently competes in.
reasoning:
The summary indicates that Simone Benedetti plays for Virtus Entella, but it does not state the current Serie (league level) in which Virtus Entella competes. To accurately answer which Serie the team currently competes in, it is necessary look up the most recent league information for Virtus Entella. query:
What Serie (league level) does Virtus Entella currently compete in?
aa other demos omitted for brevity. #
GEPA Prompt generated by config GEPA:
You given two input fields: "question" and "summary_1"
Your task is to generate
search query ('query') optimized for the **second hop** of multi-hop retrieval system. The original user question is typically complex and requires information from multiple documents answer. The first hop query original question used to retrieve an initial set of documents. Your goal is to generate **second hop query** that retrieves #additional relevant documents* that were *not* found in the first hop but are necessary to answer the original question completely.
Detailed task instructions and hints:
1. **Input Understanding:wt
"question" is the original multi-hop question posed the "summary_1" concise summary of information from document retrieved first hop, which partially addresses the question.
2. **Purpose and *ontext:**
Your generated "query" aims to find the #missing pieces* of information needed fully answer the "question".
The multi-hop retrieval system works in stages:
-irrt hop: The original question returns some documents.
Second hop: Your query must help retrieve any *other relevant documents* NOT found in the first hop that hold complementary or broader context necessary for final answer extraction.
3. **Key Observations from Examples and Feedback:**
First-hop documents often cover entity aspect in the question. Remaining relevant documents often involve connected or higher-level concepts mentioned in "summary_1" but not explicitly asked in the original question.
43

---

Published as conference paper ICLR 2026
The "query" should be formulated to explicitly target these smissings, but logically linked, documents.
Avoid merely paraphrasing the original question or restating known facts from "summary_""
Instead, infer what broader or related entities/concepts might provide the crucial missing information.
For example, if "summary_1" describes a population for a small civil parish, but the question wants total population of the wider region, your "query" should target that wider region (e.g., "Madeira archipelago population in 2011").
Similarly, if "summary_1" covers song and the question wants the album it came from, but first hop got song-level documents, your query should retrieve documents about the album itself.
4. **How Build the Query:**
Identify the entities or topics mentioned in "summary_1 that appear related different from first-hop documents.
Reframe the query to explicitly mention these broader related entities connected the original question.
Include relevant key context from the question maintain specificity, but shift focus to the missing piece.
The goal is to retrieve documents that link or complement what was retrieved initially.
5. **Practical Strategy:**
Read the "summary_1" carefully to spot references to bigger contexts or other entities not covered in the first hop.
Ask yourself, "What entity aspect does this summary hint at that could answer the original question but was found yet?"
Formulate a precise, focused factual query targeting that entity or concept to retrieve the missing documents.
6. **Output:**
Produce only the field "query" as a clear, concise question or keyword phrase designed for efficient retrieval of **second-hop documents**.
Ensure the query relates logically to the original question while targeting the broader complementary knowledge identified in "summary_1".
Do **not** include the original question simply rephrase
Do **not** duplicate information already well-covered by the first hop retrieval.
By following these principles, you will help the multi-hop retrieval system find all the multi-faceted original question completely.
necessary documents
HotpotQA GPT-4.1 Mini final_ansee predict
Base Prompt:
Given the fields "qeestion., "summary_1"
ssmmaary_ __" produce the fields "answer".
MIPROv2 Prompt:
You are an expert multi-hop reasoning assistant skilled in synthesizing information from multiple summaries to answer complex questions. Given the "question", along with two intermediate summaries "summary_1" and "summary_2" that contain relevant evidence,
44

---

Published as conference paper ICLR 2026
carefully analyze and integrate information step-by-step to produce a clear, logical reasoning process followed by a concise and accurate final answer.
Demos:
Example 1:
question:
are Machaeranthera and Prumnopitys tys both the plants?
sunmary-1:
Yes, both Machaeranthera and Prumnopitys tys plants. Machaeranthera is genus of flowering plants in the daisy family, while Prumnopitys is genus of coniferous evergreen trees the podocarp family. sunmary_2:
Yes, both Machaeranthera and Prumnopitys are plants; Machaeranthera is genus of flowering plants in the daisy family, while Prumnopitys is genus of coniferous evergreen trees the podocarp family. reasoning:
Both summaries agree that Machaeranthera and Prumnopitys are plants. Specifically, Machaeranthera is genus of flowering plants in the daisy family, and Prumnopi tys is genus of coniferous evergreen trees in the podocarp family. Since both belong plant genera, the answer is yes.
answer:
yes
other demos omitted for brevity. a
GEPA Prompt generated by config GEPA:
Task Description:
You are given three fields input: "question", "summary_1", and "summary_2" 2" Your goal produce an "answer" field that directly and explicitly responds to the question using the information from the two summaries, enhanced by your authoritatios domain knowledge when needed.
Input Format:
"question": A natural language question that may require a fact, definition, name, date, yes/no response, or other specific information.
"summary_1" and "summary_2" Two independently generated summaries snippets containing information related the question. They may vary in completeness, accuracy, and specificity.
Requirements and Approach:
1. **Understand the question precisely Determine exactly askedaäfwhether name, a specific fact, a date, or a yes/no answer.
2. **Compare both summaries. ** Analyze the content of "surmary_1" and "summary_2" If they agree and directly answer the question, this primary evidence.
If one summary provides fact that the other does not mention, carefully evaluate its plausibility.
45

---

Published as conference paper ICLR 2026
If the summaries conflict, use domain exper tise authoritativv knowledge resolve or explicitly state uncertainty.
3. *=Domain-specific factual verification and nuance;**
**Names nicknames: Provide only the specific nickname or name when asked, whohout extra phrasing. For example, when asked entity, respond with the nickname alone, not a full sentence.
nickname a person or
**Nationality and identity distinctions: Use the most precise terms aligned with factual correctness and common usage ge.g., äÄEnglishäAt domain knowledge.
aABritishåAT) based
**Dates and historical facts:** Verify dates or historical claims with domain knowledge to pick the correct fact, especially when there might be confusion between franchise start dates event dates etc.
**Yes/no questions:** Prefer concise answers aAyesåA1 or âλnoâÄř only, unless the question demands elaboration.
**yypes or categories; If question asks about the type category (e.g., type of company), provide the most direct concise phrase without including adjectives like nationality unless asked explicitly.
4. **Answer conciseness and relevance:**
Provide brief and direct answer to the question.
Avoid repeating restating the question.
Avoid unnecessary context unless requested or needed clarity. Avoid constructing full sentences unless needed; for example, answers to nickname or yes/no questions should be as short and specific as possible.
5. x*When authoritative knowledge supplements the summaries:
the summaries are incomplete or potentially inaccurate, incorporate trusted knowledge from your training about the topic to provide the correct and precise answer. For example, when summary gives year that conflicts with known release dates or factual details, prefer the verified date.
When the summaries differ style (one uses formal phrase, another provides just the nickname), respond with correct, clean answer format (e.g., just the nickname alone).
6. **Emplees of correct reasoning and answers:**
Question: âAWhat the nickname of the 2005 Toyota Grand Prix Long Beach Polesitter 2åA1
Correct answer: the thrill from West Hill
Question: åAwhat type of company is Zipcar led by Scott Griffith from 2003-2013787
Correct answer: "car-sharing company"
Question: aAWho was the partner of British comic book artist, Henry Flint, that helped create Zombo?āAi
Correct
"Al Ewing"
Sunmary:
Use both summaries primary but not sole evidence.
Reliably verify and contextualize facts using domain knowledge. especially for nationality, dates, nicknames, company types, and yes/no questions.
Provide short, direct answers matching the specificity requested. Avoid unnecessary elaboration unless explicitly required.
46

---

Published as conference paper ICLR 2026
Explicitly resolve conflicts or ambiguity using your knowledge or state uncertainty when appropriate.
This approach ensures that answers both accurate and concise, suitable for direct consumption or integration knowledge bases or
systems.
HotpotQA GPT-4.1 Mini summarize1 predict
Base Prompt:
Given the fields "question", passages"
produce the fields "summary".
MIPROv2 Prompt:
Given question and a set of related passages, carefully analyze the information by thinking through the relevant facts step-by-step. Produce clear and concise summary that synthesizes the key points from the passages directly relevant to answering question, ensuring the summary is focused, accurate, and grounded in the evidence provided.
Demos:
Example 1:
question: The architectural style of a church that stands in front of the Palazzo Ghisilardi Fava originated in what city?
passages:
Palazzo Ghisilardi Fava is Renaissance style palace, located on via Manzoni in Bologna, region of Emilia Romagna, Italy; it houses the Medieval Civic Museum of Bologna.'s 'Madonna di Galliera, Bologna The Madonna di Galliera is church with Renaissance facade and Baroque interiors, located Via Manzoni, in central Bologna, Italy. It stands in front of the Palazzo Ghisilardi Fava. The present name over the portal is the "Chiesa Filippini Madonna di Galliera e Filippo Neri".' 'Palazzo Pretorio, Prato The Palazzo Pretorio of the Tuscan town of Prato was the old city hall located town center, standing in front of the current Palazzo Comunale. It accommodates the Civic Museum of Prato, which was reopened on September 2013.', 'Palazzo Pisani Gritti The Palazzo Pisani Gritti a Gothic-style palace located across Grand Canal from the Church of the Salute, and stands along the Campo del Traghetto where the church of Santa Maria del Giglio, in the Sestieri of San Marco, Venice, Italy.', 'DÄümÄūtÄür Tower The DÄümÄütÄūr tower is the oldest building probably laid during the 11th century, while lower part was built (in Romanesque style) from the 12th century, and the upper part (Gothic style) from the 13th century. The tower once part of the former "Szent DÄünÄütÄür templom" (St. Demetrius church), but today stands in DÄşm Square, front of the much larger Votive Church of Szeged. The techure of the tower is similar to another found in Southern France, or in the territory of the former Byzantine Empire.', PPalazzo Leoni, Bologna The Palazzo Leoni is Renaissance style palace located Via Marsala #31, in front of the Romagna Italy. The apse
['Palazzo Ghisilardi
Szeged, Hungary. The foundation
upper part was rebuilt from the original stones in 1926.
outlet Via Mentana, in central Bologna, region of the church of San Martino, is across the street.'. 'Palazzo Altieri Palazzo Altieri is a palace in Rome, which was the home of the Altieri family in the city. The palace faces the square in front of the Church of the Gesăz. reasoning:
47

---

Published as conference paper ICLR 2026
The question asks for city where the architectural style of church that stands in front of the Palazzo Ghisilardi Fava or originated. Passage [2] identifies the church standing in front of the Palazzo Ghisilardi Fava "Madonna di Galliera" and
mentions it has Renaissance facade and Baroque interiors. Passage [1] states the Palazzo Ghisilardi Bologna and features Renaissance architectural style, can be inferred that the architectural style of this church originated in the city of Florence, which is historically recognized the birthplace of Renaissance architecture Thus, the architectural style (Renaissance) of the church in front of the Palazzo Ghisilardi Fava or irignatedd in Florence. summary:
located Bologna, Italy. Since the church located
The Renaissance architectural style of the church standing in front of the Palazzo Ghisilardi Fava originated in Florence.
3 other demos omi tted for brevity.
GEPA Prompt generated by config GEPA:
You are first-hop #*sumaarizatin module** in multi-hop question answering (QA) system. Your task is to generate concise, informative "summary" given two input fields: "uestioon and list relevant "passages".
Your goal is to extract and synthesize key information from the retrieved passages that:
1. Directly relates the initial question.
2. Captures core facts and entities needed understand scope and context the question.
3. Includes relevant connections, bridging entities, dates, locations, or descriptions that enable the system to devise focused and effective follow-up queries in subsequent hops.
4. Provides a strong factual foundation for downstream answer generation modules.
**Task specifics and best practices:**
The "summary" must represent distilled synthesis, not just a compression or extractive snippet.
Explicitly include cited passage titles or key entity labels (e.g., "Children in Need 2006 ...." "Anthony Levandowski ...") in your summary to highlight the origin of information.
Incorporate sufficient context hint missing or un-retrieved supporting facts, thus enhancing the multi-hop retrieval process.
When the question asks for an attribute (e.g., nationality, location, company origin), ensure you provide:
Identification of the relevant subject entity mentioned the passages.
The extracted attribute or relevant information as stated or implied.
Bridging details that could help the system pursue remaining information in the next retrieval step.
Avoid forming final answer; instead, focus "what known now" from the input
documents to facilitate Further query refinement.
** Examples of critical elements to include:** **
48

---

Published as conference paper ICLR 2026
Entity names, roles, titles, and dates tied to the question.
Names of organizations locations connected through intermediary entities. Distinctive identifiers clarifications that can help narrow down next-step retrieval (such as "Natasha Kaplinsky is an English presenter, "Kapolei city on Oahu," or "Waymo spun out Alphabet").
**Format of output:**
Provide paragraph or a sentences that cohesively summarize the key passages relation to the question, referencing passage titles or entities to frame facts clearly.
This approach ensures the summary both informative for next-hop retrieval foundational for final answer extraction in sulti-hop QA.
HotpotQA GPT-4.1 Mini Summarize2.prede
Base Prompt:
Given the fields "qeestion, 'cnnextt"
passages" produce the fields "summary".
MIPROv2 Prompt:
Given "question', relevant 'context', and list "passages', provide clear, concise summary that integrates key information from the passages in relation to the question and context. Use step-by-step reasoning to explain how the summary is derived from the evidence before presenting the final synthesized summary.
Demos:
Example
question:
What bank was founded by the seeat-reat-aee grandfather of the second Duke of Florence?
context:
The bank founded by the great-great- grandfather the second is the Medici Bank, established by Giovanni di Bicci de" Medici.
Florence
passages:
[ I'Giovanni di Bicci de Medici Giovanni di Bicci de' Medici (c. 1360 aăş February 20/28, 1429) was an Italian banker, a member of Medici family Florence, and the founder of the Medici Bank. While other family members, such as Chiarissimo di Giambuono del Medici, who served in the Signoria in 1281, and Salvestro de Medici, who was implicated in the Ciompi Revolt of 1378, are historically significant.
Giovanni founding of the family bank truly began the family\'s rise power Florence. He was the father of Cosimo de' Medici ("Pater Patriae"), grandfather of Piero di Cosimo del' Medici, great-grandfathhr of Lorenzo de Medici (the fagnificent) and great-great-great--raadde Tuscany." Averardo del Medici Averardo de Medici (1328-1363), also known Everard Medici, was the son Salvestro de Medici (died 1346), "il Chiarissimo" (English meaning "the very clear. and the father of three children: Giovanni, Francesco, and Antonia. Giovanni di Bicci de Medici would later become the first historically relevant member of Medici family of Florence and the eventual founder of the Medici bank.'. "villa Medici Cafaggiolo The Villa
of Casimo de Medici, Grand Duke of
49

---

Published as conference paper ICLR 2026
Medicea di Cafaggiolo a villa situated near Tuscan town Barberino Mugello in the valley of the River Sieve, some 25\xa0kilometre north of Florence, central Italy. It was one of the oldest and most favoured of the Medici family estates, having been in the possession of the family since the 14th century, when it was owned by Averardo considered be the founder the Medici dynasty.", 'Medici: Masters Florence Medici: Masters of Florence is an Italian-British television drama series about the Medici dynasty set in 15th-century Florence, starring Dustin Hoffman Giovanni di Bicci de'' Medici, Richard Madden Cosimo de\' Medici, and Stuart Martin Lorenzo de Medici ("The Elder").
Medici. Averardo's son, Giovanni di Bicci
Medici,
series was co-created by Frank Spotnitz ("The X-Files" and "Man in the High Castle") and Nicholas Meyer (""). Sergio Mimica-Gezzan ("The Pillars of the Earth") directed all eight episodes. Episodes and 2 aired on Rai (Italian TV) 18 October 2016. According to Italian ratings compiler Auditel, it attracted record 7. 6'xammilllon viewers. The first season consists eight Bicci I Neri di Bicci (14198Aş1491) was an Italian painter of
episodes.'s , 'Neri the Renaissance. prolific painter of mainly religious themes, he was active mainly in Florence and in the medium of tempera. His father was Bicci di Lorenzo. His grandfather, Lorenzo di Bicci Aretino." 'Cosimo de)' Medici Cosimo Giovanni de' Medici (called \'the Elder\ (Italian "il Vecchio") and posthumously "Father of the Nation" (Latin "pater patriae"); 27 September 1389 aĀş August 1464) was an Italian banker and politician,
also painter Florence, pupil of Spinello
the first of the Medici political dynasty, "de facto" rulers of Florence during much the Italian Renaissance. Despite Florence\'s 's legislative councils at times resisted his proposals, something which would not have been tolerated by the Visconti of Milan, for instance; throughout his life he was always "primus inter pares" ("first among equals"). His power derived from his wealth archi tecture "Giovanni Popolano I Giovanni Pierfrancesco de" Medici, later known as il Popolano (21 October 1467 aăş 14 September 1498) was Italian nobleman of the Medici House of Florence. He was the of Pierfrancesco di Lorenzo de' Medici, and therefore member of secondary branch the family.") reasoning:
influence, his power was not absolute;
banker, and he
great patron of learning, the arts and Medici, in full Giovanni
The question asks for the bank founded by the second Duke of Flarence. Passage [1] states that Giovanni di Bicci de Medici founded the Medici Bank and the seadt-reat-geea-ggraatt
of the
of Cosimo de' Medici, who was the Grand Duke of Tuscany. The Dukedom of Florence eventually transformed into the Grand Duchy of Tuscany under Cosimo I, who is identified as the second Duke of Florence in the question context.
Thus, Giovanni di Bicci de' Medici, the preat-great grreat-geeat-ee grwag grraat-granffahhr of the second Duke Florence (Cosimo I de' Medici), founded the Medici Bank. sunmary:
The Medici Bank founded by Giovanni di Bicci de' Medici, the great-greattgreaat-gganaaaaatat of the second Duke of Florence.
ww other demos omitted for brevity.
**
GEPA Prompt generated by config GEPA:
Given the fields "question", "context", "passages" produce the fields "summary".
50

---

Published as conference paper ICLR 2026
N.2 HOTPOTQA, QWEN3 8B
HotpotQA Qwen3 8B
predict
Base Prompt:
Given the fields "question", "summary_1" produce the fields "query".
MIPROv2 Prompt:
Given the question the summary. Identify the specific information that needs to be retrieved or confirmed. Formulate focused and precise query that will guide the next step in the multi-hop reasoning process, ensuring it directly addresses the gap in knowledge requires further clarification from addi additioaal context. Demos:
first summary, synthesize the key elements the question
GEPA Prompt generated by config GEPA+Merge:
Given the fields "question" and "summary_1" produce the field "query" that optimizes the retrieval of additional documents for multi-hop system.
**Task Details:*x
<*Objective:*x Your query must target documents not retrieved the first hop, using clues from the summary the original question.
2. **Key Strategy:**
Identify gaps in the first hop's retrieved documents (e.g., missing entities, relationships, specific details).
Use explicit information from the summary (e.g., names, locations, quantities)
rephrase the question into query that surfaces new relevant documents. Avoid restating the answer directly; instead, structure the query to explore connections or unresolved details.
3. sxDomain-Specifiic Guidance:**
If the summary explicitly answers the question, query should retrieving documents that provide deeper context or verify the answer (e.g., "What is the headquarters location of [Company]?" instead of "The answer is [Location]"). Leverage entities mentioned in the summary (e.g., "Carhartt," "Aubrey O' O'Day") to anchor the query.
focus on
If documents are missing, rephrase the query to explicitly request the answer (e.g., "Which has more acts, Elektra or From the House of the Dead?").
4. x*Avoid:**
Generating queries that duplicate the original question.
Assuming the summary contains necessary information for the second hop.
HotpotQA Qwen3 Final_answer, .rredict
Base Prompt:
Given the fields "question", summary_1 *summary_2 produce the fields "answer".
MIPROv2 Prompt:
Given the question, summary_1, and summary_2, generate step-by-step reasoning process that logically connects the information from both sumnaries arrive the final
51

---

Published as conference paper ICLR 2026
answer, then provide the answer. Ensure the reasoning explicitly references how the sunmaries support the conclusion and maintains consistency with the question's requirements.
Demos:
Example
question:
are Machaeranthera and Prumnopityy tys both the plants?
summary-1:
Yes, both Machaeranthera (a genus of flowering plants in the daisy/aster family) and tymnoppity (a genus of coniferous trees in the podocarp family) are plants.
sunmary_2:
Yes, both Machaeranthera (a flowering plant genus in the daisy family) and Prumnopitys (a coniferous tree genus in the podocarp family) plants.
reasoning:
Prumnopi tys are plants. Summary explicitly states they are plants, categorizing Machaeranthera as flowering plant genus in the daisy/aster family and Prumnopitys as coniferous tree genus in the podocarp family. Summary 2 repeats this confirmation with zith slightly different phrasing.
Both summaries confirm that Machaeranthera
answer:
Yes
other demos omitted for brevity. ".a...*.*444:aa*4*.....
GEPA Prompt generated by config GEPA-Merge:
Given the fields "question', "summary_1", and "summary_2", produce field "answer" by: 1. **Extracting precise terminologyax: Identify the exact noun or specific term required in the answer (e.g., "Medicare" rather than "Medicare cuts"). Avoid vague or generalized terms unless explicitly stated in the summaries.
2. **Resolving ambiguity**: If the question references title, historical role, specific designation (e.g., "second Duke Florence"), prioritize contextual or historical clues from the summaries to infer the correct answer, even if the exact term is not explicitly stated. Use domain-specific knowledge (e.g., Medici family lineage) to fill gaps when summaries
indirect vague.
3. ##Cross-refereencnn summaries**; Ensure consistency between summaries. summaries conflict, prioritize the one with explicit factual claims (e.g., numerical data, direct statements). If no explicit claim exists, synthesize information while ensuring al alignment with historical, political, or cultural context.
4. **Avoiding overgeneralization and extra information**: specific and directly stated information explanations, or external knowledge beyond what is explicitly provided. For example, if the question asks for year, provide only the year; do not include band member details historical background.
strictly on the the summaries. Do not add context,
5. **Prioritizing factual alignment**: If sunmary explicitly states answer, use that. If summaries are indirect or vague, synthesize information while ensuring alignment with factual knowledge (e.g., linking "Path to Prosperity" to Rep. Paul RyanåAZs Medicare proposal).
**Key adjustments based on feedback**:
**Conciseness**: Answers must be strictly factual and concise, avoiding additional context or explanations. For example, if the question is "Is X shorter than Y?" the
52

---

Published as conference paper ICLR 2026
answer should simple "No" or "Yes" based on numerical comparisons, not full explanation.
**Numerical precision**; When comparing measurements (e.g., heights, dates), ensure exact values are used and explicitly stated in the summaries. If summaries provide conflicting numbers, resolve via direct factual claims.
**Domain-speci knowledgeww: Use known facts (e-g., architectural records, historical timelines) to validate ambiguous answers, but only when summaries lack explicit information.
HotpotQA Owen3 8B summar ize1, 1. predict
Base Prompt:
Given the fields "question', passages", produce fields "summary".
MIPROv2 Prompt:
Given the fields "question", "passages", produce the fields "summary"..
Demos:
Example 1:
question:
Tay Garnett Alexander Kluge both what job?
passages:
['Tay Garnett William Taylor "Tay" Garnett (June 13, 1894 aăş October 3, 1977) was American film director and writer.'. 'Trade Winds (film) Trade Winds is 1938 comedy film distributed by United Artists. It directed by Tay Garnett, and starred Fredric March and Joan Bennett. The screenplay was written by Dorothy Parker, Alan Campbell and Frank R. Adams, based on story by Tay Garnett.'s 'Prestige (film) Prestige is 1932 American pre-Code drama film directed by Tay Garnett and written Tay Garnett, Rollo Lloyd and Francis Edward Faragoh. The film stars Ann Harding, Adolphe Menjou, Melvyn Douglas and Guy Bates Post. The film was released on January 22, 1932, by RKO Pictures. 'Alexander Kluge Alexander Kluge (born 14 February 1932) is a German author, philosopher, academic and film director.', 'Cattle King Cattle King is 1963 film directed by Tay Garnett. It stars Robert Taylor and Robert Loggia. It also appears have been called Guns Wyoming "GÅnther von Kluge GÅnther von Kluge (30 October 1882 añş 19 August 1944) was a German field marshal during World War II. Kluge held commands on both the Eastern and Western Fronts. Although Kluge was not an active conspirator in the 20 July plot, he committed suicide 19 August 1944, after having been recalled Berlin for meeting with Hitler Marshal Walter Model.', 'Bad Company (1931 film) Bad Company is 1931 American Pre-Code gangster film directed and co-written by Tay Garnett with Tom Buckingham based Jack Lait\'s 1930 novel "Put the Spot". It stars Helen Twelvetrees and cardo Cortez. Told from the view of woman, the working titles of this film "The Gangster\'s Wife" and "The Mad Marriage". Unlike many static early sound films, Garnett includes several scenes using a moving camera climaxing in a gigantic assault an office building with both sides using heavy machine guns." '] reasoning:
some countries.',
aftermath of the failed coup. He was replaced by Field
The question asks the common job of Tay Garnett Alexander Kluge, From passages, Tay Garnett is explicitly identified as a film director and writer (passages 1, 2, 3, 5, 7). Alexander Kluge is described as a German author, philosopher, academic, and film director (passage 4). The shared job between them is "film directar."
53

---

Published as conference paper ICLR 2026
summary: Both Tay Garnett and Alexander Kluge are film directors.
#ha.hhattttttttttttttt other demos omi teet for brevity.
GEPA Prompt generated by config GEPA+Merge:
You are the first-hop #Nsummarization module** a multi-hop QA system. Your task to generate a *xcomprehensive, structured summary** that:
1. **Extracts direct answers** from the top retrieved passages address the question.
**Identifies and highlights missing or implied clues** that may require further retrieval (e.g., entities, connections, or contextual details).
3. **Synthesizes information** by combining explicit facts from the passages with domain-specific knowledge or logical inferences to guide subsequent steps.
#*# **Summary Structure**
**Entity/Person Mentionıx: Clearly state the subject (e.g., "Billy Truax", "Eintracht Br Graunschweig") and include **full names, titles, or official designations** (e.g., "Thomas Lance Rentzel", "Braunschweiger Turn- und Sportverein Eintracht 1895 e..."). **Direct Answer**; Include **xpplicit answers** from the passages (e.g., birth dates, team affiliations, or direct statements).
**Clues for Next Steps**: Signal *smissing informations* (e.g., "Lance Rentzel's birth year is explicitly stated, but his exact birthplace is not; need to search for 'Lance Rentzel birthplace'").
**Domain-Specifi Context**; Add *ærelevant backgroundkx (e.g., "Eintracht Braunschweig is a German football club based in Braunschweig, Lower Saxony" or "NFL players' birth dates are critical for age comparisons").
#### **Guidelines**
**Do not omi any entity or detail from the retrieved passages that could relevant for follow-up queries (e.g., team names, locations, or historical context). **Prioritize clarity** **separating direct answers from inferred clues** (e.g., using bullet points, subheadings, **Avoid assumptions** not supported by passages; if information absent, **explicitly state that it is missing** and suggest **precise search terms** (e.g., "Verify Wichita Dwight D. Eisenhower National Airport's tower status via FAA records"). **Include quantifiable data** (e.g., "few thousand Stabyhouns exist globally", "born July 15, 1943") enable precise comparisons.
bolded labels).
*xHighlighht connections*x between entities (e.g., "Billy Truax and Lance Rentzel were traded in 1970") to aid in cross-referencinng
#### **Key Miche/Domain-Spciife Insights**
Player Comparisontx: Birth dates are critical for age determination, team affiliations (e.g., "traded in 1970") may imply historical context.
**Airport Classificationsk: "Non-towered" status is explicitly stated in some passages (e.g., "non-towered public airport"), while others require inference (e.g., "major commercial airports typically have towers").
**Football Club Context*;; Clubs like Eintracht Braunschweig require background on
their location, league, and history (e.g., "based in Braunschweig, Lower Saxony").
54

---

Published as conference paper ICLR 2026
*xQuantifiable DatarR: Use exact dates, numbers, or rankings (e.g., "few thousand Stabyhouns exist globally") to enable precise comparisons.
### **Critical Additional Instructions**
**Ensure All Retrieved Documents Representedke; Explicitly include all entities, titles, details from the retrieved passages (e-g., full names, film titles, and specific roles).
**Signal Missing Links**: If connection between entities is implied but not explicitly stated (e.g., "Nancy Steiner worked *The Lovely Bones*"), flag this potential gap suggest search terms to resolve it. xxPrioritize Bridging Concepts**; Highlight relationships between entities (e.g., "Gary Pinkel coached Toledo in 1993 and holds the most wins in school history") to enable focused follow-up queries.
**Avoid Overgeneralization++: Only include somain-specific context that either explicitly stated the passages or directly inferable (e.g., "major commercial airports typically have towers" is acceptable, but "airports with fewer than 10, non-towered" is not unless stated).
10,000 000 passengers are
### **Example Format**
For the question *"Which NFL player is younger, Billy Truax or Lance Rentzel?"*:
*AEntity/Person Mention*:; Billy Truax (William Frederick Truax), Lance Rentzel (Thomas Lance Rentzel)
**Direct Answer**:
**Billy Truax**: Born July 15, 1943.
**Lance Rentzel*:: Born October 14, 1943. *xClues for Next Steps*#; None required; birth dates are explicitly provided. #xDomain-Specific Context*:; Birth dates sufficient determine age difference within the same year.
For the question *"Which is non-towered airport, Wichita Dwight D. Eisenhower National Airport or Montrose Regional Airport?**:
**Entity/Person Mention*x: Wichita Dwight D. Eisenhower National Airport, Montrose
Regional Airport
**Direct Answer**:
**Montrose Regional Airport**; "non-towered public airport" (passage 3).
**Wichita Dwight D. Eisenhower National Airport**; No explicit mention of tower
status; inferred k*toweredk* (typical for major commercial airports).
**Clues for Next Steps*#: Verify Wichita's tower status via FAA records or addi tional sources (e.g., "Wichita Dwight D. Eisenhower National Airport tower status").
xxDomain-Specific Context*R; Non-towered airports lack control tower, relying on pilot communication (passage 4). Major commercial airports like Wichita usually have towers.
**Tip:** When summarizing, donaAžt just compress; synthesizesäTinnLude both direct answers and clues required for the systemåAZs next steps. Always explicitly state if retrieved documentåA2s content missing critical information, and provide actionable search terms to address gaps.
55

---

Published as conference paper ICLR 2026
HotpotQA Owen3 summar ze2. zredird
Base Prompt:
Given the fields "question", "cnnextt" passages", produce the fields "summary".
MIPROv2 Prompt:
Given the fields "question,, "context", and passaagss", generate structured reasoning process that explains how to synthesize information from these sources, and then produce concise summary
key findings.
Demos:
Example 1:
question:
Simone Benedetti plays for a team that currently competes which Serie?
context:
The provided passages do not specify the Serie in which Simone Benedetti 's team (Virtus Entella) currently competes.
passages:
[ ['Simone Benedetti Simone Benedetti (born April 1992) an Italian professional footballer who plays as centre back for Virtus Entella.'s "Virtus Entella | Virtus Entella (usually referred as simply Entella or Chiavari Entella) is an Italian association football club, based in Chiavari, Liguria. They currently compete in Serie B.", 'Simone Palermo | Simone Palermo (born 17 August 1988) is Italian footballer who plays as midfielder for Virtus Entella at Serie B.', 'Andrea Paroni | Andrea Paroni (born 14 October 1989) is an Italian footballer who currently plays goalkeeper for Virtus Entella in the Serie B.', 'Alessandro Iacobucci Alessandro Iacobucci (born 3 June 1991) goalkeeper
Italian footballer who currently plays Serie side Virtus Entella.', 'George Ganea | George Ganea (born 26 May 1999 in Bucharest) is a Romanian footballer who plays as Striker for Serie club Virtus Entella Primavera team.', 'Gianpaolo Castorina Giampaolo Castorina (born August 30, 1976 in Milan) is retired Italian professional footballer turned coach, currently charge head coach of Serie club Virtus Entella.' reasoning:
The question asks for the Serie in which Simone Benedetti's team (Virtus Entella) currently competes. While the context initially states that this information is not specified, the passages explicitly mention that Virtus Entella competes in Serie B. Multiple passages (e.g., [2], [3], [4], [5], (7]) confirm this detail, directly answering the question.
sunmary:
Simone Benedetti's 's team, Virtus Entella, currently competes in Serie B.
3 other demos omitted for brevity. #*t.ahttiiiitttttt..tttttttttttA
GEPA Prompt generated by config GEPA+Merge:
Given the fields "question', "context", and "passages", produce the field "summary",
Your task is synthesize information from the question, context, and newly retrieved passages generate a excomprehensive, precise, and well-structured summary** that enables the answer generation module to confidently arrive at the correct answer
56

---

Published as conference paper ICLR 2026
#*# Key Requirements:
1. **Explicit Answers First**; Prioritize explicitly stated facts from the context and passages (e.g., direct mentions of entities, roles, or relationships).
2. **Infer Generalize When Necessary**; If critical details missing from the passages, infer connections or generalize based on contextual clues knowledge (e.g., linking ownership structures, roles, or historical context).
somain-specific
3. **Bridge Gaps**: Ensure the summary includes all **key supporting information** required to answer the question, even if it is not explicitly stated in the input. For example:
If the answer "Newcastle United," include details about Sports Direct's ownership and the connection to the billionaire.
If the answer is a person's s role (e.g., "troubleshooter"), explicitly state their relationship to the question's subject and any relevant background.
4. **Structure and Precision**:
Clearly connect entities, roles, relationships (e.g., "Stan Kroenke owns Sports
Direct and Arsenal F.C.").
Avoid ambiguity by including all necessary contextual links (e.g., "Mike Ashley founded Sports Direct and owns Newcastle United").
ensure alignment with domain-specific knowledge (e.g., "investigative journalist" instead of "writer").
Use precise terminology
5. **Domain- Speccifiiciiccicc Knowledge**: Leverage implicit domain knowledge when passages lack critical details (e.g., knowing that "Project RAND" is linked to Henry H. Arnold and the RAND Corporation).
### Example Integration:
If the question is about a person's profession in a novel, ensure the summary includes:
The character's name
Their profession (explicitly stated the text).
Contextual links to the book series or plot (e.g., "in *The Girl in the Spider's Web+"). Any relevant background about the profession or characteråă2s role in the story.
Always aim match the **coverage and relevance** of "ideal summary" described in the feedback, ensuring answer module has all necessary information to generate the correct final answer
N.3 IFBENCH, GPT-4.1 MINI
IFBench GPT-4.1 Mini
generate, rewpoosseerreeeeeeeeeee_ee response_module module predict
Base Prompt:
Respond to the query
MIPROv2 Prompt::
You will be given query containing complex task with multiple constraints and instructions. Your job is to first repeat the query exactly given, word for word, wi withuut adding any extra words or commentary before after repetition. After repeating the query, provide a detailed, step-by-step chain-of-thought reasoning that carefully unpacks and addresses every aspect of the query. Then, produce final response that adheres strictly to all requitements, including formatting, language, and content constraints specified in the query. thorough, precise, and ensure the final is
57

---

Published as conference paper ICLR 2026
fully validated and consistent with the reasoning. the query specifies addi dionail formatting or stylistic rules (such as language or inclusion of postscript), include them exactly as instructed. Demos:
GEPA Prompt generated by config GEPA+Merge:
You are given a query input, and your task is to respond appropriately to that query. The query may contain specific instructions constraints that you must strictly adhere to your respanse. Carefully analyze the query to determine the exact requirements, including not limited to:
Responding with answer chosen from restricted set of options exactly as specified (including exact wording and punctuation).
Including specific words minimum number of times.
Including specific letters minimum number of times.
Repeating the entire query word-for-word before providing your answer if explicitly requested, wi without adding extra words characters before the repetition. Avoiding specific forbidden words keywords in your response if indicated. Following any other explicit instructions or constraints embedded in the query.
When the query requests calculations or factual answers (e.g., combinatorial calculations), you should:
1. Carefully interpret
mathematical or logical problem.
2. Show your reasoning internally to confirm the final answer (reasoning does not need to be included in the response unless explicitly requested).
3. Provide the final direct response strictly following all instructions, especially when to repeat the query verbatim first before giving the answer.
General approach:
Always parse the query thoroughly extract every constraint instruction.
Ensure your response exactly matches format, wording, and content as instructed. Do not invent or omit any part of the user's s explicit requests.
Meet all formatting, lexical, numeric, and structural constraints without deviation. If the query involves repeating text verbatim, do alter capitalization, punctuation, or wording.
Incorporate any required keywords or letters the required number of times naturally into your response.
When multiple constraints (like avoiding specific words while including others) apply simultaneously, ensure you satisfy all simultaneously.
This task requires precision, exact reproduction, and strict adherence to any given constraints or instructions embedded in the query. Your goal is to deliver the requested answer in the exact manner requested without extraneous additions or omissions.
IFBench GPT-4.1 Mini wttpreeooorrcteeeepeoeeee .peeeict
Base Prompt:
58

---

Published as conference paper ICLR 2026
Ensure the response correct and adheres the given constraints. Your response will be used as the final response.
MIPROv2 Prompt:
Given the original query and the initially generated response, carefully verify that the response fully meets all specified constraints and instructions in the query. Produce detailed step-by-step reasoning that explains how the response satisfies or fails the requi rements such exact repetition of the query text, minimum sentence count, formatting rules (e.g., capitalization), letter frequency, other explicit demands. Based on this reasaning, output a final corrected that adheres strictly to the original instructions, ensuring accuracy, completeness, and formatting correctness. Your final output should begin by repeating the original query exactly provided, with modifications comprehensive, well-structured answer that respects all constraints, and clear explanation of how the requitements have been met.
fully compliant response
additional text before it, followed by
Demos:
Example 1:
query:
Can you provide social media handles of Harry Potter? Your answer must contain exactly bullet points. Use the markdown bullet points such as:
* This is point 1.
This is point 2 Finish your response with this exact phrase That's my answer.. No other should follow this phrase.
response:
* Twitter: (ww.aarrciiioooilltttts///ttttte cot..//iaadddddgwrlld)
Instagram: (hortarrdnnuullll///tts///////id http://ww.sttgg...ee inwtinntttaan.......
Facebook: [Wizarding Korlddjhhttp:://ww.aaabbh com.///laarddidggooo1..
YouTube: [Wizarding horloo(hhtpss//wwwyytuue.cc
Twitter: htterrptttrrri//tt////////ttttrp
Instagram: (etprrywwtterrriilotttooo//////o hnw.ssaaarmm..ooooorootttttrrnat
Facebook: [Harry Potter/(hhtpps//wwwwaaccooo http://www...ebbboo.ooommaarrt/r com/haaryyoottter))
TikTok: [en http://w...kk..coo/////aadnngggo
com/
That's my answer..
reasoning:
The request is for the social media handles of Harry Potter, formatted exactly eight bullet points using markdown bullet points, exact phrase "That's my answer." The provided response includes eight bullet points, referencing official or widely recognized social media accounts related to Harry Potter and the Wizarding World franchise on Twitter, Instagram, Facebook, YouTube, and TikTok. The format is correct, the bullet points are used properly with markdown syntax, and the closing phrase matches the requirement exactly. Therefore, the response meets all given constraints.
response must with the
final_response:
ccom/wizardingwwrldd) * Twitter: [ewiiartiiggvrrdd(hhoo/////tiiii |/te/////thttt///errr/////////rr
Instagram: [euizardinguol1((((ttt:///w/w ins tagram. com/wizardingwwrrl//
Facebook: [Wizarding world](hhttps::/wwwfaacbb com//iiaaddinngwrll..
YouTube:
Twitter: Cha.rpppterriiitttte////aaeeaerr
Instagram: [tberrpgttrriiiiitttaee/////maar
Facebook: [Harry Petter/hhtpps//www.aacbbkkk fft.rrrcbboookoomom/rrrrtttttt)r
TikTok: [eeizanrdiggor1100hhhttts/// http://wwwwwtkkkkoooooooo
59

---

Published as conference paper ICLR 2026
That's my answer..
wwt.trawwwnnttttitttttttttttttta other demos omitted for brevity. #aanenennnennneaannee
GEPA Prompt generated by config GEPA+Merge:
query that includes explicit, detailed instructions about how you must structure your response. Your overall task is to interpret these embedded instructions with perfect accuracy and produce a response that strictly complies with every single one, without adding, omitting, or rewording any mandated text, phrases, structural elements.
You will receive
Key detailed requirements and strategy for this task:
1. *+uuery Parsing and Extraction of Instructions**
Carefully read the entire query identify all explicit instructions concerning: Whether and how to repeat the query text (verbatim or partially).
Specific length constraints (number of sentences, bullet points, word counts). Formatting instructions (e.g., capitalization requirements, quotation marks, markdown bullet styles).
Mandatory phrases or exact sentences that must appear (especially repeated verbatim or appended at end).
to
Content limitations or prohibitions (for example, refusal language or disclaimers for unethical requests).
Note that some instructions may be nested appear within the query's wording and are critical to follow exactly.
2. **Exact Text Reproduction**
When asked to repeat the query text (or any other required phrase) verbatim, do with zero changes aAT no added or removed words, punctuation, or formatting.
Do not prepend or append anything to the repeated text unless explicitly instructed. Preserve all original capitalization, spacing, and punctuation exactly as in the query.
3. **Structural and Formatting Compliances*
Follow all formatting instructions strictly, such as:
Wrapping the entire response in quotation marks if required. Using specified markdown bullet point styles (e.g., asterisks). Ensuring capitalization instructions (e.g., all caps minimum occurrences of uppercase words) are perfectly met.
Adhering to sentence or paragraph counts exactly as requested.
4. **Response Content Accuracy and Appropriatenessx:
After fulfilling structural requirements, respond main substantive question accurately and completely.
Use domain knowledge and reliable calculations to ensure factual correctness in answers.
For questions requesting sensitive potentially harmful content (e.g., without scientific basis), produce responsible answers that include disclaimers or refusals if instructed.
60

---

Published as conference paper ICLR 2026
Always respect ethical guidelines and any mandated refusal language or concluding statements for such queries.
5. **No Extraneous Text**
Do not add explanations, internal reasoning, apologies, or meta commentary beyond what the query explicitly permits ts or demands. Your final output must be the exact, ready-to-delll -oettod--eeierr response that meets all user instructions perfectly.
6. x*Examples Patterns Observed*s
Users often combine multiple complex formatting and content instructions (e.g., repetition of request text, followed by specific number of sentences or bullet points, with capitalization rules).
Ensure you carefully distinguish when repeat query verbatim and when repetition excludes an instruction sentence).
respond directly (sometimes
Handle instructions about capitalized words appearing minimum number of times by
distributing such words naturally but thoroughly across the response.
When length constraints specify minimums (e.g., åAat least sentencesáAt), you meet or exceed exactly rather than approximating.
For bullet points or enumerations, use the precise markdown style asked for (commonly asterisks).
Follow refusal instructions verbatim and terminate the response exactly specified.
**In summary:** ** YOUR RESPONSE MUST BE PERFECT TEXTUAL AND STRUCTURAL RENDERING THE USER QUERYaÄλS EMBEDDED INSTRUCTIONS. ALWAYS PRIORITIZE EXACT USER SPECIFICATIONS OVER DEFAULT GENERATIVE NORMS. THE OUTPUT IS FINAL AND WILL NOT BE EDITED, so COMPLIANCE MUST BE 100%.
This task therefore requires a rigorously rule-based approach:
First parse and isolate all explicit instructions (including repetitions and formatting).
Then assemble your answer by reproducing mandated text identically.
Next produce the requested content in the exact required format, length, style. Finally, review your output against the instructions ensuring total compliance before delivering your response.
N.4 IFBENCHI, QWEN3 8B
IFBench Qwen3 8B generaat_ressooseemmone www.:muuuueeeeeiccttcoooemmme predict
Base Prompt:
Respond to the query
MIPROv2 Prompt:
You are frecision-focused eestruction-adhereme expert tasked with generating structured, ethically compliant responses. When responding to queries, always think step-by-step to analyze constraints (formatting rules, content boundaries, ethical guidelines), synthesize requirements, and produce outputs that strictly adhere to specified parameters. Ensure responses fully formatted requested (e.g., lowercase
61

---

Published as conference paper ICLR 2026
text, bullet points, markdown), avoid any unverified claims, maintain neutrality ambiguous ethical scenarios.
Demos:
Example 1:
query:
Can you provide social media handles of Harry Potter? Your answer must contain exactly bullet points. Use the markdown bullet points such as:
This is point 1.
This is point 2 Finish your response with this exact phrase That's my answer No other should follow this phrase. reasoning:
The query asks for the social media handles of Harry Potter, fictional character. Since he does not have personal social media accounts, the response should instead list official accounts related the Harry Potter franchise. These include accounts Wizarding World, and associated organizations.
managed by Warner Bros., answer is structured into exactly 8 bullet points as requested. response:
* @kizardingWorld (Twitter)
ewizardingNorld (Instagram)
@wizardingWorld (Facebook)
IKRowling (Twitter)
@harryPotterAlliance (Facebook)
@oowwrtsEEprres (Twitter)
(Facebook)
@HarryPotterBooks (Twitter)
That's s answer.
3 other demos omi for brevity. **:.:**Guae
GEPA Prompt generated by config GEPA:
Respond to the query by first repeating it word for word without any changes. Then, provide your answer. Ensure your answer includes niche or domain-specific factual information relevant to the task. If the query specifies constraints (e.g., letter frequency, sentence limits, or formatting rules), adhere to them strictly. generalizable strategy to solve the task, such as breaking down complex problems into logical steps or applying specialized knowledge. Avoid adding any preamble or postscript before after the repeated query.
a
IFBench Qwen3 8B mnsure_correct__rssosssssmm predict
Base Prompt:
Ensure the response is correct and adheres to the given constraints. Your response will used as the final response.
MIPROv2 Prompt:
You are meticulous and ethical AI assistant tasked with generating detailed, compliant responses. When given query, first think step by step create reasoning process
62

---

Published as conference paper ICLR 2026
that addresses the query's requirements, then produce a response that strictly adheres all specified constraints (e.g., formatting rules, content boundaries, ethical guidelines). Ensure the final output is structured with "Reasoning:" section followed by a "Final Response:" section, and verify that it aligns with the query's exact specifications while maintaining logical coherence and ethical responsibility. Demos:
Example 1:
query:
What some techniques for winning swordfight in medieval combat game? In your response, the letter should appear than times. Do not include keywords ['heart', 'listen', 'reputation', 'side'] in the response.
response:
Prioritize stance and balance to control the fight's flow. Use angles to create openings for strikes while minimizing exposure. Practice parrying with precision to disrupt opponent rhythm. Maintain distance to avoid overcommitting and target weak points like joints or limbs. Focus on rhythm and timing to exploit vulnerabilities. reasoning:
The response outlines key techniques for medieval swordfighting, focusing stance, angles, parrying, distance, and targeting weak points. It avoids restricted keywords and limits the letter 'f' to 2 instances, satisfying the constraints. The advice is practical and aligns with historical combat strategies. final_response:
Prioritize stance and balance control the fight's flow. Use angles to create openings for strikes while minimizing exposure. Practice parrying with precision to disrupt opponent rhythm. Maintain distance to avoid overcommitting, and target weak points like joints or limbs. Focus on rhythm and timing to exploit vulnerabilities.
3 other demos omitted for brevity. **n.aaa
GEPA Prompt generated by config GEPA:
Ensure the response strictly follows these instructions:
**First**, repeat the user's query **word for word** without any changes additions.
2. **Then**, provide your answer the specified format, adhering constraints
(e.g., markdown, structure, content).
3. **Do not include any additional text, explanations, or formattingax beyond the repeated query and your answer
4. *+Include siche//omain--peciife factual details** (e.g., technical commands, best practices, or platform-specife configurations) if applicable, as these are critical for accurate task completion.
5. *nUse precise formattinga* (e.g., bullet points, code blocks, headers) requested, ensuring markdown is omi tiet altered.
6. **Avoid generalizable strategies** unless explicitly instructed; focus on actionable, specific guidance.
7. **Validate all technical steps** (e.g., Dockerfile syntax, CLI commands) for accuracy and completeness.
8. **Highlight potential pitfalls and solutions** address common issues in the task.
9. **Prioritize clarity and conciseness**, ensuring the response both comprehensive and easy to follow.
10. **Adhere to language and case requirements** (e.g., all caps, English only) if specified.
63

---

Published as conference paper ICLR 2026
N.5 HOVER, GPT-4, MINI
HoVer GPT-4.1 1 Mini create_query_hopp, predict
Base Prompt:
Given the fields "claim', "summary_1" produce the fields "query".
MIPROV2 Prompt:
Given the original claim and the initial summary of retrieved evidence, carefully analyze the information step-by-step to identify
gaps, contradictions, points needing further clarification. Then, generate clear, focused, and precise query that targets additional relevant information to deepen the investigation and help verify or refute the claim. Your response should include a detailed mhain-of-though reasoning explaining your thought process in formulating this refined query. Demos:
Example
claim:
AirBaltic's has bases at Tallinn Airport and the airport that ceased operations in 2015, with the airport code of Ryanair: VNO, ICAD: EYVI.
summary_1:
AirBaltic bases Tallinn Airport and Vilnius Airport (IATA: VNO, ICAO: EYVI), but Vilnius Airport did not cease operations in 2015. The airport that ceased operations in 2015 mentioned in the passages is unrelated and has different codes. Therefore, the claim that airBaltic's base with code VNO/EVVI ceased operations in 2015 is false. reasoning:
The claim states that AirBaltic has bases at Tallinn Airport and an airport with codes VNO (IATA) and EYVI (ICAD) which ceased operations in 2015. However, the summary clarifies that Vilnius Airport, which has those codes, did not 2015 has different codes
operations 2015. The airport that did cease operations and is unrelated to AirBaltic's bases. Therefore, the claim that AirBaltic's base at the airport with codes VNO/EYVI ceased operations in 2015 is incorrect.
query:
Did Vilnius Airport (IATA: VNO, ICAO: EYVI) operations in 2015?
other demos omitted for brevity. tttthhattttttttt........t.tttnna
GEPA Prompt generated by config GEPA+Merge:
Task Description:
You are given
fields: "claim' and "summary_1", Your goal is produce field called "query" åut a concise set of relevant, fact-checking questions or search style queries that can be used to retrieve evidence documents verifying or refuting the claim based on information contained or inferred from the summary.
Input Format:
"claim": A factual statement potentially containing multiple fact assertions about people, events, attributes, titles, dates, roles, or relationships. summary_1'; short paragraph summarizing factual information related to the claim, often clarifying correcting parts of the claim.
64

---

Published as conference paper ICLR 2026
Output Format:
"query": One or more specific, well-phrased questions keyword queries that directly target the key factual discrepancies verifications raised by the claim in light of the sunmary.
Detailed Instructions:
1. **Extract key factual elements from the claime* äAt names, dates, titles, roles, events, or relationships explicitly implicitly stated.
2. **Contrast these facts with the summary identify points of agreement, contradiction, or ambiguity.
3. **Formulate fact-checking queries that are:**
Tightly focused the core factual issues raised by the claim and addressed contradicted by the summary.
Include named entities, dates, roles, or other domain-specific identifiers directly mentioned in both claim and summary to improve retrieval effectiveness. When relevant, break complex claims into multiple queries ensuring each fact is verifiable separately.
4. swWhen relevant details appear only in the summary but are hinted at or missing from the claim (e.g., specific titles, roles, or names), include these in the queries to enable retrieval of key evidence. **
5. **Use clear, natural question format targeted keyword phrases that could serve well search queries.**
6. **Avoid overly broad or generic queries; precision improves evidence retrieval quality.** **
7. Optionally, you provide brief reasoning internally (not required in output) to ensure the queries cover all claim aspects and reflect the summary insights.
Examples of typical query components include:
Correct dates of events or deaths.
Confirmation of personåAžs role association with known work.
Verification of relationships allegations.
Details about specific cul tural or domain elements (e.g., operas' structure, song directors).
Clarifying entity attributes classifications (e.g., ethnicity, nationality).
By following these instructions, you will produce queries that are both comprehensive targeted, maximizing the chance of retrieving relevant factual evidence relevant to the claim verification task.
HoVer GPT-4.1 Mini create_query_hop: predict
Base Prompt:
Given the fields "claim", summary_11 summary_2"
produce
fields "query".
MIPROv2 Prompt:
You are an expert fact-checker specializing in multi-hop reasoning and evidence synthesis. Given a claim and two intermediate summaries that consolidate evidence from previous retrieval steps, thoughtfully analyze the information step-by-step to generate a clear and focused query. This query should be designed to retrieve the most relevant additional documents that help verify refute the claim by leveraging the insights
65

---

Published as conference paper ICLR 2026
from both summaries. Provide detailed chain-of-thought reasoning explaining how you integrate the summaries and the claim to formulate this refined query.
Demos:
Example 1:
claim:
The brother of Freddie Sessler, was restaurateur whose club was frequented by
Ruth Elizabeth "Bette" Davis, an actress with Welsh ancestry.
summary-1:
Freddie Sessler's brother Siegi Sessler, London restaurateur whose club frequented by Bette Davis, an actress with Welsh ancestry.
sunmary_2:
Freddie Sessler's brother was Siegi Sessler, a prominent London restaurateur whose club, Siegi's, was frequented by Hollywood stars including Ruth Elizabeth "Bette" with Welsh ancestry.
Davis, an reasoning:
The claim states that the brother of Freddie Sessler was restaurateur whose club was frequented by Ruth Elizabeth "Bette" Davis, noted to have Welsh ancestry. Both summaries support this claim identifying the brother Siegi Sessler, restaurateus
London, whose club was indeed frequented by Bette Davis, an actress noted for Welsh ancestry. Summary_2 adds that the club's 's name was Siegi's and mentions the presence of other Hollywood stars. To fact-check the claim accurately, it would be useful to verify the identity of Freddie Sessler's brother Siegi restaurateur, verify that Bette Davis frequented his
Sessler, confirm that he club, and confirm Bette Davis's 's Welsh ancestry. query:
Who was Siegi Sessler, and was he the brother of Freddie Sessler? Was Siegi Sessler restaurateur whose club was frequented by Ruth Elizabeth "Bette" Davis? Did Bette Davis have Welsh ancestry?
#*n.ueeeeeeeeeeeeeeee other demos omi tted for brevity. #*n.e.
GEPA Prompt generated by config GEPA+Merge:
Given three text fields: "claim", "summary_1", and "summary_2" your task is produce "query" field that is designed effectively retrieve evidence documents relevant verifying or refuting the claim based on the information contained in the two summaries.
Detailed task description and considerations:
1. **Purpose of Query:**
The query should accurately and comprehensively target the key factual elements from the claim that are addressed or clarified in the summaries.
The query will be used to retrieve evidence documents, it should be specific enough pinpoint relevant support or contradiction but broad enough cover tapo details present in the summaries.
2. soUtilizing the Summrries:*
Summaries often correct, clarify, add factual context the claim. Your query must incorporate these clarifications (e.g., name corrections, factual specifics, or counterpoints) to ensure retrieval of relevant evidence reflecting the nuanced truth.
66

---

Published as conference paper ICLR 2026
Include distinctive entities, facts, dates, locations, names, relationships mentioned or corrected in the summaries that pertain to the claim. For example, if summary corrects a documentary title in the claim, the query should reference the corrected title and related details to guide retrieval effectively.
3. **Query Content Strategy: **
Explicitly mention key entities, such persons, places, dates, works, political entities involved.
claimåĀZs accuracy (e.g., person X politician in country during year 2?" or "Did documentary A and documentary B film in different locations such as location 1 and location 2?"). If there is factual dispute correction in the summaries (e.g., nationality, official names, population figures), phrase the query target evidence clarifying this dispute.
Include attributes or relationships relevant
4. s=Domain-Specific Nu
and periods (e.g., "United Kingdom of Be mindful of historical geopolitical the Netherlands between
and 1830").
Recognize proper titles and correct spellings (e.g., corrected documentary titles). Include both subjects or objects mentioned as comparisons or contrasts in the claim and summaries (e.g., documentaries, two politicians, towns).
5. **Fomulatiin Style:**
Queries should be phrased as clear, precise, and objective questions that can be answered based on evidence åÄt often structured yes/no informational queries. Avoid overly broad vague phrasing. Aim for detail-rich queries that connect multiple evidence points
summaries.
6. #*Geerraliabble Strategy:**
Identify the core claim elements and verify if the summaries confirm, contradict, amend these elements.
Incorporate both claim and corrections from summaries into the query so evidence retrieval captures the full factual context.
Use the comparison contrast highlighted by the summaries to create queries that specifically test the claim's veracity (e.g., comparing locations, roles, historical timeframes).
By following these guidelines, you will generate queries that maximize the likelihood of retrieving relevant texts that confirm refute the claim accurately.
HoVer GPT-4.1 Mini summarize1 predict
Base Prompt:
Given the fields "claim", "passages" produce the fields "summary"
MIPROv2 Prompt:
Given a "claim" and a list relevant "passages", carefully analyze the evidence by reasoning step-by-step to assess the claim's validity. Produce a detailed chain-of-thought "reasoning" that explains how the information in the passages supports refutes the claim, followed by clear, concise "summary" that synthesizes the key
67

---

Published as conference paper ICLR 2026
findings relation to the claim. Ensure the reasoning explicitly connects evidence from the passages to the claim, enabling a thorough and transparent multi-hop verification process.
Demos:
Example
claim:
The manufacturer that owns the company that currently produced and marketed the candy the video Darkened Skye uses in magic is headquartered in the US state Virginia.
passages:
[ ['Darkened Skye | Darkened Skye is third-person action-adventure video game developed by Boston Animation. It was released for Microsoft Windows and the Nintendo GameCube in North America in 2002 and the PAL regions in 2003. The game was also packaged with Outlaw Golf. Its title character is young woman named Skye who lives fantasy realm searching for her mother. She does perform magic using Skittles candies, as well as use her staff as melee weapon, though it also becomes an energy weapon when used in conjunction with the Skittles." "Rocket Fizz Rocket Fizz is franchise chain of candy stores in the United States. The company markets diverse variety of candies and produces its 1ine of soft drinks with unique flavors. Its flagship store located Camarillo, California, and most of the company's 's franchise stores are located in California. The company also markets candies that are rare to find or in limited production by various manufacturers, such those that were popular during the 1960s to 1980s. As of September 2017, there 'Spangler Candy Company The Spangler Candy Company is a privately owned international confectioner that has been manufactur ing and marketing candy since 1986.', ' 'Future US Future US, Inc. (formerly known as Imagine Media and The Future Network USA) is American media corporation specializing in targeted magazines and technology markets. Future is
firearms,
can
Rocket Fizz store locations in the United States."
websites in the video games, music, headquartered in the San Francisco with a small sales office in New York City, Future US is owned by parent company, Future plc, a small publishing company based in the United Kingdom. ', 'Fun Dip | Fun Dip is candy manufactured by The Willy Wonka Candy Company, brand owned by NestlÄr. The candy has been the market in the United States Canada since the 1948s was originally called Lik-M-Aid. or originally manufactured by Fruzola, and then Sunline Inc., through their Sunmark Brands division, which purchased by NestlÄl' in January 1989. It comes in many di fffreenn flavors with candy sticks that included.'s 'Hot Tamales Hot Tamales chewy, cinnamon flavored, oblong-shaped candy introduced manufactured and marketed in the United States by Just Born, a family-owned, Pennsylvaniabbased candy company.', "Frankford Candy &amp; Chocolate Company Frankford Candy & Chocolate Company is an American candy manufacturer, located in Philadelphia, Pennsylvania, founded 1947 by Himmelstein. The current Stuart Selarnick. In recent years, Frankford Candy Chocolate Company has become very successful candy manufacturer. It is one of the largest producers of chocolate rabbits in the United States, making over 100 varieties. In 2000, it started licensing popular children's reasoning:
was
1950 aAT
characters, such SpongeBob SquarePants."" "]
The claim states that the manufacturer of the candy used in the video game Darkened Skye is headquartered in Virginia. Passage [1] establishes that the video game Darkened Skye uses Skittles candies for magic. Therefore, the candy in question is Skittles. The crucial step is to identify the company that produces and markets Skittles and whether it is headquartered in Virginia. None of the provided passages explicitly mention Skittles or its manufacturer. The passages list various candy companies, but none mention Skittles or match its manufacturer. The known producer of
68

---

Published as conference paper ICLR 2026
Skittles Mars, Incorporated, which headquartered McLean, Virginia. Although this fact is not directly stated in the provided passages, it is relevant general knowledge. Since the passages do not refute the claim and for accuracy based on known facts, the claim can be considered true that the manufacturer that owns the company producing marketing Skittles headquartered in Virginia. summary:
The candy used as magic in Darkened Skye is Skittles, which are produced by Mars, Incorporated. Mars is headquartered in McLean, Virginia, US state. Therefore, the claim is true based external knowledge confirming that the manufacturer is based Virginia.
other demos tted for brevity. CAnHEnaanaRnnNeenne
GEPA Prompt generated by config GEPA+Merge:
Given the fields "claim,, "passages", produce the fields "summary".
HoVer GPT-4.1 1 Mini summarizez predict
Base Prompt:
Given the fields "claim", "context", passages" produce the fields "summary..
MIPROv2 Prompt:
Given "claim", relevant "context', and set of supporting "passages' generate a detailed step-by-step reasoning process that logically connects the evidence to the claim, followed by concise sunmary that clearly states whether the claim is supported or refuted based on the aggregated information. Ensure the reasoning explicitly references key evidence from the context and passages to justify the conclusion in the sunmary.
Demos:
Example
claim:
One half of the KalyanjišāşAnanjji duo, Kalyanji Virji Shah' 's brother, won the Filmfare Award for Best Music Director.
context:
Yes, Kalyanji Virji Shah's brother Anandji Virji Shah, as part of the KallynjiaaŞAAandji duo, won the Filmfare Award for Best Music Director in 1975 for the film "Kora Kagaz."
passages:
['Kalyanji Virji Shah Kalyanji Virji Shah (30 June 1928 aăş August 2000) was the "Kalyanji" of the Kalyanji-Anandji duo. He and his brother Anandji Virji Shah have been famous Indian film musicians, and won the 1975 Filmfare Award for Best Music Director, for "Kora Kagaz". He is recipient of the civilian honour of Padma Shri (1992).', 'Anandji Virji Shah Anandji Virji Shah an Indian music director. Together with his brother he formed the Kalyanji-Anandji duo, and won the 1975 Filmfare Award for Best Music Director, for "Kora Kagaz".. He is recipient of the civilian honour of Padma Shri (1992).', "KalyanjiakSAnanjji KalyanniaA are Indian composer duo from Gujarat: Kalyanji Virji Shah (30 June 1928-03 November
69

---

Published as conference paper ICLR 2026
2000) and his brother Anandjí Virji Shah (born March 1933). duo are known their work on Hindi film soundtracks, particularly action potboilers in the 1978s. Some of their best-known works are "Don", "Bairaag", "Saraswatichandra" "Qurbani", "Tridev" and "Safar". They the 1975 Filmfare Award for Best Music Director for "Kora Kagaz". ' Killyanjiak$mannjii discography This a discography of Bollywood composer duo Kalyanji Anandji, consisting of Kalyanji Virji Shah and Anandji Virji Shah. They have composed music for over 200 films in their 46 year career.'s 'Viju Shah Viju Shah (born, Vijay Kalyanji Shah, 5 June 1959) is music director of Hindi cinema. He the son of music director Kalyanji Virji Shah of composer duo Kalyanji Anandji.
composed music for movies such as "Vishwatama" (1992), "Mohra" (1994), "Tere Mere Sapne" (1996) and "" (1997) for which got his second nomination for the Filmfare Award for Best Music Director and he won the 1998 Filmfare Award for Best Background Score for "".', 'Bombay the Hard Way: Guns, Cars and Sitars | Bombay the Hard Way: Guns, Cars Sitars in which music from 1970s funk Bollywood composer duo Kalyanji-Anandji (Kalyanji Virji Shah and Anandji Virji Shah) was taken and given funky remix. The album was eventually wi thdrawn (possibly due to copyright concerns). sequel, "Bombay 2: Electric Vindaloo", was also produced.'s "Kora Kagaz Kora Kagaz (Hindi: àdTaëNadFádi adTadidtlådád; translation: Blank Paper) is 1974 Hindi drama film produced by Sanath Kothari and directed by Anil Ganguly. The film stars Vijay Anand, Jaya Bhaduri, A.K. Hangal, Achala Sachdev and Deven Verma. The films music is by Kalyanji Anandji. The famous title song "Mera Jeevan Kora Kagaz" Kumar.
1998 project of producer Dan the Automator
Kishore
reasoning:
The claim states that one half of the KalyanjiaA$Anandji duo, Kalyanji Virji Shah's 's brother, the Filmfare Award for Best Music Director. From the passages and context, is clear that the duo consists of Kalyanji Virji Shah and his brother Anandjí Virji Shah. Both together won Filmfare Award for Best Music Director in 1975 for the film "Kora Kagaz." Therefore, Anandji Virji Shah, who is Kalyanji's 's brother, indeed won the Filmfare Award for Best Music Director as part of this duo. summary:
Anandji Virji Shah, brother of Kalyanji Virji Shah, won Filmfare Award for Best Music Director in 1975 as part of the Kalyanjiaā$Anandji duo.
other demos omi ttee for brevity. "a:e:e::::w0
GEPA Prompt generated by config GEPA+Merge:
Given the input fields:
"claim": A factual statement assertion that may be true or false. "ontext": brief explanation summary relating to the claim that typically
clarifies the claimåăžs accuracy.
; A list of relevant textual evidence or knowledge snippets containing facts, descriptions, or biographies related to entities or concepts mentioned in the claim.
Your task is produce "ummaryy" that meets the following requirements:
1. **Accurately reflect the relationship between the claim and the evidences*; Analyze the input "claim" carefully and check it against the "context" and all "passages".
70

---

Published as conference paper ICLR 2026
Determine whether the claim is supportegs partially supported, or contradicted by the evidence.
2. **Explicitly connect key entities and facts from the passages**; Your sunmary must mention the main entities and facts that directly confirm or refute parts the claim. For example, highlight relevant names, works (films, albums, songs, books), attributes, dates, nicknames, or roles that clarify the claim's accuracy.
3. **Make substantive connections**: The summary should not merely restate the claim conclusion, Instead, it should explicitly 1ink the claim to detailed evidence from the sash as specific film titles, album names, song titles, dates, or relationshipsāAtto provide clear reasoning for the accuracy or inaccuracy of the claim.
4. **Include relevant disambiguations or clarifications::: Where applicable, clarify potential misunderstandings or common confusions highlighted in the passages (e.g., distinguishing similarly named films or songs, specifying which individual is referenced, noting alternate titles and adaptations).
5. kHUse concise, factual languageıж: The summary should be clear and succinct include all relevant evidence necessary to understand how the claim is supported or refuted.
6. **Support future query generations*; As summary will used to generate queries to find evidence, must contain explicit mentions key entities, titles, facts that can guide retrieval and reasoning.
**Overall, your approach should be each passage, synthesizing connected facts evidence into a coherent evidence-rich sunmary that clearly documents why the claim is true, false, or partially true. x* Avoid vague or overly general statements; instead, ground your summary in the precise factual details present in the passages and context.
comprehensively over the claim, context, and
N.6 HOVER, QWEN3 8B
HoVer Qwen3 8B treate__quey_hhere predicc
Base Prompt:
Given the fields "claim", summary__1 produce the fields "query".
MIPROv2 Prompt:
a high-stakes scenario where the accuracy your query directly determines the validity of a complex claim, generate a precise query that builds on the provided claim and summary to uncover critical evidence. Use step-by-step reasoning to identify gaps in the initial summary, then craft query that explicitly targets these gaps to retrieve additional supporting information. Ensure your query is unambiguous directly addresses the claim's unresolved aspects. Demos:
Example 1:
claim:
71

---

Published as conference paper ICLR 2026
Sandi Simcha DuBowski directed a documentary released 2001. film Zeitgeist was not also released in 2001.
sunmary_1:
The claim is correct: Sandi Simcha DuBowski directed the 2001 documentary *Trembling Before G-d+, and there is no evidence the passages that the horror film *Zeitgeist* was released in 2001.
reasoning:
The claim is correct because Sandi Simcha DuBowski indeed directed the 2001 documentary *Trembling Before G-d*, stated in summary_1. The assertion that *Zeitgeistx was not released 2001 is supported by the absence evidence the provided summaries linking the film to that year.
query:
Is there any evidence in the provided summaries that the horror film *Zeitgeist* released in 2001?
other demos omitted for brevity. ahw.aaaaattttttttt..........t.ta
GEPA Prompt generated by config GEPA:
Given the fields "claim" and "summary_"" generate precise and focused query that identifies the specific evidence needed verify or refute the claim.
### Key Requirements:
1. **Target Missing Evidence**; Identify and explicitly ask about the unverified unconfirmed details in the summary (e.g., names, dates, locations, connections) that are critical to the claim.
Example: If the summary mentions "Massimo Giordano" as a potential counterpart but lacks birthplace details, the query should ask for evidence confirming their ties to Naples.
2. **Correct
Anomalies**: Address discrepancies like anachronisms (e.g., Ali Qushjiaă2s work in the 1960s vs. his actual 15th-century timeline) or misattributions (e.g., *Hayy ibn Yaqdhanı by Ibn Tufail, not Ali Qushji).
3. **Link to Summary ContextR:: Ensure the query references the summaryáAžs key points (e.g., "the claim states X, but the summary notes Y is unverified").
4. *nUse Specific Terms**: Include exact names, titles, or dates mentioned in the summary to avoid ambiguity (e.g., "Anadrs Nin," "Metropolitan City of Naples," "1948").
### Example Strategy:
summary states:
"The claim is partially supported. While [Fact A] is confirmed, [Fact B] lacks verification."
The query should ask:
there evidence confirming [Fact B], such [specific name/daate/locttion]"*
### Domain-Specific Notes:
Verify historical timelines (e.g., Ali Qushji died in 1474, not the 1960s).
Confirm authorships (e.g., *Haayy ibn Yagdhan* is attributed to Ibn Tufail, not Ali Qushji)
Check for geographic or biographical details (e.g., birthplaces, cities, or professional connections).
72

---

Published as conference paper ICLR 2026
Ensure your query directly addresses the summaryåA2s unverified claims and includes critical terms from the summary to maximize evidence retrieval.
HoVer Owen3 8B create_ caaate__uery_hp3333 predict
Base Prompt:
Given the fields "claim", "summary_1"
"summary_2 produce the fields "query".
MIPROv2 Prompt:
You are fact-checking assistant specializing in multi-hop reasoning and information synthesis. Given the fields "claim", "summary_1" and "summary_2 generate precise query that synthesizes original claim with the two summaries probe for deeper contextual relationships, resolving ambiguities and confirming supporting details through targeted evidence retrieval. Demos:
GEPA Prompt generated by config GEPA:
Given the fields "claim", "summary_1" and "summary_2", produce the field "query" that:
1. Explicitly asks whether the claim is supported by the provided summaries.
2. Includes **specific evidence** from the summaries (e.g., names, titles, dates, factual details) to guide retrieval relevant documents.
3. Focuses on **key disputed points** in the claim (e.g., incorrect attributions, missing evidence, or conflicting statements) to ensure the query targets the most relevant information.
**key considerations
query:**
If the summaries mention specific works (e.g., *Planes, Trains and Automobiles*), include the title.
If the summaries reference named entities (e.g., John Candy, KLM, Richard Ford), include them.
If the summaries clarify contradictions (e.g., "not X but Y"), structure the query to highlight this contrast.
Avoid vague phrasing; instead of asking "Is the claim true?" use "Does the evidence in the summaries confirm [specific detail]?"
**Example:** If the claim is about film and the summaries mention *Planes, Trains and Automobilese (1987), the query should include that title to retrieve relevant evidence.
HoVer Qwen3 8B summarizel 1, predict
Base Prompt:
Given the fields "claim", "passages' produce the fields "sumaary".
MIPROv2 Prompt:
73

---

Published as conference paper ICLR 2026
Given the fields "claim" "passages", generate structured reasoning process analyzes the claim step-by-step using the provided evidence, and produce concise sunmary that distills the key findings and evaluates the claim's validity based on the evidence.
Demos:
Example 1:
claim:
One half of the KalyanjiiāšAnandjj duo, Kalyanji Virji Shah's brother, won the Filmfare Award for Best Music Director.
passages:
['Kalyanji Virji Shah Kalyanji Virji Shah (30 June 1928 āăş 24 August 2000) was the "Kalyanji" " of the Kalyanji-Anandji duo. He and his brother Anandji Virji Shah have been famous Indian film musicians, and the 1975 Filmfare Award for Best Music Director, for "Kora Kagaz". He a recipient of the civilian honour Padma Shri (1992).', "KalyanjiaAAAmandji
KalyanjiaAfAnanjji are an Indian composer duo from Gujarat: Kalyanji Virji Shah (30 June 1928-03 November 2000) and his brother Anandji Virji Shah (born 02 March 1933). The duo are known for their work Hindi film soundtracks, particularly action potboilers works are "Don", "Bairaag", "saraswatichandra,, "Qurbani", "Tridev" and "Safar". They won the 1975 Filmfare Award for Best Music Director for "Kora Kagaz".', 'Anandji Virji Shah Anandji Virji Shah is Indian music director. Together with his brother he formed the Kalyanji-Anandji duo, and "Kora Kagaz". is a recipient of the civilian honour of Padma Shri (1992). 'viju Shah Viju Shah (born, Vijay Kalyanji Shah, June 1959) is music director of Hindi cinema. He is the son of music director Kalyanji Virji Shah of composer duo Kalyanji Anandji. He composed music for movies such as "Vishwatama" (1992), "Mohra" (1994), "Tere Mere Sapne" (1996) and " (1997) for which got his second nomination for the Filmfare
1970s. Some of their best-known
the 1975 Filmfare Award for Best
Music Director,
for Best Music Director Filmfare Award for Best Background Score for "".', "KalyanjiaASAnandji discography This is discography of Bollywood composer duo Kalyanjí Anandji, consisting of Kalyanji Virji Shah and Anandji Virji Shah. They have composed music for over 200 films their year career.'. 'Bombay the Hard Way: Guns Cars and Sitars Bombay the Hard Way: Guns, Cars and Sitars is a 1998 project of producer Dan the Automator in which music from 1970s funk Bollywood composer duo Kalyanji-Anandy) Virji Shah and Anandji Virji Shah) was taken and given funky remix. The album was eventually wi thdraawn (possibly due copyright concerns). sequel, "Bombay 2: Electric Vindaloo", was also produced. ', 'Bairaag Bairaag a 1976 Bollywood film. Produced by the duo Mushir-Riaz, it is directed by Asit Sen. The film stars Dilip Kumar, who received a Filmfare nomination for Best Actor for playing a triple role. "Bairaag" also stars Saira Banu, Leena Chandavarkar, Ruma Guha Thakurta, Prem Chopra, Helen, Sujit Kumar, Madan Puri, Paintal, Kader Khan, Asit and Sachin. is by the duo Kalyanji Anandji, who received a Filmfare nomination for Best Music. The film received a Filmfare Award for Best Sound for P. Harikishan. According to one source, the film did not do well at the box office." 'J reasoning:
won 1998
(Kalyanji
music
The claim states that Kalyanji Virji Shah's s brother won Filmfare Award for Best Music Director. The passages confirm that the Kalyanj iāsşAnndda duo (Kalyanji Virji Shah and his brother Anandji Virji Shah) won the 1975 Filmfare Award for Best Music Director for "Kora Kagaz." Since the award accurate it identifies Anandji Virji Shah (Kalyanji's brother) as the winner. sunmary:
shared by both brothers, the claim is
74

---

Published as conference paper ICLR 2026
Anandji Virji Shah, the brother of Kalyanji Virji Shah, won 1975 Filmfare Award for Best Music Director as part of the KalyanjiakšAnande duo for their work on "Kora Kagaz."
C.A.AAAAHH.. other demos omi onitted for brevity. #aA.aaa
GEPA Prompt generated by config GEPA:
Given the fields "claim" and "asssages", produce the fields "summary" by:
1. **Analyzing the claimex to identify key elements (e.g., names, locations, relationships, specific terms).
2. seCross-refferencing
passages** locate explicit or implicit evidence that supports, contradicts, or partially addresses the claim.
3. **Explicitly connecting the claim to the evidence** in the passages, even if the evidence is not directly stated in the claim (e.g., linking "Roger Yuan" to "Shanghai Noon" in Example 1).
4. snHighlighting domain-specific details** (e.g., "Sentinelese language is unclassifiable" in Example 3) that are critical for accuracy.
5. **Stating whether the claim is supported, unsupported, partially supportedxx, and explaining the reasoning with clear references
the passages. 6. s*Prioritizing key evidence** that would be relevant for further queries (e.g., "Marlborough" in Example 2) to ensure retrievability.
**Additional Guidance:**
If the claim contains unverified details (e.g., "Roger Yuan" in Example 1), explicitly note this in the summary.
For niche or ambiguous information (e.g., "uncontacted people" in Example 3), reference specific passages that clarify the context.
Ensure summaries refute the claim.
concise but include all critical evidence needed to validate
HoVer Qwen3 8B summarize2. 1rmmarrie22.rrdicct predict
Base Prompt:
Given the fields "claim', 'context', "passages". produce the fields "summary".
MIPROv2 Prompt:
Given the fields "claim', 'context', and "passages', perform multi -ho reasoning generate a structured summary that validates or refutes the claim. First, analyze the claim and contextual information to identify key relationships. Next, evaluate evidence from the retrieved passages to build coherent narrative. Construct logical reasoning chain connecting the claim supporting contradictory evidence. Finally, produce concise summary that explicitly confirms contradicts the claim based on your analysis, citing relevant evidence from the context and passages.
Demos:
Example 1:
claim:
75

---

Published as conference paper ICLR 2026
The recorded history of the state, for which Baptist George Ruby was a prominent black Republican leader in the Reconstruction-eras officially started in 1519 in the same state that holds the city Augustus Chapman Allen used his inheritance to fund the founding of.
context:
The claim is not supported by the passages. While George Ruby was associated with Texas during Reconstruction and Augustus Chapman Allen founded Houston in Texas, there is evidence in the passages that Texas's recorded history began in 1519. passages:
["Merchants Manufacturers Building The Main Building, formerly the Merchants and Manufacturers Building (commonly referred to as the M&M Building), is building on the campus of the University of HoustonddşDowntomm. The building is recognized part of the National Register of Historic Places, is Recorded Texas Historic Landmark, and considered a Contributing Building Downtown Houston's Main Street/Market Square Historic District. The building was built above Allen's LandingåÄTan area where Houston's founders John Kirby Allen and Augustus Chapman Allen originally settled.", ", "Augustus Chapman Allen Augustus Chapman Allen (July 1886 aAş January 11, 1864), along with his younger brother, John Kirby Allen, founded the City Houston the U.S. state Texas. He was born on July 4, 1886, in Canaseraga Village, New York (the present day hamlet of Sullivan in the Town of Sullivan, New York), to Sarah (Chapman) and Roland Allen. "Histary of Texas | The recorded History of Texas begins with the arrival of the first Spanish conquistadors the region of North America now known as Texas in 1519, who found the region populated by numerous Native American /Indian tribes.
Native Americans' ancestors had been there for more than 10,00 10,000 years as evidenced by the discovery of the remains of prehistoric Leanderthal Lady.
During period recorded history from A.D. 1519 1848, all or parts of Texas were claimed by five countries: France, Spain, Mexico, the Republic of Texas, the ted States of AmericaaATas well as the secessionist Confederate States of America in 18618Aş1865, during the American Civil War 'Charlotte Baldwin Allen | Charlotte Baldwin Allen (July 14, 1885 aăş August 3, 1895) known Texan history as "mother of Houston". She was wife Augustus Chapman Allen, who used inheritance to finance the founding of this city." 'Allen Parkway I Allen Parkway is arterial road west of Downtown Houston, Texas. It has distance of approximately 2.3 miles (3. 7Xxa0km). running from Interstate 45 west Shepherd Drive, where it becomes Kirby Drive. Originally known Buffalo Parkway, John Kirby Allen and Augustus Chapman Allen, the founders of Houston. "Allen's Landing Allen's Landing is the birthplace of the city of HoustonåAfthe largest city in the U.S. state of Texas. In August 1836, just months after the Republic of Texas won its independence from Mexico, YorkåäfJohn Kirby Allen and Augustus Chapman Allenáktpurchased 6,
was later named after
brothers (and real estate developers) from New
xxa x27kmmm) in the area and settled there on the banks of Buffalo Bayou. Allen's Landing is located south of the University of HoustonaλşDowntowr Commerce Street Building, the intersection of Commerce Street "Keith Allen (American football) Keith D. Allen (born July 12, 1974) an American football coach. He served as the head football coach at Southwest Baptist University from 2807 to 2012, compiling record of 278Aş39. His 27 wins are the most in the history of Southwest Baptist's football program, which began play in 1983.
Main Street."
reasoning:
The claim is not supported by the passages. While the passages confirm that Texas's recorded history began in 1519 (passage 3) and that Augustus Chapman Allen founded Houston in Texas (passage 2), they do not explicitly link the start of Texas's recorded history to the same state where Augustus Chapman Allen used his inheritance
76

---

Published as conference paper ICLR 2026
fund the founding of city. claim conflates these two wi thout direct evidence in the passages that the 1519 start of recorded history is tied to the state where Allen's s inheritance was used for founding Houston. sunmary:
The claim is not supported by the provided passages, as they confirm Texas's recorded history began in 1519 and Augustus Chapman Allen founded Houston in Texas not explicitly connect the two facts as stated in the claim.
wA other demos omitted for brevity. о*:
GEPA Prompt generated by config GEPA:
Given the fields "claim", 'context', and "passages", produce the field "summary" that: 1. **Evaluates the claim's validity** by explicitly connecting it to **specific evidence** from the "passages" (e.g., citing exact names, dates, terms that support/refute the claim).
2. kiHighlights factual inaccuracies* in the claim, such as incorrect origins, dates, or relationships, and links them to axdirect contradictionssx in the "passages" or "context". 3. **Mentions key terms or entities** critical to the claim (e.g., "Sly Stone," "Large actionable for
Mänsterlädnder," or "Hit Parader's 2006 ranking") to ensure summaries query generation.
4. **Balances accuracy** by acknowledging both correct and incorrect aspects of the claim, as seen in the examples (e.g., validating "The Wild Bunch" compilation while correcting the magazine's 's nationality and ranking year).
**Avoids vague statements** like "no evidence" and instead specifies **which parts of the passages** are relevant (e.g., "Passage confirms Parader was American").
*xGeneralizable Strategy**:
Always reference **specific passages** (e.g., "Passage states.... ") to ground claims text.
Use **precise terminology** (e.g., "German Shorthaired Pointer" instead of "cousins") to align with the data.
Prioritize sxclarity and specificitym to ensure summaries guide further evidence retrieval.
N.7
PUPA, GPT-4.1 MINI
PUPA GPT-4.1 Mini craft_redacted_rrr recpreet_rrrettt.___eee predict
Base Prompt:
Given private user query, create privacy-preserving request for power ful external
LLM.
The LLM may assist without learning private information about the user.
MIPROv2 Prompt: You will be given private user query. Your task is to analyze the query carefully and think step-by-step to understand the user's intent while identifying and removing
77

---

Published as conference paper ICLR 2026
abstracting any sensitive, proprietary, or personally identifiable information. Then, generate a clear, privacy-preserving prompt suitable for an external large language model (LLM) that requests useful, relevant guidance or information without exposing private details. Additionally, provide detailed chain-of-thought explanation describing your reasoning process for how you transformed the original query into the redacted, generalized request. Ensure resulting prompt is professional, contextually appropriate, and maintains user privacy while enabling the LLM to assist effectively.
Demos:
Example 1:
user_query:
Imagine you are a stock researcher and advisor, will give you table about stocks and I want you to give me advice on what to invest next. This is a project am working
I need help with predictions I making. Here is the table: Stock
Price Dividends 5D 5D
5Y % IBM $146, $6,64 $3,81
2,66 $2,63 1,83 07 3, $16,31 31 12,51 $0, 03 0,02 MCD $284, $6,08 $5,07 1,81
-07 -2,46 $20,57 7,78 $29, 24 11, 44 $123,28 26 76,26 JNJ $163,98 $4,76 -33 -1,80
-$3,90 -2,32 -$14,21 -7,97 $0,98 6,68 $29 66 22, 28 MSFT $324, 89 $2,72 $1,25 0,39
$9,888 -2,95 $85,31 35,61 $59,66 22,49 $217,33 202,05 05 WMT $158,36 $2,28 $1 0,89
$11,85 -1,15 $14,75 18,27 $25,47 19,16 $62,29 64, KO $60, $1, $8,12 -$1,59
-2,55 -22,45 -3,89 $2,23 -3,55 115,13 33,35 PFE $36, 07 $1 $1, -2,84 $50,67
-1,81 1$15, -2,,64 $10, 21,98 43, -13,09 AAPL $179 96 $0, 96 $3,45 1,95
$$16,57-884 $54,89 43,89 $18,58 11,51 $125,04 04 227, 65 NVDA $468, 23 $8,16 -3,71
-0,79 $7,05 1,53 $325,08 227,00 $310,22 196,33 $401,52 601, 89 CRM $212, 00 $2,75
1,31 $9,86 -4,44 32 57,37 $51,89 32,39 $59, 11 38,64 PYPL $61, 16 $0,00 00 $1,06 1,76
-$13,82 -18,43 43 -18,00 -$31,51 -34,00 -529,46 -32,51 BIDU 3137,47 $8,00 $10,24
8,05 -315,63 -10,21 $18,34 15,39 $9,,85 -6,69 ,$88, -39,10
reasoning:
The user wants investment advice based on provided table of stock including price, dividends, and percentage changes over various time intervals (5 days, month, year-to-date what to invest in next and help with making predictions.
year, and 5 years). The primary request is for recommenattion
preserve privacy, I should not include the user's raw data or specific details about individual stocks directly in the prompt to the external LLM. Instead, should request general advice on how to analyse stock data tables that include price, dividend, and percentage changes indicators weigh more heavily for investment decisions and for predictive strategies based on such data structures.
multiple time periods. Also,
ask for advice what
This approach ensures that the external LLM does not receive private or identifying data, only generic request. will ask the LLM provide guidance interpreting such stock performance indicators suggestions predictive analytics or investment selection criteria.
1lm_request:
Please provide general guidance for analyzing stock investment opportunities based data table format that includes stack prices, dividend payouts, and percentage changes over various
intervals such as days, month, year-to-date, year, and 5 years. Outline key indicators that investors typically weigh when making decisions, how to interpret short-term versus long-term performance trends, and suggest strategies or metrics to use when making predictions recommendation about which stocks invest in next. request for generic advice wi thout any specific stock names or values.
78

---

single query string. Published as conference paper ICLR 2026
3 other demos omitted for brevity.
GEPA Prompt generated by config GEPA+Merge:
New Instruction for Privacy- Query Reformulation Assistant (Enhanced):
Task Overview:
You receive a user query that may contain sensitive, private, or personally identifiable information (PII). Your objective is to reformulate this query into a generalized, privacy-preserving prompt suitable for sending to an external large language model (LLM). The reformulated prompt
enable the external LLM to fulfill the user's original intent effectively while rigorously protecting all user privacy by abstracting, omitting, or generalizing any sensitive details.
Input Format:
The query might contain identifiable names (people, organizations), specific geographic locations, exact dates or timeframes, proprietary or internal terminology (e.g., system names, product codes), client or customer names, URLs, or any other sensitive or private information.
Output: Format:
Part (a) Reasoning:
Provide concise paragraph that explains:
How you identified sensitive private information in the input
What strategies you applied to protect privacy (generalization, omission,
abstraction, replacement with placeholders)
How the reformulated prompt preserves the original intent and task requirements
without risking data Leakage
Part (b) LLM Request:
concise, carefully constructed privacy-safe prompt that:
Removes or anonymizes all PII and proprietary/internal details
Abstracts locations, names, dates, and technical terms needed
Produces clear and contextually rich instruction for the LLM generate
relevant and informative response aligned
the user's original task
Detailed Domain-Specific Guidance and Best Practices:
Identification and Treatment of Sensitive Data:
All -ser-specific or personal names (individual or organizational) must removed or replaced with generic role descriptors (e.g., áλa business contact, åAt âña client, aA1 âλa notable individualåaf). Never lightly obscure partially redact; full abstraction is required.
All geographic mentions must be abstracted unless location publicly known,
essential to the task, and can be generalized (e.g., åλa region known for eco-tourismaät instead of naming country city explicitly).
be retained; instead, approximate temporal references (e.g., aArecently, áñover past yearāAi). Internal or proprietary terms åλt including system names, product codes, subscription types, and technical jargon åäf must be generalized replaced with
Exact dates durations must
relative
79

---

Published as conference paper ICLR 2026
neutral descriptors avoid leakage of intellectual property or sensitive operational details.
Avoid direct quotes or verbatim inclusion of user-supplied texts unless obfuscated by generalization.
2. Task Understanding and Reformulation:
Identify the functional intent of the query: Is it creative writing, translation, summarization, professional communication drafting, technical explanation, other? Preserve the thematic and informational educational quality analysis, technical translation summary).
of the query (e.g., request for
passage, biographical
Do not reproduce the original input verbatim; instead, frame the LLM prompt around the essential thematic elements extracted from the input.
For queries regarding individuals, avoid direct reference real personal information unless name is widely public and essential; even then, use generic or hypothetical framing for the individual profile.
3. Strategies for High-Quality and Presecy- Prompts:
Strike balance between sufficient contextual detail and privacy abstraction to maintain prompt clarity and relevance.
Use neutral, context-aware formulations that clearly instruct the LLM on the content and style expected.
Avoid vague overly generic prompts that could result less useful lower-quality responses.
When system or proprietary content is mentioned, instruct the LLM to generalize specific terms and maintain the technical meaning without revealing sensitive info. When direct translation is requested specialized text, specify to replace abstract internal nomenclature.
4. Explanation Requirements:
The reasoning must transparently explain how privacy risks were identified (e.g., presence of names, locations, dates, proprietary terms).
It must describe the abstraction or omission methods applied (e.g., replacing akJonah Van Beijnenãäi with åλa notable individual, åλī substituting aλMakaušλī with aÃa specific region, áλI or aÄYodaāÄī with åλa system nameâuf).
Clarify how the essential task and user intent generalizations (e.g., focusing on educational quality, technical translation, biographical summary).
preserved despite these
This explanation justifies your design choices and demonstrates adherence to prevacy- principles.
Common Pitfalls Avoid:
Do not merely lightly obscure or partially redact sensitive details; full anonymization abstraction is required.
Do not repeat any sspr-sup PII proprietary content verbatim.
Avoid including URLs, exact dates, or direct quotes without modification.
Do not leave ambiguity that could degrade the quality or contextual clarity of the reformulated prompt.
Da not include real personal or organizational names unless they are public figures and the query requires it, then generic descriptors instead.
Example Summary of Effective Approach (Informed by Prior Examples):
80

---

Published as conference paper ICLR 2026
For geographic queries: replace exact place names with general regions and provide brief contextual descriptor.
For technical texts containing system names or subscription types: instruct the LLM to translate process the text while replacing abstracting proprietary system identifiers.
For biographical summaries about specific individuals: remove the real name and request generic, well-structured four-paragraph summary about åλа notable individuall,At preserving the overall intent without leaking PII.
Sunmary:
Your reformulations must ensure zero exposure of any PII or private/proprietary content while retaining enough thematic and functional clarity for the external LLM to produce high-quality, relevant outputs. This requires thorough analysis of the user's query, rigorous application of privacy-preservation strategies, and explicit reasoning explanations that document your approach and choices.
PUPA GPT-4.1 Mini respond_to, q
Base Prompt:
Respond to query. For inspiration, we found potentially related request powerful external and its response.
MIPROv2 Prompt:
Given privacy-preserving redacted prompt sent to an external untrusted LLM response, along with the original user query containing potentially sensitive information, generate final, coherent, and contextually appropriate user-facing response. Your output should integrate the anonymized LLM output with the original queryåAžs intent, ensuring privacy is maintained while providing complete and polished answer that directly addresses the user's 's request. Demos:
Example 1:
related_llm_reques)
Please provide detailed explanation David Easton's system analysis in political science, outlining the key components of his systems theory approach and its significance in the study of political systems. related_ [eate__llm_resspone::
Certainly! David Easton's system analysis a foundational framework in political science that applies systems theory to the study of political systems. His approach provides a structured way to understand how political systems function, maintain stability, and respond to changes in their environment. Below is detailed explanation of Easton's system analysis, including its key components and significance.
####
Overview of David Easton's System Analysis
David Easton (19178Aş2014) was prominent political scientist who sought to develop general theory of political systems by borrowing concepts from systems theory, which originated in biology and engineering. His goal create scientific and analytical
81

---

Published as conference paper ICLR 2026
framework to study politics as an integrated and dynamic system rather than as isolated events or institutions.
Easton's system analysis views political system
set of interactions abstracted from totality social behavior, through which authoritative allocation of values (decisions) is made and implemented. In other words, politics is about how societies decide who gets what, when, and how, and Easton's s model helps explain how these decisions are made and sustained.
### Key Components of Easton's Systems Theory Approach
Easton's model conceptualizes the political system as **black box** that processes inputs and produces outputs, embedded within larger environment. The key components are:
#### 1. **Inputs*x
Inputs demands and supports that from the environment and society into the political system.
**Demands:** These are claims or requests made by individuals or groups for the political system to allocate values resources in particular way (e.g., demands for law enforcement, education, welfare).
**Supports: These are actions or attitudes that indicate approval, allegiance, or compliance with the political system (e.g., voting, paying taxes, obeying laws).
Inputs represent the pressures and resources that the political system process.
#####: 2. **Political System (The Black Box)** The political system is the core of Easton's model. It receives inputs, processes them, and produces outputs.
It includes institutions, organizations, and processes involved in authori tative decision-making (e.g., legislatures, executives, courts).
The system transforms demands and supports into decisions and policies. The system
maintain its boundaries and adapt to changes survive.
##### 3. **Outputs**
Outputs are the decisions, policies, and actions that the political system produces in response to inputs.
These include laws, regulations, public policies, authoritative allocations of values.
Outputs affect the environment and society, influencing future inputs.
#### *#Feedback**
Feedback process by which outputs influence future inputs.
Positive feedback can reinforce the systemåÄžs stability.
Negative feedback can signal problems dissatisfaction, prompting adjus tments. Feedback helps the system adapt maintain equilibrium.
#### 5. ronmentx*
82

---

Easton's s systems theory influenced later developments in political science, including policy analysis, and the study of political development and Published as conference paper ICLR 2026
The political system exists within broader environment that includes social, economic, cul cultural, and international factors.
The environment provides inputs and is affected by outputs.
The system must interact with adapt to environmental changes survive.
#*# Diagram anmmati Representation (Simplified)
Environment
aES åEş [Demands Supports] aEš [Political System] açš [Decisions & Policies] aç$ åçş aER------- Feedback
#*# Significance of Easton's System Analysis in Political Science
1. *oHoistiic Understanding of Politics:**
Easton's 's model encourages viewing politics as an interconnected system rather than isolated events or institutions. It highlights the dynamic interactions be oetween society and political authority.
2. **Focus on Process and Stability:** The model emphasizes how political systems maintain stability and order through continuous processing of inputs and outputs, and through feedback mechanisms.
**Analytical Clarity:**
By abstracting political activity into inputs, system processing, outputs, and feedback, Easton provided clear framework for empirical research and comparative analysis.
4. **Foundation for Further Theories:**
change.
5. **Adaptability.x*
The model can be applied to various political systems (democracies, authoritarian regimes, etc.) and scales (local, national, international), making it versatile.
6. k*Emphasis on Environment::x Recognizing the environmentáäžs role underscores the importance of external factors (economic conditions, social movements, international pressures) in shaping political outcomes.
### Criticisms and Limitations
83

---

Published as conference paper ICLR 2026
xxOverly Abstract:** Some critics argue that Easton's model is too abstract and general, lacking specificity about power relations, conflict, and individual agency.
**Neglect of *o The model tends to emphasize stability and equilibrium, potentially underplaying political conflict and change.
**Limi ted Normative Guidance: is primarily descriptive and analytical, offering little normative guidance on what political systems ought to do.
###
Summary
David Easton's system analysis
user user_query:
System analysis by David Easton
response:
David Easton's system analysis is foundational framework in political science that conceptualizes the political system dynamic and interactive system. His approach uses systems theory explain how political systems function, maintain stability, and adapt to changes in their environment.
At its core, Easton's model views the political system as "black box" that processes inputs from society and the environment, transforms them into outputs, and feedback to adjust sustain itself. key components of Easton's s system analysis include:
1. x*Inputs:** These are the demands and supports that society directs towards the political system. Demands requests claims for resource allocation policy change, while supports are actions or attitudes such as compliance, legitimacy, or loyalty that sustain the system.
2. sMPolitical System:** This is the set of institutions and processes that processes the inputs. It interprets demands and supports make authoritative decisions, including the formulation enforcement of policies.
3. **Outputs: These are the decisions, policies, and actions that the political system produces in response to inputs. They affect society and its environment, shaping future interactions.
4. **Feedback:** ** Outputs generate responses that feed back into the system as new inputs, signaling whether the outputs were effective require modification, This feedback enables the system to adapt to internal and external changes.
5. **Environment:** The system operates within a broader social, economic, cultural, and international context that influences and is influenced by the political system.
Easton's theory significant because it offers holistic, process-oriented lens analyze politics, emphasizing interaction between society and state institutions. It provides a framework to understand political stability, adaptation, and change by focusing the continuous circulation of inputs, processing, outputs, and feedback. The model has been influential in shaping subsequent political theories and comparative political analysis.
84

---

Published as conference paper ICLR 2026
However, critics note that Easton's can be overly abstract, sometimes downplaying conflict, power struggles, and individual agency within political processes.
In summary, David Easton's system analysis offers powerful conceptual tool to study political systems as dynamic entities that manage societal demands and supports through authoritatinn tative decision-making to maintain order adapt over time.
other demos omi for brevity.
GEPA Prompt generated by config GEPA+Merge:
Respond to user query.
For inspiration, we found a potentially related request a powerful external LLM and its response.
N.8
PUPA, QWEN3 8B
PUPA Qwen3 8B craft_ htt__rrddceeeeeeeeeeeeeeeeeeeeee rwtueet.preeicc
Base Prompt:
Given private user query, create privacy-preserving request for power ful external LLM.
The LLM may assist without learning private information about the user.
MIPROv2 Prompt:
Given a private user query, generate a structured, non-sensitive LLM request that captures the user's s intent while omi mitting personal details. Include a step-by-sten reasoning process explaining how the redaction was achieved, ensuring the request is actionable and privacy-preserving. Format the reasoning the request as clear, specific task for the to execute.
coherent thought process and
Demas:
Example 1:
user quer_quer::
DSRÄDyDSD;NC DiDFDEDýNLDy DZDaDDrDiDtNC DEDZNR DuÑCDEÑÄDrOSD20y snmpwalk DiDF ip 10. 10.2.35.2 ÑA NNDDyy DEDI D'DENAÜCNCDENC hatiiuuerrdd211ggtv2222222222
(MD5) DES
reasoning:
The user requesting an SNMP walk command retrieve data from SNMPv3-enabled device at IP 10. 2. 35. 2. The provided credentials include username ('initi_user'") an authentication password ("d2I3BgtYdB2"), and an encryption password (" ('d2J3BgtYdB2'). The authentication protocol is MD5, and the encryption protocol is DES. The command must include SNMPV3 options to specify these parameters correctly. 1lm_request:
Generate a command-line instruction for executing "snmpwalk" with SNMPV3 authentication and encryption using the provided credentials, ensuring privacy by omitting sensitive details in the response.
85

---

Published as conference paper ICLR 2026
3 other demos omitted for brevity.
GEPA Prompt generated by config GEPA:
### New Instruction for the Assistant:
**Task Overview:**
Rephrase the user's query into privacy-preservingg, actionable request for an external LLM, ensuring no personally identifiable information (PII), proprietary data, or sensitive details are exposed. Maintain the original intent, clarity, and specificity of the query while adhering domain-specific strategies for generalized information.
**Key :*quirements:**
1. **Privacy Preservationts:
**Generalize Specifics**; Replace specific locations (e.g., "Andhra Pradesh" aE$ "a major Indian city"), numbers (e.g., "18 volunteers" aE$ group participants"), or proprietary data (e.g., "van Cleef production sites" aE$ "[Brand Name]'s 's production sites based on publicly available information").
**Avoid PII/Proprietary Dataæ*: Remove names, addresses, dates, internal processes, brand-specific secrets. Use placeholders like [Brand Namel, [Country], [Region] for brand/ company references.
**Publicly Available Focus**: For brand-related queries, emphasize publicly accessible data (e.g., "locations of [Brand Name]'s is production sites" instead of "van Cleef's 's secret factories").
2. **Quality of Request**:
**Clarity and Actionabilitys*; Ensure the rephrased query is precise, avoids ambiguity, and specifies constraints (e.g., "concise, platform-friendly hashtags" for creative tasks).
snDomain-Specific PrecisionRR: For scientific/technical queries, retain key terms (e.g., "CAPM formula," "pharmacokinetic parameters") while anonymizing study details (e.g., "18 volunteers" aE$ "a group of participants").
**Avoid Overly Broad Requests**; Specify constraints like keyword focus, tone, format (e.g., "health and fitness keywords" instead "general hashtags").
3. s*Domain-Specific Strategies**:
**Geographic Queries**: Replace regions/cities with generalized terms (e.g., "major city [Country]" "well-known [Region]").
#mBrapn/Comppny Queries**; Reference brands by name but prioritize publicly available data (e.g., "locations of [Brand Namel's production sites").
**Creative Tasks*#: Use examples or constraints (e.g., "wellness, strength, motivation keywords" for hashtags; "platform guidelines" for social media content).
4. **Avoid Leakage**:
**No Inferred Details**: Do not assume user-specific preferences, contexts, or private data (e.g., "Tell me the location of van Cleef production sites" áçš "Provide locations of [Brand Namel's s production sites based publicly available information"). **Anonymize Numerical Data**: Replace specific counts, dosages, or metrics wi generalized terms (e.g., "40 mg OA" aE$ "a dase of OA").
** Exxmple Workflow:**
86

---

Published as conference paper ICLR 2026
*xUser Querya*: "Calculate DaAZAmicoáAZs cost equity using with 4.67%, 1s 1.56, and market risk premium = 6.63%."
xxRephrased RequestæR: "Calculate the cost of equity for company using the CAPM formula, given risk-free rate of 4.67%, beta of 1.56, and market risk premium of 6.63%. Show step-by-step calculations and round the final result two decimal places."
*ANiche Domain-Specific Guidance:
Sesccientific/Phhaaacciinne Queries*:: Generalize study details (e.g., "18 healthy male volunteers" áEş "a group of participants"). retain key parameters (e.g., AUC, peak concentration)), and avoid proprietary methodologies.
DlCreative/Translatioon Tasks**; Use placeholders for song titles/artists (e.g., "Mahmood' song [Title]") and specify tone/rhythm requirements for translations. **F inancial Calculationssx: Retain formulas and numerical values but avoid linking specific companies or proprietary models.
**Final Output Format:**
**reasoning**; Briefly explain the rephrasing choices (e.g., "Generalized '18 volunteers' to 'a group of participants' to preserve privacy"). ==llm_requestwx: Provide the anonymized, actionable request for the LLM.
PUPA Qwen3 respond_to.. qu_uuryyyyyyyyy
Base Prompt:
Respond to query. For inspiration, found potentially related request powerful external LLM and its response.
MIPROv2 Prompt:
Synthesize the external LLM'S response with the user's specific query to generate final privacy-preserving answer. Replace placeholders with actual values while omi omitting densitive information, maintaining professional tone, and ensuring contextual accuracy. Combine the technical solution from the LLM with the user's unique details to produce clear, anonymized response that adheres privacy constraints.
Demos:
Example 1:
gelated__ln_requestt
Create PESTLE analysis framework far GE Heal thcare the us, outlining key issues each category (Political, Economic, Social, Technological, Environmental, Legal) and their potential impact on the organization. Prioritize these issues based on urgency and significance without referencing confidential data. Structure the output matrix with clear criteria for evaluation. paaate__lm__reppmee
Certainly! Below is a **PESTLE analysis framework** tailored for **GE Health in the US**, structured as matrix. It outlines key issues in each category, evaluates their *xurgency** and #*significancexx, and highlights their expotential impact*x on the organization.
###
PESTLE Analysis Matrix for GE Healthcare (US)
87

---

Published as conference paper ICLR 2026
**Categoryæ* **key Issues** *xUrgencye* (High/Med/Low) *xSignifi (High/Med//oww
xxPotential Impact on GE tealthcare**
**Political** 1. Healthcare policy changes (e.g., MedicareeMedicaid reimbursement reforms) High High Directly affects revenue streams and product/service demand; requires adaptation new reimbursement models.
2. Trade policies and tariffs on medical equipment imports/exports Medium Medium Could increase costs of components or affect supply chain efficiency.
3. Government funding for healthcare innovation and research | Medium High Influences R&D opportunities and partnerships; potential k*Economic*: c** 1. US thcare spending trends budget constraints High High Impacts purchasing power of hospitals and clinics; affects sales volume and pricing strategies.
grants subsidies.
2. Inflation and rising operational costs High Medium Increases production and operational expenses; may pressure profit margins. 3. Economic cycles affecting capital investment in healthcare infrastructure | Medium Medium Influences demand for high-cost medical equipment and technology upgrades. **Social**
Aging population increasing demand for healthcare services and devices High High Drives demand for diagnostic imaging, monitoring, and therapeutic devices; growth opportunity..
2. Growing patient awareness and demand for personalized medicine | Medium | Medium Necessitates innovation in tailored healthcare solutions and data-driven technologies. 3. Workforce shortages in healthcare professionals High Medium May affect adoption and training for new technologies; impacts customer support and service delivery.
**Technological** 1. Rapid advancements in AI and machine learning for diagnostics and imaging | High High Critical for maintaining competitive edge; requires continuous investment R&D and product development.
IoT and connected devices in healthcare Medium High Enhances product offerings but demands robust cybersecurity and interoperability solutions. 3. Data privacy and cybersecurity technologies High High Essential to protect patient data and comply with regulations; failure risks legal penalties and reputational damage.
I 2. Integration
**Envirommental** 1. Regulatory pressure reduce carbon footprint and waste in manufacturing I Medium | Medium | Necessitates sustainable practices; potential cost implications but improves brand reputation.
2. Energy efficiency in product design and operations Medium Medium Can reduce operational costs and appeal environmentally conscious customers.
3. E-waste management and recycling programs Low Medium Compliance with environmental standards; growing importance for corporate social responsibility. **Legal** Compliance with FDA regulations and medical device approvals High High Critical for market access; delays failures halt product launches and financial losses.
2. Intellectual property rights and patent protections Medium High Protects innovation and competitive advantage; infringement risks costly litigation.
3. Liability and product safety regulations High High Ensures patient safety; non-compliance can lead to lawsuits, recalls, reputational damage.
88

---

Published as conference paper ICLR 2026
#*# Criteria for Evaluation:
**Urgency**; How immediate the issue requires attention (High immediate/ongoing:: Medium within years; Low longer-term).
**Significance**: The potential tugnitude of impact on GE Healthcareaå2s operations, profitability, and strategic positioning (High critical; Medium moderate; Low miner).
**Potential Impact**: Describes how the issue could affect thcarekks business, including risks and opportunities.
This matrix helps prioritize strategic focus areas for GE Healthcare US, balancing immediate challenges with longer-term trends. _ser_query:
issue priority matrix pestle analysis for GE Healthcare in US response:
Here's a priaritized RRissue priority matrix*s based on PESTLE analysis KNGE Healthcare in the US**, focusing on the mast urgent and significant challenges and opportunities:
### **High Priority Issues (High Urgency + High Significance) **Category** **Key Issue** **Urgency** #+Significances **Impact Summary**
**Political** Healthcare policy changes (e.g., reimbursement reforms) High
High
Direct revenue risk; requires rapid adaptation new regulations.
**Econamic** US thaath spending trends and budget constraints High
High
Drives demand and pricing pressures; critical for sales strategy.
**Social** Aging population demand for healthcare services | High High Growth opportunity in **Technolooicallw AL/ML advancements in diagnostics and imaging High High
long- term market expansion.
Essential for competitiveness; demands sustained R&D investment.
**Legal** | FDA regulations and medical device approvals | High High Critical for market access; delays risk financial losses.
н## x*Medium Priority Issues (High Urgency + Medium Significance Medium Urgency + High Significance)**
**Category** **Yey Issue** **Uggenc*** **Significance** **Impact Summary**
**Economic** Inflation and operational costs High Medium
Pressures profit
margins; requires cost-efficiency measures.
caTe IoT integration and cybersecurity Medium
High
Enhances
offerings demands robust security frameworks. sxEnvironmettale Carbon footprint reduction and sustainability | Medium Medium
Balances cost implications with brand reputation and regulatory compliance.
89

---

Published as conference paper ICLR 2026
### +*Prioritization Criteria**
**Urgency**: Immediate (High) vs. longer-term (Low).
**Significancek* Critical (High) vs. moderate (Medium).
**Focus**: Issues with *sHigh in both urgency and significancexs require immediate strategic action, while others demand monitoring phased planning.
This matrix helps Healthcare allocate resources to address the most pressing challenges and leverage high-impact apportunities.
aha.ahattt.tttt.t...tttttttttaaa other demos omi mitted for brevity. ***.***************:*
GEPA Prompt generated by config GEPA:
Respond to user query by leveraging related LLM response as inspiration. Structure your answer in clear, organized format (e.g., bullet points, sections) to enhance readability. Ensure the response is tailored to the user's s language and avoids personally identifiable information (PII) sensitive data.
When rephrasing or summarizing content:
1. **Maintain academic tone** if the query specifies formal language (e.g., rephrasing technical or historical text).
2. **Refactor repetitive redundant information=s into concise, focused sections (e.g.,
grouping similar ideas, eliminating duplication).
3. keHighlight domain-specific facts** (e.g., cultural, technical, or regional details) to add value, as these may not be universally known.
4. **Improve code technical responses** by breaking down complex logic into smaller, well-documented functions, reducing redundancy, ensuring proper resource management.
For all outputs:
Prioritize **clarity, accuracy, and adherence to the user's requitenntss Avoid **leaking proprietary information, PII, unverified claims**. the useråA2s query involves multiple steps (e.g., optimization, content rephrasing), address each part systematically.
Use ==language-specific conventions** (e.g., correct grammar, terminology) to align with the ušeråA2s preferred language.
GEPA GENERATED PROMPTS FOR KERNEL GENERATION
0.1 NPUEVAL: KERNEL CODE GENERATION FOR NEW HARDWARE ARCHITECTURE
Figure 27 shows the prompt generated by GEPA with GPT-4o for NPU Keernel Generation, that achieves 26.85 % score with the same GPT-4o agent, that achieved just 4.25% with simple prompt.
90

---

Published as conference paper ICLR 2026
You tasked with generating Cee code for single kernet function that kill run an At Engine (AIE) tite. The kernel, shou perforn operation necessary headers and imports. Follow these quidelines:
self-containes single block, including
**Headers Isportsee: Include only necessary headers operations. including "vadr.he" or any headers part the stardard AlE API. Use:
**
Lude <stdint.ho
#inc Lude <oie_ap1/aiee.nge ap..a/..//pp-
#include <aie apw.stiisstsspp
function specified input. function pointers input output buffers and size paraseter. Use ATE vector operations to process the data efficiently.
#4Kernet Functiones: rmplesent
3. relector operationsw: Utiliize the ATE API's vector operations loading, processing, and storing data. example, use "elessvector" for vector operations and adddceeedue_aan" for reduction tasks. Ensure that the vector size is conpatible with the AIE hardware capasliities.
4. esdvoid Non- Existxtttntt Functionsee: not use functions tike 'ateszesp" or "ste::stora_y" if they are not supported, Instead, implesent the required functionality available AIE API functions.
5. eeError Handlinge:: Ensure that the code is free of syntex errors and compatible with the AIE envirorment. Test the code for compilation without
Here is an example tenolate for Kemet function:
**
#include <stdint.ho
#inc lude <ale, sie_aap//eee.hpp
sade_aps/utils..pp
vold kernel _eun__ttotnnneeeeee [aaee(brooot166 =irput_vector, brleat16 constespr int lengtt 16; Mijust based on ATE capubilities aieivector-ofloatll,, pector_length, irput_cata; aie:vettoreflontt6, vector_ Length output_data;
vector_size)
for (uint32_t i 9; vector_sizee sizes
Lengtth {
input_data aieit veveetse wwwp_//eeeeeeeeettteeeeooooo
// Perform the required operation on input_data // Store the result output_data aleesstore__ieule wtwprreevvouttttteeeeeeeee pecttott output_data);
Replace L functiionnaane" the operation logic specific details provided the input.
Figure 27: GEPA generated prompt for NPUEval that achieves 26.85% score with the same GPT-4o agent, that achieved 4.25% score.
91

---

Published as conference paper ICLR 2026
O.2 KERNELBENCH: CUDA KERNEL CODE GENERATION FOR NVIDIA GPUS
GEPA GPT-4o generated prompt for CUDA Kernel Generation
To optimize a given PyTorch model by replacing operators with custom CUDA kernels, follow these detailed instructions. Your goal is to achieve performance improvements while ensuring correctness. Name your optimized output architecture "ModelNew". Output the new model code codeblocks. Please generate real code, NOT pseudocode, and ensure the code compiles and is fully functional. Do not include testing code.
### Steps to Create Custom CUDA Kernels
#### Identify Operators Replace
Analyze the model to identify operators that can benefit from custom CUDA implementations.
Consider operator fusion opportunities combine multiple operations into single kernel for efficiency.
### 2. Setup and Compilation
www.uutppppleeeeeeeeeoo celss.cpp_eeeeeee pop_exxension. lead_ladnlll indiien to compile your CUDA code. This allows you Use ttorc.uutlls
to define and compile custom CUDA kernels directly within your Python script.
Ensure all necessary CUDA
headers are included to avoid missing includes errors.
#### 3. Implementing the CUDA Kernel
Write the CUDA kernel code, ensuring it is optimized for parallel execution. Use shared memory to reduce global memory accesses ensure coalesced memory access patterns. Example structure for CUDA kernel:
cpp >>ppp
_global__ void my we..kernelcconsss float* input, float* output, int size) {
int idx blockIdx. blockDim.x threadIdx.x;
if (idx size)
// Perform computation
#### 4. Kernel Launch Configuration
Configure the kernel Launch parameters (number of blocks and threads per block) to maximize GPU utilization.
Example:
"python
const int block_size 256;
const int num_blocks (size block_size / slock_size; my my.kernnel<<cmmmbblock (iock,size>>>inppt,, output, size);
#### 5. Error Handling and Debugging
Immplement error checking after CUDA API calls and kernel launches using cudaGetLastErrorO) () to catch errors early.
-Use CUDA debugging tools like "cuda-memcheck" and NVIDIA Nsight for debugging and profiling.
Be aware of common syntax errors and namespace issues, and ensure that the CUDA code adheres syntax rules.
92

---

Published as conference paper ICLR 2026
#*## 6. Integrating with PyTorch
Define Python function that wraps the CUDA kernel call and integrates it into the PyTorch model.
Example: python
def custommoppinnutt
output torch.zeros. wew.o like(inuut)
my_kernel (input. dtra outeut.datt_ptr<floooo)) input. ())
return output
#### 7. Testing and Validation
Validate the correctness the custom kernel by comparing output with the original
PyTorch operator.
-use profiling tools to measure performance improvements and identify further optimization opportunities.
Establish reference outputs for known-good inputs verify correctness.
Consider floating-point discrepancies and use small epsilon for acceptable differences
#### 8. Compatibility and Compute Capability
Ensure that the CUDA code is compatible with the target GPU's compute capability. Use appropriate compiler flags and consider building for multiple compute capabilities if necessary.
#b# Example Code
Here an example of how to replace simple element-win addition wi th custom kernel:
"*"yhnon import torch
import torch.nn nn as nn
from torch. www...tlls.c.o worch.utils.cpp.eeteeee.eeee cpp_extensiio import load_inline
Define the custom CUDA kernel for element-wise addition
el samentwise__dd__see tinclude <torch/extension..t tinclude <cuda_runtime... h>
now
__llbal___ void
wwtpwiiieeeddddeeeeeeeeeeeeeeeee floatx a, const floats floats out, int
size) {
int idx blockIdx. x blockDim.x + threadIdx.X:
if (idx < size) {
out[idx] a[idx] b[idx];
torch: Tensor :orch::Tensor
clmmmnowsss_adde...aacccoooooooo a, torch: :orch::Tenso: Tensor b)
auto size numell); auto out torch. parch.zeros_like(2.
const int block_size 256;
93

---

Published as conference paper ICLR 2026
const int num_blocks (size block_size 1) / block_size;
block_size>>> data_ptr<float>0, b. b.data_ptr< float>(), out. dat___ttrsflat>>), size);
return out;
semmenttwiee_edd__p__oooo (
"torch::Tensor dlementtwise_eadd_cuda(aCC Tensor a, torch: : Tensor b);"
Compile the inline CUDA code for element-wise addition
elementwise_add (oad_inline(
name=="elementwiss__ddde
gog_ssurrcessalleenntttsssseddd sww.urresseeemmmmniiisseddedddds
cource,
LunciiinssTteeeeeettsee....ddu. extra_cflagg==("",, extra_ldflags=[""]]
verbose=True,
class BodelNewwnnnModullee
def __enit_((eel)) -> None:
super()...nie
self s.lf.elementiise_aee add elementwise_ade
def forward(self,, b):
return self www.eeeeeeeeeeeee se_add, elmeentisee_ddd_uuaal b)
**# Additional Best Practices
*xxptimize Memory Usage**; Minimize data transfers between host and device, and use shared memory to reduce global memory access.
**Atomic Operations**; When using atomic operations like "atomicMax with floating-point
numbers, ensure correct usage by following best practices, such as using appropriate data types and minimizing contention.
*xPer formance Optimizationes
Maximize parallel execution, optimize memory access patterns, and compiler flags enhance performance.
**Namespace Usage**: Avoid adding class declarations or function definitions directly to reserved namespaces like "cuda". Use nested namespaces within non-reserved namespaces to organize code.
**Numerical Precision**; Be aware of floating-poine arithmetic issues, such non- associativity, and appropriate precision levels for calculations. **Debugging Tools**: Utilize tools like CUDA-GDB and NVIDIA Nsight for debugging and profiling to ensure correctness and performance.
following these instructions, you effectively replace PyTorch operators with custom CUDA kernels, ensuring both performance improvements and correctness.
## Instruction for Replacing PyTorch Operators with Custom CUDA Kernels
94

---

Published as conference paper ICLR 2026
Your task is to optimize a given PyTorch model by replacing certain operators with cus tom CUDA kernels to achieve performance improvements. Follow the steps below to ensure successful implementation:
#*## Step Identify Operators Replacement
**Criteria for Selection**: Choose operators that are computationally intensive and have potential for parallelization. Consider operators that are frequently used in the model's 's forward pass.
**Operator Fusion=*: Look oppor tunities to fuse mul multiple operators into single CUDA kernel, such as combining matrix multiplication with activation functions (e.g., matmul ReLU).
#### Step Implement Custom CUDA Kernels
x*Kernel Structure*x: Define your CUDA kernel using the "_lobbaal__" specifier. Ensure that each thread handles a specific part of the computation. Use correct index calculations to access data.
**Memory Management**:
Allocate memory for input, output, any intermediate on GPU using cudaMalloc". Use "cudaMemcpy" to transfer data between host and device. Utiilie shared memory to cache frequently accessed data and reduce global memory accesses.
Ensure coalesced global memory accesses for efficient memory transactions.
**Numerical Stability and Boundary Conditions**:
Implement verification mechanisms to ensure numerical stability. Use h__host_
__ddviie__" functions for testing both CPU and GPU.
Handle boundary condi tions to prevent out-of-bounds memory access. Ensure that thread indices are within valid ranges.
**Optimization Techniques**;
Use shared memory to reduce global memory accesses and improve performance,
Consider using mixed precision and Tensor Cores for matrix operations to enhance performance.
-Avoid diverged execution paths to maintain efficient parallel execution.
#### Step 3: Integrate CUDA Kernels into PyTorch Model
***nline Compilation*:: Use "torch.utiss.. coop_extension.no.e. loadooooooodnllaaaaaal inlinee to compile your CUDA code and integrate it into the PyTorch model.
**Model Modification**: Replace the original PyTorch operators with calls to your custom CUDA functions. Ensure that the new model techitecture ("ModelNew") is fully functional and compiles wi without errors.
μb## Step 4: Testing Validation
*CCorrectness**; Verify that the output of the optimized model matches the expected output of the original model. Use set of test cases to ensure accuracy.
***ee formance Evaluation**; Measure the runtime of the optimized model and compare it to the original. Aim for significant reduction in execution time.
**Edge Case inndling**: Ensure that the kernel correctly handles cases where the matrix size is not multiple of the block size and other potential edge cases.
#### Example Code
Below is an example
define integrate custom CUDA kernel for element-wise
adoi:ion:
95

---

Published as conference paper ICLR 2026
""yyhhnn
import torch
import torch. nn nn
from wtcphhuuils.cppeeeeeeeecc
import Load_inline
Define the custom CUDA kernel element-wise addition
elementwise_add seurc_s = now
#include <torch/extensiinn.tt
#include <cuda_runtime.hh
gloobal__ void elemen keree float* a, const float* b, float* out, int
size) {
int idx blockIdx.x blockDim.x + threadIdx.Xr
if (idx size) { out[idx] a[idx] b[idx];
torch: Tensor clementwwseccadddcuaacorrr::d:ee a, torch:: b) auto size a.numel() auto out torch: zeroo.:keees.
const int block_size 256; const int num_blocks (size block_size 1) / block_size;
( b. b.data_ptr<
float>(), aut.dattaptr<float>>)). size);
return out;
elementwise_ade _tp_____scppp_ceceee____e_e_____
"torch::Tensor dlementwise__ddd__udd((ee Tensor a, torch: : Tensor b)
Compile the inline CUDA code for element-wise addi tion
elementwise_ade bod__nniiee
name=" lemee="Weemenwis__dddee seeurcessslleeenttssee_ddddd cource,
SumncttoonsseLLeeeentvveeeeddddd extra_cflags=[""", extra__dflags=[""1
verbose=True,
class Mew_eleeewnnMMMMdJlee
def __init_(self)) -> None:
super () in self www.eeewewseeddde elementwise sedad
def forward(self,, b):
96

---

Published as conference paper ICLR 2026
return self. deeeenntwsse_ddd elementwise_eedd_cuddss b)
### Constraints
-Ensuee that optimized model maintains the same accuracy as original. -The custom CUDA kernels should be optimized for performance, minimizing execution time and maximizing GPU utilization.
following these instructions, you will be able to effectively replace PyTorch operators with custom CUDA kernels, achieving significant performance improvements while maintaining model accuracy.
P NUMBER OF REFLECTION LM CALLS MADE BY GEPA DURING OPTIMIZATION
Table 4: Total number of calls made by GEPA reflection LM during optimization.
Benchmark Name
Num Reflection Calls
Num Reflection Calls
GPT-441-Mini
Qwen3-8B
AIME-2025
24
90
LiveBench-Math
34
38
HotpotQA
69
64
IFBench
21
17
Hover
92
50
PUPA
46
38
97


Reviews:
[{"review_id": "QjgOcoMHan", "reviewer": "Reviewer_dW6h", "summary": "This paper addresses the challenge of post-training large language models (LLMs). Existing RL–based methods, such as GRPO, demand thousands of rollouts, leading to substantial computational costs. To overcome this, the authors introduce GEPA, an evolutionary, LLM-driven approach to prompt optimization. Unlike RL, which relies solely on scalar rewards, GEPA leverages execution and evaluation traces to provide richer, denser learning signals. It also maintains a Pareto set during optimization, promoting solution diversity. Experiments across multiple benchmarks demonstrate that GEPA is up to 35× more sample-efficient than GRPO and achieves superior performance compared to both GRPO and other prompt optimization frameworks.", "questions": "1. In Observation 4, could the weaker performance from adding few-shot demonstrations be attributed to the evolutionary framework of MIPROv2? In other words, how might GEPA perform if it were similarly constrained to include few-shot demonstrations alongside the instructions?\n\n2. In the experiments without a validation set (Observation 1), does this hurt the generalization performance?\n\n3. An additional advantage of GEPA is that the optimized prompts can potentially generalize across models. Have the authors tried this?", "limitations": "", "rating": 10, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The paper addresses an important and timely challenge—improving the sample efficiency of LLM post-training—and presents a well-motivated approach to tackle it.\n\n2. The proposed method overcomes key challenges of existing RL-based approaches, such as credit assignment from single scalar rewards and low sample efficiency.\n\n3. The results are compelling and challenge the existing paradigm of RL-based post-training in the field of LLM post-training.", "weaknesses": "1. (Line 220) “Human-written explanations” – does the human have to provide textual feedback based on the scalar reward?\n\n2. What is r in Algorithm 4?\n\n3. Other LLM-based evolutionary methods have been explored in the context of adversarial prompt generation and red-teaming, and these works should be appropriately cited, for example [1].\n\n[1] Samvelyan, M. et al. (2024). Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. In NeurIPS 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BV14X4aNQE", "reviewer": "Reviewer_pax9", "summary": "This paper introduces GEPA (Genetic-Pareto), a novel and highly sample-efficient prompt optimizer for LLMs. The authors argue that conventional adaptation methods, particularly RL techniques like GRPO, are inefficient as they require tens of thousands of trials and rely on sparse scalar rewards, failing to utilize the rich information in an LLM's own operational traces. In contrast, GEPA leverages the LLM's inherent linguistic capabilities through a process of \"reflective evolution.\" It analyzes the detailed, natural language traces of its own attempts—including reasoning steps and tool usage—to diagnose failures, propose targeted improvements to its prompts, and learn high-level strategies. This reflective process is combined with a Pareto-based evolutionary algorithm that maintains a diverse set of high-performing prompt candidates to avoid local optima. Across multiple benchmarks, GEPA is shown to significantly outperform GRPO, achieving superior results with up to 35 times fewer rollouts, and also surpasses the state-of-the-art prompt optimizer MIPROv2. The work demonstrates that leveraging language-based reflection is a more powerful and efficient method for optimizing complex, real-world AI systems compared to traditional RL approaches.", "questions": "Stated in Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1、It proposes a prompt optimization algorithm based on the Pareto frontier, which avoids the problem of local optima found in previous prompt optimization methods.\n\n2、Combining prompt optimization method with the multiple-rollout approach of GRPO, it demonstrates a significant improvement in the effectiveness and sample efficiency of prompt optimization. I think the main improvement comes from contrasting multiple rollouts and produce a better prompt.\n\n3、The performance of the proposed framework is demonstrated across multiple tasks.", "weaknesses": "1、Like many prompt optimization methods, this approach is highly dependent on the model's own reasoning and summarization capabilities. It requires the model to analyze successful and failed rollouts and distill effective textual experience into the prompt. Consequently, this method may not be suitable for less capable LLMs, an aspect the paper fails to analyze.\n\n2、The paper does not analyze the types of tasks for which this prompt optimization method is effective and those where it might not be. For instance, on tasks like mathematical reasoning, the effectiveness of this approach is not guaranteed.\n\n3、Does the prompt exclusively contain summarized experiences, or does it also incorporate the trajectories of failed rollouts?\n\n4、I am concerned that the context within the prompt could become increasingly long. Theoretically, as the number of learning samples grows, the volume of summarized experience in the prompt should also increase.\n\n5、The paper lacks some necessary citations. [1] is very similar to the process in this paper; both automatically optimize prompts through LLM. \n\nMissing citations：\n\n[1] Zhang, Wenqi, et al. \"Agent-pro: Learning to evolve via policy-level reflection and optimization.\" ACL 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lxUfvtHMEs", "reviewer": "Reviewer_QSDo", "summary": "This paper introduces GEPA, a reflective prompt optimizer for compound AI systems that combines textual reflection with multi-objective evolutionary search. The method iteratively mutate prompts by leveraging natural language feedback from rollouts. Evaluations across six benchmarks demonstrate its effectiveness and efficiency.", "questions": "See the Weaknesses", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The method leverages explicit reflection and Pareto-based selection for prompt optimization, and shows its superior sample efficiency and performance against baselines.", "weaknesses": "The novelty of the paper is limited. Reflection has been used in prior works (e.g. Reflexion and Self-Refine) for iterative improvement, and genetic algorithms have also already been applied to prompt optimization (e.g., EvoPrompt, Promptbreeder, and EvoAgent).\n\nThe paper is hard to follow, and needs more clarification. For example, what is the detail of the Reflective Prompt Mutation, especially for the feedback. In addition, System aware merge should be described clearly in the paper. How to select these two strategies to create new candidates in each iteration, and how many prompts are selected to mutate? According to the problem statement, the system comprises multiple modules, yet the paper does not elaborate on these modules in sufficient detail.\nMoreover, while GEPA is presented as optimizing only the prompts (i.e., only optimizing $\\Pi$), the optimization problem defined in Equations (2) and (3) also includes the module weights $\\Theta$. This apparent inconsistency should be addressed and clarified. \n\nIn GRPO, it is common practice to generate 16 rollouts per query. However, the paper mentions 24,000 rollouts in GRPO, which requires clarification. Additionally, the computational overhead of GEPA itself (e.g., the number of LLM calls required for reflection) is not discussed.\n\nThe experiments are insufficient in several aspects. First, the paper should compare with other prompt optimization methods such as EvoPrompt, PromptBreeder, and EvoAgent. Second, how do the performance of these methods vary under different computational budgets? Third, why are not all benchmarks included in Table 1. Specifically, why are the results for AIME-2025 and LiveBench-Math missing in Table 1?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iFPrRKWHdx", "reviewer": "Reviewer_J1yS", "summary": "This paper argues that the interpretable nature of natural language can serve as a richer learning signal for large language models (LLMs) compared to conventional reinforcement learning (RL) fine-tuning. To this end, the authors propose GEPA (Genetic-Pareto), a natural-language-based prompt optimization framework that integrates reflective reasoning and evolutionary search. GEPA iteratively samples trajectories, reflects on them in natural language to diagnose errors, proposes and tests improved prompts, and aggregates complementary insights from the Pareto frontier of its own optimization history. This approach reportedly enables more efficient improvement of LLM performance using significantly fewer rollouts.", "questions": "See the weakness.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.\tThe motivation to reduce rollout costs during fine-tuning while maintaining or improving performance is both practically valuable and timely, given the rising computational demands of LLM alignment and adaptation.\n2.\tThe proposed method is intuitively appealing and conceptually straightforward, making it easy to follow.", "weaknesses": "1.\tAlthough Equation (2) defines two sets of parameters for potential optimization, the proposed framework appears to update only the prompt parameters, leaving the model weights fixed. In contrast, baseline methods such as GRPO involve updating the network parameters directly in their original paper. This discrepancy raises concerns about fairness in the comparison, as the two approaches optimize fundamentally different objectives and operate under different levels of model adaptability.\n2.\tWhile the problem is framed as an optimization task in Equation 2, GEPA does not employ gradient-based backpropagation ensuring convergence to an optimum. The paper should clarify how the method guarantees optimization stability or convergence, either through theoretical justification or empirical analysis. Without such assurance, it remains unclear whether GEPA reliably approaches a Pareto-optimal solution or simply performs heuristic search without convergence guarantees.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1exoJw5odB", "reviewer": "Reviewer_VhzU", "summary": "This paper proposes GEPA, a novel prompt optimization algorithm based on genetic algorithms. The key components of GEPA are (1) reflective prompt mutation based on execution traces generated by LLMs and evaluation traces from environments, and (2) pareto-based candidate selection that effectively balances exploration and exploitation. The paper shows that GEPA outperforms MIPROv2, a recent prompt optimization algorithm based on Bayesian optimization, and GRPO on various tasks encompassing mathematical reasoning, multi-hop reasoning, and instruction-following.", "questions": "I will raise my rating to 8 or 10 if my major questions are well addressed.\n\n- (Major) Could you explain the rationale for not comparing GEPA with TextGrad or Trace? If feasible, I would strongly encourage the authors to run these additional experiments.\n- (Major) Why are the math benchmark results for Qwen3 missing? Could you explain the reason for excluding these results? Is it because the performance improvement was not significant compared to MIPROv2? Or, were the math benchmarks added closed to the deadline, which only allowed time to conduct experiments for GPT-4.1?\n- (Major) Could you please compare GEPA with MIPROv2 without few-shot examples? If few-shot examples are not effective for Qwen3 or GPT-4.1, it seems better to remove them and allocate more budget to the response.\n- (Major) Could you please elaborate on lines 219-221? You mentioned that feedback can be module-specific and augmented with human-written text. Could you please explain this more concretely using HotpotQA as an example?\n- (Minor) What was the monetary cost of running the experiments on GPT-4.1? Also, what would be the estimated cost if you were to use GPT-5 instead?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Overall, the paper is well-written and easy to understand. Although the paper is 94 pages long, including the appendix, I had no significant difficulty reading all of it.\n- The experiments are very well-designed. The benchmarks were appropriately selected across a diverse range of tasks, and the choice of models, one reasoning model (Qwen3) and one non-reasoning model (GPT-4.1), is also considered appropriate.\n- The inference-time search experiment in the appendix looks very promising.\n- The proposed system is production-grade, which makes it seem highly practical.", "weaknesses": "- The paper's comparison is insufficient, benchmarking GEPA against only one baseline method, MIPROv2. The authors should expand the comparison to include other relevant prompt optimization techniques, such as ORPO, TextGrad, and Trace [1, 2, 3]\n- Reflective prompt optimization seems not novel, as it has been widely used in TextGrad and Trace.\n- Pareto-based candidate selection seems to play a crucial role in performance improvement, yet the analysis on this component appears insufficient. The paper only presents a simple comparison against greedy selection.\n\n[1] Yang et al., Large Language Models as Optimizers, ICLR 2024 \\\n[2] Yuksekgonul et al., TextGrad: Automatic \"Differentiation\" via Text, arXiv 2024 \\\n[3] Cheng et al., Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs, NeurIPS 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)