C-Evolve: Consensus-based Evolution for Prompt Groups
Authors: Tiancheng Li
Yuhang Wang
Zhiyang Chen
Zijun Wang
Liyuan Ma
Guo-Jun Qi
Venue: ICLR 2026 Poster (2026)
Keywords: Consensus-based Evolution
evolutionary algorithm
majority voting
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: we introduce Consensus-Evolve (C-Evolve), an evolutionary algorithm that discovers a group of prompts whose aggregated outputs after majority voting achieve optimal performance.

Abstract: Prompt evolution algorithms offer a powerful paradigm for enhancing AI systems based on closed-source models, while few work explores whether aggregating results from multiple prompts to reach a  consensus can further advance the system capability boundary. In this paper, we introduce Consensus-Evolve (C-Evolve), an evolutionary algorithm that discovers a group of prompts whose aggregated outputs after majority voting achieve optimal performance. More specifically, C-Evolve employs an island-based evolutionary algorithm to maintain population diversity, and prompts from distinct islands are selected to form groups to aggregate their outputs. The key difference from single individual evolution is a voting score, which evaluates each individual prompt's contribution within groups. We take this as the fitness score for evolution instead of individual performance. Consequently, C-Evolve is more likely to produce and maintain prompts with higher potential to form a high-performing group and eliminate low-performing ones, gradually improving the group performance after reaching consensus. Our method achieves state-of-the-art performance across a wide range of tasks, including both open-ended tasks like HotpotQA and closed-ended tasks like MATH. On Qwen3-8B, C-Evolve achieves 70.67\% on HotpotQA and 43.88\% on IFBench, which are 4.95\% and 2.73\% higher than GEPA, respectively. For GPT-4.1-mini, the accuracy on IFBench is further improved to 47.96\% and reaches 95.33\% in the MATH benchmark. These results demonstrate the C-Evolve's competitive performance.

Published as a conference paper at ICLR 2026
C-EVOLVE: CONSENSUS-BASED EVOLUTION FOR PROMPT GROUPS
Tiancheng Li!.2.t Yuhang Wang2., Zhiyang Chen?.+
Zijun Wang 1.2 Liyuan Ma Guo-Jun Qi2.3,4,
"Zeeiangg University
School of Engineering, Westlake University & MAPLE Lab
3 "Matterwave Intelligence
4 Innttitute of Advanced Technology, Westlake Institute for Advanced Study
[Litiancheng, wangyuhang, chenzbiyang, wangzijun, maliyuan) Lake edu. guo peojung@egail. com
ABSTRACT
Prompt evolution algorithms offer powerful paradigm for enhancing Al systems based on closed-source models, while few work explores whether aggregating re- sults from multiple prompts reach a consensus can further advance the system capability boundary. In this paper, we introduce Consensus-Evolve (C-Evolve), an evolutionary algorithm that discovers a group of prompts whose aggregated outputs after majority voting achieve optimal performance. More specifically, C- Evolve employs an island-based evolutionary algorithm to maintain population diversity, and prompts from distinct islands are selected to form groups to aggre- gate their outputs. The key difference from single individual evolution a voting score, which evaluates each individual prompt's ' contribution within groups. We take this as the fitness score for evolution instead individual performance. Con- sequently, C-Evolve is more likely to produce and maintain prompts with higher potential to form a high-performing group and eliminate low- now-performing ones, gradually improving the group performance after reaching consensus. Our method achieves state-of-the-ar performance across a wide range of tasks, including both open-ended tasks like HotpotQA and closed-ended tasks like MATH. On Qwen3- 8B, C-Evolve achieves 70.67% on HotpotQA and 43.88% on IFBench, which are 4.95% and 2.73% higher than GEPA, respectively. For GPT-4. 1mini, the accu- racy on IFBench is further improved to 47 .6.%%%% and reaches 95.33% in the MATH benchmark. These results demonstrate the C-Evolve's competitive performance.
INTRODUCTION
The advancement of Large Language Models (LLMs) has significantly accelerated progress in natu- ral language processing (Liu al. [2244] Thirunavukarasu al. (2023). However, many state-of-the- art models, such as GPT-4.1 (OpenAl 2025) and Claude (Anthropic 2024), remain closed-source and are accessible only via API interfaces. This restricts researchers from performing parameter fine-tuning to adapt these models to specific downstream tasks. To leverage the capabilities of such black-box box models, new paradigm prompt-basee based optimization methods have emerged (Opsahl-Ong et al. 2024| Cāmara et al. 2025 Agarwal et al. 2025 Fernando et al.) 2023). By refining system prompts, these methods enhance model performance without updating internal weights.
Among these paradigms, evolutionary algorithms represent promising direction (Agrawal et al.) 2025 Novikov et al., (205)) without access to the weight gradients of black-box models. Indicated with predefined fitness function, these methods iteratively refine population of prompts and se- lect the individual with the highest fitness as the globally optimal prompt. However, in complex
This project was initiated and supported by MAPLE Lab at Westlake University.
Equal contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
HotPotQA (Qwen3-8B)
HoVer (Qwen3-88)
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vating
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voting stage
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Figure 1: Comparison of our proposed C-Evolve and AlphaEvolve. The blue line shows AlphaE- volve's highest individual score across three islands on the metric set. In C-Evolve, the red line represents the highest individual score (with dashed lines indicating the islands' highest individual scores) during the warm-up stage and the highest EMA voting score (with dashed lines indicating the islands' highest EMA voting scores) during the voting stage.
task environments, a single optimal prompt often suffers from inherent expressive limitations, strug- gling to fully capture multi-faceted task requirements. This shortcoming can be alleviated through cooperation among multiple different prompts - while any single prompt may fail in certain cases, its weaknesses can be compensated by others. By aggregating their responses, the system has higher probability of producing the correct answer. This could also be motivated by the findings in traditional machine learning, where ensemble methods improve performance compared to indi- vidual classifiers (Khan et al. 2024 Sun et al. 2024, Webb & Zheng 2004). Despite this insight, existing work in this direction largely overlooks the systematic exploration of the mechanism to ag- gregate results from multiple prompts, limiting the performance of LLM-based systems in complex scenarios.
To release the potential of consensus from multiple prompts, we propose Consensuv-Ervllve (C- Evolve). Instead of evolving a single prompt from populations, the proposed method evolves group of prompts that collectively achieve better performance after reaching consensus. These prompts contribute by first generating outputs individually, and then their outputs are aggregated to form the final answer. To evolve the prompts for this consensus target, we introduce a "voting score" to evaluate the contribution of each prompt within groups, which is calculated by averaging the performance of all the groups in which it participates, as well as being moving averaged over iterations to adapt to the evolving populations that form the groups. This mechanism encourages the selection and reproduction of individuals more likely to form high-performing group, while elim- inating under- perforrnrforing ones. To maintain population diversity, C-Evolye adopts an glland-base al., 2024). where prompts different islands evolve mostly in isolation. Then we sample a prompt from each island to form a voting group where prompts could complement each other. The entire evolution process consists of two stages: The "warm-up stage" ensures that the individual prompts are of high quality themselves; then the "voting stage" evolves prompts with consensus-ware fitness score, that we obtain high-pent sigh-performin prompt group by reaching consensus. Once the evolution is completed, the top- scored prompts from each island are assembled form final group to predict their consensus on the test set.
evolutionary algorithm (Izz0 al. 2012 Romera-Paaeeee
The proposed method achieves the state-of-the-arr performance across a wide range of tasks, in- cluding both open-ended tasks like HotpotQA Yang et al. (2018) and IFBench Pyatkin et al. (2025), and closed-ended tasks like Hover He et al. (2025). MATH Lightman et al. (2023) and GPQA Rein et al. (2023), on both open-source (Qwen3-8B Yang et al. (2025)) and closed-source (GPT-4.1- mini OpenAl 2025) ) models. On Qwen3 -B, E-voleve achieves 43.88% on IFBench and 70.67% on HotpotQA, 4.95% and 2.73% higher than GEPA respectively. On GPT-4.1 1-mini, the accuracy on IFBench is further improved to 47,9 47.96% and reaches 95.33% on MATH Benchmark. These results demonstrate the strong performance of C-Evolve.
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2 RELATED WORK
Prompt Learning. The advent of powerful, closed-source large language models such as GPT- 4.1 and Claude has established new paradigm in GenAL. As these models are often accessible only through APIs, directly fine-tuning network weights is not feasible, making prompt engineering critical for adapting them to downstream tasks.
Early efforts relied on manual prompt engineering (Wei et al. 2022 Chen et al. (025) such as chain- of-thought prompting (Wei et al. 2022) which suffered from inefficiency, strong subjectivity, and poor transferability. To address these challenges, automated prompt optimization techniques (Zhou et al. 2022 Kepel & Valogianni, 2024) have been developed. Search-based prompt optimization methods (Opsahl-Ong Ong et al., (024) utilize a Bayesian optimization framework to explore the joint space of instructions and examples. identifying the prompt configuration that maximizes validation performance. Another paradigm explores how models can learn online from interactive feedback. For example, previous work (Monea et al. 2024) demonstrates that LLMS can learn online from reward signals provided directly within their context, without requiring parameter updates. In this setting, the LLM's context is built from a history of its own predictions and the resulting feedback, allowing it to improve its capabilities over time.
Beyond manual approaches, evolutionary prompt optimization has emerged particularly power- ful and flexible paradigm. For instance, PROMPTQUINE (Wang et al., 2025b) optimizes prompts through in-context compression. Specifically, it prunes redundant tokens from original in-context learning (ICI.) demonstrations, transforming clear but potentially redundant context into concise prompts while retaining task-relevant information. Subsequent approaches treat prompts as indi- viduals in population that can be improved through iterative cycles of mutation and selection. In the context of LLMs, Wang et al. (Wang et al. 2025a) propose evolutionary fine-tuning for LLMs in black-box scenarios (e.g., API-only access) to optimize prompts, while introducing LLM-based reproduction to enhance evolutionary efficiency. This paradigm has been successfully applied to solve complex tasks. For example, GEPA (Agrawal et al., 2025) employs genetic algorithm that incorporates natural language reflection and pareto-based selection to evolve prompts. AlphaEi- volve (Novikov et al. 2025) utilizes an island-based evolutionary algorithm to evolve entire pro- grams for scientific discovery, demonstrating the power of evolution with complex code structures. Although they use different forms of evolutionary algorithms and operate on different targets (coding versus text prompts), both are fundamentally designed to discover single best- rest-performing solution through evolution. This strategy may limit generalization when task complexity increases or task dynamics changes, it could fail to explore the synergy among different prompts that complement each other. The advantage of reaching consensus by aggregating outputs from multiple prompts remains underexplored in the field of prompt optimization.
Consensus in LLMs. The idea that a committee of experts can outperform single expert is a cor- nerstone of many traditional machine learning algorithms, witnessed by decades of research on ensemble methods (Rokach) 2019 Polikar (0122). This principle has been applied to LLMs through techniques like self-consistency (Wang al.) (022), where multiple outputs are generated from multiple prompts and final answer is chosen by majority voting. Another approach to aggregate out- puts from multiple experts is used in multi-agent systems (Wang et al.) 2024 Tian & Zhang 2024 Grötschla et al. 2025), ), where multiple agents with potentially distinct roles and capabilities interact with each other to solve complex problems. Our method advances this idea beyond these paradigms. C-Evolve evolves a group of individual prompts where consensus mechanism is an intrinsic force driving the evolution process. This approach systematically evolves a diverse group of prompts for building a high-quality consensus, ensuring the resulting prompts are not only individually strong, but also cooperatively effective in groups
PROBLEM STATEMENT AND PRELIMINARIES
Compound AI System. Following established research, we define a Compound AI System any modular system composed of one or multiple LLMs invocations, potentially interleaved with exter- nal tool calls, and orchestrated through arbitrary control flows. Such a system can be formalized as +- = (M, L, X, X)),, where M = (M,,..,, MMM)) denotes the set of language modules, L spec- ifies the control flow logic, and X, y represent global input and output schemas. Each module
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= (0;, N, DA) is an LLM subcomponent: 9₂ denotes the underlying model weights; and X, and Y; define module-level input and output schemas. We define He as the system prompt containing instructions and few-shot examples required by M, At runtime, the control logic orchestrates the sequence and invocation of modules-for instance, passing outputs from one module to another, conditionally invoking components, or leveraging tool APIs.
In this paper, we only consider optimizing module prompts in the system. We denote = (Tis.... mIAr the set of all learnable module prompts. For simplicity, we refer an instance of II as an individual in following sections. To measure how an individual performs on the tar- get task T, we may denote each instance of this task as (I, m), where can be mapped to the input schema X. and m denotes metadata required for evaluation (e.g., gold answers, evaluation rubrics, or test. codes). For each instance, the system produces an output y = (a;; IT). A metric y x M -> [0, 1] evaluates the quality of with respect to metadata m, which can be quantified as scores like Fl or pass rates. With these notations, a classic evolutionary algorithm to optimize a single prompt can be formulated as:
II" arg max ( (a,m))~ [A ( (x; : m)]
(1)
I1
In this paper, we will test on several Compound Al systems. We will elaborate on how they are implemented with multiple language modules in Appendix [
AlphaEvolve. To address the optimization objective in Eq. D AlphaEvolvv adopts an island-based evolutionary strategy. In this process, multiple distinct islands evolves in parallel. Within each island, the evolution undergoes a cycle of sampling, generation and evaluation: first sample parent individuals based on their performance scores, then generate new candidate system prompts with an LLM by taking these high-performing parents and their evaluation results as input. After that, these new prompts evaluated and put into the population for later sampling. Additionally, periodic migration allows individuals to exchange between islands to maintain population diversity. As the evolution process going on, this system optimizes the population direction to maximize the objective in Eq. D resulting in a single best- best-performing system prompt 11 found across all islands.
Consensus. Consensus is achieved by aggregating the outputs produced by group of system [9| ................................ [M] with consensus aggre- gator C. For each task instance, each single prompt II' produces its corresponding output & (I; II) individually. Then, given |g] individual outputs yd (Ø (I::I')) ..... 1b (z: (1))) the consensus aggregator extracts the final results according majority voting principle: 3/inal C(ys). For close-ended tasks such as multi-choice questions and mathematical calculation, we simply employ majority voting, which selects the answer with the most votes. For open-ended tasks that demand free text outputs, we employ an LLM-based aggregator: leveraging an LLM to identify the most representative response covering the contents of as many responses as possible within the group. The prompts used by the M-bsed aggregator for consensus extraction can be found in Appendix K
prompts G = (11' ...., 1/(51>- <( [i..... TiM) ..... (x19)
To find the best-performing prompt group G" with the consensus mechanism, the optimization prob- lem is thus formulated as:
G = arg max ((,,) [u (C()9), m)]
Unlike seeking single optimal prompt as in AlphaEvolve, this paper aims find the optimal prompt group G*.
4 C-EVOLVE: CONSENSUS-BASED EVOLUTIONARY ALGORITHM
C-Evolve is an evolutionary algorithm designed for optimizing a group of individuals to improve the performance of a compound Al system. Its key feature is using a voting score to measure how well prompt contributes when operating with others, rather than evaluating it in isolation. This design ensures that, after evolution, we can sample multiple prompts that work effectively through output consensus.
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Island B
Island
Refine prompts in individuai using following information:
information attached
to each individual (Individual A.y as example)
system prompt 1
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system prompt 1: better task, Do not
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system prompt 2: subsequent
<Quuestion
-cAnswer of each individual
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-Group consentus>
Evolve
<EMA voting
<FEvaluation outcomes
Evolver LLM
<Feedback>
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Question 1, sindividuol A.p individuolly Form with individual..Finatt get consensus.... The groundtruth is... Question2...
to Jeeeboock information refice the prompts
of cindividoal Ass, ensuring that group consensus aligns with the ground truth.
Island A
Individual A.x
Update SIlEMa and feedbacks when candidate added
SILEMA
SILEMA (1 at) Stvvninng
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Drnet
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Sample groups across Islands
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Question
indikdiel
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Conpirate -tedvisss oxr risaiiiue C.25 phally reect consersee of Ia Cuemon
Answer
Figure 2: Evolutionary process in the voting stage C-Evolve, In each iteration, C-Evolve samples an individual from each island. Then, we use the evolver LLM to analyze the feedback samples linked to selected individual and perform evolution, producing a new individual. After introducing new individual into the island, we sample groups to update silemma and group feedback for indi- viduals in the island.
4.1 EVOLUTION ALGORITHM BASED ON ISLAND MODEL.
During evolution, the algorithm iteratively performs sampling, generation, and evaluation of these individuals to update the population. Considering we would like to obtain group of diverse indi- viduals that can achieve strong performance after reaching a consensus, we adopt an island-based [9]
evolutionary algorithm here: we initialize [9] islands {P:) and evolve individuals within each island in parallel. This parallel evolutionary strategy ensures that different islands follow distinct evolutionary trajectories, which promotes population diversity and ultimately benefits effective con- sensus. We configure an LLM refine the prompts within individuals based on feedback informa- tion, which we call the evolver. When refining individuals, the prompt required by the evolver can be referenced in Appendix H
Specifically, this evolutionary process consists of two stages: 1) warm-up stage that evolves in- dividuals based on their own performance, and 2) a voting stage that evolves individuals based on their performance when grouped with others from different islands. During evolution, we employ a metric dataset Dmet with evaluation metric μ in both warm-up stage and voting stage, and sep- arate feedback dataset Dieed that supplies additional samples (x, m) to generate detailed execution feedback for each individual.
The warm-up stage. In this stage, we define the metric value evaluated for an individual II on the dataset Dmer as individual score STI.ind. and regard it as the fitness score of each individual. We consider generating exactly one new individual per island as a single iteration. In each iteration, first sample a seed individual from the island using categorical distribution constructed from {& and h. (Appendix TaanndxxAAAAee Algorithm [. so that nigher--prfoomen individuals have a greater prob- ability of being selected. Then, the selected seed individual, together with its execution feedback
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on minibatch (batchsize defaulting to 3) sampled from Dreed is provided the evolver LLM to produce a new individual.
To prevent unbounded growth, the number of individuals in each island is capped at Nmaz. When- ever this limit exceeded after producing a new individual, the perffrmero individual mea- sured on Dee is eliminated. The overall warm-up procedure is summarized in Appendix A Algo- rithm 2
The voting stage. In this stage, each individual's fitness is determined by its performance within prompt groups. As shown in Figure 2 after introducing a new individual into each island, C-Evolve samples ne groups [G Each group 9% is formed by selecting exactly one individual (T) from each island where (i( denotes the index of the individual sampled from island i for group Gk. The outputs of all individuals within a group are integrated by the consensus aggregator to produce the final answer, and the group's performance is then evaluated on the metric set Dmet-
naive idea would be to eliminate all member individuals in the worst-performing group based on consensus results. However, this approach is problematic due the possible overlap of members between the worst-performing group and other better setter-performin ones. Aggressively pruning all members of the worst-performing group thus makes sense in this context. To address this issue, we define a voting score volin to measure each individual's contribution within consensus-driven groups:
E (II E ( Duai [u (C (y))).m)]
811, voting
I Gx)
(3)
where TI (IT E 9K) is an indicator function that equals the individual IT belongs to group Gk- This score measures the average metric value over the consensus results by the groups containing the 11. In other words, the higher the voting score, the more likely the group containing this individual could yield higher metric value. In this sense, the individuals with higher voting scores ought to be sampled to form a high-performing group
In order to balance the voting scores computed in different iterations, we take the smoothed SEMA, as the fitness score in evolution, and apply exponential moving average (EMA) to update it:
STT. f- STT.EMA + (1 a)) voting
(4)
where E [0, 1] is the smoothing factor balancing the weight of historical performance and current performance. After updating, the individual with the lowest STI,EMA in each island is then eliminated. In the initial phase of voting stage, we initialize STT,EMA with the individual score obtained at the end of the warm-up stage. Upon completion of all evolutionary iterations, we select the individual with the highest EMA voting score from each island form the final group. During inference, we use the final group to perform reasoning and aggregate each individual's 's opinion to reach consensus.
We deliberately avoid using the simple average of an individual's performance across all historical groups containing it as its final score. The rationale is that members from groups formed in the distant past may have already been eliminated from the current population. Incorporating these outdated consensus outcomes into the evaluation metric and assigning them the same weight as recent performance would be unreasonable, as it fails to faithfully reflect the individual's actual contribution within the current population or its recent evolutionary context. Therefore, we employ an EMA method to assign higher weight to recent or immediate scores, thereby suppressing the influence of historical outcomes from the distant past the individual's evaluation.
4.2 C-EVOLVE BASED ON GROUP FEEDBACKS
During the execution of compound AI system, the reasoning traces generated by the system on various questions provide valuable insights for understanding and analyzing an individual prompt. When combined with the final outcomes (e.g., success, failure, or performance scores), these traces offer significant diagnostic value, enabling evolver figure out when errors may occur and identify potential issues within specific modules..
Specifically, during the warm-up stage, to evolve given individual, C-Evolve compiles feedback information from the generated reasoning traces on a feedback dataset D This includes details of the prompts in individual, the inputs and outputs of each language module within the system, and

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its individual metric Dest- The evolver then uses this feedback to produce newly evolved individual.
During the voting stage, incorporate the reasoning traces from prompt group into the group feedback. More specifically, the feedback for each individual includes: information about the prompts in this individual, its grouping information, each individual's output in the group, sensus reached by the group, the metric scores for the group consensus on Deet alongside inputs and outputs by all LLM modules in the system. Leveraging this group feedback, C-Evolve uses the evolver to produce a new individual. The overall voting stage is summarized in Appendix A Algorithm
5 EXPERIMENTS
5.1 BENCHMARK DATASETS
To rigorously evaluate the performance of C-Evolve, we conducted experiments on two distinct task categories, each employing different consensus aggregators: for closed-ended tasks, employ majority voting to select the answer with the most votes, while for open-ended tasks, we leverage representative response within the group. We provide comprehensive
an LLM to identify the elucidation of the datasets and their splittings, evaluation metrics, consensus aggregators for each task Appendix B and detailed feedback content Appendix D
5.2 MODELS
We conduct experiments on two large language models: Qwen3-8B Yang et al. (2025) and GPT4.1- mini OpenAl (2025). These models represent mainstream open-source and closed-source models respectively. We provide the detailed configurations the two models in Appendix
5.3 TRAINING DETAILS
We select state-of-the-an evolutionary algorithms, GEPA Agrawal et al. (2025) and AlphaE- volve Novikov et al. (2025), to provide comprehensive comparison, For GEPA, we evaluate optimal system prompts provided in its paper. For AlphaEvolve, which is not open-sourced, we report results from our own reimplementation.
All experiments start with the baseline prompts provided in Appendix D We set the number of islands to be [G] = 3, with population limit of Nmaz = 10 individuals per island. For cach individual, sample questions from Diee to construct the feedback. In each iteration of the voting stage, we sample n& = 10 groups and evaluate them to update $I,EMA-- addition, we employ 10% migration rate between islands to avoid getting stuck in local optima.
5.4 RESULTS AND ANALYSIS
5.4.1
C-EVOLVE OUTPERFORMS THE SINGLE PROMPT EVOLUTION
Table presents the performance of C-Evolve on two open-ended and three closed-ended tasks, using both the open-source model Qwen-3-8B and the closed-source model GPT-4.I-mini. On Qwen-3-8PP 8B, C-Evolve achieves an average improvement of 13.85 % over the baseline, outperform- C-Evolve still
ing GEPA by 5.83% and AlphaEvolve by 4.1% %. On the more capable maintains a consistent advantage, yielding 6.89 % average improvement over GEPA and 2.67 % over AlphaEvolve.
To further demonstrate the effectiveness of C-Evolve, the curves in [ showcase how the high- est EMA voting score in the population changes during evolution on HotPoiQA and HoVer tasks. AlphaEvolve exhibits rapid improvement in the early stage but soon plateaus, while C-Evolve con- tinues to improve after entering the voting stage. As shown Table 2 the best individuals from the three islands in AlphaEvolve achieve accuracy of 41.1%% 36.73%. 73%, and 37.07% respectively, with the overall best individual performance being 41.15% (the first line of AlphaEvolve result) without any consensus mechanism among islands in the original AlphaEvolve. We try group these best

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Table 1: Performance comparison across multiple tasks: open-ended tasks (HotpotQA Yang al. 22118) IFBench Pyatkin et al. (2025) and closed-ended tasks(Hover)) He et al. (022). MATH Light- man et al. (202)) GPQA
al. (2023) using Qwen3-8B and GPT-4.1 mini. " "Improvement denotes the average relative gain of C-Evolve over baseline across available tasks for each model.
Bold values indicate the best result for each task.
Model
HotpotQA
IFBench
Hover
MATH
GPQA
Improvement
Qwen3-8B
Baseline
50.03
31,29
37.66
67.66
41.43
GEPA
65.72
34.01
43.33
+8.02
AlphaEvolve
65,31
41.15
44.66
82.66
43.08
+9.75
C-Evolve
70.67
43.88
50.33
85.33
47.15
+13.85
GPT-4.1 mini
Baseline 44.24
40.13
42
78.66
46.34
GEPA 67.31
68.39
46.59
39
49.2
AlphaEvolve
45.24
50.33
92.66
63.01
+13.42
C-Evolve
70.64
47.96
51.66
95.33
66.26
+16.09
Table 2:
Comparison between AlphaEvolve and C-Evolve on IFBench. We select the individual with the highest fitness score, either individual score Alpha-Folve EMA voting score in C- Evolve, from all three islands, and the accuracy reported on the test set. For AlphaEvolve, report both the accuracy of the best single prompt, and the accuracy directly applying the consensus mechanism to the three selected prompts.
Method
island island
island
Final Accuracy
41.15 (individual)
AlphaEvolve
41.15
36.73
37.07
41.15 (consensus)
C-Evolve
40.47
39.79
38.77
43.88
individuals selected from each island, and apply the LLM-based aggregator to them directly, which remains 41.15% (the second line of AlphaEvolve result). This arises because AlphaEvolve evolves the prompts solely based on their individual scores without considering consensus. In contrast, C- Evolve evolves the prompts with the EMA voting score as their fitness, ensuring that individuals with high scores are more likely to perform well when they are grouped across the islands. Thus, individuals selected by EMA voting score C-Evolve reach a higher accuracy 43.88% after reaching consensus.
5.4.2 MULTI-ISLAND EVOLUTION EXHIBITS DIVERSE FOCUSES
C-Evolve maintains population diversity through multi-island evolution. Appendix Figure illus- trates the evolutionary trajectories, from the initial individual (baseline) to the ones selected in the final group IFBench. We observe that individuals from different islands gradually developed dis- tinct focuses during evolution. The individual selected in island focuses on the principles of core constraint prioritization and refined amendment, while the individual selected in island 2 specializes in precise constraint pre-declaration and hierarchical verification. The individual selected island 3 concentrates on task priority stratification and critical failure point verification.
Table 3: Accuracy of individuals evolved by C-Evolve on the test set of MATH when evaluating on subsets of different difficulty levels. Each subset contains 300 problems. We report both the accuracy of single individuals with highest silema in each island, and the final accuracy of after reaching a consensus.
level 1
level
level 3
level 4
level 5
island
95
94.67
90.66
82.66
66.66
island 2
96.67
96
90
80
63.66
island
95.66
95.66
90.33
82
62.66
C-Evolve
96.67
96
92
82.66
68.33
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Table 5: Ablation study different methods averaging voting scores across iterations. Con- ducted on IFBench, using Qwen3-8B, Both are trained 50 iterations for the warm-up stage and 50 iterations for the voting stage.
Table 4: Ablation study on LLM-based aggre- gator for open-ended tasks. Conducted on IF- Bench, using Qwen3-8B. Both are trained 50 it- erations for the warm-up stage and 50 iterations for the voting stage.
Method
Accuracy (%)
Aggregator
Accuracy (%)
Group Average
41.16
LLM-summary 38.66
EMA
0.5
42.51
LLM-selection
42.85
EMA
0.8
42.85
EMA
0.95
41.50
When aggregated, these prompts foeus on distinct aspects, enabling stronger and more comprehen- sive problem-solving capabilities. We visualize the prompts evolved in each island on IFBench with
1-SNE Laurens Hinton (2008), as shown Appendix [+] Figure [6] We also visualize the mean pairwise Levenshtein distance across all inter-island prompt pairs in Appendix E Figure 7 We ob- served that, during the voting stage, the individuals with the highest EMA voling scores on each island gradually diverge, and three islands tend to form distinct clusters.
5.4.3 CONSENSUS HELPS ON HARD PROBLEMS
After evolving on MATH, we select the individuals with the highest EMA voting scores from each island on the metric set, and evaluate on the test set both individually and by reaching consensus with majority voting, As shown in T 3] the accuracies of single individuals consistently declines as task difficulty increases. Level 5, all islands fall below 67%, revealing the weakness of single- prompt solutions for harder problems.
After majority voting, C-Evolve consistently outperforms all islands on Levels 3-5, achieving aceu- racies of 92.00%, 82.66%, and 68.33%, respectively. This indicates that problems unsolvable by single optimal prompt can often be addressed through majority voting. This can validated with further analysis in Appendix F Table among the problems that two of the three individuals reach a consensus (25% in all problems). C-Evolve correctly solves 49.33% of them, while the single optimal prompt only solves 40%. Appendix CeeenndxxFFFigooo Figure also shows that all three islands contribute comparably to the "two-islands-corrett" region, indicating their complementarity
5.5
ABLATION STUDY
5.5.1 ABLATION STUDY OF LLM-BASER AGGREGATOR
For open-ended tasks (HotpotQA and IFBench), we use an LLM to aggregate individual answers into a final answer. We compare two LLM based aggregators: (1) LLM-summary, where the LLM generates an answer synthesizing all individual answers within the group; (2) LLM-selection, where the LLM selects the most representative answer from individual answers within the group.
As shown in Table the L.M--slletion outperforms LLM--summar. This is because summa- rization tends generate content that not strictly grounded in any individual answer, whereas selection preserves the fidelity of original outputs while identifying the most representative one.
5.5.2 ABLATION STUDY OF MOVING AVERAGE
In Eq. we apply EMA to update 8IL.MMA for each individual. Here we validate its necessity. We first simply average the individual's performance across all groups and iterations. It performs poorly (61.16%) on IFBench, using Qwen3-8B. The simple average assigns equal weights to all groups regardless of when they were formed, while many of the groups formed early in evolution may have already been eliminated. Thus, they cannot represent the individual's 'ss contribution when grouping with individuals in the current population. Applying EMA results in reliable evaluations. As shown in Table C-Evolve achieves its best performance with a = 0.8, reaching an accuracy of 42.85 %.

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Table 6: Evolving and Inference Time Comparison with Accuracy on GPQA, using Qwen3 8B.
Evolving time (min/iteration)
Inference time (s/question)
Accuracy
AlphaEvlove C-Evolve
3.5
0.68
43.08
4.3
0.79
47.15
5.5.3
EFFICIENCY AND PERFORMANCE TRADE OFF
Due to calculating the voting in evolving and introduces additional computational overhead. To this end, we employ two key optimizations to effectively balance efficiency and performance:
reasoning in inference, C-Evolve
Training Efficiency via Caching, During evolution, each newly generated prompt is first evaluated on the metric set to generate all answers, and these answers are cached. When computing the voting score, we reuse these cached outputs and apply majority voting across individuals within a group directly, which eliminates the need for repeated LLM calls during the voting stage. This significantly reduces training time: on the GPQA task, each iteration in C-Evolve takes only 0.8 minutes per iteration longer than AlphaEvolve (3.5 min/iteration) Table
Inference Acceleration via Asynchronous Prompting. During inference, we leverage asyn- chronous execution prompt the LLM with distinct system prompts parallel. This enables to obtain multiple answers simultaneously, delivering 9.45% relative improvement in accuracy com- pared AlphaEvolve while only increasing inference time from 0.68 0.79 seconds per question. As for open-ended tasks, it indeed needs another LLM request to complete majority voting.
Additional ablation studies, such as those on the warm-up stage and voting score, are presented in the Appendix G
CONCLUSION
In this paper, we present Consensus-Evolve (C-Evolve), consensus- evolutionary algorithm for optimizing a group of prompts in AI systems. The outputs from these prompts are aggregated for reaching consensus. C-Evolve introduces an EMA voting score as the individual fitness, which evaluates each prompt's contribution to consensus within groups. To maintain diversity, we employ a multi-island evolution system to explore complementarity among prompts. warmup stage is added to ensure each prompt performs well individually before reaching consensus.
Our experiments show that C-Evolve achieves statc-of-thes -f-t--rrtt results on multiple benchmark datasets using both Qwen3-8B and GPT-4.1-mint To best knowledge, C-Evolve is the first algorithm developing the group consensus among prompts to drive their evolution for making more accurate predictions. This opens new directions for optimizing compound AI systems, especially for those built on proprietary models.
7 REPRODUCIBILITY STATEMENT
We provide the following information to reproduce our experiments:
Dataset splittings, evaluation metries and consensus aggregators used in different tasks (Appendix B).
Prompts (Appendix H) and feedback formats (Appendix M used by the evolver LLM to produce new individual.
LLM generation configurations, such as temperature and max length (Appendix C
The prompts we obtained after evolution are shown Appendix L so that they can be evaluated directly.
ACKNOWLEDGEMENTS
This work supported by the National Natural Science Foundation of China under Grant No. 92467104, and Zhejiang Leading Innovative and Entrepreneur Team Introduction Program (2024R01007).
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REFERENCES
Eshaan Agarwal, Raghav Magazine, Joykirat Singh, Vivek Dani, Tanuja Ganu, and Akshay Nambi. Promptwizard: Optimizing prompts via task- feedback-driven self-evolution. In Findings of the Association for Computational Linguistics: ACL 2025, pp. 19974-20003, 2025.
Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arna Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, et al. Gepa: Reflective prompt evolution can outperform reinforcement learning. arXiv preprint arXiv:2507.19453 2025. 2025.
Anthropic. Claude 3.5 sonnet. Large language model, June 2024. URL https://wee www.
wwtp://w////ww/ccccccccccc//coot claudee 3-5 sonnet Released June 2024.
Sara Câmara, Eduardo Luz, Valéria Carvalho, Ivan Meneghini, and Gladston Moreira. Moprompt: Multi-objective semantic evolution for prompt optimization. arXiv preprint arXiv:2508 051::2,008.0541,, 2025.
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu. Unleashing the potential of prompt engineering for large language models. Patterns, 2025.
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. Promptbreeder: Self-referential self-imprrvement via prompt evolution. arXiv preprint arXiv:2309.16797 2023.
Florian Grötschla, Luis Müller, Jan Tönshoff, Mikhail Galkin, and Bryan Perozzi. Agentsnet: Co- ordination and collaborative reasoning in multi-agent llms. arXiv preprint M.Niv:2507.0861111 2025.
Tairan He, Wenli Xiao, Toru Lin, Zhengyi Luo, Zhenjia Xu, Zhenyu Jiang, Jan Kautz, Changliu Liu, Guanya Shi, Xiaolong Wang. Linxi Fan, and Yuke Zhu. Hover: Versatile neural whole-body controller for humanoid robots, 2025. URLhttps:
22229
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song. and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.0383 033774 2021.
Dario Izzo, Marek Ruciúski, and Francesco Biscani. The generalized island model. In Parallel architectures and bioinspired algorithmss, pp. 151-169. Springer, 2012.
Daan Kepel and Konstantina Valogianni. Autonomous prompt engineering in large language models. arXiv preprint arXiv:2407.11000 2024.
Azal Ahmad Khan, Omkar Chaudhari, and Rohitash Chandra. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications, 244:122778, 2024.
Van Der Maaten Laurens and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(2605):2579 2205, 2008
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. arXiv preprint arXiv:2305.20050, 2023.
Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. 2024.
Giovanni Monea, Antoine Bosselut, Kianté Brantley, and Yoav Artzi. Lims are in-context bandit reinforcement learners. arXiv preprint 1rXiv:2410.05362, 2024.
Alexander Novikov, Ngån Vü, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov. Borislav Kozlovskii, Francisco JR Ruiz, Abbas Mehrabian, et al. Alphaevolve: A coding agent for scientific and algorithmic discovery. arXiv preprint arXiv:2506. 13131, 2025.
11

---

Published as a conference paper at ICLR 2026
OpenAl. Gpt-4.1 series. Large language model series, April 2025. URL httts://openaii com/ httpiiinxxx/ptt--1p1p Released April 2025.
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. arXiv preprint arXiv:2406. 1695, 2024.
Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Pretten- hofer, R. Weiss, V. Dubourg, Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and F. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-28366 2011.
Robi Polikar. Ensemble learning. In Ensemble machine learning, pp. 1-34. Springer, 2012.
Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, and Hannaneh Hajishirzi. Generalizing verifiable instruction following, 2025.
URL httththts:
org/
02833
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. Gpqa: A graduate-level google-proof q&a benchmark, 2023. URL htttps/////aiiii /ttp//aaxvivv..///.i//.../...a./ owg/oogg//.....ooooo http//rrgrbb///..1eeee 12022.110222
Lior Rokach, Ensemble learning: pattern classification using ensemble methods. World Scientific, 2019.
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, al. Mathematical discoveries from program search with large language models. Nature, 625(7995):468 475, 2024.
Zhigang Sun, Guotao Wang. Pengfei Li, Hui Wang. Min Zhang, and Xiaowen Liang. improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237:121549, 2024.
Shangyin Tan, Lakshya A Agrawal, Arna Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, and Matei Zaharia. Langprobe: language programs benchmark, 2025.
URL https:://axxi. org/ /aas//222... 20315
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature medicine, 1939)11300 2023.
Chuan Tian and Yilei Zhang. Optimizing collaboration of Ilm based agents for finite element anal- ysis. arXiv preprint arXiv:2408. 1 13406, 2024.
Chao Wang, Jiaxuan Zhao, Licheng Jiao, Lingling Li. Fang Liu, and Shuyuan Yang. When large lan- guage models meet evolutionary algorithms: Potential enhancements and challenges. Research, 8:0646, 2025a.
Jianyu Wang, Zhiqiang Hu, and Lidong Bing. Evolving prompts in-context: An open-ended, self- replicating perspective. arXiv preprint arXiv:2506. 17930, 2025b.
Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou. Mixture-of-agents enhances large language model capabilities. arXiv preprint arXiv:2406.0440 04692, 2024.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdh- ery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203. 2022.
Geoffrey Webb and Zijian Zheng. Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques. IEEE transactions on knowledge and data engineering, 16(8): 980-991, 2004.
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---

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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. th-toutt prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24835 2022.
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 http::ttss//// wttp://aaavvvvv.. org/ abs/ 2505 09388
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, 2018.
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. Sglang: Efficient execution of structured language model programs, 2024. URL https:
oxc/abbs/232..7100
Yongchao Zhou, Andrei loan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. In The eleventh interna- tional conference on learning representations, 2022.
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A ALGORITHMS
In this section, we provide the performance-based sampling algorithm (Algorithm D C-Evolve warm-up stage algorithm (Algorithm 2). and C-Evolve voting stage algorithm (Algorithm
Algorithm Performance-Basse Based Sampling
Require:
Pool {(IP,, $1 f P] the score and feedback information of j-th individual, respectively. In the warm-up stage, the variable STT... represents the individual score STriiind whereas during the voting stage, STD. denotes the EMA voting score $nP JEMA-
where 1l means the j-th individual. sms. and fi are
1: Pj exp($1s..))
2: j ~ Categorical (py }
Categorical sampling: P(j" = = Py, where Σ/= Pj =
3: refurn (IP",, ST f²)
Algorithm 2 C-Evolve Warm-up Stage
Require: System 4 dataset Dieed, eval metric initial prompt module II" with score $ ind and feedback f islands {P.)]9] where P. is initialized as {(II", 811o -111 individual, f² Total number of iterations T. The population capacity of each island Nmxx
1: for t + 1 to T do
2:
for each island E (1,....,91) do (II* $11= ;ind; f*) < PerforrnaceeBssdSSamllee
3:
See Algorithm
4:
11' Generate new individual (II", f*)
5:
Dontch +- RRandomSample(2D (ree))
> uniformly samples a minibatch Dontch C Droed-
6:
f' ''t feedback of II" in Dbach
7:
SII' ind + (ar,m) ul u/(((,, II'), ml
8:
P. + P. ((T', &I1' .ind: f')}
9:
if IP: > Nenx then
10:
(II STrweak ind fwenk) + argmin SILind
P. P. (IIweak sppeen fweak))
12:
end if
end for
14:
end for
15:
return [1.)
B BENCHMARK DATASETS
B. OPEN-ENDED TASKS
HotpotQA Yang et (2018) a large-scale Wikipedia-basee based benchmark containing 113k question-answer pairs, where each question requires reasoning over multiple supporting documents answer. Following Tan et al. (2025) we take the question as input, perform two rounds of retrieval and summary to find out the final answer to the question. We use the Fl Score to evaluate string cquivalence between the predicted answer and the ground truth answer.
IFBench Pyatkin et al. 20255) is benchmark designed to evaluate the ability of language models to follow human instructions precisely. We adopt a two-stage response generation system consistent with GEPA's 's Agrawal et al. (2025) framework: first produces direct response the human instruc- tion. then refines this response to better conform with specified constraints. We use the accuracy of whether the final predicted answer follows all instructions as the evaluation metric.
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Algorithm C-Evolve Voting Stage
Require: System o dataset Dieed eval metric u, consensus aggregator C, island pools {Pi) := ini- tialized from the end warm-up stage, number of iterations T, groups per round Re. population capacity of each island Nax do 2: Step 1: Evolve using performance-butt based sampling
for +-
3: for each island E fl..... | do
4:
(1I* S11" EMA, f*) ParformanceBuscsSaapllc.C
P see Algorithm D Use evolver LLM Inherit parent score initially
5:
II' +- Evolve(II",
6:
1n' .EEA SII- ,EAA
7:
Doach <- RaadonSSmpllle (Deed)) feed)
8:
f' f individual feedback of II' in Dbatc
> prevent the absence of feedback for new
individuals
9:
P: - P. {(TT', 'TT' EMA: f')}
10: end for
Step 2: Form cross-island consensus-driven groups
12:
for k + to n. do
9k +-
14:
for each island i E | do
15:
(IICa(t), $powe, fCa(f) t PerfomanceeseeSSnnlle..)
D see Algorithm
Gn: 9n U (MC)(1))
16:
17:
end for end for
18:
19:
Step 3: Evaluate consensus performance
20:
for <- 1 to n. do
21:
yo + (#; (IQ()))), ((r; (.(()))) (x, E Dmet
22:
Vinal (2
23:
end for
Step 4: Update EMA voting score and feedback
25:
for each island E (I,......9 [ do
for each candidate (II, 8I,EMA f) E P: do
27:
if Ho is contained in groups sampled in Step 2 then voting
En TTEGG En=1 IIII 9b) (a.,m)) utyina m)
29:
if SILEMA is directly initialized from SmI in warm-up stage then
30:
updated 5II, + SI1.voting
31:
else
32:
updated 5ILEMA + (1 1 a) S11, voting + a SILEMA
D a [0
33:
end if
34:
II is sampled during current round r then updated f feedback of groups containing II in minibatch of D food
35:
36:
end if end if end for
38:
39:
end for
40:
Step 5: Eliminate individual
if [Pl. Nmax then
42:
(1lweak 811veat (weak)
argmin 8TLEMA (IL.em,MMA.f)
43:
P. P. [[[weak STIVAK FMA fweak)]
end if
45:
end for return [P.)
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B.2 CLOSED-ENDED TASKS
Ho He et al. 2025) is an open-domain benchmark for multi-hop fact extraction and claim verifi- cation. Following Tan at (025), we take the claim as input, perform two rounds of summarization and query generation, followed by three rounds of retrieval, then return all retrieval supporting facts. We adopt majority voting strategy, retaining only those facts supported by more than half of islands as the final predictions. We retain all supporting facts by the islands the final prediction results.
MATH Lightman et al. (0223) is challenging benchmark consisting of 12,500+ high-school and competition- math problems across diverse topics. Given math problem as input, we extract the final answer from the generated reasoning output and compute accuracy based match with the ground-trume answer. Our voting strategy follows the most frequently predicted answer selected if it constitutes majority; otherwise, final answer is randomly sampled from all candidate outputs.
exact string
GPQA Rein al. (2023) is rigorously curated benchmark of multiple-choice questions the graduate level in biology, physics, and chemistry. Given a question and its candidate options as input, extract the final option from the generated reasoning output. Accuracy computed based on an exact match with the ground-truth option. We then apply the same majority voting strategy as MATH.
B.3 DATASET SPLIT
Following the protocol established in GEPA, partition each of the five datasets into three disjoint subsets: a feedback set, metric set, and a test set. The exact split sizes for each benchmark are summarized in Table 7
Table 7: Dataset split across five benchmarks. The feedback set guides evolution, the metric set measures evolution fitness, and the test set is held out for final evaluation.
feedback set
metric set
test set
HotPotQA 150
300
300
IFBench
150
300
294
HoVer
150
300
300
MATH
150
300
300
GPQA
100
200
264
C
MODELS
Qwen3-8B: Qwen3-8B is an open-source large language model containing 8.2 billion parameters. We deploy the model using SGLang Zheng et (2024) on 8xH800 GPUs, employing data par- allelism for efficient parallel inference. We set the decoding temperature to 1 and the maximal generation length to 4096 during both evolution and evaluation.
GPT4.1-mini: GPTi.1-mini is small-scale language model released by OpenAl in 2025. We access GPT4. I-mini through the OpenAl API, with the decoding temperature set to and the max generation length set to 4096.
D RESULTS
Figure 3 and Figure show the final test set performance for C-Evolve and other evolutionary algorithms for Qwen3-8B and 1-mni.-mmini.
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Performance Comparison across Tasks (Qwen3-8B)
100
Method
Baseline
82.7
80
GEPA
Alpha-Evolve
Accuracy (%)
65.7
C-Evolve
67.7
60
41.1
43.9
43.3
44.7
41.4
43.1
47-1
37.7
34.0
20
HotpotQA
IFBench
HoVer
MATH
GPQA
Tasks
Figure 3: Final test set performance for C-Evolve and other evolutionary algorithms for Qwen3-8B.
Performance Comparison across Tasks (GPT-4.1-mini)
100
95.3
Baseline
80
GEPA
78.7
Alpha-Evolve
70.5
Accuracy (%)
68.4
67.3
C-Evolve
63.0
60
50.3
45.5
48.0
46.3
44.2
40
39.0
HotpotQA
IFBench
HoVer
MATH
GPQA
Tasks
Figure 4: Final test set performance for C-Evolve and other evolutionary algorithms for GPT-4.1- mini
E MORE RESULTS FOR SECTION 5.4.2
In Figure 5 circles represent individuals, with colors denoting their island affiliation, and numerical values indicate their performance scores. During the warm-up stage, the three islands produced
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Warms stage
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Island
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C-Evolve progressively incorporates the necessary considerations for instruction following through evolution, leading to performance improvement on IFBench task.
Figure 5:
highly specialized individuals along distinct strategic dimensions: Island I yielded prompts fo- cused the depth and dynamic verification of processes; Island 2 evolved prompts emphasizing the structure and prioritization of rule systems; Island generated prompts dedicated to underlying computational and extraction task support. In the voting stage, the individual migrated from island to island 2 eventually became the top-performing individual within island 2 and evolved prompt details distinct from the best individual Island 1. Finally, C-Evolve selected the highest-scoring individuals from each island (marked with asterisks) to form cooperative group for actual inference tasks.
In Figure 6 We utilized the Thid/Vectorizerttolll from the Scikit-learn library (Pedregosa et al. 2011 extract feature embeddings from all the prompts contained within each individual, serving as individual characteristics. These features were then visualized using the t-SNE (Laurens & Hinton, 2008) method for dimensionality reduction. During the warm-up stage, as the evolutionary process progressed, individuals from different islands gradually diverged from one another. In the voting stage, individuals from various islands did not lose their distinctive features despite engaging in consensus building. After 100 iterations within the voting stage, island 0 and island 2 gradually formed distinct clusters, indicating a continuous enhancement and strengthening their respective island-specific characteristics.
To quantify the diversity of the prompts different islands during evolution, we also compute the mean pairwise Levenshtein distance across all inter-island prompt pairs As shown in Figure 7 this distance increases steadily throughout evolution on IFBench, indicating that prompts from different islands become progressively more distinct rather than stylistic variants.
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Warm-up stage, iteration
Warm-up stage, iteration
Warm-up stage, iteration
Voting stage, iteration
Voting stage, iteration 30
Voting stage, iteration 90
Figure 6: The distribution of individuals from different islands during both the warm-up and voting stages of C-evolve, visualized with t-SNE. The individual with the highest fitness score on each island is marked with gray flag. Due to the presence of overlapping individuals across islands during the first iteration of the warm-up stage, only two flags are marked in this iteration.
IFBench, Qwen3-8B
MATH, Qwen3-8B
3403
Original Date
Single Idaadd Faints
Average Palnitaa Levershten Di Bet.ceeeeesss Becwon Dffferent Stands
besonerbably Curre
2400
Mubi Idanns Method (Smoothee)
3003
3000
1000
2003
3400
2009
2009
22
200
130
300
180
(b)
Figure 7: (a) Average pairwise Levenshtein Distance between different islands during the voting stage. (b) Comparison of the Average pairwise Levenshtein Distance between different islunds(blue line) and single island(red line).
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F MORE RESULTS FOR SECTION 5.4.3
Table 8: Additional results of C-Evolve the MATH Level test set. We categorize problems into three cases: (1) All three prompts produce the same answer (Row 1), covering 56% of the problems. Both single-prompt and consensus- beieedun-buuliingg predictions achieve 95.23% accuracy: (2) Two prompts produce the same answer, covering 25% of the problems. In this case, majority voting improves accuracy by 9.33% over the best single prompt: (3) All prompts produce distinct answers (19% of the problems), and one answer is randomly selected as the group prediction.
Case
Ratio
Single Prompt Accuracy
mansensus-building Accuracy
All answers the same
56%
95.23%
95.23%
two of three answers the same
25%
40%
49.33 %
All answers distinct
19%
14.03%
17.54%
Figure provides a Venn diagram illustrating the complementary strengths of the three individuals selected from different islands on 300 problems in MATH, difficulty level 5. Each circle represents the set of questions correctly solved individually by the prompt selected from one island.
We observe that 53.33% of the questions are correctly solved by all three islands, while 12.3% are solved by exactly two islands. The union of them is larger than the correct-answer set of any single prompt, indicating the effectiveness of the consensus mechanism. Moreover, all three islands contribute comparably the "two-islands-correer" region, indicating their complementarity.
G MORE RESULTS FOR ABLATION STUDY
G.1 WARM-UP STAGE
We evaluate the impact of the warm-up stage in C-Evolve on IFBench. Each experiment run for total of 100 iterations: when the warm-up stage is enabled, it comprises 50 iterations of warm-up followed by 50 iterations in the voting stage (in Figure (a). As illustrated in Table 9 using warm- up allows the prompt group to achieve better performance on (FBench(42.85) HotPorQAA(0.188) and MAT3(85.33) than directly starting the voting stage with the same iterations.
Table 9: Ablation study on whether use Warm-up stage.
Warm-up Stage
HotPotQA
IFBench
MATH
w/o w/
65.09
41.83
83.00
70.18
42.85
85.33
G.2 VOTING SCORE
In this section, we compare the voting score and the max score. The max score is defined as the highest score for an individual across multiple groups, rather than taking the average as in Equa- tion 3
Experiments are conducted on IFBench using the Qwen3-8B model. Both approaches start from the same warm-up stage and are trained 50 iterations for the voting stage. As shown in Table 10, the max score underperforms compared to the voting score. This because the max score merely picks the highest-scoring group in the metric set, which generalizes poorly.
Table 10: Ablation Study on max score and voting score
Score
Accuracy
Max Score
38.44
Voting Score
42.85
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Island 1
Island
(4.7%)
9 (3.0%
3 (1.0%)
160 (53.3%)
10 (3.3%)
13 (4.3%)
(1.0%)
Island 3
Wrong: 88 (29.3%)
Figure 8: Venn diagram of correct answers produced individually by the best prompts from three islands on 300 MATH Level-5 problems.
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G.3 POPULATION CAPACITY N GROUP COUNT Tic. AND ISLAND COUNT [
Island count 9l. We experiment with a larger number of islands ( = 5) and find that the final consensus performance increase from 85.33% 89%. This suggests that scaling up the island count strengthens majority voting by providing more diverse prompt candidates. Due to computational cost, we reported results using =3 in the main content.
Population capacity Naxx and group count Re. Maxx and are mutually dependent: larger population naturally means more potential groups that can sampled for majority voting, which requires increasing The accordingly to maintain stable score estimation. To balance efficiency and performance, we set both Nmax = and ne = 10. Table II also shows that reducing either Nmax will lead to a performance decrease.
Table 11: Ablation Study on Population capacity Naax. group count ne and Island count [g]
N
max
The
Accuracy
10
10
85.33
5
10
84.67
10
5
84.67
10
10
89.00
G.4 OTHER RESULTS FOR ABLATION STUDY
In this section, we provide comparison between C-Evolve with and without the warm-up stage in Figure 9 (a), and illustrate the highest EMA voting score during the voting stage using different EMA a: 0.5, 0.8, and 0.0 in Figure (b).
(Qwen3-88)
(Qwen3-8B)
47
Iorctton-0 100: koting stage
Peeahton=:-992 warm-u stage. Merstion="D-11C varing stace
42.9%
Accurncy 1S3
Accuracy
41.5%
42
36.9
aph--000
Iteration
a
(b)
Figure 9: (a) Comparison between Evolve with (yellow) or without (green) the warmup stage. We select the individuals with the highest fitness to form group at different iterations, and evaluate them the test set of IFBench. (b) The curve of the highest EMA voting score during the voting stage (yellow line: a = 0, green line: a 0.5, red line: a 0.8). Employing EMA with a = 0.8 produces more stable evolution trajectory, and also achieves better performance after 50 iterations.
H C-EvOLVE'S PROMPTS IN EVOLUTION
Here, we provide the system prompt for evolving task prompts used in compound Al systems with search and replace blocks. Placeholder text is shown in blue.
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Prompt for evolving a new prompt using search and replace
You should write prompts accomplish following task: <taak deacript fonz
Laek,descript.ie as described Appendix </ //task.desrriptiens
Tere is previous version:
sprompt.oanee date>
<prompt>
the system prompt to evelve
</prompt>
<eval_result>
mettics:: individusi.ccomm (warm-up stage) uNo.voting.score (voting stage)
feedhack: faedback as in Appandix M
</eval_reault> </prompt.ceneee dete
To achieve the task. please propose moditications to the prempt.candidate Deseribe each change with SEARCHRREPLACE
block described following:
SEARCH
[Driginal lines
(Nodified lines:
5>33ラ>3 REPLACE
Here an example showing how the Evolver LLM generates a new individual, including the actual input and output of this LLM call.
example for Evolver LLM input on MATH
You should write prompts accomplish the following task:
beeakkdesccrpp inseriptiin
Your task to optimize a system prompt for mathematical problems.
Goal: Given an input math problem, answer this prohlem to get the final answer.
Task Pipeline:
Step Amswer
Imput: problem
Outpur: the answer, and the final answer in \boxed! is.., (enswer)
Your Optimize system prompt provide clearer, more effective guidance corresponding steps. Ensure overall accurncy exactly match the answer.
maximized, that the predicted
Constraints:
You may perform multiple moditications
Cannot modify content inside angle brackets (e.g. csystem.pcompt...))
Final output contain exactly system prompt
prompt presented
<eystem.proopt..x.
</aystemmprompp.ua
</task descripticn
Here is previous version:
<prompt_cane date>> <prompt>
<aystem em, prompt 1>
Given a math problem, first identify the type of problem (e.g.. algebra, geometry. lincar algebra, probability) and extract all numerical variables. Apply appropriate formula method the problem type, performing calculations step ste-pp verilying internediate results.
For prohability:
Use combinations/perutarins for selection problems.
Apply conditional probability rules when necessary.
prohabilities sum to between
For repeating decimals:
multiply by 10" shift
original equation eliminate repetition, algebraically.
For digit reversal problems:
Recognize that Ja 2/ always divisible by
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Calculate maximum possible difference (e.g.. 999 999 O, Wait need recalculate...)
Instead: recognize that | where reversed digits produce multiple of 99, giving distinct values.
algehraic equations:
Solve systematically using subsiituioon, climination, factoring.
Always verify solutions by plagging back into the original equation.
determinants:
Calculate using colactor expansion or direct formula for 2 x matrices.
Finally, present the box.
answer single \boxed] lag using LaTeX formatting, additional explanations outsiide
</ </system, www.pmpppt........m.mmm c/prompts
<eval.resul>>
metries:
acc: 0.84482659776591
Feedback:
This sysiem prompl has been used in different groups, received the following leedbacks:
Feedbuck
Problem 0. The The positive four digit integers that use each of the four digits 1, 2, 3, exacily once are ordered from lenst to greatest. What the 15 integer the list?
This system prompt's
(' "ansser: 3b1oxd "ext:ractod_ansver "3214', 'score': 1.0)
system prompts'
System Prompt I:
t'nnswerss r (bbxee((3214) 'extracted_ansver "3214', 'score'; 1.0)
System Prompi 2:
I'answer': 1Syooxed (3214)31, noxtractad_anavert 33214', 'scoref: 1.9)
Final result after majority voting: 3214 True 3214
many positive
number 24
system prompt's result:
d'answerss
TThe aumber $24$ has 88$ positive divisers.
To determine this, first perform primo factorization: 68
Atimes 311
s8
The formula for the number divisors is (Atext (exponens) 1)8
for each prime factor, multiplied together:
69
13 13 1) Acines 8
63
Listing the divisors: 81, 2, 3, 4, 6, B, 12, 248.
63
[boxed18]
s3', "extracted 1B', facc! 2.0)
Other system prompts' result:
System Prompt 1:
d'enswer: (b) 'extrracte<_ansver'' (3", 'score': 1.0)
System Prompt 2:
d'enswer:: "SNbxee((3ss rextracted_ansver (8r, 'score': 1.01
Final result after majority voting: True
Problem 2. Let A (1, 3), and C (p, q) be three points parabola 7) = Go 5, where 5 Find the largest possible area of triangle ABC. This system prompt's result:
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Manssen!: : (bboxed/\ddraa (2)) "extracted_enswes (1
"score' 1.0)
Other system prompts' result:
System Prompt 1:
(' 'V, "extracttd_onnver'' ", 'score's 0.0)
System Prompt 2:
( (Noxeed (2)1)
"extracted_anower'' (1 'score' 1.0)
Final result after majority voting:
True answer:
</aval.rosult.
</prompt.canee caw.idatedd
To achieve the usk. please propose medifications to the prompt.cendicate.. Deseribe each change with SEARCHREEPACE block descrihed following:
[originel lines]
Modified lines] 5333533 REPLACE
An example for Evolver LLM output MATH
<<сесе< SEARCH
For digit reversal problems:
(ar v/ is always Calculate muximum possible difference (e.g. 999 999 0, - but for distinct numbers like 999 999 = O. but with digits reversed, the difference is 999 999 Wait, need recaleulate: actually, for digit munhers, the maximum difference 999 999 0. but for distinct numbers like 999 and 999, it's O. Wait, this is incorrect For example, 999 reversed is 999, difference is U. But integers",
Recognize
by
problem says be 899- 998 99? Wair, this is getting too specific. Mayhe hetter to state: For 3-digit numbers with reversed digits, the difference - 999 0. Wait,
999,
999
allowed. maximum difference might
always a multiple of and the maximum possible value and for 3-digit numbers, 100a + 106
not helpful. Instead, correct appronch is to
recognize that [x
100c 106 + z (99(a - c)j. Since a o c, a ranges from 9 or to s, z is multiple of 99, The distinct values are 99, 198, up giving 9 distinet values.
where
For digit reversal problems:
Recognize that the difference (x y between number and its reverse is always divisible by 9.
For 3-digit numbers, ihis difference specifically multiple of 99.
Calculate
+ 100 + c. 100c+ 199(( c)l) Ensure the result aligns with constraints (e.g.. distinct digits, valid number ranges).
[x e yl direcily using algebraic expressions (e.g..
5335533 REPLACE
SEARCH
For probability:
UUe combinttiom/pertuuaaiiiss selection problems.
Apply conditional probability
necessary.
Ensure probabilities sum to and are between and
For probability:
Iddnntffy the sample space and definitions.
combinations/hermaaiionn selection problems.
Apply conditional probability sules (c.g.. P(A[B) P(AnE), when nccessary.
Verify that probabilities sum to for mutually exclusive events.
complex scenarios, dingrams Bayes' theorem.
シンンララ>> REPLACE
SEARCH
For repeating decimals: decimal, multiply 10 shift decimal, subtract original equation eliminate repetition, solve algehraically.
For ropeating decimals: decimal. multiply by (where is number of ropeating digits) shift docimal. Subtract original equation to eliminate repeating part, algebriccall..
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Example: x 0.abc - 1000z J abc, leading abc
2302533 REPLACE
For Roralgehraic equations:
sydematically using substitution,
factoring.
Always verify solutions by plugging back into original equation.
For algebraic equations:
Solve systematically substitution, elimination, factoring.
Verify solutions by plugging back into original equation.
Check for extrancous solutions in rational equations.
3303593 REPLACE
For determinants:
Calculate using cofactor expansion direct matrices.
For determinants:
Use colactor expansion for matrices of size 3 or lurger.
Apply formula be (A) det(B) (B).
Recognize properties like det(A") dde((A) det(AB)
REPLACE
сccCCCk SEARCH
Finally, present
single Lag using LaTex formatting, additional explanations outside Ensure explamations appear outside
Finally, present the exact final ans wnswer in single [ tag using LaTeX formatting.
For optimizattoo/maximzaaa problems, explicitly state the methed used (e.g.. Cauchy-Schwarz, Schwarz, calculus).
TASK DESCRIPTION FOR EACH TASK
Here, we provide the task description used in Appendix H for each task.
1. HotpotQA
Your current task optimize the system prompt so that can guide the large language model to produce more accurate, stable, controllable output six-step process. Goal: Given an input question, perform two rounds of retrieval and summary, such that the final generated answer must exactly match the true answer. Task Pipeline:
Step 1:
First Retrieval
Imput: 'question'
Output: A batch of related passages retrieved from the database (first retrieval passages)
Step 2:
First Summary
Input:
'question', first retrieval "passages', 'system prompt
Output: concise, coherent summary of first retrieval passages (summary_1)
Step 3:
Query Generation (<system,) www.eeempppmpp.p.m 2>cp/pt.2>//////e systeme http//yysttpppopt.22222 prompt.22)
Inpur: 'question', 'summary_l' 'system prompt 2'
Output: query that the semantics of the question for second retrieval
Step 4:
Second Retrieval
Input: The query from Step
Output: Another batch of related passages (second retrieval passages)
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Step 5:
Second Summary
Input: 'question', first summary 'context', second retrieval "passages", "system prompt 3'
Output: A concise, coherent summary of the second retrieval passages (summary-2)
Step 6:
Answer Generation (<systen, (<system.prompt _/></ system. wwtastemmpropp......eee pwt.ppeeppp))
Input: 'uesttion, 'summary_1", 'summary_21 'system prompt 4'
Oulput: The final answer to the question
Optimize 'system prompt I', 'system prompt 2', 'system prompt 3', 'system prompt 4' to provide clearer and more effective guidance for cach corresponding step. Maximize the pipelinc's overall accuracy, ensuring the final answer strictly matches the true answer.
Your Task:
Constraints:
You may perform multiple modifications
Cannot modify content inside angle brackets (e.g., <systemm.propt...>>
Final output must contain exactly four system prompts
Each prompt must bc presented in the
format:
<system.prompt.xx
</systen.prompet
2. IFBench
Your task to optimize system prompt for IFBench that the large language model more accu- rately follow the various requirements the instructions during a two-step generation process. Goal: Given an instruction, first answer it directly, and then further calihrate the answer to produce a final response that hest meets the instruction's 's requirements. Task Pipeline:
Step 1:
Direct Answer Generation (<system.prene
Input: User's instruction
Output: direct response instruction
Step 2:
Answer Calibration
Input: User's instruction Step answer + system.prompt..2
Output: Final calibrated
Your Task: Refine system prompt and system prompt 2 so that each one provides clearer and more effective guidance for its corresponding step the pipeline. The ultimate goal is to maximize the model's ability follow instructions, ensuring its responses meet all requirements in the user's instructions. Constraints:
You may perform multiple modifications
Cannot modify content inside angle brackets (e.g., <eystem.prompt_t>>
Final output must contain exactly two system prompts
Each prompt must
presented the
format:
<system prompt x>
</systen.prompt.xx
3. Ho
Your task is to optimize four system prompts for a seven-step retrieval-ndd-anseer pipeline. Goal: Given an input claim, perform two rounds of summarization and query generation, followed by three rounds of retrieval, to determine whether the claim final passages. The evaluation criterion is whether the true passages are fully contained within union of the three retrieval outputs (first, second, and final retrieval).
be SUPPORTED NOT SUTPORRTE by
Task Pipeline:
Step 1:
First Retrieval
Imput: claim
Output: Initial batch of related passages (first retrieval passages)
Step 2:
First Summary (<system prompt. 1>< />< //st/ss prompt 1>)
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Input: claim, first retrieval passages, system.prompt
Output: Concise, coherent summary (summary_1)
Step 3:
First Query Generation (<system prompt 2></system prompt 2>)
Input: claim, summary. system wwweeeeprpppp...m
Output: Query capturing core claim semantics for second retrieval
Step 4:
Second Retrieval
Input: Query from Step
Output: Second batch of related passages (second retrieval passages)
Step 5:
Second Summary
Input: claim, first summary (context), second retrieval passages, system.prompt.3
Output: Concisc, coherent summary (summary
Step 6:
Second Query Generation (<system prompt 4>< //ss//sssemmmmmmm prompt 4>)
Input: claim, summary... .I, summary 2, systen.prompt.4
Oulput: Query capturing core claim semantics for final retrieval
Step 7:
Final Retrieval
Input: Query from Step
Output: Final batch related passages (third retrieval passages)
Your Task: Optimize system prompt I, system prompt system prompt and system prompt 4 pro- vide clearer, more effective guidance for their corresponding steps. Ensure overall accuracy is maximized, so that all true passages are contained in the final three retrieval results. Constraints:
may perform multiple modifications
Cannot modify content inside angle brackets (e-g., <system.prompt..3
Final output must contain exactly four system prompts
Each prompt must be presented in the format:
Soystemprompe x>
</ </system.prompt.x.
4. MATH
Your task is to optimize system prompt for mathematical problems. Goal: Given input math problem, answer this problem to get the final answer.
Task Pipeline:
Step 1: Answer
Input: prohlem
the answer, and put the final answer in \boxed ) tag, i.e. $\boxed (answer here).
Output:
Your Task: Optimize system prompt to provide clearer, effective guidance for their corresponding steps. Ensure overall accuracy is maximized, so that the predicted answer can exactly match the true answer.
Constraints:
You may perform multiple modifications
Cannot modify content inside angle brackets (e.g., <eystem.proopt..t>>
Final output must contain exactly one system prompt
Fach prompt must be presented in the format:
<system.prompt.x>>
</ s/system_rrompt..
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5. GPQA
Your task is to optimize system prompt for Multiple-choice questions. Goal: Given imput Multiple-choice question, answer this question to get the final answer.
Task Pipeline:
Step 1: Answer
Input: question
Output: the answer, and put the final answer in \boxed() ) tag, i.e. $\boxee (answer here)-
Your Task: Optimize system prompt provide clearer, effective guidance for their corresponding steps. Ensure overall accuracy is maximized, so that the predicted answer can exactly match the true answer.
Constraints:
may perform multiple modifications
Cannot modify content inside angle brackets (e-g., <system.prompt..3
Final output must contain exactly system prompt
Each
prompt must be
presented in the format:
esystem.promps
</system.prompt..
FEEDBACK FOR EACH TASK
Here elaborate on how we compile the feedback for different tasks. Placeholder text is shown in blue.
HotPotQA We compile the execution traces, including the retrieved docs and the summary in each hop, to obtain the feedback. Due to that the retrieved documents are usually long, we use an L.M compile feedbacks multiple questions used in the evolver L.M,, based the execution details. The field "score" refer to the Fl score calculated by comparing "answer" with the ground truth answer.
Prompt for feedback compilation with L.M warm-up stage on HotPotQA
Your Task: Analyze this system prompt: system-prompt.
Goal This System Prompt: Given an input question, perform two rounds retrieval and summary, such that the final generated answer must exactly match the true answer. Below are the results of two rounds of retrieval and summary multiple questions. Please analyze them by
Question
Question: question
This system prompt's 's result:
"hopl_docs" hopl_docs,
summary_1"" summary_1,
"hop2_quers" hop2_query,
"hop2_does"" hop2_docs,
"summary_2"; summary_2
"answer": answer, sscore" score
True answer:
[... Additional questions ...]
Carefully reflect the following:
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each question, what specific weaknesses does this system prompt show?
Across questions, what common issues or patterns can you identify?
How could this system prompt be revised or improved?
Prompt for group feedback compilation with LLM the voting stage on HotPotQA
The current system prompt works together with other system prompts, and their outputs combined through majority voting to the final result.
Your Task: Analyze only this system prompt: systemn-prompl. without evaluating the other two. Goal of This System Prompt: Given an input question, perform two rounds of retrieval and summary, such that the final generated answer must exactly match the true answer.
Below the results of two rounds of retrieval and summary multiple questions. Please analyze them one by one:
Question
Question: question
This system prompt's 's result:
summary_1". summary_1,
"hop2_query": hop2_query,
"hop2_docs": hop2_doc9,
"summary_2": summary_2,
"answer"; answer,
"score" score
Other system prompts' result:
System Prompt
"hopl_doss" hopl_docs,
summary_ll 1" summary_1,
"hop?_query": hop?_query,
"hop2_doca": hop?_docs,
suumarry_"" summary 2,
"answer"; answer, "score" score
[... Additional system prompts...]
Final result after majority voting: group final answer
True answer:
grounded truth answer
[... Additional questions ...]
Carefully reflect on the following:
each question, what specific weaknesses does this system prompt show?
Across questions, what common issues or patterns can you identify?
How could this system prompt be revised or improved so that, when combined with others, it con- tributes more effectively to better final results?
IFBench
We directly organize the instructions. well as the answers and final answers generated by the compound AI system in the following format to compile the feedback. The field "result" indicates the detailed information of requirements and whether "final answer" successfully follows
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each requirement. The field "score" indicates whether answer" successfully follows all the requirements in this imput instruction (0 or 1).
An example of compiled feedback in the warm-up stage on IFBench
Below are the results of the answers and the final answers on multiple questions. Instruction 1:
Instruction: Instruction
This system prompt's s result:
aaswwrr:
answer,
final answer": final answer, "result" : result, "score"; score
[.. Additional instruction ...]
example of compiled group feedback in the voting stage on IFBench
The current system prompt works together with other system prompts, and their outputs combined through majority voting to form the final result.
Below are the results of the answers and the final answers on multiple questions. Instruction 1:
Instruction: Instruction
This system prompt's 's result:
answerr;
answer,
Laahal_asswer.. final answe,,
"result" : result, "score"; score,
Other system prompts' result:
System Prompt 1:
answer, Final_answen final Final_answer, result" : result, "score": score,
answer";
[... Additional system prompts...]
Final result after majority voting:
group final answer
[.. Additional instruction... ...]
Ver We compile the execution traces, including the retrieved does and the summary in each hop, to obtain the feedback. Due to that the retrieved documents are usually long, we use an L.M to compile feedbacks on multiple questions used the evolver LLM, based the execution details. The field "score" indicates whether all documents listed in ground truth are successfully retrieved during execution (0 1) for the given claim.
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Prompt for feedback compilation with LLM in the warm-up stage Ho
Your Task: Analyze this system prompt: system. prompt.
Goal This System Prompt: Given input claim, perform two rounds of retrieval and summarization and determine whether the "true answer" are subset of the "answer". Below are the results of applying this system prompt to multiple claims. Please analyze them one by one:
Claim 1:
Claim: claim
This system prompt's 's result:
"hopi_docs" hop1_docs,
s summary_1" summary_1,
"hop2_query" hop2_query
"hop2_doca". hop2_docs
"summary_2": summary_2,
"hop3_query": hop3_query,
"hop3_ :os33_docs:: hop3_docs
"answer": : popl_docsthop/ decpoedddcstthp___oocm docs,
"score" score
True answer:
grounded truth answer
[... Additional claims
Carefully reflect on following:
1. For each claim, what specific weaknesses does this system prompt show?
2. Across all claims, what common issues patterns you identify?
How could this system prompt be revised improved?
Prompt for group feedback compilation with LLM in the voting stage on HoVer
The current system prompt works together with other system prompts, and their outputs are combined through majority voting to form the final result.
Your Task: Analyze only this system prompt: system-prompt without evaluating the other two, Goal of This System Prompt: Given input claim, perform two rounds of retrieval and summarization and determine whether the "true answer" subset of the "answer".
Below are the results applying this system prompt to multiple claims. Please analyze them one by one:
Claim 1:
Claim: claim
This system prompt's result:
"hopi_docs"" hopl_docs,
summary__1" summary_1,
"hop2_query" hop?_query
"hop2_docs": hop2_docs,
"summary_2" summary_2,
"hop3_query"; hop3 _uery,
"hop3_docs": docs": _dp3s
"answer": : hopl docsthop2 abcs+hop2_doccsthag3 wwwpthopp_sccccooooooo docs,
score" score
Other system prompts' result:
System Prompt 1:
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hhop__doss;; hopl_docs,
summary_ 11; summary_l,
hhop__uery"", hop2_qucry,
"hop2_doca": hop2_docs,
summary_2"; summary_2,
"hop3_query"; hop3_query,
"hop3_docs" hop3_docs,
"aasswe": "score" score
pooosshop2_dccsteeee docs,
[... Additional system prompts..]
Final result after majority voting:
group final answer
True answer:
srruddedtruth.annee
[... Additional claims
Carefully reflect the following:
1. For cach claim, what specific weaknesses does this system prompt show?
2. Across all claims, what common issues patterns can identify?
3. How could this system prompt be revised improved that, when combined with others, it con- tributes more effectively better final results?
We directly organize the problems, as well as the answers generated by the prompt we evolved in the following format compile the feedback. The "extracted answer" refers to the answer extracted from the model response using the script of Hendrycks et al. (202T). The "score" indicates whether the answer matches the ground truth (0 or 1).
MATH
An example of compiled feedback in the warm-up up stage on MATH
Below the results of applying this system prompt multiple problems.
Problem 1:
Problem: problem
This system prompt's 's result:
"answer"; answer, "extractee_answer" extracted_ "score" score
True answer:
grounded.truth.anse .rruh
[.. Additional problems ...]
example of the compiled group feedback in the voting stage on MATH
The current system prompt works together with other system prompts, and their outputs combined through majority voting to form the final result. Below are the results of applying this system prompt to multiple problems.
Problem 1:
Problem: problem
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This system prompt's 's result:
answerr; answer,
eextracted_answert extracted_ansee _nsseer, "score"
Other system prompts' result:
System Prompt 1:
"aswwrr; answer, "score" score
extracted_answer
[... Additional system prompts...]
Final result after majority voling:
group finalflaswlaaaaaaa Lanswr
True answer:
grounded truwhh answer
[.. Additional problems ...]
GPQA We directly organize the problems, well as the answers generated by the evolved prompt in the following format to compile the feedback. The "cxtracted Lanswer" refers to the chosen option (A, B, C or D) extracted from the generated answer. The "score" indicates whether this choice matches the ground truth (0 or 1).
An example of compiled feedback in the warm-up stage on GPQA
Below are the results of applying this system prompt to multiple questions.
Question 1:
Question: question
This system prompt's result:
aanswer; answer,
ractedcce__ansseee extracted_ansver sccore" score
True answer:
grounded truth answer
[.. Additional questions ...]
An example of compiled group feedback in the voting stage on GPQA
The current system prompt works together with other system prompts, their outputs are combined through majority voting to form the final result. Below the results of applying this system prompt multiple questions.
Question 1:
Question: question
This system prompt's 's result:
answwrr; answer,
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"extracted, aasserr: extracted_answer, "score" score
Other system prompts' resilt:
System Prompt
answerr: answer, eettracted_asserne extracted_ansee annwer,, "score" score
[... Additional system prompts..]
Final result after majority voting: group final answer
True answer:
grounded truth answer
[..
Additional questions ...]
K
SYSTEM PROMPT FOR LLM-BASED AGGREGATOR
Here, we provide the system prompt for the LLM-based aggregator on open-ended tasks (HotPotQA and IFBench). Placeholder text is shown in blue.
Prompt for ALM-basel A Aggregator (HotPotQA, IFBench)
You given question several candidate generated by LLMs. Your select represen- tative auswer among three islands to get the consensus.
Instruction:
Only output the index number (just the number the chosen
words).
Question: (question)
L.M Answers:
1. Answer
2. Answer
3. Answer
L BEST PROMPT GROUPS FOR EACH TASK
We provide the baseline prompt, the prompt and the prompt group generated by AlphaEvolve and C-Evolve for each task.
1. HotpotQA (Qwen3-8B)
Baseline:
<system.prompt, 1>>
Given the fields ' 'question', 'passages', produce the fields summary'.
</aystem.prompt.1>>
<aystem.prooptt.2
Given the fields 'question', 'summary_1', produce the fields 'query'. </system.prompt.2>
<aystem.proopt..35 Given the fields 'question', 'context' passages', produce the fields 'summary'.
</system prompt <system taystem.promtt.ee 4>
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Given the fields ' 'question', summaayy 1', summaary 2', produce the fields 'answer'.
</system.prompt..9
Alpha-Bvolve:
csystem.prompt..2
Given the fields 'question', 'passages', produce the fields 'summary'. Focus on extracting: 1) **all explicit entities** (names, numbers, dates, titles) directly related to the question, 2) **all explicit statements** that answer the question, 3) any contradictions or ambiguous information. Organize the summary to Asprioritize entities and statements critical for disambiquations Kingdom" if "Magic Kingdon theme park" is mentioned). **Do not exclude explicit mentions** of entitics statements. Ensure the sumnary contains **all information required to disamoiguate entitics**, including **exact format matches** (e.g., "Julie Cooper (MP for Burrley) instead of just "Julie Cooper"). Include **granular atcributes** like roles, dates, or locations resolve ambiguities in later steps.
(e.g., include "Magic
**Add**:
**Format preservationv:: Copy exact phrasing from passages (e.g., "two-time" instead of "two") to align with true answer structure. **Descriptive entity inclusiones: For entities like award counts, require inclusion descriptive terms (e.g., "two-time winner" instead of "two").
**Strict format matching+4; Only include disanbiguated terms if they are *explicitly zequired by the true answer's exact format (e.g., "Magic Kingdom" if the true omi: "Stoney" unless the true answer includes it) Ensure the summary includes only the **exact format** of the true answer (e.g., "Nakoda" instead of "Nakoda (Stoney) </aystem.prompt.1>
is "Magic Kingdom", but
<aystem.prompt..2>
Given the fields 'question', 'summary 1', produce the fields 'query'. Create precise query that: 1) incorporates the core semantics of the question, 2) includes *oonly entities and attributes from summary.1 that explicitly required by the true answer's format**,
3) explicitly requests information missing from the first retrieval, **Prioritize single-entity focus** (e.g., "astronaut" vs. "career") and s-disambiguate ambiguous entities with attributos** (c.g., "Magic Kingdom" if "Magic Kingdon theme park" is mentioned). Format the query *excclde ambiguous granular detailsvx such as specific dates, roles, or attributes critical for disambiguation summary not yet confirmed as answers** (e.g., "Julie Cooper (MP for Burnley) ensure targeted retrieval. **Add*+:
exteous details*.. IInclude
**Explicitly name entities from
**Descriptive term inclusion*:: Explicitly request exact phrasing (e.g., "Magic Kingdom" instead of "Magic Kingdom theme park") to match the true answers structure.
**Format alignment**: Ensure the query includes **must-matel terms** (e.g., "Magic Kingdom" for venues, "two-time" for awards). Use sxbracketed keywords** (e.g., "[Magic Kingdom] to enforce exact match during retrieval.
**Entity disambiquation::: Only include disambiguated terms if they are *explicitly required by the true answer' exact format** (c.g., "Nakoda" instead of "Nakoda "Stoney)"). Avoid including additicnal details not critical to the answer. </aystem prompt 2> <system prompt 3>
Given the fields 'question', 'context', 'passages', produce the fields 'summary'. Focus on: 1) cross-referencing the context (sumnary.l) with the new passages to **validate er refute inferred
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information in summary.1**, 2) extracting any infermation that resolves ambiguities adds critical details from the first summary, 3) explicitly identifying numerical values, direct quotes, or specific attributes required by the question. **Prioritize information that directly matches the exact true answer format** (o.g., "Magic Kingdom" vs. "Magic Kingdom theme park"). Include *onny information from the new passages** that could resolve ambiguities add critical details. **Expiciitly confirm or refute all inferred claims from summary.1 with direct evidence from the new passages**. Ensure all statements in the summary are wvdirectly supported identically to the true answer** (e.g., include "Magie Kingaem" the exact phrase requized by the answer).
the new passages** and **fermatted
**Add*+:
**Format validations:: Ensure numerical values (e.g., "14") and descriptive phrases (e.g., "two-time") are preserved verbatim from the passages.
**True answer alignment**: Cross-check summaries against the true answer's s structure using **exact phrase matchinge* and **regex patterns**. If the true answer requires "Magic Kingdom", ensure the summary includes +only that exact phrase** and alternativest* (e.g., "Nakoda" vs. "Nakoda (Stoncy) **Format enforcementa:: Use **strict regex matching** to ensure the summary strictly matches the true answers structure (e.g., "Magic kingdom" instead of "Magic Kingdom theme park"). </system prompt 3> <system.prompt..t
Given the fields 'question', 'sunmary-1', 'summary-2', produce the fields 'naswr''. Generate the answer by: 1) **strictly extracting only explicit statements from summary.244, 2) *resolvigg contradictions by prioritizing the most specific directly quoted information from summary_2*4, 3) Amatching the exact format the true answer.. (e.g., "Las Vegas Strip" vs. "Las Vegas"). Use **regex validationss to ensure the answer matches the true answer's structure. **Reject answers that deviate in format inferred informationes. Ensure the answer is **singular phrase** (e.g., "astronauts" vs. "astronaut") and is **exactly the same as the true answer.*.
include
**Add++:
**Rxact phrasing regex*::
**dynamic regex** based on the
true answer's s exact format (e.g., [NakodallHancaa Hancock County [Mickey Spillane) $") to enforce strict format matching for critical terms. **Format enforcement**: If the true answer requires "Nakoda", the system must generate **only** "Nakoda" without any modifiers (e.g., "Nakoda (Stoney) ")
**Singlo-tozm output**: Output only the singular phrase, no additional context or oxplanations.
**True answer alignment**: Cross-reference the generated answer with the exact phrasing of the true answer. there is mi mismatch structure or formatting, **reject the answer and request a re-evaluation-..
**Regex override**: For questions where the true answer is singular term (e.g., "Nakoda"), the regex must **strictly match the exact phrase** without any disambiquation terms. </aystem.prompt.tt
C-Evolve:
Group Prompts
<system.prompt.1>
viven the fields ' 'question', 'passages', produce the fields 'summary' by: 1) extracting named entities (e.g., organizations, locations, people) that directly answer the question or its sub-questions **add explicitly resolve all entities mentioned in the question's s gramnatical structure (e.g., "are breeds of hounds?" "Scottish
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Deerhound [type] and Jämzhund [typel")**, 2) identifying numeric values (e.g., counts, dates) that resolve ambiquities constraints, 3) including exact phrases from the passages that explicitly name or describe these entities, 4) cross-referencing entities with the question's grammatical structure (e.g., "Which company?" "Yum! Brands") *+and mapping entity types (c.g., "person" for "who," " "organization" for "which company") to question's 8 intent**, and 5) **validating entities against the current hop's a documents only, not future hops, to avoid unnecessary prior checks**. Prioritize the classification explicitly stated in the text alternative names (e.g., if "Spitz-type" is mentioned, it overrides "hound breed" unless "hound breed" explicitly cetined in the text). **Optional full names for people** only if the question or text explicitly specifies full names (e.g., "Marianne Craig Moore") if truncation is noted in the text. "Explicitly include full dates (e.g., "May 5, 1838") rather than year-only values... DoCommtnt unresolved entities and their constraints**, **Avoid automatically deprecating hopl.docs without timestamped evidence from hop2.docs* **Explicitly map grammatical structures like "seventh and final" to entity types (e.g., "novel in series positicn") to prevent conflation with embedded entities like characters or books.** </system.prompt..>>
alignment with the
<aystem_prompt..2>
Given the fields ' 'question', 'summary 1', produce the fields 'query' by: extracting named entities (e.g., companies, people, countries) from the question and summary.1 that remain unresolved, 2) structuring the query to directly ask for these entities with explicit constraints (e.g., "Are Scottish Deerhound and Jänthund breeds of hounds?"), 3) avoiding vague terms like "related to" "about," 4) including exact entity names from summary.1, 5) incorporating temporal or numeric constraints from the question (e.g., "population in 2016") to avoid ambiguous matches, and 6) *eexpicittly resclving entity types in the query (e.g., "breed classification: instead of "which") to prevent attribute conflation. If the question uses grommatical structures like "who" or "which," " the query **MUST isolate the required entity type (e.g., "breed classification' vs. "organization") with explicit constraints. ++rrigger the second hop only if summary.1 contains unresolved entities semantic gaps.** Prioritize constraints from the question over summary 1, and eliminate conditional phrasing like "if any" to enforce strict query resolution. Require the query to explicitly reference hop2 docs' contextual domains (e.g., "population in 2016" + "demographics") to ensure aligrment with the second-hop retrieval scope. **When the question uses grammatical structures like "who" or "which, ensure the query explicitly resolves entity types (e.g., "which breed?" "hound breed classification" to prevent conflation of attributes with core entities... If the query's resolved entity type does not match the and annotate the query with the required type. **Include full names (e.g., "George William Hill, born 1838") in queries for individuals only it the question explicitly requests full names or if truncation is noted in the text.** **When resolving entity types, prioritize the entity type implied by the question's grammatical structure (e.g., "which" "breed classi ficationi"
answer's entity type, explicitly flag the inconsistency
flaystem_prompe
poystem.prompt.33
Given the fields 'question', 'cntext'' 'passages', produce the fields "summary' by: 1) extracting all named entities (e.g.,
organizations, people) from the passages that match the query's focus, 2) including exact quotes phrases that explicitly name or describe these entities, 3) cross-referencing entities with the question's grammatical structure (e.g., "Are Scottish Deerhound and Jämthund breeds of hounds?") **and explicitly resolving entity types (e.g.,
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"breed classification" vs. "organization") to prevent conflation of attributes with entities**, and validating entities against the current hop's documents only, with a fecus on timestamped evidence to resolve conflicts. **Explicitly document contradictions with timestamps, source documents, and entity type mismatches**. ++If hop2.docs provide unambiguous, timestamped evidence that contradicts hopl_docs, **only then** automatically deprecate conflicting hopl_docs and annotate them with the reason**. **Validate all entities in hopl.docs against hop2 docs for all possible conflicts, but prioritize entity type consistency and constraint alignment over timestamped conflicts**. **When resolving entity types, is the text is ambiguous, prioritize the entity type implied by the question's s grammatical structure (e.g., "which" "breed classifications type consistency and constraint alignment are unambiguous.
only when entity
**Prioritize entities from hop2.docs when they resclve ambiguities satisfy constraints, and ensure entity type consistency (e.g.,
"Spitz-type" overrides "hound breed" if explicitly defined)** **.
+*Additionally, when the question involves "who" or "which, ensure the summary explicitly isolates the required entity type (c.g.,
"breed classification" vs. "organization") to prevent attribute conflation*s. **Explicitly map grammatical structures like "seventh and final" to entity types (e.g., "novel in series position") to prevent conflation with embedded entities like characters or books**, **Include fu11 names (e.g., "Field Marshal John Standish Surtees Prendergast Vereker, 6th Viscount Gort") for individuals only it the question or documents explicitly require full names or if truncation is noted.**
</system.prompt..3> <aystem.prompt..4
Given the fields 'question', 'summary_1', 'summary22, produce the fields 'answer' by: 1) extracting the +exact named entity or queted phrase from sunmary.2** that resolves the questicn's intent, *without any additional context descriptionss, 2) cross- this entity with summary1 and the original question to ensure consistency, 3) resolving contracictions by esprioritizing entities from hop2 does only when they provide timestamped, unambiguous evidence that resolves ambiguities or satisfies the question's constraints, and explicitly deprecating conflicting -occs If hop2_docs are incomplete or timestamped evidence exists, "xproortiiz hopl decs only if the entity type and constraints match**, **If no hop? docs are available or popl_doce contain the only valid entity, proceed with hopl explicitly flag any constraint mismatches**. and 4) **formatting the answer strictly as the true answer's structure** (e.g., "ne" for contradictions, "Scottish Dserhound" for breeds, "20 October 1951" for dates) with *explicit validation against the question's grammatical structure, temporal constraints, numeric pattern matching, and entity type constraints... +IIE the true answer is "no" or "yes," the answer must explicitly state "no" or "yes" with additional qualifierser. *xAdditionally, when the question uses grammatical structures like "who" or "which, enforce strict entity type checks (e.g., "which breed?" 4 "breed classification" not "organization") to ensure the answer matches the true answer's entity type and excludes conflated atcributes**. **f the answer the only possible one but does not fully satisfy constraints, flag the discrepancy and return the answer with note explaining the mismatch. **Only enforce full names (e.g., "Field Marshal John Standish Surtees Prendergast Vereker, 6th Viscount Gort") if the question or cocuments explicitly require full names or if truncation is noted.** **Explicitly map grammatical structures like "seventh and final" to entity types (o.g., "novel in series position") to prevent conflation with enbodded entitics like characters books, and ensure the answer strictly matches the true anawer's entity type.** **Whhen resolving entity types, if the text is ambiguous, prioritize the entity type implied by the question's
docs docs and
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grammatical structure (e.g., "which" "breed classification" only when entity type consistency and constraint alignment unambiguous **If the question's entity type does not align with the summary's s entity type, explicitly flag the inconsistency and annotate the answer with the required type.** "ssystem.prompt.4>
Group Prompts
pyystem.promp....
Given the fields 'question', 'passages', produce the fields 'summary'. Extract only: (1) *eexact values** (dates, numbers, entity names) as **airect quotes** regardless of whether they resolve the question, (3) *explicit comparisens** (e.g., "A has x, B has Y") if the question involves comparative reascning, and (4) **taxonoiic classifications Orchidaceae") when entities are biological or botanical. *Prioritize literal values over antity-relationshipa.. produced the radio program") unless the question directly asks for zelationships. **Format the summary as a list of concise, isclated facts with strict singular/plural consistency** (e.g., "Goorge Martin is the producer of the radio program", not "astronauts" if the scurce uses "astronaut"), **Expiccitly capture temporal values (c.g., dates, time periods) as literal text**, not derived metrics (e.g., durations). **For questions involving collaborations, explicitly include all entities mentioned in the collaborationx Ramirez is key member of Sublime with Reme") to (e.g., "Rome completeness.
raw text **withou derivation**,
(e.g., "Bletilla is genus in
(e.g., "George Martin
*+II an entity not mentioned in the passages, explicitly flag it as "unreferenced" to prevent assumptions about its existence**. </aystem.promptt.1>
<aystem.prompt.22
Given the fields 'question', 'summary_1', produce the fields 'query.. First, **check the answer is fully resolved in summary.l.. (e.g., boolean, numeric, direct quote, or explicit entity). If resolved, **return the answer immediatelyex without generating query. If unresolved, request:
the exact value (s) from summary-1 that unresolved, (2) specific numeric values if required, (3) direct quotes if the answer requires textual evidence, (4) explicit comparisons between the entities involved in the question (e.g., "Which film explicitly mentions political themes and how does it compare to the other?") if the question involves comparative reasoning, and (5) *explicit hierarchical relationships** (e.g., "what league is the Toronto Blue Jays in?") to resolve parent-child entity dependencies. +*Add explicit entity roles** (e.g., "producer," " "star, " "host")
in the query to resolve ambiguities. prioritize exact start and end dates (c.g., "22 April 1963 to 20 April 1958") over derived values (e.g., duration) Phrase the query as a closed-ended question with clear target (c.g., "What is the exact start and end date of tenure?"). **For comparative questions, if one entity is explicitly defined in summary.l and the other is not, request the explicitly defined entity as the answer** (e.g., "If *Saint Motel> is explicitly mentioned with 4 members and *Curve* is not, the answer is *Sain= Motel+"). +IIf an entity is flagged as 'unreferenced' in summary.l, explicitly include 'unreferenced' in the query to signal missing information* (e.g., "what is the team of the unreferenced entity Karnig Sarkissian?"). </aystem perstemm.rompp...
**For temporal questions,
</aystem.prompt.22 prompt.22
Given the fields 'question', 'context', 'passages', produce the fields "summary', Extract only: (1) numeric values explicitly mentioned, (2) direct quotes that answer the question, (3) exact values from the passages that resolve unresolved clements in the context, (4) explicit comparisons based direct quotes from the passages (e.g., "A has stated in [quote], в has stated [quote]") the question involves comparative reasoning, (5) entity
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disambiguation by vexppicittly specifying the primary entity and its taxonomic categozy** (e.g., "Bletilla is genus in Orchidaceae"), (6) **resolve entity relationships at the category level** (e.g., "Vernonia is a genus in Astoraceac"), and (7) **explicit hicrarchical relationships** (c.g., "The Capitol Theatre is a component of the Hamm Building") resolve parent-child entity dependencies, +IInclude direct entity relationships*A (e.g., *Rana Daggubati starred in *The Ghazi Attack=") to enable cross-referencing involving collaborations, explicitly include all entities ment mentioned in the collaborationes (e.g., "Rome Ramirez is a key member of Sublime with Rome") to ensure completeness. Format the summary as a list of isolated, verifiable facts **but retain direct links between entities** (e.g., "Rana Daggubati is the star of *The Ghazi Attack*"), *+If a fact or entity is already included in summary.l, avoid repetition by flagging it as 'redundant' to prevent processing inefficiencies... ++If the exact date is stated in the text, use the literal text; do not calculate durations**. **Do not include entities flagged as 'unreferenced" in summary-1 unless explicitly requested in the query**.
**For question
</systee.prompt.3>
<system.pronpt..9
Given the fields 'question', 'sunmary_1', 'summary_2', produce the fields 'answer'. First, **check if the answer is fully resolved in aummary 1** (e.g., boolean, numeris, direct qucte, explicit entity). resolved, *rreturn the consulting summary...
inmediatelyes without unresolved, the answer must be: (1) a boolean ("yes"/"no"), (2) a specific numeric value, (3) a direct quote from summary_2, or a specific phrase explicitly stated in the summaries without inferred context, "Sum 41") that directly answers the question. **oor comparative questions**, the answer must be a descriptive entity representing the explicitly compared entity (e.g., "The One and Only, Genuine, Original Family Band"). *+If the true answer is a specific entity, pricritize it over data discrepancieses. If conflicting, explisitly state the conflict and the true answer. If no exact value exists, return "UNRESOLVED". **ddditionall,, verify hierarchical relationshipa** (e.g., "The Toronto Blue Jays belong to the American League") to ensure the answer is the correct parent entity when the question involves nested relationships. entity is explicitly defined in the context and the other is not, default to the explicitly defined entity as the answer.* (e.g., if *Saint Motelx is explicitly mentioned with 4 members and *Curve* is not, the "unreferenced' entities, cross-reference summary.1 to determine if the answer is resolved or if it remains 'URRESOLVED' due to missing data**. Do not add interpretation or context beyond the extracted value. +IIf an exact date is stated in the text, use the literal text; do not calculate durations**. *IIf entity is flagged "unreferenced" summary.1 and summary explicitly flag the answer as UNRESOLVED' Group Prompts
(4) descriptive entity (e.g.,
one **Por comparative questions,
is *Saint Motel*). **If sumnary.2 contains
<aystem.prompt.1>
Given the fields 'question', 'passages', produce the fields 'summary'. Extract only: (1) exact values (dates, numbers, entity names) swithhot derivation+*, (2) direct quotes that resolve the questien, (3) explicit comparisons (e.g., "A has X, has Y") if the question involves comparative reasoning, (4) #vcategory-level elassifications* (e.g., "Bletilla is a genus") rather than full taxonomic hicrarchy, and (5) **contoxtual relationships+. (c.g., "George Martin produced the radio program"). Format the summary isolated facts ..wihh explicit category-level links*+ (e.g., "Bletilla belongs to the genus category") to enable hierarchical
list of concise,
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resolution. Exppiiiitly capture temporal values (e.g., dates, time periods) as literal text**, derived metrics (e.g., duraticns). +*Additionally, for anbiguous entities (e.g., "Karnig Sarkissian"), explicitly disambiguate by specifying their category (e.g., "singer", "bascball player") and role in the question.**
</system.promppt.>>
<aystem.prompt.22
Given the fields 'question', 'sunmary-1', produce the fields 'query'. First, **check if the answer is fully resolved in summary (e.g.r boolean, numeric, direct quote, or explicit entity). If resolved, **return the unresolved, request: (1) the category-Level value from summary.1 that are unresolved (e.g., "what is the shared category of Bletilla and Vernonia?"), (2) specific numeric values if required, (3)
inmediately** without generating query. If
direct quotes if the answer requires textual evidence, (4) explicit comparisons (e.g., "Which band has involves comparative reasoning, and (5) #xcategory-level hierarchical relationships** (e.g., "What category do Bletilla and Vernonia belong to?") to resolve parent-child entity dependencios. **Add explicit entity roles** (c.g., "producer, " "star, "host") in the query to resolve ambiguities. ++For temporal questions, prioritize exact start and end dates (e.g., "22 April 1963 to 20 April 1968*) over derived values (e.g., duration). Phrase the query as a closed-ended question with clear target (e.g., "What is the shared category of Bletilla and Vernonia?"). *IIf the entity is ambiguous (e.g.r "Karnig Sarkissian"), explicitly specity its role the query (e.g., "baseball player", "singer") to disambiquate its identity.** </aystem.prompt.2>> <aystem.prompt.3>
albums?") if the question
Given the fields 'question', 'context', 'passages', produce the fields 'summary'. Extract only: (1) numeric values explicitly menticned, (2) direct quotes that answer the question,
(3) exact values from the
passages that resolve unresolved elements in the context, (4) explicit comparisons (e.g., "A has X, has Y") if the question involves comparative reasoning, (5) entity disambiguation by **specifying the primary entity, its role, and category-level classification= (e.g., "Bletilla is a genus of orchid"), (6) resoovv entity relaticnships at the category level** (e.g., "vernonia belongs the genus category"), and (7) *exxpicitt hierarchical relationships.. (e.g., "The Capitol Theatre is a component of the Hamm Building") to resclve parent-child entity dependencies. +IIncluee direct category-level links** (e.g., "Bletilla belongs to the genus category") to enable cross-referencing. Format the summary as list isolated, verifiable facts **but retain category-level links** (e.g., "Bletilla is classified in the genus catogory"). *addittonnlly, explicitly difforentiate between direct quotes (c.g., "The quote is from a speech") and allusions (e.g., "This refers to literary work") and provide source attribution for each..* </aystem.ppl prrsstee.promp..3..
<system taystemproopt...... 4>
Given the fields 'question', summary_1'' 'uumaary22, produce the fields 'answer'. First, **check it the answer is fully resolved summary.1** (e.g., boolean, numeric, direct qucte, explicit entity). If resolved, *xreturn the answer immediately without consulting su If unresolved, the answer must be: (1) a boolean ("yes"/"no"), (2) specific numeric value, (3) direct quote from summary.2, or (4) #*category- descriptive entity** (e.g., "genus") that directly answers the question. **For comparative questions**, the answer must be a descriptive entity representing the explicitly compared entity (e.g., "The One and Only, Genuine, Original Family Band"). +IIf the true answer is specific entity, pricritize it data discrepancies:. If conflicting, explicitly state the conflict and the true answer. If no exact value exists, return
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"UNRESOLVED". AAdditionally verify hierarchical relationshipse (e.g., "The Toronto Blue Jays belong the American League") to ensure the answer the correct parent entity when the question involves nested relationships. Do not add interpretation or context beyond the extracted value. **Finally, ensure the answer's phrasing matches the true answer's exact terminology* (e.g., "genus" instead of "they
both genera"). ++If the answer involves conflicting sources (e.g., one claims "California" and another "New York"), explicitly state the conflict and return the true answer based on the most authoritative source.* s/aystem.prompt.e
2. IFBench (Qwen3-8B)
Baseline:
kaystem.prompt.Ir
Respond to the query
</system.prompt..1 <system.prompt.25
Ensure the response is correct and acheres to the given constraints. Your response will be used as the final response. </aystem.prompt.22
Alpha-Evolve:
<systom.prompt.l>
Anawer the query directly by first parsing and confirming all *xxplicittly specified** requirements (e.g., word count, sentence structure, puretuation rules, formatting, keyword placement, text processing tasks, and language constraints). If constraints are ambiguous, explicitly state the uncertainty. **Prioiitize only the requirements directly mentioned in the instruction*s. It the instruction includes **atructural elements** like PARAGRAPH or formatting rules, vvvrrify their presence**. If required to avoid commas, ensure none are present; otherwise, follow natural language conventions. .EEplicittly include all requested keywords with exact case sensitivity If text processing operations are specified (e.g., extracting substrings, slicing), execute them precisely and include the results. Additionally, **cenfirm that the entire response is in the target language (e.g., Tamil) and contains no other languages**, +Veriiy that the first word of each sentence matches the exact case and word specified the instruction*.. </aystem.prompt.l> <system prompt. 2>
Review the initial
with exprecise alignment** the original
instruction:
1. **Ianguage compliancer+: Confirm the **entire response is in the target language (e.g., Tamil)** and contains +*no other languages**. **keyword verification*:: confirm all requested keywords are present, placed in the **exact positions** specified instruction, and +.contexuualyy appropriate.*. **Ensure first-letter casing matches the instruction.
2.
3. **Text processing compliancerv: If the instruction involves text manipulation (e.g., extracting substrings, slicing), confirm the output includes the *results these operations with indices**, **including the exact span of characters or words*. If indices are out-of-range, explicitly state "Index out of bounds" and +*do not include the result**.
4. #*Structural val validationss:
**Paragraph count*:: Verify the number of paragraphs matches the instruction (e.g., "9 paragraphs").
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e*Sentence structurews: Confirm *eaach sentence's first word matches exactly** the instruction's specified word (case-sensitive required).
**Format compliancexs: Validate dividers (+ *) and formatting (e.g., PARAGRAFE X markers, bullet points, numbered lists).
5. **Content constraints44:
**Sentence endding consistency44: Ensure every sentence ends with
the exact word specified (case-matching if required).
**Prohibited elements**: Eliminate unauthorized commas, exclamation marks, punctuation.
6. **Final word requirement**: Confirm the **last word the
response matches exactiy** the instruction's specified word (case-sensitive if required).
7. kwword enclosurexs: If the instruction mandates enclosing every word in brackets (e.g., [word]), confirm *every single word is enclosed in square brackets**. **Count the total number words
and ensure all are bracketed+*.
8. **Case consistencys:: If the instruction mandates all caps, ensure **sontonce-flret words match the exact case**.
9. **Formatting uniformity*:: Ensure alignment with requested styles (c.g., bullet points).
10. **Final validationss: Cross-check all requirements from the original instruction, sxincluding structural elements** (e.g., PARAGRAPH X, JSON wrapping) and **content constraints** (e.g., prohibited elements), *swith focus Language compliance and word enclosure**. </system.prompt.22
C-Evolve
Group Prompts 1:
-system.prompt.1>
Answer the query directly first confirming the +*primary instruction requirements+ (e.g., language, structure, core formatting). If any instruction is ambiguous, explicitly state uncertainty. Prioritize **соо language, syntax, and structural rules** (e.g., number of paragraphs, markdown dividers, paragraph spacing) before applying detailed formatting. Ensure every sentence ends with the required word before punctuation, and **remove all commas**. **Explicitly include all specified keywords**, even if placement is ambiguous. If the instruction specifies exact word placoment (e.g., "as the 2nd word of the 16th sentence"), ensure it is done with precision. **Verify paragraph count and spacing** (exactly two newlines).
**First-pass validationss:
If the instruction requires extracting text between character indices (e.g., 193-324), ** iimmediately trace and quote it verbatim** before addressing other requirements.
Ensure the response includes **xactty*** the required number of placeholders (e.g., [address]) and reppatte keywords** (e.g.r "repeat 'emergency' 3 times").
Replace **all sentence-ending words.
with periods... Strip punctuation before
Finally, ensure the reaponse satisfies the *suser's final geal**, even if it requires rephrasing. *IIf the response is incomplete due to strict parsing, annotate incomplete parts and proceed to step ***, </system.prompt.1>>
<aystem_prompt.22
Review the iritial answer with focused attention:
1. **Core validationss: Confirm **language, atructure, and primary formatting rulesv (e.g., paragraph count, markdown dividers). any requirement is missing, directly accress it. If the instruction requires excting text between character indices (e.g., 193-324), **imeddiately trace and quote it verbatim**.
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2. *KSenterce-levee checks**: Ensure every sentence ends with the zequired word before punctuation. Replace **comas with periods**, Check for hyphens without spaces and replace words starting with consecutive letters (e.g., "Apple Banana") with synonyms. Confirm all keywords are explicitly included. Check for repeated keywords and their placement. If the instruction specifies word order or position, restructure sentences accordingly.
3. Keyoord verifications.
4. **Structural validationex: Count paragraphs, verify sentence counts per paragraph (e.g., "exactly 3 sentences"), validate markdown dividers (+ *), and ensure paragraph separation with exactly two newlines.
5. **Pormatting compliance**: Ensure al alignment with requested styles
(e.g., bullet points). Strip all trailing whitespace.
6. **Final validationss: Confirm all requirements
satisfied,
including **keywodd placement, placeholder counts, and paragraph structurex*. Ensure **no two adjacent words start with consecutive letters**.
7. **Final adjustment**: If incomplete due to parsing, annotate missing parts. Expand content if the instruction requires a minimum word count. Replace adjacent words with consecutive letters with aynonyms.
Your final answer must strictly follow the exoriginal instruction's requirementsw prioritizing core language, structure, and formatting rules.
</system.prompt.22
Group Prompts 2:
<aystem.prompt.l>>
Answer the query directly by first parsing and confirming all explicit requirements (e.g., word count, sentence structure, punctuation rules, formatting). any constraints are ambiguous, explicitly state the uncertainty in the zesponse. Prioritize exact specifications over general guidelines. Enaure every sentence ends with the required used throughout
word before punctuation, and verify that commas the response. vrExplicitly include all specified keywords in the response, even if their placement is ambiguous**. If the instruct ion zequires inserting keywords in specific positions (e.g., *as the 2nd word of the 16th sentence"), ensure this is done with exact precision **For all keywords with positional constraints, state the exact sentence/word placement in your response before generating content**. Secyystem.promp.... <aystem.prompt.2>
Review the initial answer with obsessive attention to detail: 1. **Extract verify exact character spans**: If the instruction zequires retrioving a substring between specific charaster indices (e.g., "indexes 117((19"), extract and highlight this span precisely, If the instruction requires inserting keywords in specific positions (e.g., "as the 2nd word of the 16th sentence"), confirm this placement. explicitly in your response.
2. **Validate structural requirementsw:: Count paragraphs, verity sentence counts per paragraph, check markdown dividers (+ *), and confirm exact formatting specifications (e.g., bullet points, numbered lists). Ensure paragraphs are separated by *exactly two line breaks** (\nin), not markdown syntax. Verify the **umber of paragraphs and their separation method matches the instruction*.. 3. **Confirm content constraints.:: Verify keyword placements (including exact sentence/vors positions), word counts, specific wording, and sentence-ending consistency (every sentence must end with the exact word specifled before punctuation). Ensure all specified keywords are explicitly included, and if any require unique word usage, confirm word is repeated. If the instruction requires inserting keywords in specific positions, restate their placement your response before generating content.
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4. **Check for prohibited elements**: Ensure no commas, exclamation marks, inappropriate capitalization, Verify the last word of your response matches the specified word (o.g., "a"). Ensure the **last word of every sentence matches the instruction's specified word+*.
unauthorized punctuation.
5. #*Enforce formatting uniformity..: Ensure alignment with the
requested styles (e.g., bullet points, numbered lists) instruction requires a specific starting word (e.g., "essay"), ensure the first word of the specified paragraph matches. Verify **all headers and section markers** (e.g., "SECTION x") are present and correctly formatted.
6. **Final validation*:: Confirm all requirements from the original instruction are explicitly satisfied, including word count, placeholder usage, prohibited elements, and keyword placement. Ensure the letter 'h' appears least 8 times if required. *CCheck for duplicate words and Your final answer must demonstrate 1008 compliance with every specified criterion, including the last word of each sentence, the final response, and all formatting and content constraints.
all unique word requirements
s/aysteem.rommp.2>>
Group Prompts
<aystem.prompt.l>
1. **Directly answer the instructions io without additional analysis.
2. s*Prioritize these tasks in orderv*:
a) **Exact repetition** of the request (verbatim copy)
b) #MMathematica//ooicaal computations** (arithmetis, algebra, combinatorics)
c) **Text extraction+> (span/position, precise character indexing) d) **Formatting requirements*4 (case conversion, markdown, postscripts)
e) **Content structure** (paragraph count, sentence structure, paragraph/word counts)
f) **Keyword placement** (exact sentence/word positions) g) **Forbidden elements** (no commas, repeated words, etc.) a) **Special rules** (e.g.r no consecutive Letter-start words)
3. **Ensure no text is omitted or addea** beyond the request.
4. ++Do not apply formatting, logic, or reasoning to the request itaelf** follon low instruction ons in the request. </aystem prompt
<aystem prompt..ppppp..... 2>
1. *vVerify Step 1 output meets all requirementsr in this
prioritized order:
a) **Request repetition** (exact verbatim match with input)
b) =#Mathemati.cl/llogccaal accuracy** (correct computations)
c) **Text extraction (span/position correctness)
d) **Formatting complianc (case, markcown, postscripts)
e) **Content structure.* (paragraph counts, sentence rules)
f) **Keyword placement** (exact sentence/word positions)
g) **Forbidden elements** (no commas, no repeated words, etc.)
2) **Special rules*x (e.g., no consecutive letter-start words)
2. ++Focus on critical fail points**:
a) **Ensure all required placeholders** (e.g., [address]) present
b) **Check for exact word/character counts**
c) **validate bigram wrapping** (e.g., <<wordl word2>>)
d) **Confirm no markdown formatting errors** (balanced brackets, correct syntax)
3. **Resolve conflicts by prioritizingss:
a) **Numeriiallsstrueuue constraints** over ambiguous instructions
b) **Explicit keyword inclusion** (ensure all required keywords are present in response)
4. **Final check+*;
a) text omitted added++ beyond the request
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b) **Al1 formatting, keywords, and structure rules are strictly satisfied** </system.prompt.22
3. HoVer (Qwen3-8B)
Baseline:
esystem.prompt.lr Given the fields 'claim,, 'passages', produce the fields summary'' </aystem prompt.1
<system prompt 2>
Given the fields 'claim', 'summary-1', produce the fields 'query,. e/systemuprompt.2>
<aystem.proomt..35
Given the fields r rclaim', 'context', 'passages', produce the fields 'summary'.
</system.proomtt.3
<aystem.prompt.4r
Given the fields 'claim', 'summary-1', 'summary produce the fields 'query'. </system.prompt.4>
Alpha-Bvolve:
csystem.prompt..l>
Given the fields cllam', 'passages', and the trueLanswer (if available), produce the fields 'ummarry' that explicitly includes *oony the exact entities from the claim+ (e.g., names, locations, specific titles) and *exxcldee all contextual information Focus on summarizing **only the passages that directly confirm or refute the claim's core entitiesux. If the true is available, ensure the summary aligns **exactly with the answer's structurev (e.g., lists of names, specific dates, precise terms). not include any work titles, biographies, or indirect references. </system.prompt.11
<system <aystem.prompt. 2>
Given the fields 'claim', 'summary_1', and the crue_anawe (if available), produce the sields 'query' that exxppicittly lists the exact entities from the claimes (e.g., names, locations, specific titles) sswithout any descriptive or contextual terms** The query must be formatted as **strict entity list** (o.g., "Adelfo Bioy Casares, Iris Rainer Dart") and avoid terms like "works" or "biography." If the true.answer *exacctyy the structure of the true_answer (e.g., lists of names, specific years) This will ensure retrieval of documents that only contain these entities.
available, ensure the query matches
</system prompt 2> <aystem.prompt..3
Given the fields 'claim', 'context', 'passages', and the trueer (if available), produce the fields 'summary' that **xtt only the exact entities from the claim+ (e.g., names, locations, specific titles) **enttoned the retrieved passages**. Focus on *paassages that directly identify or confirm these entities**.
If the true.answer is available, ensure the summary **matches the tzue_answer's exact format** (e.g., list of names, specific years) Exclude all contextual details, indirect references, unrelated content. Output the summary as a ++list entities** without any explanatory text.
</aystem.prompt..3 <aystem.prompt.44
Given the fields 'claim', 'summary 1', *summary 2', and the true answer (if available), produce the fields 'quer'' that **combines all exact entities from the claim, sumnary 1, and summary 2** into a **strict
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entity list (e.g., "Christopher Robin, Nimiie-the-Pott") The query must *exxclude any contextual terms** and match the **xxactly the same format as the true.answer** (e.g., lists names, specific dates). This ensures the final retrieval only includes documents that *ddrrectly mention these entities** without any oxtrancous information indirect references. </system.prompt.4>
C-Evolve:
Group Prompts
wwrstem.prompt...m
Given the fields 'claim', 'passages', and the true (if available), produce the fields summary'' that explicitly includes **only the exact entities from the claimes (e.g., names, locations, specific titles) and **excludes all contextual information Focus on summarizing **only the passages that directly confirm or refute the claim's core entities**. If the true_answer is available, ensure the aummary aligns **xacctyy with the true_answer's structure:* (e.g.r include any
lists of names, specific dates, preci terms). work titles, biographies, or indirect references. </system prompt 1> <system.prompt.2>
Given the fields 'claim', 'summary-1', and the true.answer (if available), produce the fields 'query' that **explicitly lists the exact entities from the claim+ (e.g., names, locations, specific titles) **ithhout any descriptive or contextual terms*.. The query must be formatted as #*tricc entity list** (e.g., "Adclfo Bioy Casares, Iris Rainer Dart") and avoid terms like "works" "biography." If the truwer is available, ensure the query matches **exactly the structure of the trueeenswerex (e.g., lists of names, specific years) This will ensure retrieval of documents that only contain these entities.
</aystem.prompt..2>
<ayatem prompt 3>
Given the fields 'claim', 'context', 'passages', and the true answer (if available), produce the fields 'summary' that vextracts only the exact entities from the claimes (e.g., names, locations, specific titles) **mentioned in the retrieved passages**. Focus on **passages that directly identify or confirm these entitics If the true.answer is available, ensure the summary **matches the true_answer's exact format** (e.g., list of names, specific years). Exclude all contextual details, indirect references, unrelated content. Output the summary as a **list entities** without any explanatory text.
</system prompt 3> <aystem.prempt..>
Given the fields 'claimm, 'summary-l', 'summary-2', and the trueLanswer (if available), produce the fields 'query' that **combines all exact entities from the claim, summary.l, and summary.2 into a **strict entity list.. (e.g., "Christopher Robin, winnie-the-Pooh") query must +eexclude any contextual terms** and match the +exactly the seme format as the trueLanswer** (e.g., lists names, specific dates). This ensures the final retrieval only includes documents that **directly mention those antities** without any oxtrancous information indirect references.
The
llsystee.prompt..4
Group Prompts 2:
csystem.prompt.l>
Given the fields 'claim', 'passages', produce the fields 'summary', Extract **key entities** irom the claim and passages, and ssexplicitly link these entities to the claim's core assertions* Flag any *xcontradictory informations* in the passages. Focus exclusively on entities directly tied to the clain's a specišic scope (e.g., names,
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dates, locations, roles) and avoid tangential details. Decuplicate repeated entities and ensure the summary reflects the **most relevant evidencex* for evaluating the claim's s truth. </aystem.prropt..1
<system.pronpt..25
Given the fields 'claim', 'summary_1', produce the fields 'query'. Gererate search query that eexplicttly incluces exact temporal/spatial markers** (e.g., "1998 New York Cathedral*) and **logical constraints** (e.g., "AND", "NOT") to narrow results. Prioritize entities from the claim and summary 1 that are **most eritical to the claim's semantic core** (e.g., specific people, dates, locations). Avoid ambiguous terms like "cathedral" without specitying exact names or locations. Use **nttit linking** to ensure precisicn. </system.prompt.2>
poysstemprromp..3
Given the fields 'claim', 'summary.1', 'passages', produce the fields 'summary', Focus exclusively on *eetiitis and relationships from the second retrieval passages** that are **directly tied to the claim's s core scope** (c.g., specific dates, roles, locations). **Flag contradictory evidenes** if present. Deduplicate repeated entities and ensure the summary **prioritizes evidence supporting or refuting the claim's assertions*.. Avoid generalizations and fecus on *xprecise, unambi guous details** (e.g., "elected" vs. "appointed"). </aystem prompt 3>
<system prompt 4>
Given the fields 'claim', 'summary-l', 'summary_2', produce the fields 'query'. Combine **core entities** from the claim and summaries to generate a final query that *xprecisely captures the claim's semantic core.. (e.g., exact roles, dates, locations). Use *entity prioritization to emphasize terms most critical for evaluating the claim (e.g., "Geoff LaTulippe AND 2010"). Avoid redundancy by eliminating *rpeetitive or tangential terma**. Include **smantic constraintsev (e.g., "NOT [irrelevant entityl") to exclude noise. Easure the query aligns with the claim's **specific scope** and *xtempora//spatia precisionex.
</systeen.prompt.t Group Prompts 3:
<aystem.prompt.1>
Given the fields 'claim', 'passages', produce conci summary that: 1. Extracts only entities eexplicitly mentioned in the claims and their axdirectly stated relationships (e.g., specific names, dates, titles that directly appear in the claim)
2. Identifies vvdirect evidence** (e.g., unanbiquous dates, official statements) and **contextual clues** that could support or rebut the claim, but **flags all contextual links as needing validation+*
3. clearly separates **confirmed direct evidencо** (explicitly stated in passages) from +iindirect or inferred relationships.. (contextual implied), explicitly marking these unverified
4. Excludes **all entities not directly tied to the claim's assertion**, even if they appear in the passages (e.g., avoid unrelated articies tangential mentions)
5. Prioritizes exdocument-Level evidence** (e.g., explicit mentiens of the claim's entities) over *xpassage-level inferences** unless the passage itself contains a clear statement that directly corroborates or contradicts the claim
s/aystem.prompt.l) <system.prompt.2>
Given the fields 'claim', 'summary.1', produce search query that: 1. Uses **oxact terminology** for =<critical entitios** (c.g., specific names, dates, titles) from the claim and summary-1; *exxclude semantic variants** unless explicitly necessary for resolving ambiguity
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2. **Avoid dynamic expansion** of entities unless they are directly tied to the claim's verification; for example, if "Friendship" is not found, **do not pivot to broader terms** from summary-1
3. Apply geographic constraints **only if the claim explicitly requires them** (c.g., "Prince George's County, Maryland" must be used if the claim references it explicitly)
4. Use Alooical filters exclude irrelevant categories (e.g., "NOT (movie OR film)"
the claim is about documentary evidence) 5. **Prioritize direct entity matches** over implied relaticnship (e.g., "infiuenced by" or "connected to") unless the claim explicitly references such relationships
6. +*Use strict terminology** for critical roles (e.g., "CEO") and **exclude semantic equivalents** unless the role is explicitly defined in the claim
</aystem.prompt.22
<system.prompt.35
Given the fields 'claim', 'context', 'passages', produce summary that:
1. Extracts *ronly direet evidenco.. from the second retricval (e.g., explicit mentions of claim entities, dates, affiliations) and **flags all indirect evidence as requiring valication**
**Rxcludes all tangential entities or speculative relationships** not directly tied to the claim's assertion, even if they appear in the passages
2.
3. **Clearly separates confirmed evidencexx (explisitly stated in passages) from *uncerraain or ambiguous claims*t, explicitly noting which antities/relationship
require further verification 4. **Filters out all entities not directly linked** to the claim's assertion (e.g.,
if passage mentions an unrelated article, exclude unless provides direct evidence)
5. *eRtaas only the first summary's critical entities**, and *excclues any new entities*A from the second retrieval unless they are explicitly tied to the claim's verification
6. **Labels all indirect evidence as "tentative"** and explicitly states that such evidence must be validated in later steps </system.prompt.3:
csystem.prompt.4s
viven the fields 'claim', 'summary_1', summary_2' produce final search query that:
1. Uses **exac torminology** for sscritical entities** from both summaries; vexcluuee semantic variants** unless the claim explicitly references such relationships (e.g., "influenced by")
2. **Avoid dynamic geographic adjustments** unless the claim explicitly requires them (e.g., use "Frince George's County, Maryland" if the claim references it)
3. **Exclude irrelevant catogorics** only when explicitly required by the claim (e.g., "NOT (movie OR film) " if the claim is about documentary evidence)
4. **Prioritize exact entity matches** over broader terms (e.g., use "Incesticide" instead of "Nirvana album" for the claim's core assertion)
5. **Structure the query emphasize airect entity matches**, with **n semantic flexibility** for secondary terms unless the claim explicitly uses such phrases
6. **Use a strict tiered inclusion strategy**:
First, include all documents with 4.full entity matches.. (exact terms in the claim)
Then, include documents with espartial matches only they explicitly mention the claim's entitics** and provide ssdirect evidence** (e.g., dates, affiliations), not contextual links </aystem.prompt...
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4. MATH (Qwen3-8B)
Baseline:
<system prompt 1> Given the fields 'problen', produce the fields 'answer'. </system.prompt.13
Alpha-Bvolve:
<system.prompt.1>
Given the 'problem' field, analyze and solve the mathematical problem by following these stepa:
1. *xUnderstand the problemxs: Carefully read the question and identify all mathematical operations, constraints, and conditions zequired. For functional equations, focus identifying patterns or substitutions that simplify the equation. For algebraic problems, determine whether factoring, substitution, or elimination is most appropriate. For number theory problems, consider modular arithmetic prime factorization.
2. *.Break it into stops**: Show your reasoning process explicitly, including calculations, formulas, and logical deductions. For problems involving multiple variables or constraints, enumerate all possible cases and validate each. For functional equations, test specific values of derive relaticnships between coefficients. For binary decomposition problems, explicitly convert the number to its binary form and calculate the sum of the exponents of the powers of 2 used.
3. **Perform calculation culations**:: Execute each step presisely, ensuring arithmetic accuracy. For algebraic problems, solve systematically (e.g., factorization, substitution, elimination). equations, substitute expressions and simplify algebraically. When decomposing numbers into powers of 2, confirm the binary representation is correctly calculated and exponents appropriately.
For functional
summed
4. +*Verify the result**: Confirm that the final answer satisfies +all* conditions in the problem, including constraints on variable domains (e.g., positivity, distinctness, integer remuitements). For functioral equations, verify that the solution works for all valid inputs. For binary decomposition problems, ensure the binary representation is used to determine the exponents and that no alternative combinations of powers of yield valid solution with Lower sum of exponents. Recheck calculations for errors.
5. **Format the answer**: Enclose the final numerical result in box using \boxedí).
Provide the answer in the 'answer' field as a mathematical expression (numerical symbolic) directly inside \boxed(). </aystem.prompt.15
C-Evolve:
Group Prompts
<system esystem.prompt. 1>
Given a math problem, first identify the type of problem (e.g., algebra, geometry, linear algebra, probability) and extract all numerical values and variables. Apply the appropriate formula method the problem type, performing calculations step-by-step and verifying intermediate results. For probability:
Use combinations pot.raattioo/ppmmmaatiinns for selection problems. Apply conditional probability rules when necessary. Ensure probabilities sum to and are between and 1.
For repeating decimals:
Let = \text (decimal) muitiply 10'n to shift decimal, subtract original equation to eliminate repetition, solve algebraically.
For digit reversal problems:
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Recognize that
yl is always divisible by 9.
Calculate maximum possible difference (e.g., 999 999 0, 999 999 0, but for distinct numbers like 999 999 = 0, but with digits reversed, the difference is 999 999 - 0. wait, need to recalculate: actually, for 3-digit numbers, the maximum difference is 999 999 but for distinct numbers like 999 and 999, it's 0. Wait, this incorrect. For example, 999 reversed
999, difference 0. But for numbers like 999 and 999, but the problem says "distinct integers", 999 and 999 are not allowed. So maximum difference might be 899 998 99? Wait, this is getting too specific. Maybe better to state: For 3-digit numbers with reversed digits, the difference is always a multiple of 9, and the maximum possible value is 999 - 999 = 0. Wait, this is not helpful. Insteac, the correct approach is to recognize that
yl where y is 100a 10b c., y 100c + 1cb a so 2 (9 - c 1. Since a \neg c a - ranges from 1 to 9 or -1 to -9, so z is a multiple of 99. The distinct values of are 99, 198, .... to 891, giving 9 distinct values.
the reverse of
and for 3-digit numbers,
For algebraic equations:
Solve eystemaallll using substitution, climination, or factoring, Always verify solutions by plugging back into the original equation For determinants:
Calculate using cofactor expansion direct formula for 2x2
matrices.
Finally, present the exact final answer in a single \ooxe( tag using LaTeX formatting, with no additional text or explanations outside the box.
</system.prompt.1> Group Prompts 2:
esystem.prompt..1.
Given the 'problem' field, analyze and solve the mathematical problem by following these steps:
1. *xUnderstand the problem**: Carefully read the question and identify all mathematical operations, constraints, and conditions zequired. For geometry problems, *explicitly identify whether the problem involves common internal/external tangents, similar triangles, circle properties*.. For algebraic problems, identify variables and relationshi selationships. *xSpecifically for funation-relates problems, determine ne whether the problem involves even/odd properties, periodicity, or transtormationsw.. involving geometric contigurations (e.g., parallelograms), **nalyyee the symmetry and midpoint conditions roots**.
For polynomial root problems
2. **Break it into steps**: Show your reasoning process explicitly, including calculations: formulas, and logical deductions. For problems involving multiple variables constraints: (e.g., different configurations,
Enumerate all possible positive/negative values).
Validate each case against the problem's constraints. For word problems, explicitly define variables and derive equations from the problem's s narrative.
For function. transformations (e.g., rotations, translations, reflections), identify the transformation type and its effects on the function's form.
For geometric problema involving distances areas, Hclarify whether it involves direct measurement, Pythagorean theorem, tzigonometric identities*
For polynomial root problems, consider all possible root arrangements and verify their geometric conditions.
**For complex number problems involving roots of unity, explicitly apply symnetry properties and eyclotomic polynemial identitics**.
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**For summation problens with recurring patterns (e.g., gecmetric series, telescoping sums), identify the pattern and simplify before sunning**.
3. **Perform calculationsee: Exccute each step precisely, cnsuring arithmetic accuracy. For algebraic problems: Solve systematically (e.g., factorization, substitution, elimination).
For geometric problema, ensure proper application of theorems (e.g., Pychagorean theorem, triangle similarity).
For problems involving sequences or summations, **break the problem into intervals ranges** and calculate contributions systematically. For polynomial root problems, solve for all possible values of parameters that satisfy the geometric conditions (e.g., parallelogran vertex conditions)..
**Por complex number problems, properties of roots of unity (e.g., x'n implies x*k (n(kk) for expressions before substitution+s.
and simplify
**For summation problems, identify telescoping patterns or symmetry to simplify the sum analytically before numerical calculationee.
4. **Verify the result**: Confirm that the final answer satisfies +all+ conditions in the problem, including:
Constraints on variable domains (e.g., positivity, distinctness, integer requi rements).
Consistency with given data (e.g., units, diagram labels). Logical validity (e.g., contradictions in intermediate steps). For word problems, ensure the answer matches the question's requested value (e.g., "how many days, "what is the length"). **For geometric problems, recheck tangent iongenttintersection configurations and ensure coordinates aectaatteseeeaseeeeee /measuremeetss align with diagrams**.
**For summation problems, verify interval ranges and summation
logic, especially for series involving complex numbers or symmetry**.
**For functior-related answers, verify parity/transformatioo match the problem's description (e.g., confirm (-xx) (x) for functions)
**For polynomial root problems, verify that all possible solutions are included, especially when multiple values are requested, and ensure symmetry conditions (e.g., mi midpoints, reflections) are explicitly checked**.
**Por complex number problems, ensure answers are simplifien fied using properties of roots of unity or algebraic identitiesus.
Recheck calculations using alternative methods (e.g., plugging values back into original equations).
5. **Format the answer**: Enclose the final numerical result in box using \boxed(}. For symbolic answers, provide the exact expression in a boxed format. For multiple answors, separate then with commas and ensure all values are explicitly stated. Provide the or numerical value (as appropriate) directly inside \boxed(). </system prompt 1>
in the 'answer' field mathematical expressicn
Group Prompts 3:
<system.prompt..1>
Given the 'problem' field, analyze and solve the mathematical problem by following these steps:
1. **Understand the problemиs: Carefully read the question and identify all mathematical operations, constraints, and conditions zequired. For algebraic problems, explicitly note variable domains (e.g., integers, real numbers) and functional constraints. For vector problems, identify dot products, cross products, or orthogonality conditions. For repeating decimals, recognize patterns and consider algebraic conversion to fractions.
2. **Break it into steps**: Show your reasoning process explicitly, including calculations, formulas, and logical deductions. For
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problems involving multiple variables constraints, enumerate all possible cases and validate each. For polynemial identities, compare coefficients of like terms systematically. For substitutions or simplifications, consider symmety or variable replacement strategics. 3. **Porform calculations*4: Exccute each step precisely, cnsuring arithmetic accuracy. For algebraic problems, solve systematically (e.g., factorization, substitution, elimination). For vector problems, calculate dot products or magnitudes as needed. For repeating decimals, set up algebraic equations to express them as tractions and solve. For functional problems, verify constraints for all domain values (e.g., check the equation holds for all in the domain).
4. **Verify the result**: Confirm that the final answer satisfies *all conditions in the problem, including constraints on variable domains (e.g., positivity, distinctness, integer requirements). For vector problems, ensure orthogonality conditions dot products equal zero. For functional problems, recheck that the functional equation holds for all values in the domain. Recheck calculations for errors.
met by verifying
5. **Format the answer**: Enclose the final numerical result in box using \boxed(].
Provide the answer in the 'answer' field as a mathematical expression (numerical or symbol ic) directly inside \boxed(). </aysten prompt 1>
5. GPQA (Qwen3-8B)
Baseline:
<aystem.prompt.il
Given the fields question', produce fields annswrr'. </system.prompt..1>
Alpha-Bvolve:
<aystem-prompt.l>
Given a multiple-choice question, follow these steps to determine the correct answer:
1. **Parse question zigorously4:: Identify the objective, key
terms, and domain-specific context (e.g., quantum mechanics, genetics, biochemistry, or thermodynamics). Extract numerical values, units, and conceptual constraints (e.g., "refractery tumor," "molccular weight, or "spin states"),
2. ++Eallute options methodically+.: For each option, assess scientific validity using domain-specific principles (e.g., quantum states in qubit space, hyperfine transitions in hydrogen atoms, or chromosomal mechani in meiosis). Verify alignment with standard references (e.g., Bloch sphere coordinates, known transition trequencies, or genetic marker functions).
3. *EEiminatee contradictionse:: violate numerical constraints (e.g., mismatched units or impossible probabilities), conceptual inconsistencies (e.g., incorrect molecular interactions), or empirical data (e.g., IR absorption ranges or reaction mecharisms).
Systematically discard options that
accuracy**: Select the option that satisfies all question conditions while aligning with domain-specific knowledge (e.g., quantum state vectors, hyperfine splitting formulas, or opigenetic mechanisms).
4. **Prioritize context-speciee
5. *+Validate with precision**: Ensure the answer adheres to establishi scienti fic interpretations aveids common mi misconcept i ons (e.g., confourding spin values meiotic errors), and incorporates critical reference data (e.g., quantum state geometry, nuclear spin statistics, or chromatin modification patterns). </system prompt 1>
fiientific
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C-Evolve
Group Prompts 1:
<eystem.prompt.ls
Given a multiple-choice question, follow these steps to determine the correct answer:
1. **Parse the question meticulouslye:: Identify the core objective, key terms, and context (e.g., reaction mechanisns, physical principles, or biological pathways). Focus on domain-specific terminology (e.g., "cyclohexane rings, "electron-positron annibilation, or "electric field symmetzy").
2. **Analyze all options systematicallyxx chemical questions, verify reaction mechani sms, stoichi ometry, and product stability (e.g., azonolysis patterns, scid-catalyzes dehydration).
For physics/mate /math questions, check unit consistency, formula application, and numerical validity (e.g., energy thresholds, electric field equations).
For biological questions, confirm pathway fidelity, genetic marker interactions, or enzymatic specificity (e.g., URA3/LEU2 functicns).
3. **lliminate contradictory options**:
Discard choices that violate conservation laws (e.g., /herrgy/ommnttm), thermodynamic principles, or known chemical behaviors (e.g., ozonolysis products).
Rule out options inconsistent with provided data (e.g., CMB photon energies, molecular symmetry groups)
4. **Select contextually optimal answers**:
For organic chemistry, prioritize stereochemical accuracy (e.g., absolute configurations in drug synthesis).
For physics, ensure solutions match boundary conditions (e.g., electric field inside/outside shell).
For biology, validate alignment with empirical data (e.g., drug discovery workflows, genetic marker interactions)..
5. *Vaalidate against domain-specific standards**:
Confirm answers match auchoritative references (e.g,, PDB formats for protein structures, standard IR spectroscopy ranges).
Avoid common misconceptions (e.g., confusing "d2h" with "c2h" symmetry groups)
For anbiguous questions, explicitly state assumptions and justity aligument with scientific principles. '/systeem.rromp.1>>>
Group Prompts 2:
<aystem.prompt.l>
Given multiple-choice question, follow these steps to determine the
Identify the core concept being tested (e.g., reaction mechanisms, NMR spectroscopy, quantum mechanics, molecular orbital theory). Focus on the specific context, such
1. **Understand the question**:
Organic chemistry: functional group transiormations, aromaticity, or hydrogen bording.
NMR spsctroscopy: splitting patterns, coupling constants, or shielding effects.
Quantum mechanics: spin interactions, energy transitions,
resonance phenomena.
**Numerical calculationses: pł, concentration, or equilibrium problems (e.g., weak acids/bases, buffer calculations).
2. **Analyze options**: For each choice, evaluate its validity by: a) Applying domain-spocific knowledge:
For organic chemistry: track electron flow, reaction conditions (e.g., t-BuOK, cupric acetate), and aromaticity rules (e.g., Huckel's Rule).
For NMR: determine splitting patterns based neighboring hydrogens, coupling constants, and shielding eifects.
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For quantum mechanics: calculate energy levels, spin transitions, magnetic interactions.
For numerical calculations: apply relevant formulas (e.g., \text(pH) -\log(H"+] equilibrium expressions).
b) Verify logical consistency with the question's premises (c.g.r
final product formation, reaction pathways, experimental data).
c) Cross-check against established principles (e.g., Diels-Alder
rules, NMR splitting rules, quantum mechanical spin statistics).
3. **Eliminate distractorss:: Rule out options that:
a) Conflict with the question's context (e.g., incorrect reaction intermediates unveritied side products).
b) Propose mechanisms incompatible with known chemistry (e.g., non-aqueous conditions for hydrolysis or invalid resonance structures).
c) Include irrelevant details (e.g., excess hydrogenation steps in one-step reaction mismatched aromaticity counts). d) Contain rumerical errors (e.g., incorrect på calculations, mismatched concentrationa).
4. **Select the best answor**; Choose the option that: Most directly addresses the question's requirements (e.g., final product structure, reaction mechanism, or spectral pattern). Aligns with both theoretical rules and experimental evidence (e.g., Huckel's Rule for aromaticity, NMR splitting for shielding).
For numerical problema, ensures accurate calculations (e.g., proper use of K_a stoicnicmetry, 5. **Verify**: Ensure the answer:
logarithmic relationships).
a) Satisfies stoichicmetric or structural constraints (e.g., valence, stereochemistry, or aromaticity).
b) Matches known experimencal data (e.g., NMR spectra, reaction yields, or quantum mechanical predictions).
c) Avoids overgemeralization. (e.g., assuming a non-aromatic structure when Huckel's Rule applies).
d) Accurately solves numerical problems (e.g., correct pH or concentration. values based given K_a and initial concentratiens). e) Explicitly checks for **formula application errors** (e.g., Nernst equation, Berderson-Haasellalleh or stoichicmetric ratios).
i) Ensures **unit consistency** and **correct significant figures** in numerical answers.
</aystem prompt 1> Group Prompts 3:
<system.pts wrrstem.prompt... 1>
Given a mittppee-chice question, follow these steps to determine the correct
1. +*Identify the domain-specific principles+:: Determine whether the question rolates to physics (c.g., redshift, oleatn spectra), chemistry (c.g., molecular structure, reaction mechanisms), or other fields (e.g., molecular biology, biechemistry), and recall the *xcore principles and factual knowledge** specific to that domain.
2. **Analyze each option methodicallyer: a. For w=plyelcss/sttzoppyyieees questions: Apply formulas like redshift energy equations, electromagnetic wave properties. **Verity unit consistency**, +*validate formula applicability to the question's context**, and *ccross-check with experimental results or theoretical models**. Frioritize options that align with mestabbished empirical data.*.
b. For **chemistzy** questions: Use reaction mechanisms (e.g., metathesis, imine formation), stoichicmetric ratios, catalytic behavior (c.g., T6Ol vs. нCl in and **commor. spectroscopic data+* (e.g., FTIR: C=0 ~ 1700 C=C 1600 "). + stoichiometric and structural valioity**, and *sprioritize options with known calmical shifts reaction patterns**.
reactions),
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For axmolecular biology/biochemissty questions: Apply knowledge of gene regulation (e.g., Te: promoter functionality in fungal species like +Saccharomyces cerevisiaer), protein engineering (e.g., CAR T-cell design), and **factual biological syştems** (o.g., known mechanisms of antigen transport through the Golgi). **Roject optiens that contradict established biological or biochemical facts*..
3. **Compare validity across options**: Rank options based en:
**Diirect application of domain-specific formulas or facts** (e.g.,
C-0 peak position, Tet system presence in specific species) **Alignment with experimental observations theoretical
predictions** (e.g., known gene expression outcomes in model organisms)
**Absence of contradictions
the question's conditions.. (e.g.,
logical consistency with Tet promoter requirements. **Elimination of options with unsupported assumptions unmet prerequisites:* (e.g., unexplained protein localization, missing cofactors)
4. **Select the most robust answer<*: Choose the option supported by **highost empirical evidenco**, -=mathematical/chhmee *+logical consistency with the question's context**, and *xxplicit exclusion invalid options**.
accuracy**,
5. Pinal answer must be in the format (bxswwearswer)) using the exact letter (A/B/C/D) corresponding to the correct choice.
</aystem prompt 1>
HotpotQA (GPT-4.I-min)
Baseline:
<aystem.prompt.il
Given the fields question', 'passages', produce the fields summary'. </system.prompt.1>
<aystem.prompt.22
Given the fields ' 'question', 'summary_1', produce the fields 'query'. </system.prompt..
<system.prompt...
Given the fields 'question', 'context', poneext,,'pasasen ppassages' produce the fields 'summary',
</system.prompt.3>
<aystem.prompt..>>
Given the fields 'question', coummary_1;, ssmmaryy 2', produce the fields 'answer'. </aystem.prompt.
AvolveEvolve:
<ayatem prompt 1>
Given the fields 'question' and 'passages', produce concise, strictly factual, and structured summary focused on key facts that directly answer the question. Exclude all peripheral, aneccotal, legal, or irrelevant details. Clarify entity membership precisely when relevant (e.g., distinguish original band members from collaborators or guests) and include timeline details only if they directly affect core facts. When strong evidence supports a probable partial inference resolving a unclear fact, you may cautiously include it, clearly framed as "probable" or "likely" to guide retrieval while avoiding unsupported assumptions.
Organize the summary in these labeled sections:
1. Key facts: List confirmed and strongly supported facts directly anawering the question, including any safe probable inferences clearly noted.
2. Missing information: Hullet point concrete factual gaps unknowns requiring follow-up retrieval, avoiding vague or broad itens. Omit this section if no missing info remains.
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3. Anbiguities: Specify any contradictory, uncertain, unresolved facts found using precise language. Omit this section if exist. Avoid repeating the question text or providing opinions. Write clearly, precisely, and unanbiguously to support accurate downstream steps. This sunmary is a focused knowledge anapshot for guiding the next retrieval.
</system.prompt..>>
<aystem.prompie
Given the fields 'question' and 'summary_14 1', generate concise zetrieval query that precisely targets enly the explicitly 1isted missing information and unresolved ambiguities from summary.11.
Exclude all facts already known or confirmed.
Avoid repeating wording verbatim from the original question or previous summaries to reduce redundancy in retrieval.
Formulate the query focused precise phrase designed to retrieve essential complementary oz clarifying facts that fully resolve the identified gaps.
minimal, direct natural language question
Use clear, neutral, and unambiquous wording.
Avoid verbosity, claboration, background details, or irrelevant content.
If no missing information or ambiguitics remain, respond exactly with: "No further query needed."
The query must maximize precision and relevance to enable effective, focused second retrieval aimed at thorough knowledge closure. </system prompt
<system.prompt.3>
Given the fields 'question', 'context' (the first summary), and second retrieval 'passages', produce a concise, strictly factual, and integrated summary synchesizing all evidence from both retrieval rounds.
Use 'context' as the baseline.
Augment it only with new, directly relevant, and complementary facts found in the second retrieval 'passages'.
imprecise language. Explicitly discard or ignore any irrelevant, off-topic, or Low-quality passages or facts; do not include them in the summary.
Exclude any speculation, assumptions,
If large parts of second retrieval are irrelevant, note this brietly retrieval roise.
Organize the summary into these labeled sections:
1. Updated key facts: Integrate, reconcile, and update confirmed facts from both retrievals, including safely framed probable inferences supported by concrete evidence.
2. Resolved missing information: List each previously missing information item now conclusively answered. Omit this section if none remain.
3. Persisting ambiguities or contradictions:
Specify any remaining unresolved conflicts clearly and
uncertainties, contradictions, unambiguously. 0mit this section none persist.
4. [Optional] Irrelevant or rejected information: Briefly note it significart portions of the second retrieval were irrelevant, unreliable, or discarded, to indicate retrieval noise.
Avoid repetition, unrelated content, fully refine and narrow knowledge gaps, enabling confident, precise, and unambiguous final answer generation. Desystem.prompt... poysteemprompt.4.
vague phrasing. The geal
Given the fields 'question', 'summary_1', and 'summary-2', generate the final answer that exactly matches the true canonical answer both precise content and formatting.
Output only the final answer text with no explanations, commentary, citations, additional context.
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If the collected information is insufficient, anbiguous, or contradictory preventing confident and exact determination, respond exactly with:
Answer not found.
Strictly follow these formatting rules by question type:
Yes/no questions: output exactly "yes" or "no" (lowercase, punctuation).
output only the exact year, full date, or date format as appropriate, without any extra words or formatting. Entity or name questions: output the full canonical proper name, including all middle names, suffixes, official variants, spellings exactly as expected and verified. any parts.
Date questions:
precise
not abbreviate or cmit
List or multiple-entity answers: include all required entities completely and explicitly, formatted in their full canonical forms (e.g., all official collaborators appropriate standard delimiters, and in canonical order known. All other question types: output a concise, precise, and minimal answer with no filler words, qualifiers, claboration, or punctuatien beyond what is canonical.
members), separated by
Ensure absolute accuracy, exact canonical form, completeness, and precise formatting to maximize exact match correctness. Normalize and sanitize final answers to conform exactly with canonica cal expected forms, avoiding minor mismatches caused by extransous details, spelling variants, punctuation. </system.prompt.99
C-Evolve:
Group Prompts
taystem.prompt.... 1>
Given the fields ' 'question' and 'passages', produce concise, strictly factual, and unanbiguous summary synthesizing all key information directly relevant answering the question exactly. explicitly highlight and retain every important qualifier such as geographic locations, temporal references, precise numeric specifics, and other contextual details/only is they are critical every numeric quantifiable
exact final answer match. fact, standardize the expression to the most precise form supported by the passages; if only approximations ions or ranges are present, clearly flag these as ambiguities and specify the precise numeric detail needed for exact matching. Carefully identify, enumerate, and explicitly flag every missing, ambiguous, contradictory, uncertain, insufficient piece of information including entity or name confusions, multiple plausible interpretations. partial or inconclusive evidence, and temporal or ownership uncertainties. For each flagged issue, clearly specity precisely what further information clarification required to fully resolve it. Always include structured section titled 'Missing or Anbiguous Information' that itemizes all identifies gaps, uncertainties, or conflicting points. Avoid any redundancy by including only new, pertinent evidence that meaningfully advances understanding and reduces ambiguity. Do not include any speculation, assumptions, or unsupported inferences; however, if indirect inferences are made from evidence, explicitly mark them with confidence levels (e.g., [INFERENCE, HIGH CONFIDENCE)). When stating facts that could contribute to the final answer, phrase them as concisely and minimally as possible, omitting generic or redundant terms that do not affect the exactness of the answer (e.g., omit words like \company" if \American fast food" suffices). This summary must serve a clear, evi evidence-groundee, and comprehensive foundation for generating narrowly focused and gap-targeting second retrieval query that addresses every flagged uncertainty or anbiguity with maximal precision.
</system.prompt.it
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<aystem-prompt.22
Given the fields ' 'question' and 'summary-1', generate precise, narrosly tailored second retrieval queries that explicitly and comprehensively target each specific gap, ambiguity, contradiction, unresolved point, or entity disambiquation need identified and enumerated in the 'Missing or Anbiguous information' section of 'summary1'. Do not include or reiterate any information already confirmed or clearly resolved by 'summary1'. Each query must be focused exclusively on retrieving complementary, clarifying, or di disambi disamigguating guating evi that directly resolves a flagged uncertainty or mi missing qualifier in order to enable final answer that exactly matches the ground truth. Avoid general, broad, that could introduce noise or irrelevant results. Ensure that each query is as minimal as possible while fully covering the targeted ambiguity or information gap. Organize queries to maximize efficiency and effectiveness using torminology and key terms aligned closely with the original question's s semantics to maintain retrieval precision and relevance. If multiple ambiguities exist, create separate, distinct queries for each and prioritize them to enable staged or parallel retrievals. povsstemprompp.25
unrelated topics
minimizing redundancy or overlap. Phrase queries
<system www.eep.rrpp.....
Given the fields 'question', 'context' (the first summary), and 'passages' from the second retrieval, procuce concise, logically coherent, and comprehensive summary integrating all available evidencs. kigorously compare, cross-validate, and reconcile facts and qualifiers from both the first summary and second retrieval passages, explicitly addressing and resolving any contradictiens, ambiguities, or discrepancies confidence levels, source reliability, and directness of evidence. indirect inferences are made, flag them clearly with appropriate confidence annotations (e.g., [INFERENCE, MODERATE CONFIDENCE)) Include every key qualifier and disambiguating detail essential to produce exact and fully speci fied final answer. If any conflicting information remains unresolved after thorough analysis, clearly and explicitly enumerate the residual uncertainties in dedicated 'Residual Uncertainties' section, and if feasible, specity what further information or data would be required to definitively resolve them. Do not repeat restate conclusively confirmed. facts without integration concisely into an integrated fact set. When integrating facts, phrase all sunmaries as concisely as possible, avoiding generic or superfluous terms that do not affect final answer precision. This second summary must constitute definitive, meticulously checked, and confidence- answer generation strictly matching the true answer without deviation. Confirm that all previously flagged gaps are addressed or explicitly declared unresolved.
assessing internal consistency,
refinement; instead, synthesize them
factual basis that directly enables final
</aystem.prompt.33>
cayatem_prompt..>>
Given the fields 'question', 'summary 1', and 'summary 2', produce the final answer text that exactly matches the expected true answer word for word with deviations whatscever, The answer must be strictly concise, unambiguous, and dry without any explanation, filler, opinion, speculation, or extra sentences. Do not paraphrase, zound, generalize, or elaborate under any circumstance; output only the exact text that yields exact match to the ground truth label. they appear
Include all critical qualifiers only if and strictly the ground truth answer nno additions or cmissions. Exclude any additional context, background information, or details beyond the true answer content. Before ousputting, rigorously verify that the answer completely and exactly resolves the question with respect to wording, format, punetuation, and all critical qualitiers as needed for an
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exact evaluation match. Follow case formatting conventions exactly expected for the answer (e.g., lowercase 'no' if the 'no'). any uncertainty, ambiquity, or insufficient evidence persists preventing an exact match, respond with the exact phrase: "Insufficient data to provide the exact answer, Do not output anything else. Only produce the final answer after all verifications; do not hallucinate produce summaries.
from incomplete contradictory
</aystem prompt 4>
Group Prompts 2:
<system.prompt.1.
Given the fields 'question' and 'passages', produce a concise, strictly factual, and structured summary focused on key facts that directly answer the question. Exclude all peripheral, anecictal, legal, irrelevant details. Clarify entity membership precisely when relevant (e.g., distinguish original band members from collaborators or guests) and include timeline details only if they directly affect core facts. When strong evidence supports a probable partial inference resolving a unclear fact, you may cautiously include it, clearly framed "probable" avoiding unsupported assumptions.
"likely" guide retricval while
Organize the sunmary in these labeled sections:
Key facts: list confirmed and strongly supported facts directly answering the question, including any safe probable inferences clearly noted.
1.
2. Missing information: Bullet point concrete factual gaps or unknowns requiring follow-up retrieval, avoiding vague or broad itens. Omit this section if no missing info remains.
unresclved facts found using precise language. Omit this section if none exist. Avoid repeating the question text or providing opinions. Write clearly, precisely, and unanbiguously to support accurate downstream steps. This sunmary is focused knowledge anapshot for guiding the next retrieval.
3. Ambiguities: Specify any contradictory, uncertain,
</system.prompt..
<system.prompt..2
Given the fields 'question' and 'summary-1', preduce concise, precise retrieval query that exclusively targets the explicitly listed missing information, unresolved ambiguities, 'aummary which are required to fully answer the question.
hypotheses flagged in
Do not include any facts already confirmed or known.
Avoid duplicating exact phrasing from the question prior summaries to reduce redundancy.
Formulate the query a minimal, direct natural language question or a focused, precise search phrase.
Use clear, noutral, and unambiguous language focused solcly en resolving identified knowledge gaps or testing hypotheses. Do add background, elaboration, 'summary_l' contains no missing info, ambiguities, or hypotheses that prevent answering, respond exactly with: "No further query needed."
unrelated details.
This query should maximize retrieval precision and relevance close knowledge gaps effectively the second retrieval. </aystem_ph </aystem.prompt.22 pww.ppppompp>>..m...mm.... <system eeyystem.promp.33
Given the fields 'question', 'ontextt' (the first summary), and second retrieval 'passages', produce concise, strictly factual summary that integrates evidence from both retrieval rounds to provide the most complete and reconciled knowledge snapshot for final answer generation.
Use 'conextt' the foundation, expanding only with newly discovered, directly relevant, and well well-supported facts from the second retrieval.
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Cross-check, reconcile, and resolve conflicts or inconsistencies retrievals, explici=ly stating resclutions and noting confidence.
where relevant, Integrate partial or probabilistic inferences supported by evidence, clearly flagged with confidence levels. Do not include speculation beyond what be cautiously inferred nor vague language.
Exclude irrelevant, low-quality, or off-topic passages or facts unless briefly sunmarizing their exclusion as retrieval noise.
If a significant portion of the secona retrieval is irrelevant or discarded, note this succinetly Organize into these sections with clear bullet points:
retrieval noise.
1. Updated key facts: Fully integrated and reconciled confirmed
facts and strongly supported probable inferences from both retrievals, clearly marked by confidence.
2. Resolved missing information: List previously missing
hypothesized details now conclusively answered or disproven. Omit if none.
3. Persisting ambiguities, hypotheses, or contradictions: Clearly
1ist any remaining unresolved uncertaintics, conflists, uncenfirmed hypotheses. Omit none.
4. [Sptional] Irrelevant or rejected information: Brief note if substantial content from second retrieval was discarded as retrieval noise.
Avoid repetition, vague phrasing, unrelated material. This summary must to fully close knowledge gaps and ambiguities support exact and confident final answer generation. </system.prompt.3>
<aystem.prompt..4
Given the fields 'question', 'summary_1', and 'uumaary_2', generate the final answer that exactly matches the true canonical answer both precise content and formatting.
Output only the final answer text with explanations, commentary, citations,
additional context.
If the collected information is insufficient, ambiguous, unresolved, or contradictory preventing a confident and exact determination, zespond exactly with: Anawer not found.
strictly adhere to these formatting rules by question type: Yes/no questions: output exactly "yes" or "no" (lowercase, no punctuation).
date format, with output only the exact year, full date, precise words punctuation.
Date questions:
Entity or name questions: output the full canonical proper name, including all middle names, suffixes, honorifics, official variants, and exact spellings exactly as expected. Do not abbreviate or emit any parts.
required red entities explicitly, using their full canonical forms, separated by standard delimiters, and in canonical order if known.
List miltiple-entity answers: include
Numeric answers (counts, amounts) spell out numbers (e.g., "two" instead of "2") in standard canonical form unless the canonical answer format explicitly requires digits.
All other question types: output a concise, precise, minimal answer with no filler, qualifiers, elaboration, or extraneous punctuation beyond what is canonical.
Ensure absolute accuracy, canonical form, completeness, formatting to maximize exact match scoring.
Apply normalization and sanitization rigorously to avoid minor mismatches caused by extrancous details, spelling variants, punctuation, formatting differences. s/aysteemprompt.ht Group Prompts
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kaystem-prompt..1 You are given the fields ' 'question' and 'passages' (a batch of retrieved documents). Generate a concise, coherent summary that captures all key facts directly relevant to answering the question precisely and unambiguously. Present the direct candidate answer or finding upfront in clear, minimal sentence or phrase exactly matching the expected answer type and canonical format (e.g., date in canonical form, a full name, or a simple "yes" or "nc"), the question expects a yea/no answer, respond with "yes" or "ne" di recetly unless ambi guity exists. If multiple valid candidates exist and the question on auggests plurality, include all clearly distinguisted; if singular, select the most authoritative Explicitly state any information missing
canenical
answer with justification. from the passages that prevents a definitive answer, using clear, unambiguous language such as "No information about X is provided."
Identify and explicitly describe any ambiguities, contradictions, or conflicting evidence found within the passages, summarizing their nature succinctly without hedging. When the question invelves temporal, ownership, authorship, or attribution nuances, clarify the relevant time frames or roles, prioritizing authoritative sources, and distinguishing founder group authorship as appropriate. For questions involving comparative or multi-part conditions, explicitly state whether sufficient evidence exists to conclude or if information is insutricient, summarizing asymmetries. Avoid including extraneous background details; fecus only on information essential speculate or introduce external knowledge beyond the passage content. Verify and present named entities, organizations, or official terms in their current, official, canonical forms; if uncertain, note this missing information downstroam query refinement and answer accuracy, so provide clear, decisive facts with precise, minimal, and non-redundant wording. </aystee.prompt.1>> <aystem.prompt.22>
current ownership
individual
answering the question. Do not
retrieved. This summary will support
fields 'question' and 'summary (the initial summary of retrieved passages) Analyze the summarized information in along with any explicitly identified missing details, amoiguities, or contradictions in summary.1. Cenerate precise, fecused retrieval query that directly targets the minimal missing information resolves the noted uncertainties. Ensure the query accurately captures the core semantic intent of the original question, emphasizing the retrieval of new, complementary, and authoritative evidence specifically addressing gaps, temporal or attributicnal nuances, or conflicting points. Avoid vague overly broad query terms; prioritize clarity and specificity to maximize downstream retrieval relevance and improve answer completeness and exact correctness.
You are given
</aystem.prompt.22>
<aystem.prompt..3>
You are given the fields 'question', 'context' (the first summary), and 'passages' (a new batch of retrieved documents). Integrate and synthesize all information from the first summary and the new passages into concise, coherent summary that explicitly resolves clearly articulates previously idensified ambiguities, contradistions, or missing information. Provide clear, decisive conclusions supported by the combined evidence, ensuring all named entities and relevant data are presented in their full canonical and official forms.
Where conflicts in evidence persist, clearly describe the nature and implications of these conflicts without hedging, prioritizing the most authoritative info. Include any necessary reasoning, temporal context, comparisons relevant to precisely answering the question. Avoid redundancy and exclude information not strictly necessary to support the sinal answer. This summary should maximize
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completeness, clarity, and precision to effectively guide the final
answer generation.
</system.prompt.3>
<aystem.prompt..4
You are given the fields 'question', summarryl1', and summaryy22 (the two summarics from retrieval hops), Produce the final answer single, concise text string that exactly matches the expected answer in canonical and official form/no explanations, clarifications, or additional context. Prioritize exact match, clarity, and unambiguity, ensuring the answer is definitive, directly supported by the provided summaries, the expected answer type (e.g., full names, dates canonical format, yea/no). Do not add any information beyond what is supported by the summaries or infer beyond the provided evidence. If the summaries do not contain sufficient evidence to produce confident answer, respond exactly with "Insufficient information." </system.prompt.4t
appropriately formatted to
7. IFBench (-Pnn).I-mini)
Baseline:
Syyssem.prompt.. Respond to the query
</system.prompt.15
<system.prompt..2
Ensure the response is correct and adheres to the given constraints. Your response will be used as the final response. </system.prampt.25
Alpha-Evolve:
syyatem.prompt.i
You are giver a user instruction. Provide a *Adirect, concise, and precisexx answer that **striatly** fulfills **every explicitly statedæ* requirement in the instruction, including content, language, formatting, style, structure, numbering, punctuation, and length constraints. Match these **exactly and without deviation.s. Do **no*** include explanations, rationales, clarifications, er any information beyond what is explicitly requested. Address **all parts and subparts**
the instruction fully, avoiding assumptions or interpretations beyond the literal text. Your output must be **onyy* the answer that exactly matches the user's request; do **no*** add any commentary, prefatory remarks, or extra content.
</aystem.prompt.1>
<aystem.prompt..2>
Given the user instruction and the answer generated in Step 1, perform thorough and critical review of the answer for any mistakes, omissions, contradlctions, and implicit requitements** in the instruction. Correct all such issues to ensure **full and exact compliance** with every instruction detail, including content accuracy, formatting, style, length, vocabulary restrictions, repetition rules, Language, and required structure. Enhance the answer's elarity, ccherence, completeness, and adherence without adding explanations, rationales, or meta-commentary. Output **only** the final, polished response that best fulfills every aspect of the user's instruction, including any subtle or implied requiremente, guaranteeing maximal alignment with the original instruction. </systemmprompt..2
nonconformitics with **all explicit
C-Evolve:
Group Prompts <system prompt 1>
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When given the instruction, produce a direct and exact answer that fully addresses every explicit requirement without omitting any part. Follow all specified formatting rules, language or style constraints, mandatory inclusions or exclusions of words or phrases, and precise output structure exactly as stated. Do not include explanaticns, reasoning,
additional Comenntaryyyour output should be strictly the answer required. IE multiple parts or complex directives present, answer each completely but concisely, adhering tightly to the user's a exact wording, formatting, and style instructions. Pricritize literal and explicit compliance over paraphrasing, elaboration, inferred interpretation, ensuring deviations from the instructien's demands.
</system4prompt.1>
<eystem.prompt.22
Thoroughly examine the Step 1 answer alongside the original
instruction identify any errors, omissions, anbiguities, failures to meet the instruction's requirements. Correct all such issues by enhancing the reaponse to fully and precisely satisfy every explicit requirement, constraint, formatting rule, and specified style. Refine and polish the answer for clarity, coherence, completeness, and natural flueney, while strictly adhering to all exact wording mandates, required phrases, forbidden words, and formatting instructions, The final output must be a perfectly cal salibtated, comprehensive, and ready-to-del iver response that complies for ambiguity
flawlessly with the user's instruction, Leaving or error.
</system.prompt.22 Group Prompts 2:
<aystem.prompt.1>
Carefully and thoroughly read the user's instruction. Identify and understand ALL requirements, conditions, constraints, including formatting, style, language, punctuation, keywords, length limits, or any other explicit elements. Produce direct, precise, and faithful answer that strictly adheres every instruction detail, without omissions, additions, or deviations. Do NOT include explanations, zeasoning, or commentary unless the instruction explicitly requests them. Ensure the response fully respects requested output structure and style. Your output must salely and exactly fulfill the inatruction as given.
</aystem prompt.... jaystem.prompt 2>
Carefully re-examine the answer generated in Step 1 together with the original instruction. Detect and inconsistencies in content, format, style, language, punctuation, or failure to meet explicit instruction constraints such as required keywords, forbidden words, exact counts of sentences or paragraphs, specified structure. Refine the response for clarity, completeness, coherence, and overall quality. The final comprehensive, and perfectly compliant version meating ALL instruction requirements without changing the intenced meaning. Confirm that formatting, style, and all explicit demands from the user instruction and Step 1 output </aystem.prompt.25
any errors, omissions,
must be polished,
strictly fulfillea.
Group Prompts 3:
poystem.prompt...
You are giver user instruction. Your task is to generate precise, direct, and thoroughly complete response that fully satisfies every explicit and implicit requirement stated in the instruction. This includes all aspects such as factual accuracy, computational correctness, required language, formatting conventions, stylistic tone, organizational structure, numbering schemes, punstuation rules, reaponse length, mandatory keyword usage, section and paragraph counts, and any other details. Your output must. be solely the direct
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answer requested do not include explanations, rationale, commentary, or any extraneous information. Rigorously adhere to all formatting, style, and structural zequirements specified in the instruction unless they are explicitly reserved for Step 2 calibration. Ensure your response is clear, logically coherent, unambiguous, concise, and exhaustively addresses every part of instruction without emissions or assumptions.
</aystem.prompt..]
caystem.prompt.2>
Given the user instruction and the step 1 answer, carefully analyze the response to identify and correct all errors, missing compenents, formatting or style issues, length constraints, redundant or repeated wording, violations of Language or keyword constraints, mismatches in section paragraph count, explicit and implicit instruction requi rements. Improve, refine, and polish the Step answer to produce a final response that is error-free, fully compliant, maximally clear, coherent, precise, and optimized to satisfy exceed all user requirements. Provide only the firalized, calibrated answer; not include explanations, justifications, commentary, or any content beyond what the final instruction demands. </aystem.prompt.22
any other failures to fully meet
HoVer (GPT-4.1-mini)
Baseline:
<system.prompt.Ju Given the fields claim', 'passages', produce the fields summary', </system.prompt..>>
<aystem.prompt.22 ,2>
Given the fields 'claim', 'summary.1', produce the fields 'query'. </aystem.prompt.25
<aystem.prompt..3>
Given the fields 'claim', 'context', 'passages', produce the fields 'summary'.
</systemm.prompt.3>
<system.prommtt.>>
Given the fields 'claim', 'summary_1', 'summary2', produce the fields 'query'.
</aystem
Alpha-Bvolve:
<aystem.prompt.I>>
You are giver the fields 'claim' and 'passages' retrieved in the first retrieval step.
Your task to produce the field 'summary' as a concise (max sentences), coherent synthesis that balances strict factual accuracy and proactive evidence bridging to support effective claim verification.
Explicitly identify and relationships mentioned or implied in the claim, especially when multiple similarly named candidates appear. Rank these candidates by relevance or likelihood using passage evidence.
disambiguate all key entities, attributes,
When essential entities relationships refereneed by the claim missing from the passages, infer or hypothesize plausible identities or links strictly based on provided evidence without unsupported speculation, to proactively fill critical information gaps needed for verification.
Clarify normalize ambiguous unusual claim by referencing possible standard equivalents or synonyms found in the passages where justifiable.
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Integrate complementary facts strictly from the passages, combining evidence logically reduce unresolved gaps while maintaining clarity and confidence levels.
clearly state all remaining ambiguitics, missing evidence, or unresolved points that require further targeted invostigation, distinguishing them from conjecture.
Exclude extraneous verifying the claim components. Avoid redundancy, excessive detail, or unsupported speculation/ include only facts grounded in the retrieved passages or justified bridging inferences.
peripheral information unrelated directly
Ensure the sunmazy is precisely claim-focused, highlights evidence strengths and gaps, and prepares the next query to astively resolve missing or ambiguous claim facets.
Provide summary that both synthesizes entities and core evidence and recommends key unresolved aspects for follow-up.
</system.prompt.1>>
<aystem.prompt..25
You are given the fields 'claim' and 'summary.1'.
Your task is to generate the field 'query' that pinpoints and actively targets all unresolved, anbiguous, or partially covered claim aspects revealed or hypothesized in "summary_1', refining and sharpening the original claim semantica for precise second retrieval.
Formulate concise, compound query that explicitly includes all necessary entities, attributes, relationships, to resolve remaining ambiguities, disambiguate similar or competing entities, or fill critical evidential gaps noted or inferred in 'summary_l'.
qualifiers needed
where helpful, incorporate negations, exclusions, temporal qualifiers avoid conflation of similar entities or contexts, enhancing retrieval specificity.
Prioritize maximal specificity and logical combination of multiple claim facets (e.g., entity attribute contextual relation) to minimize retrieval noise and maximize relevance.
Avoid vague, overly broad, or generic queries that not directly resolve outstanding ambiguities or missing evidence necessary for claim verification.
uzilize all known identifiers, Distinctive details, temporal markers, or normalized terms surfaced hypothesi in 'summary 1' to improve query precision without introducing unsupported facts. Provide only the final query text, without explanation, formatting, or additional content.
</aystem.prompt.2>
<aystem.pronpt..3
You are given the fields 'claim', 'conextt' (the first summary), and 'passages' retrieved in the second retricval step.
Your task is to produce the field 'summary' as a concise (max 3 sentences), integrated synthesis that explicitly compares, recenciles, and combines the new passage evidence with 'context', focusing tightly on claim verification and information completeness.
Summarize only the most relevant and novel information that supports, refutes, clarisies, points from the first summary, including any newly discovered evidence or hypothesized ciarifications.
resolves ambiguous incomplete
where inconsistent, overlapping, or competing evidence exists, explicitly weigh, rank, or differentiate candidate entities, interpretations, claim facets by evidential strength, likeliheod, or confidence.
clearly identify and articulate all remaining unresolved aspects, contradictions, ambiguitios, or uncertainties critical to the final truth evaluation. of the claim, explicitly stating what has been resolved or narroved down since the first summary.
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Exclude redundant, peripheral, background information not directly relevant clain verification query focus. Maintain clarity, factual accuracy, and logical coherence to guide focused, effective final query generation.
If multiple candidate entitics, interprotations, or claim facets remain ambiguous, 1ist them explicitly to inform the final retricval focus.
Include explicit confidence or evidence strength markers where appropriate to indicate the reliability of conclusions or ongeing gaps.
Provide precise, integrative, and confidence-aware summary that nighlights evidence progress and remaining verification neecs.
</system.prompt.3>>
<aystem.prompt.4t
You are given the fields 'claim', summaryy1', and summary_2' (the two prior summaries). Your task to generate the field 'query' that precisely targets all remaining unresolved, weakly supported, or ambiguous aspects identified by synthesizing 'summary_1' and 'uumarry_2', aiming for final, comprchensive, and focused retricval.
Connstuutt a concise, compound query that integrates all outstanding claim facets, employing logical operators, exclusions, temporal qualifiers, or specific attribute combinations as needed to maximize retrieval relevance and minimize noise or redundancy.
Explicitly prioritize disambiquation among similar competing
candidate entities or interpretations, emphasizing terms that promote zetrieving novel or unique passages not covered in prior retrieval steps to improve answer coverage and reduce redundancy.
Avoid vague, generic, or overly broad queries that risk retrieving irrelevant or overlapping passages.
Use exact entity names, attributes, temporal markers, normalized terms, or relationships surfaced in previcus sumnaries, strictly without introducing unsupported facts assumptions. Where useful, prioritize query terms that target claim exclusivity, identity or name disambiguation, term normalization, or completeness gaps identified in prior summaries.
Ensure the query is optimized to retrieve passages offering maximal novel evidence for final claim Provide only the final query string formatting. e/systemmprompt..t 4>
without explanations
C-Evolve: Group Prompts 1:
<systen prompt 1>
You are given the fields 'claim' and 'passages' which have been retrieved as the first batch relevant to the claim. Your goal is to produce a concise, factual, and coherent 'summary' strictly focused on verifying all claim components, including all relevant entities, roles, dates, and relations mentioned.
**Instructions:..
Limit the sunmary to 3-4 well-structured sentences.
Explicitly confirm, contradict, or report absence of evidence for each distinct factual clement in the claim (e.g., individuals, events, roles, dates). If component is not found, state "no direct evidence found, "
Identify and enumerate alternative plausible entities or hypotheses relevant to the claim especially if initial evidence is missing or contradictory. For example, 1ist other possible directors or films connected to involved actors.
Emphasize temporal, positional, and role-related consistencies or discrepancies with clear statements.
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Synthesize all relevant evidence, integrating multiple claim parts, and exclude unrelated duplicate information.
Avoid vague or hedging language; be precise and clear about confirmations, contradictions. and gaps.
Explicitly 1ist any ambiguities or unresolved points and recemmend retrieval query (c.g.,
concrete focuses targets for the alternative entities, missing films, conflicting dates) Include a structured aummary in terms of: *Confirmed facts*, *Contradi cted claims*, *Ambiguities or unknowns*, *Evidence gaps*, and *Next retrieval focus*.
The summary should search queries that aim to close evidence gaps and resolve contradictions.
comprehensive basis refining
Output the field: 'summary'.
</systeemprompt.1>> <aystem.prompt.22
You are giver 'claim' and 'summary.l' (a concise summary the first zetrieval).
Your task is to produce a clear, focused, and precise 'query' to guide the second retricval aimed resolving all identificd gaps, ambiguities, and contradictions from ssummary.1'. **Instructions::>
Carefully analyze 'summary.1', especially the sAmbiquitiesy, *Rvidence gaps*, and *Next retrieval focus* sections to ident ify which claim components exploration.
alternative entities need targetea
Formulate a quezy that explicitly targets these unresolved aspects using specific qualifiers such as exact names, dates, roles, film titles, or locations.
Include alternative hypotheses or related entities suggested by 'summary.1l' to broaden evidence collection and cenfirm or refute competing possibilities.
Avoid vague, generic, or overly broad terms; do not reuse prier queries verbatim but refine them to maximize relevance and evidence discovery.
Ensure the query preserves the original claim's core semantics but focuses the retrieval on bridging gaps and resolving contracictions. Output the field: 'query'.
</aystem prompt
<ayatem prompt.3 3>
You are given the fields 'claim', 'cotext'' (which is 'summary 11) and 'passages' (the second retrieval batch). Your goal is to produce concise, factual, and coherent 'summary' that moaningfully enriches or revises the evidence landscape regarding the claim boyond what is captured in 'contxx''. *IInstruttion::*
Limit the summary to 3-4 well-structured sentences that present relevant evidence supporting, refining, or contradicting individual components of the claim.
Clearly integrate and compare this new evidence against 'context' and the claim, explicitly highlighting contirmations, contradictiens, emerging ambiguities between sumnaries.
Explicitly account for multi-entity relationshipe and temporal, role, and positional consistencies or inconsistencies that emerge.
If alternative hypotheses or entities were proposed in 'context', zeport whether the new passages confirm, refute, further complicate them.
Avoid restating unchanged information from 'context' or introducing of off-topic details.
Summarizo remaining gaps, ambiguitics, evidence still required for thorough claim verification.
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Structure the output with discrete elements: <New confirmed facts*,
*New contradictions or rečinements*, *Remaining ambiquities gapst, and *Recommerdations for next retrieval focus*.
Emphasize clarity, precision, and relevance to guide the final query generation effectively.
Output the field: 'summary'. </system.prompt.>>
byyystem.propp....
You are given 'claim', 'aumnary 1", and 'summary.2 2' (summaries from previous retrievals).
Your task is to produce final, narrowly targeted 'query' designed retrieve definitive, authoritative evicence that conclusively contirms or refutes the claim by resolving all remaining contradictions gaps, and ambiguities identified in the combined summaries. IIIntructioon:**.
Thoroughly analyze both summaries, focusing unresolved questions, contradictions, ambiguous or missing links crucial for final claim verification.
Formulate precise, refined query that explicitly targets these outstanding issues using specific qualificrs names, titles, dates, roles, film titles, locations, or other contextual identifiers maximize retrieval relevance and precision. Include any alternative entities or hypotheses still unresol ved to ensure comprehensive coverage towards conclusive evidence.
exact personal
Avoid reusing broad vague terms from prior queries without refinement; strive for the narrowest effective scope that can yield definitive passages.
The query must be designed to enable retrievals that provide final closure the claim's truth status, isolating any remaining uncertainty.
Output the field: 'query'.
dlaystem.prompt... Group Prompts 2:
<system.prompt. 1>>
You are given the fields 'claim' and 'passages' retrieved in the first retrieval step.
Your task is to produce the field 'uumary'' as concise (max 3 sentencss), coherent synthesis of the key information strictly relevant to assessing the claim.
Summarize only the most pertinent facts from the passages that support or contradict the claim.
Avoid repetition, duplication, and extraneous details unrelated to the claim specifics.
Focus on factual accuracy and clarity to enable precise query generation in the next stop.
Do not include new information not present in the passages. Provide clear and concise summary suitable for query refinement. </aystem propstte.prmmp.1>>>> <aystem <system.prompt.22 prompt.2>
You are given the fields 'claim' and summaayy 1',
Your task is to generate the field x query' that captures the cere semantics the claim retined by insights from 'ummaary.1' to improve retrieval focus.
Formulate a precise and focused query that directly targets missing, ambiguous, or critical aspects suggested by the first summary. Avoid mere paraphrasing; instead, narrow or clarify the claim-based information retrieve step.
relevant cocuments in the next retrieval
The query should be concise, specific, and constructed for effective database search.
Provide the generated query only.
</system.prompt.25 <aystem.prompt.3>
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You are given the fields 'claim', 'contett' (which is the first summaryy, and 'passages' retrieved in the second retrieval step. Your task to produce the field 'summary' as concise (max 3 sentences), integrated synthesis of the second retricval passages, focusing on information that complements or clarifies the first summary regarding the claim.
Summarize the claim, emphasizing new or refined facts emerging from these passages. Avoid redundant or irrelevant information.
relevant evidence supporting refuting the
Your summary should prepare for precise query generation in the next step, highlighting any remaining uncertainties details needed. Ensure factual clarity and avoid repetition. </system.prompt.3>
<aystem.prompt.4t
You are given the fields 'claim', suumaayy.1', and summaayy2" (the prior summaries).
Your task to generate the field 'query' to retrieve the final set of passages relevant to clain verification. Construct a focused and preciso query that targets unresolved aspects or key evidence gaps identified by synthesizing summaryy_l' and 'suumarry22'
The query should be specific, concise, and designed to retrieve non-duplicate icate, highly relevant passages that maximize coverage of the true evi needed to confirm or refute the claim. Avoid broad, vague, redundant queries. Provide only the final retrieval query. //systemmprompt.49
Group Prompts 3: <aystem.prompt.1>
You are given 'claim' and a batch of retrieved 'passages' related it.
Your task is to produce a concise, coherent summary (up to 5 sentences) that synthesizes unique, clain-critical facts directly refuting the claim.
relevant to verifying Aggressively exclude all tangential, auplicate, off-topic, or unrelated information, especially facts involving similarly named or ambiguous entities not related to this claim.
explicitly identify and 1ist any such unrelated or ambiguous entities excluded,
demonstrate clear sambiiggtiio... Explicitly link all entities, numeric values, dates, and geographic details to exact components of the claim for unanbiguous clarity. Organize your summary strictly into three labeled sections:
(1) Facts Supporting the Claim,
(2) Facts Contradicting the Claim, and
(3) Remaining Ambiguitics, Missing Information, or Conflicts. Use precise, focused language that highlights insightful nuances and unresolved gaps.
End with clear, brief statement of the current evidential status: whether the claim is supported, contracicted, or remains unresclved based on this evidence.
Your summary should serve as clean factual foundation free of noise, enabling sharp, targeted query generation for the next retrieval step.
</aystem_prome
persstemmpropptt. 2>>
You receive a 'claim' and the first summary (' d'suunary.l) 11) synthesizing initial evidence.
Generate a single, concise (one or two sentences), highly focused query that targets the single most critical unresolved information gap, ambiguity, or contradiction explicitly identified in 'summary.1,, The query must directly seek evidence to resolve that specific gap verify uncertain claim components, avoiding broad, general, or multi-faceted questions.
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Do not repeat prior queries restate the claim without narrowing focus; aim solely reduce uncertainty effectively.
Formulate a query inmediately actionable for database retrieval, emphasizing precise entity linkages, numeric or date specifics, and exact fact ronfiomations (e.g., "Which actress starring in +Menyy Monster+ also appeared in +One Fercent More Humid+?" "What is the exact eldership subdivision containing Pervalka?").
If 'summary_1' indicates no missing or ambiqucus information, produce query that seeks explicit confirmation or refutation of the claim's main assertion.
Keep queries clear, unanbiguous, and free unnecessary verbosity oz multiple unrelated parts. </aystem.prompt..2
<aystem.prompt..3>
Given a claam', the first summary ('context' 'summary_1"), and batch of passages retrieved in the second retrieval: Produce a concise, coherent summary (up to sentences) synthesizing new, unique, directly relevant facts that were not covered in 'cotext'.
Focus deeply evidence that supports, contradicts, further disambiguates the claim and its key entities. Explicitly link all entities, numeric data, dates, or geographic details to exact claim components.
Rxclude all facts already summarized in 'context' and remove any off-topic information.
tangential, redundant, irrelevant, clearly identity and list any passages or facts associates with unrelated or ambiguous entities that have been excluded, especially if already settled.
Structure the summary into three labeled sections:
(1) Newly Supporting Facts,
(2) Newly Contradicting Facts, and
(3) Persisting Ambiguities, Missing Details, or Conflicts.
End with concise statement specifying which uncertainties remain, prioritizing those that the final query should target for verification.
Ensure this summary provides a precise factual update that sharpens the evidence base for final query formulation.
</aystem.prompt.3>
<aystem prompt 4>
Given a 'claim' and the two prior summaries ('sumaary 1' and 'sumnary generate a single, sharply focused, and precise query (one or two sentences) for the final retrieval step.
Integrate evidence from both summaries critical unresolved, ambiguous, or contradictory aspect the claim. The query must explicitly target these remaining gaps or key verification points, aiming to conclusively confirm or refute the essential relationships or facts.
identify the single most
Avoid repeating previous queries or broad restatements the claim; prioritize novel, decisive retrieval questions.
Prefer queries that articulate explicit entity-to-claim mappings, include precise numeric or geographic cetails, or request direct confirmation negation.
Enaure the query retrieval.
guous, concise, and inmediately usable for
If the summaries indicate no remaining major ambiguities or missing information, produce final query that seeks authoritative adjudication of the claim's overall truth falsehood status. </system.prompt.45
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MATH (GPT-4.1-mini)
Baseline:
<system prompt 1>
Given the fields 'problen', produce the fields 'answer'. </system.prompt.15
Alpha-Bvolve:
<systeemproompt..>
You will be given mathematical problem the input field named 'problen'. Your task is to produce the exact final answer by sclving the pm step-by-step, Follow these instructions carefully: Provide clear, logical, and fully justified step-by-step sclution that guides from the problem statement directly to the final answer. Show all recessary calculations and explanations in sufficient
detail to ensure full correctness and completeness.
The final answer must be explicitly presented at the end, enclesed precisely in LaTeX \boxed() notation, for example: \boxed (andwe<r)))))) where <answer> is the exact final result.
Make certain. that the content inside \boxed() matches exactly the mathematically correct and exact final answer no approximaticn, rounding, ambiquity is allowed.
Do not include any irrelevant information, extraneous commentary, outputs beyond the solution and the final boxed answer. Your response will be evaluated for exact mathematical correctness and clarity. Prioritize accuracy, thoroughness, and clear reasoning to ensure the final answer is fully reliable. </system.prompt.b
C-Evolve:
Group Prompts
caystem.promp..1>
You will be given a math problem as input in the field 'problem', Your task is to produce clear, logically structured, and rigerously accurate solution that directly leads and includes the final answer.
Follow these instructions precisely:
1. Read the problem carefully and ensure full understanding before starting the solution.
2. Provide detailed, step-by-step reascning with all necessary mathematical justifications, formulas, and calculations. Each step must be mathematically valid, complete, and clear.
3. Simplify all expressions fully, unless the problem explicitly instructs otherwise. Perform arithmetic and algebraic operatiens accurately.
4. when units are involved, include them explicitly and consistently throughout the solution, especially in the final answer.
5. If the problem has multiple valid final answers, present all anawers together inside single \boxed() command, separated by commas, without extra spaces.
6. At the end of your solution, write the final answer exactly once its own line, enclosed precisely within a LaTeX \boxed() command. For example: \boxed[Xtext (anxweeerr]() (answer)).
7. The content inside the \boxed() command must exactly match final answer in value, format, and notation I no extra spaces, line breaks, or formatting beyond what the problem requires.
8. Do not include any additional commentary, explanations about the process, or unrelated information. Frovide only self-contained, precise solution followed imnediately by the boxed final answer.
9. Output the full detailed solution immediately followed by the final answer on the next line, with no blank lines or extranecus spaces between them to ensure maximum clarity and correctness. proamtt.prompt1>> Group Prompts 2:
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<aystem-prompt..r
You are given math problem in the input field labeled 'problem'. Your task is to provide complete, clear, and accurate solutien in the output field labeled 'answer". Instructions:
1. Provide well-organized, step-by-step solution. Number or title each step clearly. Every step must include detailed calculatiens, explanations, and logical reasoning to support your conclusions.
2. Use precise mathematical notation throughout, formatting all expressions and equations using lalee within math delimiters.
3. At the end, explicitll state the final answer alone, enclosed exactly within single LaTeX math mode \boxed!1 tag, like this: \boxed/final) answer)
4. Ensure that the content inside \boxed( exactly matches the true final answer in both value and format. Do not approximate unless explicitly required. or explanation after 5. Make your entire reaponse clear, complete, and logically structured, so that the reasoning and final answer can be easily followed and independently verified.
not include any additional text, commentary, boxed final answer.
Strictly follow this format and instructions maximize accuracy and clarity.
Example:
Step 1: Step 2:
**Final answer:..
\boxed[...)
</aysteem.roopt..
Group Prompts
<system.prompt..l You will receive a mathematical problem provided in the input field named 'problem'. Your task is to solve the problem completely and present the exact final answer.
Follow these precise instructions:
Solve the problem thoroughly, showing a clear, togical, and fully justified step-by-step solution that leads directly from the problem statement to the final answer.
Include all recessary intermediate computations, explanations, and reasoning detail, ensuring each step is machematically accurate fully rigorous.
Conclude your response by explicitly stating the final answer exactly once, enclosed in LaTeX \boxed() notation, in this format: (boxed(<answer>)) where <answer> is the mathematically exact final result.
The expression inside \boxed() must be exact: no approximaticns, rounding, or ambiguous forms are allowed.
Do not include any extraneous outputs other than the solution steps
irrelevant information, commentary,
the final boxed answer. Use clear, standard, and consistan consistent mathematical notation and terminology throughout to maximize precision and comprehensicn. Your answer will be evaluated based on exact mathenatical correctness and clarity. Prioritize precision, thoroughness, and lucid exposition to ensure the final answer completely accurate and can be fully trusted,
</system.prompt.1s
10. GPQA (GPn-4.1-mini)
Baseline:
<system prompt 1>
Given the fields 'question', produce the fields 'answer'.
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</system.pronpt..1
Avoha-Bvolve:
<system.prompt...
You will be provided with multiple-choice question labeled 'question' along with answer options ceenifiee by uppercase letters (e.g., A, B, C, D) Your task to carefully analyze the question and select exactly one correct answer from these options.
Instructions:
Read the entire question and all reasoning.
options thoroughly before
Provide a detailed, precise, and step-by-step explanation that clearly and logically leads to the final answer.
Support every step of your reasoning with relevant facts, principles, formulas,
evidence directly tied to the question; de not omit any critical reasoning steps.
Avoid assumptions unless explicitly justified; ensure your reasoning fully supports the chosen answer.
State your final answer exactly corresponding to one of the given options.
single uppercase letter
Enclose the final answer letter within \boxed() tag, for example, (b)xed(A).
Provide only one definitive and confident final answer; do not present multiple possibilities quesses.
Your output should consist solely of your detailed reasoning followed immediately by the final boxed answer. Exclude any additional commentary, headings, or unrelated text. Before submitting, rigorously verify that your explanation and final answer are consistent and accurate to avoid mistakes.
Output Format:
[A comprehersive, step-by-step, logically sound explanation concluding with your final answer]
Final answer: \boxed(<letter))
where <letter> the one selected option (A, B, C, or D)
Strict compliance with these instructions will maximize accuracy, clarity, and ensure your answer precisely matches the correct choice. </system.prompt..>>
C-Evolve:
Group Prompts 1:
<aystem.prompt..l You will receive a multiple-choiee question consisting of a question
followed by answer options labeled with letters (e.g., A, B, C, D). Your task is:
1. Carefully read and fully comprehend the entire question and all answer options.
2. Provide a thorough, clear, and stop-by-step explanation that logically supports why the chosen answer is correct, explicitly addressing why the remaining options incorrect. Include all relevant reasoning, calculations, accuracy.
evidence ensure complete
3. After the explanation, output only the single final answer letter enclosed exactly within LaTeX \boxed() tags, with no extra characters, outside the box. For example: \boxed[B) spaces, punetuation, requirements:
Strictly follow these
Keep your response focused strictly on the step-by-step raticnale and the final boxed answer; do not include any unrelated comments, disclainers,
extraneous information.
The final answer letter inside the \boxed() tag must exactly match the correct choice to maxinize answer accuracy.
Use concise, precise, and unambiguous language throughout your explanation to avoid any confusion.
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Following this format ensures your answer is clear, correct, and perfectly aligned with the question demands.
</system.prompt.i> Group Prompts 2:
tsystem.prompt.1>
You will receive multiple-choice question labeled 'question' together with answer options labeled is to determine the single correct answer by executing a rigorcus, step-by-step reasoning process.
and D. Your objective
Instructions:
Carefully read the entire question all answer choices before starting the analysis.
Provide a detailed, precise, and logically coherent explanation that systematically justifies the correct answer. Each reasoning step must be supported by relevant facts, calculations, the question.
Avoid making unstated assumptions and do not skip essential zeasoning steps; make sure your explanation fully supports your final answer.
final answer, correspending the uppercase letter the provided options.
Select exactly
Enclose your final answer letter in the \boxed() tag, for example, (A)
Do not provide de quesses, partial answers, multiple answers; your output must conelusively identify the single correct choice. Your output should consist solely of the step-by-step explanatien inmediately followed by the final boxed answer. Avoid any extra commentary, headings, or unrelated content.
Double-check the reasoning and final answer to ensure maximum accuracy and alignment with the question. Output Format:
[Your detailed step-by-step reasoning leading to the final answer] Final answer:
D) Follow these instructions exactly ensure clarity, precision, and correctness.
where <letter> is the chosen option (A,
</system.prompt.11
Group Prompts 3:
<aystem prompt 1> Given a multipie-choice question with answer options labeled by letters (A, B, c, D, etc.), your task is as follows:
1. Carefully read the entire question and all provided answer options ensure complete understanding.
2. Provide detailed, step-by-step logical explanation that definitively justifics why the selected answer is correct and all other options are incorrect. Base your reasoning on accuratc facts, relevant caleclations, precise definitions, or domain-specific knowledge directly pertinent 3. Conclude your response with **only** the single final answer letter exactly as shown in the options, enclosed in LaTeX \boxed(] brackets, with no extra text, spaces, punctuation, or symbols. For example: \boxed(C).
the question.
important instructions: Output solely your detailed answer and reasoning, followed immediately by the final boxed answer no headings, preambles, or postambles.
Avoid any vague language, filler words, generalities, off-topic information.
Ensure the final boxed answer exactly matches the correct option letter with zero deviation to maintain maximal accuracy. Precise adherence to this format guarantecs maximum clarity, riger, and correctress in your responses. </aystee.prompt..>>
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M THE USE OF LARGE LANGUAGE MODELS
In this submission, we use LLMs for the following purposes:
Polishing paper writing;
Serving as a component in our algorithm, introduced in Sec.
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Reviews:
[{"review_id": "mlUMFpUUOY", "reviewer": "Reviewer_jKJ8", "summary": "The paper introduces a methodology to evolve groups of prompts optimized for consensus-based prediction. It uses island-based evolution to maintain diversity and introduces a voting score which evaluates each prompt's contribution within groups as a fitness function. The strategy achieves state-of-the-art performance across different tasks like HotpotQA and MATH.", "questions": "-", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper introduces a novel approach for prompt optimization to maintain diversity, which is well motivated by the ensemble learning principles\n- Experiments cover different tasks\n- The problem is well defined and paper well written", "weaknesses": "- I am not entirely clear about the individual contribution, redundancy, and complementarity between the islands, especially from table 3 where the performance are rather close. It makes it rather hard to evaluate the soundness of the underlying decision making and tradeoffs for this optimization pipeline. \n\n- There is a significant computational aspect to such prompt optimization methodology, and the optimization pipeline is rather complex / slightly convoluted overall with hardcoded parameters. When would such computational cost be justified?", "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": "c2iLwsPDNn", "reviewer": "Reviewer_Lrmy", "summary": "This work proposes C-Evolve, an evolutionary framework that does not search for a single best prompt, but instead evolves a group of prompts whose predictions are aggregated via consensus voting. The key technical idea is to use a voting score that reflects each prompt’s contribution to consensus performance across dynamically sampled groups, smoothed by an EMA update to stabilize fitness estimation. An island-based evolution design maintains diversity among prompts, encouraging complementary behavior rather than converging to the same style.\n\nThe method is tested across open-ended tasks like HotpotQA and IFBench and closed-ended tasks including MATH, HoVer, and GPQA. On Qwen3-8B, performance improvements reach +13.85% relative to baseline (e.g., HotpotQA 70.67%) and similar gains apply to GPT-4.1-mini (e.g., MATH 95.33%). Ablations analyze consensus strategies and the necessity of EMA scoring.", "questions": "1. How does this differ fundamentally or committee-based prompt ensembling techniques used in self-consistency? Can you prove that evolving cooperative behavior is better than ensembling individually strong prompts?\n\n2. What happens when you scale the metric dataset ×10? Do the improvements persist or vanish once generalization pressure increases?\n\n3. Can you provide diversity statistics (pairwise prompt edit distance, output disagreement entropy)? Otherwise, the “complementarity” narrative feels speculative.\n\n4. Does the EMA voting score unfairly reward overly conservative prompts that avoid taking risks? What safeguards ensure prompts do not converge to generic evasive behavior?\n\n5.Did you try evolving only one island but selecting the top-k prompts for consensus? Would that match your results with far lower complexity?\n\n6. What is the robustness of the LLM aggregator? Any adversarial test where a minority prompt is correct but consensus fails?\n\n7. How sensitive are results to α? α=0.5 and 0.8 are not a sufficient exploration of learning dynamics—did α>0.9 destabilize selection?\n\n8. Why is the warm-up stage needed at all? Could the system evolve directly under consensus supervision?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The shift from individual optimality to cooperative optimality is a timely and relevant topic.\n\n2.Demonstrates consistent performance improvements on several benchmarks (e.g., HotpotQA 70.67%, MATH 95.33%).\n\n3.Attempts to investigate diversity via multi-island evolution and t-SNE visualization.\n\n4.Some ablations are meaningful (e.g., EMA vs. averaging scoring).", "weaknesses": "1.Using ensemble voting as a fitness signal has been explored in mixture-of-agents literature and existing self-consistency / committee prompting frameworks. The authors do not clarify what is fundamentally new beyond applying majority vote during evolution.\n\n2.The metric set is tiny (only 300 samples per task). Evolution overfits exactly the data used to determine fitness. There is no indication that the final group generalizes beyond the metric distribution.\n\n3.The islands may be stylistic variations of the same base prompt. There’s no quantitative diversity measure—no entropy, no semantic edit distance, no response disagreement statistics.\n\n4.a). Majority vote may amplify systemic bias when mistakes correlate.\n   b). The LLM-selection aggregator can hallucinate entirely new answers.\n   c). No error-type breakdown or trust-calibration analysis.\n\n5.Nearly all “operators” are outsourced to the evolver LLM. How is behavior shaped beyond “please improve”? This makes reproducibility dubious despite claims otherwise.\n\n6. Reporting AlphaEvolve’s grouped performance only after evolution not designed for groups is a questionable comparison. A fair baseline would train AlphaEvolve with a consensus-aware objective.\n\n7. Iteration×islands×groups evaluation produces substantial additional overhead. A ~2–5% gain over an existing strong baseline should be examined against the cost of 3× more calls.\n\n8. Table 3 improvements on Level-5 MATH are marginal (+1–2%). Very small sample sizes lead to statistically weak conclusions.", "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": "ViIz4yE381", "reviewer": "Reviewer_6Sx9", "summary": "The authors propose C-Evolve, a consensus-based evolutionary algorithm for optimizing groups of prompts rather than a single prompt in complex task environments. The method introduces a two-stage evolutionary process (warm-up stage and voting stage) within an island-based evolutionary framework. In the warm-up stage, individual prompts are optimized for standalone performance, while in the voting stage, prompts are evaluated based on their contribution to group consensus through majority voting. The approach demonstrates strong empirical performance across multiple benchmarks (IFBench, HoVer, MATH) and models (Qwen3-8B, GPT-4.1-mini), particularly for complex tasks where single prompts struggle to capture multi-faceted requirements.", "questions": "1. Can you explain why single prompts have \"inherent expressive limitations\" in complex tasks? Is this limitation fundamental to the prompt format or specific to current LLM capabilities?\n2. Why does the warm-up stage contribute positively but modestly compared to the voting stage? What specific types of tasks benefit most from warm-up?\n3. How does the 10% migration rate between islands specifically contribute to performance? Could you provide an ablation study varying this parameter?\n4. In Algorithm 2, what exactly do \"Categorical\" and \"RandomSample\" operations do? Please provide definitions to clarify these critical implementation details.\n5. Could you provide wall-clock time comparisons showing how runtime scales with |G| and N_max? What is the computational overhead of maintaining multiple islands compared to a single large population?\n\nShould the authors address these concerns in their response, I would be willing to increase my recommendation to a stronger accept.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper makes a valuable contribution by shifting focus from optimizing single prompts to optimizing groups of cooperative prompts, which addresses a genuine limitation in complex task scenarios.\n2. The warm-up stage followed by voting stage provides a principled approach to balance individual prompt quality and group collaboration effectiveness, with empirical evidence showing their complementary benefits.\n3. The paper demonstrates consistent improvements over baselines across multiple tasks and models, with particularly notable gains on complex tasks.", "weaknesses": "1. The paper claims that \"single optimal prompt often suffers from inherent expressive limitations\" but lacks rigorous theoretical or empirical evidence. While Table 8 shows consensus improves accuracy (40% -> 49.33% when two prompts agree), this is insufficient to justify the core premise. The authors should provide more systematic error analysis showing complementary failure patterns across different prompts.\n2. Despite Table 9 showing warm-up stage contributes +1.02% absolute accuracy (41.83% -> 42.85%), the paper completely overlooks this stage in the abstract, introduction, and conclusion. Figure 7(a) further demonstrates its importance, yet the paper fails to explain why this initial optimization phase is necessary.\n3. The paper extensively compares with AlphaEvolve in experiments but fails to discuss their fundamental differences in methodology sections. The key distinction should be explicitly stated in Section 1/2/4 rather than merely implied through experimental results.\n4. The paper claims the island model \"maintains population diversity\" but doesn't adequately justify why this approach is superior to simpler alternatives. In warm-up stage, all islands follow identical evolutionary processes (Algorithm 2), raising the question of why parallel islands are better than sequentially running warm-up stage multiple times. Figure 6 shows different islands develop different characteristics, but there's no ablation showing the contribution of the 10% migration rate.\n5. The paper lacks systematic analysis of key parameters that likely impact performance. Island count |G|=3 is used without justification (Figure 7(b) shows EMA α sensitivity but not |G| sensitivity). Population capacity N_max varies across islands (Table 3) but no analysis of its impact. Groups per round n_c affects voting score calculation but no sensitivity analysis is provided.\n6. All results appear based on single runs (no standard deviation reported), which is problematic for stochastic evolutionary algorithms. Evolver LLM details are sparse (Appendix H shows examples but not full implementation). Table 7 shows time per iteration but lacks scaling analysis to assess computational efficiency.\n7. The related work section misses several relevant recent papers including \"Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective\" and \"When large language models meet evolutionary algorithms: Potential enhancements and challenges\". The discussion of AlphaEvolve is particularly superficial given its prominence in the experiments.", "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 (Poster)