Eigen-Agent: Adaptive Multi-Agent Scientific Reasoning with Monitor-Based RAG
Authors: Xiangru Tang
Wanghan Xu
Yujie Wang
Zijie Guo
Daniel Shao
Cixuan Zhang
Ziyi Wang
Lixin Zhang
Guancheng Wan
Zhenfei Yin
Wenlong Zhang
LEI BAI
Philip Torr
Hanrui Wang
Di Jin
Venue: ICLR 2026 Poster (2026)
Keywords: LLM Agents
Reasoning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Large language models (LLMs) have recently shown strong progress on scientific reasoning, yet two major bottlenecks remain. First, explicit retrieval fragments reasoning, imposing a hidden tool tax of extra tokens and steps. Second, multi-agent pipelines often dilute strong solutions by averaging across all candidates. We address these challenges with a unified framework that combines implicit retrieval and structured collaboration. At its foundation, a Monitor-based retrieval module operates at the token level, integrating external knowledge with minimal disruption to reasoning. On top of this substrate, Hierarchical Solution Refinement (HSR) iteratively designates each candidate as an anchor to be repaired by its peers, while Quality-Aware Iterative Reasoning (QAIR) adapts refinement to solution quality. On Humanity’s Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy—the highest reported to date, surpassing the strongest agent baseline by 13.4 points and leading frontier LLMs by up to 18.1 points, while simultaneously reducing token usage by 53.5% and agent steps by 43.7%. Results on SuperGPQA and TRQA confirm robustness across domains. Error analysis shows that reasoning failures and knowledge gaps co-occur in over 85% of cases, while diversity analysis reveals a clear dichotomy: retrieval tasks benefit from solution variety, whereas reasoning tasks favor consensus. Together, these findings demonstrate how implicit augmentation and structured refinement overcome the inefficiencies of explicit tool use and uniform aggregation.

Published as a conference paper at ICLR 2026
EIGEN-AGENT: ADAPTIVE MULTI-AGENT SCIENTIFIC REASONING WITH MONITOR-BASEE RAG
Xiangru Tang Wanghan X Yujie Wang' ZZjie Guo Daniel Shao' Jiapeng Chen Cixuan Zhang', Ziyi Wang', Lixin Zhang'. Guancheng Wan', Wenlong Zhang', Lei Bai', Zhenfei Yin Philip Torr Hanrui Wang", Di Jin
I Yale University Shanghai Jiao Tong University Fudan University
*Wuhan University Tongji University UUniversity of California, Los Angeles UUniversity of Oxford 'Eigen AI
"Shanghai AI Lab
ABSTRACT
Large language models (LLMs) have recently shown strong progress on scientific reasoning, yet two major bottlenecks remain. First, explicit retrieval fragments reasoning, imposing hidden "tool tax" of extra tokens and steps. Second, multi- agent pipelines often dilute strong solutions by averaging across all candidates. We address these challenges with unified framework that combines implicit retrieval foundation, a Monitor-baver retrieval module
and structured collaboration. operates at the token level, integrating external knowledge with minimal disruption reasoning. On top of this substrate, Hierarchical Solution Refinement (HSR) iteratively designates each candidate as an anchor to be repaired by its peers, while Quality-Aware Iterative ReasoRing (QAIR) adapts refinement to solution quality. On Humanitys Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy-the highest reported to date, surpassing the strongest agent baseline by 13.4 points and leading frontier LLMs by up 18.1 points, while simultancously reducing token usage by 53.5% and agent steps by 43.7%. Results on SuperGPQA and TRQA confirm robustness across domains. Error analysis shows that reasoning failures and knowledge gaps co-occur in over 85% of cases, while diversity analysis reveals a clear dichotomy: retrieval tasks benefit from solution variety, whereas reasoning tasks favor consensus. Together, these findings demonstrate how implicit augmentation and structured refinement overcome the inefficiencies of explicit tool use and uniform aggregation. The code is available at https: // httgt/gttt////t///////// com/ tangxiangru/E.ce
INTRODUCTION
Eigen Agent (48.3%)
48.3
+26.8
+25.5
+37.6
+25.5
+13.4
Accurary
40-
34.9
30.2
21.5
22.8
22.8
13.4
15.4
10.7
10.7
10-
6.7
Kimi k2
DeepSeek
DeepSeek Owen3
Gemini 2.5 pro
Claude
GPT
Grok 4
Biomni
OpenAl Sci-Mester Eigen Agent
r1
V3.1
2350
4 Opus
Deep-Rasearch
04 mini
Figure 1: HLE Bio/Chem Gold overall accuracy. On the 149 149-problem HLE Bio/Chem Gold split
@ 1, auto-judged by o3 o3-mini), our system attains 48.3 % accuracy, exceeding the strongest agent baseline (SciMaster) by +13.4 points and leading frontier LLMs by up to +18.1 points.
"Equal contribution.
1 'Coreesonddenc: xiangru. tang@ya edu.

---

Published as a conference paper at ICLR 2026
Query: In population genetics, how can we estimate the effective population size N. from sample polymorphism data?
Error Type 1: Over Confident, No RAG
Error Type 2: RAG Calling Disrupted Reasoning
Answer: Okay, know this is a <population genetics>
Answer: This looks like a <population genetics estimation problem>. think it involves Watterson's estimator, but not entirely sure of the exact formula.
problem. Effective population size N is calculated through formula 0 2N So if 0 can be estimated from polymorphism data, can just plug into the formula and got: N. 8/2
don't want to make mistake...
maybe should call retrieval.
<code>
output watterssn''''' estimator)
</oode>
Okay, so 0 4N But now need to re-align my reasoning: how to connect back to the original origlnal statement? [Execuation Error]
Correctness:
Actually, the correct formula is 0 4N So the answer is [wrong].
Interrupted Thinking
Figure 2: Population genetics case with two failure modes. Left (Error Type I): the model confidently recalls an incorrect formula (0 = 2NN, and derives N. = 0/2gs, yielding the wrong answer. Right (Error Type 2): the model retrieves the correct relation (0 H) via explicit RAG, but the reasoning flow is disrupted and the result is not reintegrated into the original problem, illustrating the tool Our Monitor-based RAG avoids this context suspension by injecting the correct formula directly into the reasoning stream.
Recent advances in large language models have enabled impressive performance on a spectrum of reasoning benchmarks, from general-purpose evaluations such as MMLU [21] and mathematical prob- lem solving [9; 43] to domain-speciff. tasks including ScienceQA [38], MedQA [29], and GPQA [42]. These results indicate that LLMS can already handle factual recall and mid-level reasoning across diverse domains. However, when moving to demanding benchmarks such as Humanity's 's Last Exam (HLE) [39; 47], which targets expert-level biology and chemistry problems, performance de- grades substantially, and systematic failures persist when problems require deep domain knowledge and complex multistep reasoning [7]. Through comprehensive analysis of error patterns across 149 HLE Bio/Chem problems, we identify two fundamental architectural limitations: (I) the fragmentation of logical flow through explicit tool invocation, and (2) the inefficiency of democratic multi-agent collaboration.
Current eurrenel-angmmenee generation systems [3; 19; 33] require explicit interruption to access external knowledge. Each retrieval breaks the reasoning flow: suspending the logical state, formulating queries, processing results, and reconstructing the context. This tool tax compounds quickly: solving population genetics problems requires Watterson estimators requires 8-10 such interruptions, doubling the number of agent steps compared baseline without information retrieval (see Table 3) while reducing coherence. The problem persists in all RAG paradigms: single-round approaches [1; 45] cannot adapt to emerging needs, iterative systems [27; 44] compound interruption costs, and reasoning- aware methods [51; 55] remain bound by explicit invocation, as shown Figure 2.
Simultancously, most current multi-agent systems [4; 52] employ rigid democratic workflows: genera- tion, criticism, synthesis, selection, treating all solutions equally regardless of quality. This contradicts both cognitive science research on hierarchical expert reasoning [8; 32] and observations of scientific collaboration where ideas naturally organize into anchors and support [14]. Our analysis reveals that 92.8% of the failures involve reasoning errors, while 88.7% involve knowledge gaps, with substantial overlap, indicating that these challenges are fundamentally intertwined, as shown in Figure 7.
We present EIGEN-AGENT tool tax through implicit augmentation, operating continuously at the token level to detect knowl- edge gaps via semantic uncertainty, generate contextual queries, and inject information seamlessly. Hierarchical Solution Refinement (HSR) rotates each candidate solution an anchor and applies peer-informed repair from the remaining candidates, allowing structured cross-solution refinement rather than uniform averaging. Quality-Aware Iterative Reasoning (QAIR) replaces fixed workflows with adaptive cycles that respond dynamically quality trajectories and problem characteristics. While
efficient agent framework that unifies Monitor-
RAG eliminates
2

---

Published as a conference paper at ICLR 2026
experiments focus integration within multi-agent reasoning framework, the design of Monitor- based RAG model-agnostic and can in principle incorporated into other reasoning systems without architectural modification.
Our system achieves 48.3% accuracy in Humanity's ' Last Exam Bio/Chem Gold, surpassing SciMas- ter [4] (34.9 (3499) by 13.4 percentage points while reducing token consumption by 53.5% and agent steps by 43.7%. Solution pattern analysis further validates our framework: retrieval tasks benefit from diversity, whereas reasoning tasks favor consensus. These results demonstrate that eliminating the tool tax and embracing hierarchical collahoration enables both superior performance and computational efficiency, with potential implications that might extend beyond scientific reasoning to any domain requiring complex knowledge integration with logical inference.
RELATED WORK
2.1 EVOLUTION RETRIEVAL-AUGMENTER GENERATION
The integration of external knowledge into language model reasoning has evolved through three main paradigms. Single-round RAG systems [19; 25; 33] employ linear pipelines seciiee-retriiee-eeeeeeee
and are effective for factual queries. REALM [19] enabled end-to- end retrieval training, while RAG [33] extended this to knowledge- intensive tasks. More recent variants such as REPLUG [45] and In-Context RALM [41] improve robustness via black-box integration, but they lack adaptivity when knowledge needs emerge mid-reasoning. Iterative RAG introduces retrieval- generation loops for dynamic knowledge acquisition. ITER-RETGHE [44] alternates retrieval and generation, Self-RAG [1] uses self-reflection to decide retrieval, FLARE [27] predicts future content, and DRAGIN [48] updates datastores in real time. These improve grounding but typically incur 35 x more API calls. Reasoning-aware RAG embeds retrieval into reasoning itself. Chain-of-Note [57] produces reading notes, RAT [51] couples retrieval with thought generation, IRCoT [49] interleaves retrieval with chain-of-thon of-thought, and ReAct [54] unifies reasoming with action. While more integrated, they still depend explicit tool calls, fragmenting reasoning and increasing latency.
Table I summarizes these paradigms against our baaeeo approach. Unlike step-level methods that pause to query, Monitor-based RAG operates globally at the token level: it monitors uncertainty signals and implicitly injects evidence into context, reducing retrieval overhead while preserving reasoning continuity. Moreover, its retrieval granularity is finer, enabling more precise and frequent evidence integration without overwhelming the reasoning process.
Table 1: RAG paradigms key capabilities. Single- round RAG is efficient but inadaptable; iterative RAG improves grounding but increases latency; reasoning-aware RAG offers tighter coupling yet still relies explicit calls. Monitor-based RAG integrates evidence implicitly at the token level, improving continuity and efficiency.
System
Triggering
Fine-grained
Continuity
Efficiency
Adaptivity
Single-round RAG
X
Iterative RAG
X
X
Reasoning RAG
X
Monitor-based RAG (Ours)
2.2 MULTI-AGENT REASONING SYSTEMS
Multi-agent frameworks have shown promise through collaborative problem solving, yet many rely on rigid orchestration assumptions.
Democratic collaboration systems treat all agents equally. SciMasters [4] employs solvercriti-
crewriter pipelines with selector over candidate solutions, while LLM-Debate [13]. Debate- Onlyy
When-Necessary [15], and Multi-Agent Debate [35] use argumentative dialogue different scales.
MetaGPT [22] assigns role-based responsibilities. and CAMEL [34] explores autonomous cooperation. Table-Critic [56] extends these ideas structured domains such as tabular reasoning. Such approaches, however, may devote substantial computation to low-quality candidates and do not explicitly capture hierarchical relationships among solutions.
explore non non-linear organizations. Tree-of-Thoughts [53] enables branching exploration with backtracking, Graph-offThoouhhe [2] allows arbitrary reasoning topologies, and Everything- of- of-Thoughts [11] combines multiple reasoning patterns. CoMM [5] introduces multi- path prompting, while HM-RAG [37] couples hierarchical agents with multimodal retrieval. Although these methods capture richer reasoning structures, they lack quality-awar aware adaptation and can rapidly expand search spaces.
Structured reasoning systems
3

---

Published as a conference paper at ICLR 2026
(a) Monitor- based RAG
Reasoning
Monitor
Confusion Detected, triggers
Resume Reasoning
solve this problem, I first know
I know little about Pl... [Original Context
Querier
RAG
onighai-AddtiionnCContar Now that I have more information, I continue my reasoning.
Injector
what is
literaturee Pi 3,14
[Additional
(b) Framework
Proposer
Corrector
Refinement
init
Generate Initial solutions
critically check & Enit by
For each solution
init 2
Apply repair strategies:
Query
LLogic Completion
init
-Numericel Correction
Mathod Replacement
Inii
Expression Refinement
anchor
Solurion Quality Rubric:
Select Best Solution
Logical Reasonable
suggestion
Aroweer Correct
Explanation
High Confidence e
corrector
reference
Answer
Ochervise
repaired
Corwerge
solutions
Ranker
Quality-Aware Iterative Reasoning Hierarchical Solution Refinement (HSR)
(QAIR)
Figure 3: Framework overview. (a) Monitor-based RAG operates globally during reasoning: the Monitor detects insufficiency in the reasoning stream, the Querier generates targeted queries, and the Injector integrates retrieved evidence into context with minimal disruption.
(b) Building on this substrate, the Proposer generates initial candidate solutions. Each candidate is revised individually by the Corrector, which applies local targeted fixes without access to other solutions. The improved candidates are then passed HSR. which enables cross-solution refinement via anchor-rect reffeerree relationships. Finally, QAIR evaluates overall quality and may invoke the Corrector again if needed, while the Ranker selects the strongest solution as the final answer.
Recent advances attempt more flexible adaptive coordination. Veest [6] supports dynamic team assembly, AutoGen [52] enables configurable conversation patterns, and Reflexion [46] incorpo- rates self-improvement signals. Further, evolving orchestration [10], intent-propagation strategies [40], RL-enhanced planning with graph-based policies [26]. and collaborative leaderfollower training [16] highlight the need for adaptive depth and role specialization. Hierarchical orchestration frameworks such AgentOrchestra [58] and HALO [23] exemplify this trend, emphasizing scalable coordination via layered or logic-oriented control.
In contrast, our HSR and QAIR modules introduce hierarchical refinement and quality-driven iteration. Rather than following critic-correcto or debate pipelines [35; 46; 52] that operate under democratic comment-rewrite loops and risk over-investing in weak candidates, HSR organizes solutions into anchor-reference structures for targeted repair, while QAIR applies muality-thresholde guided revisions with early stopping. Crucially, both mechanisms operate on top of monitor-based implicit RAG, enabling hierarchical, quality-aware convergence without suspending the reasoning process, echoing cognitive science findings on expert problem solving [8].
suggestion-
Declarative vs. Procedural Frameworks (DSPy vs. Ours) Declarative frameworks such as DSPy [31] compile tasks into prompl programs and retrieval policies, providing stability but with adaptation largely the stage level. Our approach is procedural and run-time: Monitor, Querier, and Injector operate during inference to adapt reasoning on the fly. This shift from compile-time templates to run-time control enables finer-graine adaptivity and seamless knowledge infusion.
3 METHOD
Overall workflow. AGENT integrates global retrieval, role-based reasoning. and higher- refinement into unified workflow, shown Figure 3 and Algorithm 1. Monitor-based RAG operates globally during reasoning: Monitor detects insufficiency in the reasoning stream, the Querier formulates targeted queries, and the Injector seamlessly integrates retrieved evidence back into context. Based on this substrate, Proposer generates diverse candidate solutions, each which is individually revised by Corrector through targeted local repairs. The refined candidates are then passed to Hierarchical Solution Refinement (HSR), which introduces cross-solution repair through anchor-reference interactions. Next,
4

---

Published as a conference paper at ICLR 2026
Quality-Aware Iterative Reasoning (QAIR) evaluates overall quality and may invoke the corrector again for additional improvement. Finally, Ranker compares candidates and selects the strongest as the final solution. All agents can use web search tool (Serp API [30] by default.
3.1
MOSEDOR-BASED RETRIEVAL-AUGMENTI GENERATION
Our Monitor- based RAG system augments reasoning implicitly, without fragmenting the workflow through explicit tool calls. Instead of forcing the LLM to pause, formulate query, and inject evidence, Monitor continuously inspects the reasoning trace, identifies potential knowledge insufficiencies, and invokes retrieval only when strictly necessary. The construction of the RAG database is shown in Appendix A.I.
3.1.1
MONITOR: DETECTING UNCERTAINTY AND TRIGGERING RETRIEVAL
The Monitor acts sentinel that periodically examines the reasoning trace and determines whether external knowledge is required:
(1, if retrieval is required,
Monitor(contexx) 0, otherwise.
Here, context refers to the partial reasoning sequence. Once insufficiency is detected, the retrieval is immediately triggered. To balance timeliness and efficiency, the Monitor runs in a streaming setup: checks the reasoning fixed intervals of 512 characters with an overlap of 128 characters. This overlapping design ensures that uncertainty markers that cross boundaries are not missed while keeping latency low. Details of RAG Monitor are in Appendix A.5.
3.1.2 QUERIER: IDENTIFYING UNCERTAINTY AND GENERATING TARGETED QUERIES
Triggered by the Monitor, the Querier converts the uncertain fragment into one more retrieval queries: [query,.... .uery,, Querier Querier(context). (context). Here, the Querier maps the reasoning context into one or more concise, contextually appropriate queries. A key requirement of the Querier is to precisely extract the minimal set of keywords that capture the essential uncertainty in the reasoning trace. Depending on the task, this may result in a single keyword or a small collection of terms, each corresponding to a distinct retrieval perspective. The number and specificity of the generated queries directly determine the granularity of retrieval, which in turn controls the trade-off between recall and precision in RAG. By ensuring that queries are as fine- traiegr as possible, the Querier avoids unnecessary expansion of the search space while maximizing the relevance of retrieved evidence.
3.1.3 INJECTOR: EVIDENCE COMPRESSION AND CONTEXTUAL INTEGRATION
The Injector first filters and compresses raw RAG outputs into concise, utility-focused snippets to avoid redundancy and irrelevant noise. Then it rewrites and integrates the selected evidence in the Proposer's reasoning context, ensuring coherence and preserving the natural flow the reasoning narrative. This two-step design allows the knowledge retrieved to improve accuracy without introducing stylistic or structural disruptions: additional context injector(context RAG results).
Figure shows a population genetic problem that requires the Watterson estimator. Baseline I. L.MS exhibit two characteristic errors: (1) confidently recalling the wrong formula (P 2N and deriving an incorrect effective population size, or (2) retrieving the correct formula (0 4N., via explicit RAG but failing to reintegrate it into the original reasoning chain, classic case of rool tax. Our Monitor-based RAG resolves both issues: the Monitor detects semantic uncertainty, the Querier generates targeted query, and the Injector seamlessly injects the correct formula into the reasoning stream, allowing the solution to proceed without disruption and converge to the correct answer, as shown in Figure 4.
3.2 HIERARCHICAL SOLUTION REFINEMENT (HSR)
HSR challenges the assumption that all solutions should contribute equally to the final output. Instead of democratic averaging, HSR establishes structured relationships among solutions that mirror expert collaboration patterns. Let S = {$1. . Sn denote the candidate solutions. Each solution is iteratively designated as the anchor 8;, while the remaining set R = S {si} provides references. This rotation ensures that every solution benefits from peer- infornnfr repair, preventing premature convergence to a single trajectory.
Formally, the process can be described as s/ Refine(s;, R), where Refine(-) denotes the LLM--riven mechanism that applies multidimensional repairs to the anchor. Specifically, logical completion fills missing reasoning steps or implicit assumptions, numerical correction resolves arithmetic inaccuracies, method replacement substitutes stronger strategies for weaker ones, and expression refinement improves
5

---

Published as a conference paper at ICLR 2026
Question:
Start from diploid autosome that contains 5 SNPs from two inbred strains of the organism, and allowing exactly iiiecioo per gamete in each generation (F1 F2 F3), determine the number of possible unique haplotype sequenccs
generation.
Step 1-1: LLM Begins Reasoning:
"We inbred strains each homozygous. generation carries haplotypes from both parents. In F2 and F3, recombination introduce change- but uncertain how many unique haplotypes can arise by F3"
Monitor-based Triggered:
<Detected> "Uncertainty": "Maximum number reccombinationninnuccse change-points
Querier
<Generate> "Query": "How many haplotype recombinstion change-points during single crossover melosis population genetics."
RAG Tool
Retrrieee> "Knowledge": "During meiosis, single crossover introduces at most one new breakpoint per gamete,
successive generations, recombination petterns typically require more crossovers than rounds of melosis..."
Injector
<inject Information Reasoning> (1) most breakpoint per gamete fully atternating haplotypes cannot appear within few generations as F2 FF
Step 1-2: LLM Resumes Reasoning:
fram collactions Legart Contter
"Okay, then haplotypes SNPs can be represented as binary strings. Sequences with change-pole pegee-sints (01010, 10101) cannot by F3, but
Lanes Iroraati,, 18601 cengrizuell
change-points are possible. should compute many binary strings exist."
eet chares, pnietslazzt:
Coding
ceants to teeeterchaage wetetscses) for seg) chegraennst
Executor Output:
Counts by change points: 10 2;
Exacuation
petne valld anplamppe checpppeaneiis, lesivalte))
Total valid haplotypes (0- (0-3changg-pointa).30
Step 2:
Final Summary
"So, among the 32 possible binary haplotypes of SNPs, only change-points are excluded. <answer=30</answer Therefore, F3 can produce 30 oame
Figure 4: Haplotype counting with single crossovers (FI++F3) The Proposer exhibits uncertainty about recombination constraints; Monitor detects insufficiency, Querier issues targeted query, and Injector integrates two retrieved facts. This enables the reasoning to exclude invalid cases and converge on correct count 30 haplotypes.
clarity without altering substance. These dimensions ensure that the weaknesses of the anchor are addressed systematically while preserving its original strengths.
Figure 5 shows pathway reasoning problem where multiple proposers generate partial but inconsistent solutions. Baseline multi-agent synthesis averages across candidates, often propagating contradictions omitting critical intermediates. Instead, HSR designates targeted corrections from reference solutions (e.g., filling missing intermediates or fixing reaction links). This yields coherent and biologically valid pathway, demonstrating how HSR consolidates fragmented contributions into unified solution. QAIR then evaluates the refined set and can terminate the process once the quality stabilizes, avoiding unnecessary additional cycles.
solution the anchor and integrates
3.3
QUALITY-AWARE ITERATIVE REASONING (QAIR)
QAIR introduces an evaluation- control mechanism to refine candidate solutions after the HSR stage. Let S (s'...., s',,
denote the initial set of refined candidate solutions. Each solution s' E S' evaluated by an LLM-based evaluator on three quality dimensions: logic, answer, and explanation,
and textual suggestion for improvement is generated. Each dimension is scored on a scale from 0 5, and the three quality scores are then combined into composite score: q(s') 0.2 Glogie (s')
0.6 (inswer(s') ) + 0.2 "expllmation (s'), where the higher weight on the answer dimension emphasizes the correctness of the final answer while still allowing for logical consistency and explanatory clarity. Candidates meeting the threshold - 3 are retained, while those failing are marked non-passing and passed to the corrector for targeted revision: ă Corrector(s' suggestion(s"))
Fe denote the set of solutions that fail the evaluation round I, and & denote set of solutions evaluated at round f. Iterative refinement continues exclusively on the subset of failed solutions, forming the evaluation set for the next round E = s' E F.), until all solutions pass maximum rounds

---

Published as a conference paper at ICLR 2026
Question:
Tem entomologists collect 500,000 camera trap images (3MP, 10 cm FOV) of pollinators on Solidago altissima. They want to identify all pollinators and count flowers fed on in each image. Which single method would be easiest to process these images for the required data?
Answer Choice
A. EfficientNet,, species (36h train + 13.8h deploy)
D. Manual, 410h
EfficientNet, 500 species (126h train 13.8h deploy)
A
ResNet, 500 species (128h train 11.8h deploy)
F.B++C
Anchor Solution
EfficientNeet A, B) and ResNet( C) are classification models can't identify multiple pollinators and count flowers the same image Option is correct
Reference
C. ResNet
Solutions
500 species
Deployment time: Option C 126 + 13.8 = (139.8 hours), less than A: (49.8 hours) and (139.8 hours) Option C the fastest for deployment
Although EfficientNet generally faster than ResNet in practice, but proceed with the given number. Hence, Option chosen as the most appropriate.
Targeted
Numeric correction: the comparison of deployment time incorrect; Option is the fastest rather than Option C.
Correctness:
Method Correction: Classification models such as EfficientNet and ResNet cannot
simultaneously recognize multiple insects. Counting requires either detection model (like
Faster R-CNN, YOLO) or density map approach. So, only Option remains correct.
Logic Correction: Time cannot be the only criterion, even though C has fewer hours.
Clarify Correction: Even if given times suggest C is fastest, it cannot be considered solution because the fundamental requirement. Thus, the correct answer under the single-
method rule is Option D (manual).
Figure 5: Illustrative example: HSR. The system rotates anchors among candidate solutions and integrates targeted corrections from references (e.g., fixing arithmetic mistakes, filling missing steps). Instead of averaging inconsistent candidates, HSR applies targeted improvements yield coherent final answer.
Taax is reached. By coupling structured quality assessment with suggestion-driven repair and avoiding re-evaluation of already validated candidates, QAIR efficiently converges toward high-quality solution set while maintaining logical consistency, answer correctness, and explanatory clarity.
4
EXPERIMENTS
Table 2: Benchmark comparison under matched protocol. HIL
Bio/Chem (149 problems; 03-mini judge), SuperGPQA Biology (hard split), and TRQA Literature (multiple-choice).
4.1
EXPERIMENTAL SETUP
We evaluate approach Humanity's 's Last Exam (HLE) Bio/Chem Gold [47]', compris- ing 149 graduate-level problems in biology, medicine, and chem- istry. HLE Bio/Chem Gold sub- set was manually curated and cor- rected by domain experts to en- sure label fidelity. Additionally, we test on 92 hard-difficulty prob- lems from SuperGPQA [12] Biol- ogy and 172 problems from TRQA Litcrature [59]. Our framework uses DeepSeck-V3. V3.1 [36] the base model with temperature 0.5 and 64K token limit. Follow- ing HLE protocol, we employ o3-mini for automated evaluation (Sec Appendix A.3). Beyond accuracy, we log total generated tokens and agent steps in the ablation experiments, quantitative measures of
Model
HI.E Bin/Chem
SuperGPQQ Hard
TRQA
Rase Models
Kimi K2
6.71
48.91
38.37
DeepSesk
Claude Opus 4.1
21.48
63.04
42.4 42.44
Gemini 2.5 Pro GPTS
18.79
65.22
45.93
22.82
61.96
50.58
Grok-4
30.20
66.30
46.51
Agent Systems
StiMaster (GPT 4.1) [4]
9.45
19,78
47.67
Autogen (GPT 4.1) [52]
7.38
29.35
$1.74
OpenAl Deep Reseucc (o4-mini)
22.82
39.13
Biomni (GIP 1) [24]
10:74
SciMaster (DeupSock V3.1)
34,92
66.30
51.74
EIGEN-AGENT (DeepSeck V3.1, Puss(
48.30
69.57
54.65
EIGEN-AGENT (DeepSeck V3.1. Pascee
61.74
78.26
79.07
the tool tax.
4.2 MAIN RESULTS
In the HLE Bio / Chem dataset, our system achieves 48.3 % accuracy (Pass @ l), substantially outper- forming the strongest baseline Grok-4 (30.2%) by nearly 18 absolute points and more than doubling
/
cie-
7

---

Published as a conference paper at ICLR 2026
the performance of general purpose models such GPT-5 (22.8%) and Claude Opus 4.1 (21.5%). This margin is particularly notable, given that HLE problems require domain-specific reasoning rather than surface-level recall.
In SuperGPQA hard biology, method reaches 69.6%, exceeding all competing large models. The improvement is consistent across question categories, suggesting that our framework not only boosts correctness but also improves robustness on especially challenging scientific queries.
Finally, in TRQA benchmark, which emphasizes Information retrieval, integration and reasoning, our method obtains 54.7% % Pass@1 1, surpassing both Grok-4 (46.5%) and Gemini 2.5 Pro (45.9%). Furthermore, under the more permissive Pass@5. counting success if any of five attempts is correctaccuracy rises to 79.1%. indicating robustness under a best-of-N setting.
Figure 6: Comparison of retrieval backends within Monitor-based RAG.
40
30
Acourasy
Together, these results establish the advantage of our design in three heterogeneous benchmarks: biomedical, chemical and medical, demonstrating not only raw accuracy gains, but also improved adaptability across domains (Table 2). For more results of baseline models, please refer to Appendix A.
20
HippoRGG LighTRAG
Within the Monitor-based RAG framework, we experimented
RAG Methods
with four retrieval backends: Vanilla [17]. Vanna, HippoRAG [28], and LightRAG [18]. Among these, HippoRAG achieved the most consistent gains when coupled with uncertainty detection. We attribute this its finer-graie aner-grained retrieval and graph- graph-structured indexing. which better capture relevant context fragments without overw herinng the reasoning stream. Based on these results, we adopt HippoRAG as the default retrieval backend in our Monitor module (Figure 6).
4.3 COMPONENT ANALYSIS
To understand the contribution of each architectural component, we performed systematic analysis on the full HLE Bio/Chem benchmark (149 problems), considering both incremental build-up and component ablation (Table 3).
The baseline configuration uses five parallel Proposers with access to a generic web search tool but without any paper retrieval (no RAG). The Explicit RAG setting adds scientific paper database queried via embedding- baaedd similarity. Unless otherwise noted, all settings use the same five- five-Proposer architecture with CriticCorrector refinement and Ranker selection.
The baseline system without external knowledge achieves 25.3% accuracy, underscoring the limitations of parametric knowledge alone for graduate-leve science problems. Adding an explicit paper database improves accuracy to 1,4, h but at the cost of a sharp increase in workflow iterations (from 43.4 to 94.8). This reflects the high overhead of explicit retrieval: each tool call suspends reasoning, requires query formulation, and forces reintegration of results, fragmenting what should be continuous reasoning flow. While the first and compound this "tool tax." "
retrievals may be helpful, repeated interruptions often add little value
Our Monitor-based RAG mitigates this overhead through implicit augmen- tation. By continuously monitoring generation and injecting knowledge only when necessary, it reduces token consumption by more than half (from 470.6K to 218.4K) and cuts workflow iterations nearly in half (from 94.8 to 51.3), while maintaining competitive accuracy (34.5%). Adding the Querier improves query precision, leading
OOG
200
OOS
15.40%
BO
92.78%
88.66%
Reasoning Pracess Error 92.78%
Execution & Adherence Error 13.40%
Knowledge Application Error 88.66%
Comprehension Error 9.28%
Figure 7: Error type distribution. Analysis of incorrect solution logs shows reasoning- and knowledge-relane errors as dominant. Note that a single problem may involve multiple error types, so percentages do not sum to 100.
modest gain 36.8% The limited margin compared to Monitor alone suggests that the primary bottleneck lies not in query formation but in evidence integration, which is addressed by the Injector. With the Injector, accuracy rises further to 40.3% with minimal additional overhead.
Hierarchical Solution Refinement (HSR) then contributes complementary gains, raising accuracy to 43.7%. Instead of naive aggregation, HSR leverages anchorreference interactions to apply targeted
8

---

Published as a conference paper at ICLR 2026
Table 3: Component analysis from two perspectives on the full HLE Bio/Chem benchmark (149 problems), (a) Incremental build-up: modules are added one by one. (b) Component ablation: each module is removed from the full system. The baseline configuration uses five parallel Proposers with web search but without external paper retrieval (no RAG). Steps = agent-level workflow iterations (not token-level reasoning).
Incremental build-up
Compunent
Configuration
Accuracy (%)
Tokens
Steps
Configuration
Accuracy (5)
Tokens (K)
Steps
Boseline (no ext, knowl & RACG)
25,3
483.6
43.4
Pell system
48.3
218,9
53.4
Papers (Faplichi RAG)
41.4
-Menitor, Injecter)
48.5
46
95.1
Monitor only
34.5
218.4
Querier
234.1
53.1
Moninor Querier
3688
213.0
51.7
Injector
44.7
202.5
52.1
Moniter Querier + Injector
40.1
239.5
53.1
HSR
44.8
234.1
53.5
Querier Injector
43.7
QAR
214.0
Montoo Querier e Injector HSR
QAIR
48.3
218.9
53.4
repairs, focusing revisions where they matter most (c.g.. filling missing reasoning steps or correcting arithmetic). This adds some extra reasoning steps but yields proportionally higher accuracy.
Finally, Quality-Awarc Iterative Reasoning (QAIR) builds on HSR by selectively invoking the Corrector when evaluation indicates further refinement is necessary. This yields the best overall result in the incremental sequence: 48.3% accuracy with 218.9K tokens and 53.4 iterations. Although QAIR introduces slight additional overhead, it ensures that every revision contributes meaningfully, preventing uncontrolled exploration redundant cycles.
The ablation analysis further validates these findings: removing the Monitor results in significant increase in the number of tokens and agent steps; and omitting HSR or QAIR lowers final performance to 44.8% and 43.7%, respectively. Together, these results show that Monitor-based RAG reduces the tool tax, HSR provides structured cross-solution repair, and QAIR ensures convergence through selective correction. Their combination achieves both state-of-the ahrt accuracy and controlled computation.
5
ANALYSIS
5.1 ERROR TYPE DISTRIBUTION
Analysis of failed prob-
Condenintyy Actumey by Quertion Type
Aesening Trom 9323
Internteon Rdvtovel (1-000))
lems reveals two dominant error modes: reasoning process errors (92.78%)
and knowledge applica- tion errors (88.66%) as shown in Figure 7. These frequently co-occur, sug- gesting that successful sci- entific reasoning requires seamless integration of do- main knowledge with log- ical inference. Execution errors (13.40%) and com- prehension errors (9. 9.28%) comparatively rare, in- dicating that the primary challenge lies not in un- derstanding problems or following instructions, but in maintaining coherent reasoning while access- ing relevant knowledge. The strong overlap also suggests interdependence:
Jwang wabainnggg Stite
Figure 8: Diversity vs. consensus. Task-dependent effect of solution diversity: retrieval tasks benefit from variety, while reasoning tasks benefit from agree- ment. The horizontal axis reports the average pairwise consistency score among Proposers, computed by an L..M-based judge that evaluates semantic overlap between answers
0-1 scale. The vertical axis shows the average accuracy score, also judged by an L.LM, which rates the degree correctness of each an- swer relative to ground truth on a continuous 0-1 scale (rather than a binary 0/1). This continuous evaluation enables us to capture fine- ene-grained trends between diversity and correctness across tasks. The fitted trend lines further highlight the contrast: retrieval tasks show relatively flat slope (ss 0.36)), whereas reasoning tasks exhibit a much steeper positive slope (#s 0.851), indicating a stronger dependence on consensus.
missing knowledge often manifests as faulty reasoning steps, and disrupted reasoning in turn prevents effective incorporation retrieved facts, For more examples of these different errors, see Appendix A.6.
5.2 DIVERSITY VS. CONSENSUS IN MULTI-AGENT SOLUTIONS
Our framework employs multiple parallel Proposers to generate candidate solutions and utilizes a Ranker to select the final answer. A natural assumption is that higher agreement among Proposers

---

Published as a conference paper at ICLR 2026
should correlate with higher accuracy. However, our analysis reveals more nuanced picture: the relationship between solution diversity and correctness depends strongly on problem type.
To investigate this, we divide the benchmark into two categories: information retrieval tasks, which rely heavily external knowledge, and reasoning tasks, which require longer chains of inference. For each problem, we measure the level of agreement among Proposers and evaluate how correlates with accuracy. Both metrics are scored continuously by an LLM judge on [0, 1] scale: consistency reflects the pairwise agreement among candidate answers, while accuracy measures the graded alignment between a candidate and the ground-truth solution (see Appendix A.3). This continuous evaluation enables fine-grained correlation analysis beyond binary conrectness. As shown in Fig retrieval tasks benefit from diversity (low agreement), whereas reasoning tasks benefit from consensus (high agreement), with correlation slopes of 0. 369 and C 851, respectively.
This dichotomy suggests that different ranking strategies optimal for different task types. In retrieval- heavy settings, the Ranker should preserve diversity and aggregate complementary perspectives, whereas in reasoning-heavy tasks, it should prioritize high-consensus answers as indicators of reliability. These observations highlight the complementary roles of HSR and QAIR, which operationalize the transition from diversity to consensus in a task-adaptive manner.
5.3 TOoL TAX QUANTIFICATION
The computational burden of explicit tool invocation ex- tends beyond simple latency. As shown in Table 3, the explicit RAG baseline (with Proposers equipped with paper retrieval and web search) more than doubles agent-level workflow iterations compared to the no-IR setting (43.4 + 94.8). This quantifies a hidden tool tax from context switching between reasoning and retrieval modes: each call requires the system to pause the evolving chain of thought, formulate a query, process external results, and then recon- struct the local context before continuingfragmeningg what should be continuous inference process.
Eigen-Agent Basellne+RAG
Accuracy Increase
Token: 216.9K
Accursty: 48.3%
0.50
Tool Tak
Accuracy
0.45
Token Decrease
Fig. visualizes this trade-off. Explicit RAG produces substantially longer traces without commensurate gains, whereas our Monitor-based RAG maintains concise, inter- pretable reasoning by injecting only the evidence that is needed, precisely when it is needed. Operating implicitly at generation time, it delivers comparable knowledge augmen- tation with markedly fewer tokens and iterations, avoiding repeated context suspensions.
Tolknn 41.4%
0.3g
Consumption
Figure 9: Quantifying the tool tax. Com- parison of accuracy and coherence relative compute cost, showing the overhead of explicit retrieval vs. implicit augmentation. Note that in this analysis, the baseline refers to the explicit RAG configuration (i.c., Pro- posers equipped with paper retrieval and web search), which represents the standard setup most existing agent systems.
More broadly, these findings argue for implicit augmenta- tion and adaptive tool policies in agent design: systems should not treat all retrieval calls equally, but modulate re- trieval frequency and depth based on emerging uncertainty, problem structure, and expected utilitypreserving continuity of reasoning while still accessing external knowledge when it truly helps.
6 CONCLUSION
Our experiments validate three key architectural innovations in EIGEN-AGENT: First, implicit knowl- edge augmentation through Monitor- RAG substantially reduces explicit retrieval overhead while preserving reasoning coherence. Second, HSR improves over uniform multi-agent aggregation by introducing structured mnchor-refrrence relationships. Third, QAIR adaptively balances exploration and early stopping, achieving an effective trade-off' between diversity and consensus. On HLE Bio/Chem, EIGEN-AGENT reaches 48.3% accuracy under compute- matthed settings. with a 53.5% token reduction, showing that targeted architectural design can enhance both effectiveness and efficiency. By integrating external knowledge with minimal disruption to reasoning flow, our framework addresses key limitations of prior approaches in complex scientific problem solving. Future work will extend these principles to additional scientific domains, assess robustness and transferability, and explore integration into broader scientific workflows.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our study aims to improve the scientific reasoning capabilities of large language models (LLMs) by introducing a unified framework that combines implicit retrieval and hierarchical collaboration. All datasets used in this work, including Humanity's Last Exam (HLE), SuperGPQA, and TRQA, are publicly available under open licenses. No private, sensitive, or human-identifiable data are involved. All annotations, where applicable, are derived from public benchmarks generated using synthetic processes, ensuring that no ethical concerns regarding data privacy or misuse arise. The broader societal impact of this research lies in its potential to enhance scientific reasoning and complex problem- solving abilities in AI systems, which be applied to fields such education, scientific discovery, and decision support. Care has been taken to avoid overstating capabilities or drawing misleading conclusions, and we encourage further research to validate our findings across other high-stakes domains.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide detailed descriptions of our dataset preprocessing procedures, agent prompting strategies, and iterative refinement workflow in the appendix. These include full pipeline configurations and experimental setups required to reproduce the reported results with high fidelity.
REFERENCES
[]] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection. 2024.
[2] Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pp. 17682-17690, 2024.
[3] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptist Lespiau. Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference machine learning, pp.
PMLR, 2022.
[4] Jingyi Chai, Shuo Tang, Rui Ye, Yuwen Du, Xinyu Zhu, Mengcheng Zhou, Yanfeng Wang, Yuzhi Zhang. Linfeng Zhang, Siheng Chen, et al. Scimaster: Towards general-purpose scientific ai agents, part i. x-master as foundation: Can we lead on humanity's last exam? arXiv preprint arXiv:2507.000 204,2244,,,, 2025.
[5] Pei Chen, Boran Han, and Shuai Zhang. Comm: Collaborative multi multi-agent, multi-reasoning path prompting for complex problem solving, arXiv preprint arXiv:2404.1 17729, 2024.
[6] Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents. arXiv preprint arXiv:23088. 10848, 2(4):6, 2023.
[7] Ziru Chen, Shijic Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, al. Scienceagentbench: Toward rigorous assessment of language agents for data-driven scientific discovery. arXiv preprint arXiv:2410.05000, 2024.
[8] Michelene TH Chi, Paul Feltovich, and Robert Glaser. Categorization and representation of physics problems by experts and novices. Cognitive science, 5(2): 1981.
[9] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Rejichiro Nakano, al. Training verifiers to solve math word problems. arXiv preprint 1rXiv:2110.14100 2021.
[10] Yufan Dang et al. Multi-agent collaboration via evolving orchestration. arXiv preprint 1.Xiv:2505.1159)) 2025.
[1I] Ruomeng Ding, Chaoyun Zhang, Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. Everything of thoughts: Defying the law of penrose triangle for thought generation. arXiv preprint arXiv:2311.0422 1.424,, 2023.
11

---

Published as a conference paper at ICLR 2026
[12] Xinrun Du, Yifan Yao, Kaijing Ma, Bingli Wang. Tianyu Zheng, King Zhu, Minghao Liu, Yiming Liang, Xiaolong Jin, Zhenlin Wei, et al. Supergpqa: Scaling Ilm evaluation across 285 graduate disciplines. arXiv preprint arXiv:2502.1433, 2025.
[13] Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. In Forty-first International Conference on Machine Learning, 2023.
[14] Kevin Dunbar. How scientists think: On-line creativity and conceptual change in science. 1997.
[15] Sugyeong Eo, Hyeonseok Moon, Evelyn Hayoon Zi. Chanjun Park, and Heuiseok Lim. Debate only when necessary: Adaptive multiagent collaboration for efficient Ilm reasoning, arXiv preprint uxXiv:2504.05047 2025.
[16] Andrew Estornell al. How to train a leader: Hierarchical reasoning in multi-agent llms. arXiv preprint arXiv:2507.0800 08960, 2025.
[17] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. survey. arXiv preprint arXiv:2312.10997, 2(1), 2023.
generation for large language models: A
[18] Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. Lightrag: Simple and fast retrieval- auugeetl- generation. arXiv preprint arXiv:2410.05779, 2024.
[19] Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval aug- mented language model pre-training. In International conference on machine learning, pp. 3929-3938. PMLR, 2020.
[20] Conghui He, Wei Li, Zhenjiang Jin, Chao Xu, Bin Wang, and Dahua Lin. Opendatalab: Em- powering general artificial intelligence with open datasets. arXiv preprint 2rXiv:2407.13773 2024.
[21] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint wrXiv:2009.03300 2020.
[22] Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang. Steven Ka Shing Yau, Zijuan Lin, et al. Metagpt: Meta programming for multi-agent collaborative framework. International Conference on Learning Representations, ICLR, 2024.
[23] Zhipeng Hou, Junyi Tang, and Vipeng Wang. Halo: Hierarchical autonomous logic-oriented orchestration for multi-agent Ilm systems. arXiv preprint arXiv:2505.13516, 2025.
[24] Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Yusuf Roohani, Ryan Li, Lin Qiu, Gavin Li, Junze Zhang, et al. Biomni: A general- purpose biomedical ai agent. biorxiv, 2025.
[25] Gautier Izacard and Edouard Grave. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.0/282. 2020.
[26] Ziqi Jia, Junjie Li, Xiaoyang Qu, and Jianzong Wang. Enhancing multi-agent systems via reinforce- ment learning with lim-based planner and graph- based policy. arXiv preprint arXiv:2503. 10049 2025.
[27] Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi- Yu, Yiming Yang. Jamie Callan, and Graham Neubig. Active retrieval augmented generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7969-7992, 2023.
[28] Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. Advances in Neural Information Processing Systems, 37:59532-59569, 2024.
[29] Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. What discase does this patient have? a large-scate scale open domain question answering dataset from medical exams. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)), pp. 2397-2407, 2021.
12

---

Published as a conference paper at ICLR 2026
[30] Nils Kautto Ernberg. Analyzing google serp: Swedish search queries, 2019.
[31] Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vard- hamanan, Saiful Haq, Ashutosh Sharma, Thomas Joshi, Hanna Moazam, et al. Dspy: Compiling declarative language model calls into self-improving pipelines, arXiv preprint arXiv:2310.03714, 2023.
[32] Jill Larkin, John McDermott, Dorothea P Simon, and Herbert A Simon. Expert and novice performance in solving physics problems. Science, 208(4450):1335 1342, 1980.
[33] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmenntd generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459-999 99744, 2020.
[34] Guohao Li, Hasan Hammoud, Hani Itani. Dmitrii Khizbullin, and Bernard Ghanem. Camel: Communicative agents for" mind" exploration of large language model society. Advances in 2023.
Neural Information Processing Systems,
[35] Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang. Shuming Shi, and Zhaopeng Tu. Encouraging divergent thinking in large language models through multi- agent debate. arXiv preprint arXiv:2305. 19118, 2023.
[36] 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.
[37] Pei Liu et al. Hm-rag: Hierarchical multi-agent multimodal retrieval augmented generation. arXiv preprint arXiv:2504. 0t.55044..3550 12330, 2025.
[38] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai- Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35: 2507-2521, 2022.
[39] Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, et al. Humanity's 's last exam. arXiv preprint arXiv:2501. /4249, 2025.
[40] Xihe Qiu, Haoyu Wang, Xiaoyu Tan, al. Towards collaborative intelligence: Propagating intentions and reasoning for multi-agent coordination with large language models, arXiv preprint arXiv:2407.12533 12532, 2024.
[41] Ori Ram, Yoav Levine, Itay Dalmedigos. Dor Muhlgay, Amnon Shashua, Kevin Leyton- Brown, and Yoav Shoham. In-context retrieval-augmenneed language models. Transactions of the Associa- for Computational Linguistics, 11311,113 2023.
[42] 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-proot q&a benchmark. First Conference on Language Modeling, 2024.
[43] David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. Analysing mathematical reasoning abilities of neural models. In International Conference on Learning Representations (ICLR), 2019.
[44] Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. Enhancing retrieval-augmenieed large language models with iterative eetrieval-generation synergy. arXiv preprint arXiv:2305. 15294, 2023.
[45] Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. Replug: Retrieval-augmentee black-box language models. arXiv preprint arXiv:230I. 12652, 2023.
[46] Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Re- flexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36:8634-866 86522, 2023.
13

---

Published as a conference paper at ICLR 2026
[47] Michael Skarlinski, Jon Laurent, Albert Bou, and Andrew White. About 30% of humanitys last exam chemistry/biology answers are likely wrong. https: / /www. future org/ research anno www.uuncemeettt//llleeeeeeeeeeee
July 2025, Accessed: 2025-09-23.
[48] Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. Dragin: dynamic retrieval augmented generation based on the information needs of large language models. arXiv preprint 0rXi::2433.1088/ 2024.
[49] Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with preprint arXiv:2212. 10509, 2022.
reasoning for chowledge-intensiig multi-step questions. arXiv
[50] Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, et al. Mineru: An open-source solution for precise document content extraction. arXiv preprint arXiv:2409. arxIv:2409.1883, 18839, 2024.
[5]] Zihao Wang. Anji Liu, Haowei Lin, Jiaqi Li, Xiaojian Ma, and Yitao Liang. Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation. arXiv preprint arXiv:2403.055 13, 2024.
[52] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. Autogen: Enabling next-gen Ilm applications via multi-agent conversations. In First Conference on Language Modeling. 2024.
[53] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, Advances in neural information processing systems, 36:1 1809-118222 2023.
[54] Shunyu Yao. Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. International Conference on Learning Representations (ICLR), 2023.
[55] Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. Making retrieval-augmenned language models robust to irrelevant context. arXiv preprint arXiv:2310.0/558, 2023.
[56] Peiying Yu. Guoxin Chen, and Jingjing Wang. Table-critic: A multi-agent framework for collaborative criticism and refinement in table reasoning. arXiv preprint arXiv:2502. 200:255..//7999 1799, 2025.
[57] Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. Chain-of-note: Enhancing robustness in arXiv:2311.09210. 2023.
language models. arXiv preprint
[58] Wentao Zhang al. Agentorchestra: A hierarchical multi-agent framework for general-purpose task solving. arXiv preprint arXiv:2506. 12508, 2025.
[59] Zhongyue Zhang, Zijie Qiu, Yingcheng Wu, Shuya Li, Dingyan Wang, Zhuomin Zhou, Duo An, Yuhan Chen, Yu Li, Yongbo Wang, et al. Origene: A self-evolving virtual disease biologist automating therapeutic target discovery. bioRxiv, pp. 2025-06, 2025.
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 RAG DATABASE CONSTRUCTION
To construct the RAG dataset, sourced 10,876 PDF papers in biology and chemistry from Open-
Datal [20]. We then converted these PDFs to plain text using MinerU [50] to ensure downstream readability and analyzability.
Because the corpus spans many topics, designed two-stage semantic filtering pipeline focus the final corpus on biology- and chemistry-centric research.
We defined set of positive keywords to capture target research areas, and in parallel, we curated negative keywords to exclude off-topic tangential materials, demonstrated in Figure A1.
Positive and Negative Keywords
Positive Keywords
Biology: biology, DNA Replication, RNA Transcription, Protein Synthesis, Gene Editing, Viral Infection, Cell Signaling, Nucleic Acid Probes, Genomic Sequenc- ing. Transgenic Technology. Immune Response, Biomarkers, Cell Culture, CRISPR Technology, Viral Vectors, RNA Interference, Gene Expression Regulation, Cell Differ- entiation, Metabolic Pathways, Apoptosis, Bioinformatics
Chemistry: chemistry, Organic Synthesis, Inorganic Chemistry, Catalysis, Poly- mer Chemistry, Spectroscopy, Crystallography, Chemical Kinetics, Thermodynamics, Electrochemistry, Quantum Chemistry
Negative Keywords
Cosmetics, Food Additives, Drug Advertising, Environmental Pollu- tion, Ecological Balance, Medical Ethics, Social Sciences, Psychology, Nutrition, Educational Methods
Non-biology:
Household Chemicals, Industrial Wastewater, Pesticide Residues, Fertilizer Application, Chemical Engineering Safety, Petrochemical Produc- tion
Non-academic chemistry:
Figure Al: Positive and Negative Keywords.
This design concentrates positives on fundamental research fronts (e.g., gene editing, molecular signaling, organic synthesis, spectroscopy. thermodynamics) while negatives cover applied or peripheral themes (e-g., chemical production, pesticide residues, environmental pollution), improving separation of target papers from irrelevant content.
For filtering, we encoded each paper's title and abstract with pretrained Transformer and computed cosine similarities against the positive and negative keyword sets. Papers were retained only
cos Eppper, Epusitive) 0.2, cos Eppeer Enegative) < 0.1,
where Epaper denotes the vector representation paper's 's title and abstract, and Epositve/ Eneaatve denote aggregate vectors of the positive/negative keyword sets. This step effectively removed content unrelated biology and chemistry. The post-filter distribution is summarized in Table Al.
Table A1: Paper Classification Statistics by Domain (Side-by-Side)
Biology Category (n=2029)
% of Domain
Chemistry Category (n=359)
% of Domain
Molecular and Cell Biology (777) Immunology and Microbiology (482)
13.399
Organic Chemistry (172)
47,91
23.76%
Physical Chemistry (71)
19.78%
Geneties, Genomics Computa-
20.26%
Materials Chemistry (68)
18.94%
tion (411)
Neuroscience (205)
10.11%
Analytical Chemistry (37)
10.31%
Ecology and Evolution (149)
7.35%
Inorganic Chemistry (11)
3.06%
15

---

Published as a conference paper at ICLR 2026
Overall Summary
Total Biology Papers: 2029 (100%)
Total Chemistry Papers: 359 (100%)
Total Papers: 2388 (100%)
After semantic filtering, we used large language model (LLM) to extract structured text suitable for Auggeetee Generation (RAG). We designed a paper-specific prompt that guides the LLM to segment each paper into retrievable knowledge units (e.g., definitions, methods, experimental results, discussion) with consistent formatting across papers.
Prompt of RAG Bullet-Pode Summarization
Role: You are an information synthesis assistant.
General Rules:
Use ONLY the paper content provided below. No outside knowledge or invented facts.
Do NOT include verbatim quotes, citations, or page references.
The final output MUST be single CSV code block with rows containing ONLY synthesized, self-contained bullet-point summaries for RAG.
Complete Paper Content:
[paper.content]
Objective: Read the entire paper content and internally construct self-contained knowledge paragraphs. Then derive standalone, self-contained bullet-point summaries from those paragraphs for retrieval-augmenne
generation (RAG). The intermediate knowledge paragraphs internal step and MUST NOT be printed in the final output.
Process:
Phase Internal Knowledge Paragraphs (DO NOT OUTPUT):
After reading the full content, synthesize set of self-contained knowledge paragraphs.
Each paragraph must be strictly grounded in the provided text, define acronyms upon first use, include concrete details when available (lasks, datasets, sample sizes, metrics, effect sizes, confidence intervals, ablations, baselines, hyperpa- rameters, assumptions, limitations), written in neutral third-person factual style, and able to stand alone without context from other paragraphs.
Phase RAG Bullet-Point Summaries (FINAL OUTPUT ONLY):
Produce around 3 bullet points in total.
Each bullet must be self-contained, concise (13 sentences), define acronyms upon first use, include concrete quantitative or methodological details when available, state scope/assumpiooss/iiiaaa factual style.
when given, and use neutral third-person
Output Format (CSV ONLY):
Output EXACTLY ONE CSV code block and NOTHING ELSE.
Header MUST be: name, year, locator, topic, quote
For EACH bullet point, create ONE row with:
name = WWWNTHSSSIZADD.OOOO SOMMAR
year N/A
locator = N/A
topic N/A
quote the bullets full self-contained text (escape quotes as needed; no line breaks inside cell)
Do NOT include the intermediate knowledge paragraphs the output.
Do NOT add extra columns or any prose outside the CSV block.
Begin: Output only the CSV code block.
16

---

Published as a conference paper at ICLR 2026
Through this pipeline, obtained topic-focused, structurally consistent research corpus that provides high-quality knowledge support for downstream RAG systems.
A.2
AGENT PROMPT
A.2.1
REFINEMENT
The following shows the prompt we in the HSR stage.
Prompt of Refinement
Assistant Prefix Prompt
<think>
Okay, I will answer user's problem by deep reascning together with writing python code in <code></code> format. I should review and check the solution from student first with web functions to identify errors if exist, then present my so solution and answer. For example
1. If want to use the function of web.search (keywords), will say:
keywords....
resultsswwe. (keywords)
print (results)
2. If I want to use the function of web.parse (link, query), will say:
link=...
query-.... (link,, print (results)
results=web.parse (lirk, query)
3. If want to use the function of sarchhloccl..dccmeets (query) will say:
query="..."
(query)
print (ressulss)
4. If want to do computation, I will write code for accurate result:
123
456
print (a+b)
Now, lez me analyze the user's question. </think>
User Prompt Problem {query}
Anchor Solution
(anchor.solution)
Student I's Solution (reference.1)
Student 2's Solution (reference.2)
Student 3's Solution (reference_3)
Student 4's Solution (reference 4)
17

---

Published as a conference paper at ICLR 2026
Your Job
You should critically check the Anchor Solution to the problem, then correct it if needed and write your own answer.
1. Identify its weak points (missing reasoning steps, ca calculation errors, unclear logic, etc.).
2. You refer to other students' solutions for targeted improvements relevant to those weak points.
3. Please note that other students' solutions may have errors. Please refer to the points worth learning and make improvements.
4. Apply repair strategies if needed:
Logic Completion (fill missing reasoning)
Numerical Correction (fix calculation errors)
Method Replacement (use better method if needed)
Expression Refinement (clarify presentation)
Only use improvements that directly address anchors weak points. Avoid unnecessary information merging.
You can solve the problem with the help of feedback from a code executor. Every time you write piece of code between <code> and </code>, the code inside will be executed. For example, when encountering numerical operations, you might write piece of code to inteprete the math problem into python code and print the final result in the code. Based on the reasoning process and the executor feedback, you could write code to help answering the question for multiple times (either for gaining new information or verifying). There are also several integrated functions that can be used to help you solve the problem. The available functions are:
1. search query string as input, and the output is a JSON string containing list relevant document snippets from local, private knowledge base. This function should be your first choice for answering questions.
(query: str) this function takes
2. web web.search (keywords) this function takes keywords as input, which is string, and the output is string containing several web information. This function will call web search engine to return the search results. This function is especially useful when answering knowledge
questions. 3. web.parse (link:str, query:stz) this function takes link and query as input, and the output is a string containing the answer to the query according to the content in this link. This function is useful when looking into detail information of a link.
Your workflow for solving the problem must follow these steps: Step 1: Local Document Search (Mandatory First Action): You must always begin by using search local documents to check for relevant information in the private knowledge base. Step 2: Evaluate and Supplement: After receiving results from search.locl..ocueentt this information as supplement to your background knowledge, not as absclute truth. This supplementary context may incomplete or require further verification.
evaluate them carefully. Treat
Step 3: Web Search & Parse (Verification Detail): After your initial local search, use web search to find relevant web pages for verification or supplementation. If a specific link from the search results seems particularly useful, use
18

---

Published as a conference paper at ICLR 2026
web parse to extract detailed information from that page.
You should not be overconfident in your know and reasoning.
Each time you write code put the code into <code></code> snippet. Put your final answer in <answer></answer with
The following shows the prompt we use in the QAIR stage.
A.2.2 QUALITY EVALUATOR
Prompt of Quality Evaluator
User Prompt
You are expert evaluator. Your task is to evaluate the given solution for the problem from multiple perspectives.
Problem
{query}
Candidate Solution
{solution}
Evaluation Dimensions
1. Logical Reasonableness (0-5) Does the reasoning process follow valid logic?
Answer Correctness (0-5) Is the final answer correct and reasonable?
2.
Explanation Completeness (0-5) Does the solution explain the reasoning clearly and completely
3.
Output Format
Return your answer strictly in JSON:
"quality scores": [float, float, float], // [logic, answer, explanation]
"suggestion": "Provide an improvement suggestion for this solution that could help refine it in the next iteration."
The following shows the prompt we use in the RAG Monitor.
A.2.3 RAG MONITOR PROMPT
Prompt of RAG Monitor
Role:
You helpful assistant.
Task: Analyze the following text and determine if responding to it accurately requires retrieving information from external source.
Instructions:
If you find any doubt or uncertainty about a concept or term the text, consider necessary to retrieve information (RAG).
If retrieval is required, answer: yes.
If retrieval is required, answer: no.
19

---

Published as a conference paper at ICLR 2026
Text:
[text}
Judgment:
The following shows the prompt use in the RAG Querier.
A.2.4 RAG QUERIER PROMPT
Prompt of RAG Querier
Role: You are a helpful assistant.
Task: Generate single, concise, and effective search query for retrieving the information required by the text below.
Instructions:
Return only the search query itself.
Do not include explanations, punctuation, quotation marks, or other text.
The query should be direct and contain only the most essential keywords.
Text:
[text]
Search Query:
The following shows the prompt we use in the RAG Querier.
A.2.5 RAG INJECTOR PROMPT
Prompt of RAG Injector
Role & Core Objective: You are an information integration specialist. Your sole task is to process the provided RAG (Retrieval-Augmented Generation) output. Maximize the utilization of all relevant information substantively support the reasoning, argumentation, or conclusions presented in the main text. Do not perform additional reasoning or generate new conclusions.
Content Integration Principles:
Comprehensive Extraction: Extract all valuable information from the RAG outputs that enhances the logical depth, robustness, and persuasiveness of the main text's arguments.
Seamless Cohesion and Minimal Completion: Maintain smooth contextual coher- ence and stylistic consistency. Perform minimal completion only if the main text ends mid-thought.
Neutral Representation: Present all information objectively. Do evaluate, ques- tion, or add subjective commentary.
Output Specifications:
Output should follow the template: "imain text completion if necessaryč. Wait minute, by searching information about ürag queryů. found that irag result;, Now that have more relevant information, I can continue my reasoning."
Directly appendable to the end of the original main text.
Do not include process summaries, headings, bullet points, labels like "Supple- ment:".
Instruction Recap: Only select, filter, organize, and polish the RAG content. Do perform external reasoning or add new information.
20

---

Published as a conference paper at ICLR 2026
Main Text: {text}
RAG Query: (rag.query)
RAG Result: {rag result)
A.3 EVALUATION PROTOCOL AND ANSWER SCORING GUIDELINES
The o3-mini model was employed as automatic judge verify model-genennn responses against the reference answers, following the official HLE Evaluation Prompts.
Prompt of Answer Evaluation
Role: You are expert evaluator.
Task: Judge whether the following response to a question is correct or not based on the precise and unambiguous correct answer provided.
Question:
(question]
Response: {response]
Correct Answer:
(correct.answer)
Evaluation Instructions:
extracted answer:
Extract the final exact answer from the response. If there is exact final answer, put 'None..
reasoning:
Explain why the extracted final answer is correct or incorrect based on the correct answer. Focus only on differences between the response and correct answer. Do not comment on background, do not attempt to solve the problem, do not argue for any alternative answer.
Answer "yes' if extracted final answer matches the correct answer (allow small margin for numerical problems). Answer 'no" otherwise (any inconsistency, ambiguity, or non-equivalency counts "no").
correct:
confidence: Extract the confidence score from the response between 0% and 100%. If no score available, put 100.
Output Format:
" "extracted
"...",
"reasoning": ".
"correct": "yes/no", "confidence":
In Figure 8, the vertical and horizontal axes represent the scores assigned by the LLM for answer continuation, with ouiput values ranging from 0 to Below are the prompts used to assess the accuracy and consistency of the answers.
21

---

Published as a conference paper at ICLR 2026
Prompt of Answer Accuracy Evaluation
You are meticulous grader. Evaluate solver responses (up five) for one stage of medical/hiological question by comparing each responses FINAL, full RESP reasoning to the ground truth GT official rationale R. Output continuous accuracy in [0.1] for each response. Inputs:
Q: question stem (may or may not have multiple-choice options).
GT: ground-truth answer. This can be multiple-choice letter or short text for free-response questions.
official rationale (may be empty).
FINAL[i]: the solver's extracted final answer from < answer > (may be letter short text).
<//<//nnuur /aanswer
RESP[i]: the solver's entire assistant message for response in this stage.
How to grade (read carefully):
Grade onc solver's response at time, each solver's grading process should be independent, and should not rely on anything else except the solver's response, final answer, Q, R, and GT.
The grading process for one solver:
Determine the solver's FINAL answer from FINALii]. If missing, infer only RESP[i] makes the choice unambiguous; otherwise treat unanswered.
Compare against GT:
For multiple-choice questions, check if the letter matches GT (case-insensitive). or free-response questions, check semantic equivalence to GT (normalize word- ing, allow synonyms or equivalent phrasing).
Evaluate reasoning quality: Does RESP[i] align with R (key findings, mechanisms, exclusions)? Does it avoid contradictions, hallucinations, or irrelevant statements?
Scoring recipe (simple, smooth, continuous); Use continuous score reflecting BOTH aspects:
0.94-1.00 + FINAL matches GT and RESP closely aligns with R with sound, non-ccontradicorry reasoning.
0.69-0.94 - FINAL matches GT but RESP shows minor gaps, superficiality, small inconsistencies.
0.34-0.69 FINAL * GT. yet RESP shows substantial, partially-correct rea- soning aligned with R (good differential, one key mistake).
0.00-0.34 - FINAL * GT and RESP shows weak/mostly incorrect reasoning (some relevant bits).
0.00 > Off-topic, unsupported, self-contradictory. or clearly wrong with no meaningful alignment R.
Penalize confidently wrong statements or contradictions; do not reward verbosity.
Return ONLY valid JSON in the following form:
{
Witems":
{ ("accurrac"; <float in 10,1]> "reason": "<<= 40 words justifications ("accuracy": ..., "reason": ...)),
}}
Now grade the following batch of responses:
Q: q
GT: gt
R:
FINALS: (final items)
RESPONSES: (resp_items)
22

---

Published as a conference paper at ICLR 2026
Prompt of Consistency Evaluation
You are an expert biomedical exam grader. Below are two independently generated solutions to the same question. Your task is to evaluate how consistent these two solutions are.
Instructions:
Compare the reasoning processes, scientific logic, and final answers.
Assign a consistency score from 0.00 to 1.00 (two decimal places):
-1.00 Solutions are highly consistent (nearly identical reasoning and conclusion).
0.00 Solutions are completely inconsistent (different reasoning and conclusion).
Solution A:
{solution1}
Solution B:
{solution2]
Please provide your consistency score (e.g., 0.85):
A.4 EXTERNAL VALIDATION AND LIMITATIONS
Primary evaluation. As detailed above, our main results are scored automatically by o3-mini using the official HLE judging prompts and our continuous scoring rubric (Sec. A.3). No human expert adjudication is included the reported metrics.
Risk of bias and robustness. Automatic judging ensures scalability and reproducibility but may introduce grader-specific biases and failure modes, especially for free-response rationales. To mitigate this concern and to support future replication, we pre-register small-scale expert validation protocol focused on HLE free-response items:
Sampling. Randomly sample n=20 items from HLE Bio/Chem (stratified by topic and difficulty), prioritizing free-response questions where judging more nuanced than multiple choice.
Blinding. Two independent domain experts (blinded to model identity and to each other's scores) will grade each item using the exact same criteria as our c3-mini rubric (binary correctness and a continuous accuracy score in [0, 1]).
Outputs. For each response, experts record: (i) extracted final answer, (ii) binary correctness, (iii) a continuous accuracy [0. with 540-word justification.
Agreement metries. We will report expert-expert agreement (percent agreement, Cohen's K for binary correctness; Pearson/Spearna for continuous accuracy) and sxpert-o3-mind agreement (macro-FI for binary correctness; Pearson/Spearman correlations for continuous accuracy).
Release. We will release the sampled IDs. anonymized expert score sheets, and scripts to recompute all agreement statistics in our artifact package.
Takeaway. While our main findings rely on automatic judging for scale and consistency, the above protocol provides a concrete path to independently verify fairness and robustness on the subset of free-response items where grader discretion matters most. We will include the full results of this expert validation in the camera-ready or artifact release.
A.5 RAG MONITOR HYPERPARAMETER SETTINGS
In our implementation of the RAG-enhanced reasoning agent, several key hyperparameters are used. Table A2 summarizes these hyperparameters and their functions.
The choice of query top k ==3 motivated by our design of frequent and fine-grained monitoring. Fach retrieval must be highly precise, since too many retrieved documents would unnecessarily lengthen the context, slow down reasoning, and introduce redundant noisy information.
23

---

Published as a conference paper at ICLR 2026
Hyperparameter
Value 1pt-4.1-min
Description
model
LLM model used in RAG Monitor
query_top..
Maximum number retrieved documents for each query.
rag_chunk
512
Text chunk size for RAG monitoring.
rag.overlapping
128
Number of overlapping characters between consec- utive chunks to maintain continuity.
max rag
Maximum number of RAG insertions allowed in one reasoning step.
temperature
0.5
Controls generation diversity; higher values lead to more randomness.
Table A2: Main Hyperparameter Settings used in RAG Monitor.
The parameters rag-chunk and rag- overlappen control the monitoring frequency of the RAG module. A large sh_uchnk would make detection too sparse, causing some uncertain reasoning fragments to miss external knowledge injection. The overlapping setting ensures continuity between consecutive windows and avoids missing potential triggers.
The parameter max_rag limits the maximum number of retrievals that can be inserted within agent step. This prevents the monitor from triggering too frequently and ensures that the reasoning process remains stable and forward-moving.
In practice, the RAG monitor is triggered average 3.64 times per 10,000 generated characters. Each trigger adds about 176.17 tokens new context, resulting in an average 641.2 1,25 additional tokens per 10,000 characters. Although this introduces extra tokens, it reduces the need for explicit tool calls, which significantly lowers the tool usage cost. As result, the overall reasoning process requires fewer steps and consumes fewer tokens, as shown in Table
A.6 ERROR CASE ANALYSIS
Here, examine three representative failure modes of model: reasoning application errors, and comprehension errors. the subsections that follow, we present real case for each and analyze how HSR and QAIR contributed to the failure.
ccasoning-process, process errors, knowledge-
A.6.1 CASE REASONING PROCESS ERROR
HLE Question: Transgenic Arabidopsis lines constitutively expressing wheat proteins AKP1, RIB3, 1,B1, and YKL23 were tested in three assays: (i) luminol-based ROS over 60 min to MAMPs (flagpep25-40 flagpep140- 1668 csp192-20 1sp192-208), (ii) split-luciferase complementation in tobacco leaves, and (iii) GFP localization under water vs flagpep140 140-168. Choose the correct statement.
Answer Choices (A-H):
A. AKP1 and RIB3 redundant receptors for pepflag22.
B. KIBI is the receptor for flagpep25-40 and flagpep 40-168 but not for csp192-208.
C. RIB3 is the coreceptor of AKP1; KIBI acts downstream of RIB3.
D. All tested proteins are transmembrane proteins...
E. YKL23 acts upstream of KIB1; RIB3 does not act upstream of KIBI.
F. flagpep25-40 is the ligand for AKPI and csp192-208 for YKL23.
G. Tobacco lacks endogenous homolog of AKP1.
H. None of the above.
HSR (Hierarchical Solution Refinement)
Anchor s* (initially correct): From the cross-modal evidence, the solver first forms the anchor "AKPI requires RIB3 to sense flagpep140-1688 KIBI acts downstream" " favors C.
24

---

Published as a conference paper at ICLR 2026
early pass supporting C:
the correct statement is choice 'RIB3 is the coreceptor of AKPI;
K1B1 acts downstream of RIB3.'
ROS: AKPI+RIB3 -> 2lagpep140-168 (2e6 RLUs), neither alone responds.
Spiit-Luc: ARPI<->RIB3 baseline suggests Ligand-dependent
complex;
<IBI<->AXP1 and KIBI<->YKL23 are positive.
GFF: KIBI zelocalizes under flagpep140-168; stay at PM.
HSR Error Note
Misweighting across modalities. HSR over-weighted split-luc magnitudes and under-weighted ligand dependence implied by ROS. Missed consistency repair. No reconciliation step to explain baseline <KPI++RIB3 (no ligand) vs. positive ROS (with ligand).
Where HSR goes wrong (pivot to faulty anchor): When later refinement step oveghweights split- split-luciferase magnitudes and under-weights ligand dependence, the anchor flips to Two faulty moves are visible in the refinement trace:
Fault 1 (magnitude direction): Interprets strong KIB1i-; YKL22 (8e5 RLU) directional upstreammesss YKL23 over KIB1, even though magnitude does not encode causal order.
Fault (baseline + absence): Treats AKPI++IIB baseline (2e2 RLU) without ligand evidence against co-reception, igmoring the ROS gain-of-function with AKP1+RIB3 under 1agpep140-168 (a classic lgandddeeenneet complex pattern).
refinement pivot toward E:
"... search local documents timed out. Proceed from assays. KIB1<->YKL23 is strong (8e5), AKPI<->RIB3 is baseline (2e2), YKL23 likely acts upstream of KIB1 and RIB3 does not. Final choice: R."
HSR diagnosis: The error is reasoning process, not missing knowledge. All requisite facts (ROS synergy for AKP1+RIB3 at 140-168; KIBI relocalization; split-luc positives with KIBI) are available, but the refinement applies invalid inference rules that overturn the initially correct anchor C.
QAIR (Quality-Aware Iterative Review)
Checks logged as performed vs. missed:
Performed:
Parse ROS matrix (AKP1+RIB3 -> 140-158; YKL23 -> csp192-208):
Parse split-luc matrix (KIB1<->AKPL, KIBI<->YKL25 positive; AXF1<->RIB3 baseline) OK
Consistency of the "R" narrative with split-luc magni Sagnitudes: OK
Missed:
Cross-modal reconciliation: Ligand-depandent complexes
yield
baseline split-luc (no ligand) positive ROS (with ligand).
Directionality audit: interaction magnitude != causal
upat
Sanity link: GFP relocalization KIB1 implies downstream role,
which conflicts with "YKL23 upstrean of KIB1" narrative.
25

---

Published as a conference paper at ICLR 2026
QAIR Error Note
Missed mandatory audits. QAIR should have enforced:
Cross-modal reconciliation requires an explanation for baseline split-lue vs. positive ROS under ligand.
Directionality audit disallow inferring causal order from interaction magnitude alone.
Downstream sanity link KIB Is relocalization supports downstream role; flag conflict with the YKL22 upstream of KIBI story.
Skipping these allowed self-consistent but incorrect E narrative to pass.
Observed plateau: QAIR accepts internal consistency of the E narrative without enforcing cross- modal reconciliation, the faulty anchor persists. finalization kept by QAIR:
Based strong KIB1<->XKL23 luminescance and no AKP <<-RIBB signal, YKL23 acts upstream of KIB1, and RIB3 does not act upstream of KIB1. <answer>E</answer>>
A.6.2 CASE 2: KNOWLEDGE APPLICATION ERROR
HLE Question: A university field trip samples bats on an island for one month (methodology assumed valid). The student computes Simpson's 's diversity index using the formula D 1 N(N-1)/sum n(n-1) and obtains D= Which statement best describes this result?
Answer Choices:
A. Mathematically and ecologically valid
Mathematically inconclusive with the index value provided
C. Not mathematically valid, but ecologically valid
D. Mathematically valid, but not ecologically valid
E. Not mathematically ecologically valid
HSR (Hierarchical Solution Refinement)
Initial anchor (as formed): Accept the problems formula exactly as stated, derive what D implies under that formula, then judge mathematical vs. ecological validity. problem framing and anchor start:
the standard $ Simpson's diversity index is either (1 lambca) or (1/lambda)
where lambda sum n_i (n 1) (N-1)). The formula given in the problem,
1 (N-1) /sum (n-1), is not standard. However, for the purpose of this
problem, we must work with the formula as stated.
The student obtained a value of 0, which occurs when sum (n-1) (N-1) ...
What HSR should have applied (correct knowledge use):
Ecological validity is evaluated against the observed community in the stated sampling frame (the one-month survey), not against lifetime site anecdotes.
Given the solver already decided valid and, the months sample indeed shows one species, ecologically valid for that sample
the provided formula stated, D 0 is mathematically
A.
26

---

Published as a conference paper at ICLR 2026
HSR Error Note
Misapplied knowledge at refinement. Instead of auditing ecological validity within the sampling frame, HSR imported out-of-frame site priors (known island diversity). which overrode the sample-level conclusion. Missed consistency repair. After explicitly deciding work with the formula as stated, HSR later allowed a pivot to wrong formula = option 4, without reconciliation step.
What actually happened in HSR (knowledge misapplication): The refinement step imported site-level prior knowledge (known island diversity) overrule the sample-level ecological judgment and flipped away from A.
knowledge misapplication trace:
Ecological validity: Ecologically, it is invalid because it contradicta. the known diversity of the islard, as multiple species are known to exist."
"The student chose D, which is option 3."
refinement concluding to option in this run:
using wrong formula makes mathenatically invalid. Thüs, the correct answer is option 4; Not mathematically scologically valid. <ansmer>E</answer>
The error is knowledge application. The solver had all needed facts (including the decision to use the given formula and the implication /) 0) but applied ecological knowledge at the wrong level (site history rather than the sampled community), causing the anchor to settle on D or E instead of A.
HSR diagnosis:
QAIR (Quality-Awar) Iterative Review)
Checks recorded performed missed (from the run text):
Performed:
Algebra under the given D-formula: D 0 <=> sum (n-1) N (N-1) (OK).
Consistency of "mathematically valid" under the accepted (given) formula (OK).
Missed:
Ecological validity audit constrained to the stated sampling frame
one-month sample, not Consistency check: if the month legitinately observed single-species sample, then both mathematical and ecological validity hold => Choice A.
(evaluate representativeness
lifetime site knowledge)
QAIR Error Note
Missed mandatory audits. QAIR should have enforced:
Sampling-frame ecological audit judge ecological validity only within the months survey. Formula-consiseencc audit after use the formula as stated, reject later pivots to wrong formula unless reconciled.
Gold-aligned sanity check if the accepted sample contains one species, then both mathemati- cal and ecological validity hold A.
Skipping these allowed self-consistent but incorrect narrative (ecologically invalid due to island history) to pass.
27

---

Published as a conference paper at ICLR 2026
Observed plateau: QAIR validated internal consistency of the ecologically invalid due island history narrative and did not enforce a sampling-frame frame ecological audit, so the faulty anchor persisted.
finalization kept by QAIR this run:
Ecologically ... known diversity the island ... making the result ecologica cal ly invalid, Thus corresponds to opt ion 3."
fanswer><</answer>
A.6.3 CASE 3: COMPREHENSION ERROR
HLE Question: A university field trip samples bats an island for one month (methodology assumed valid). The student computes Simpson's 's diversity index using the formula D 1 N(NN-)/sum n(n-1) and obtains D O. Which statement best describes this result?
Options:
A. Mathematically and ecologically valid
Mathematically inconclusive with the index value provided
Not mathematically valid, but ecologically valid
D. Mathematically valid, but not ecologically valid
E. Not mathematically or ecologically valid
HSR (Hierarchical Solution Refinement)
Anchor s* (as formed by the solver): correct mechanism up the enal stage (thermolysis of the sulfoxide - vinyl ether - [3.3]-Claisen - unsaturated aldehyde).
excerpt from the run (mechanistic anchor):
thermal elimination at 180 C gives the vinyl ether CH2-CH-O-C (CH3) 2-CH-CH2,
which undergoes [3, 31-33-s80 -iggatrropiic Claisen rearrangement to an aldchyde ...
Where HSR should have repaired comprehension (nomenclature layer): For aldehydes, the parent chain must (i) include the carbonyl carbon as and (ii) maximize chain length while assigning the lowest locants jointly to C=O and the C=C. Under the rearranged connectivity, the correct parent is hex, absorbing one methyl into the main chain; the double bond is C4 and the remaining methyl is at C5, yielding 5-methylhex-44cm (Gold).
HSR Error Note
Missed comprehension repair. HSR failed to:
apply the aldehyde parent-chain rule (chain must include C=O and be maximized);
apply the lovest-locant rule for the C=C within that parent;
re-evaluate pent vs. hex-* after the [3,3]-shift mapping.
Result: the naming anchor should have flipped to 4-mettyylexx--enall, but did not.
What actually happened in HSR (missed repair; wrong anchor kept): The refinement layer accepted pent-based chain and locked the name accordingly. excerpts from the run (mis mis-naming kept by refnement):
28

---

Published as a conference paper at ICLR 2026
"The IUPAC name is derived follows:
The longest carbon chair. containing the aldehyde group is five carbons (pentanal).
The double bond is betweer. carbons 4 and 5 (pent-4-enal),
Two methyl groups are attached to carbon 3 (3, 33,3mmimth Thus, the correct name is 3, 4
<answer>
\boxed - </answer>*
HSR diagnosis: The failure is comprehension of IUPAC chain selection rules (parent-chain identification when both C=O and alkene must be included), mechanism recall. HSR improved mechanistic clarity but did not apply a naming-level repair to flip from pent to the hex-* parent required by the Gold answer.
QAIR (Qullit--Aaar Aware Iterative Review)
Checks recorded (as reflected in the run text):
Mechanistic plausibility (elimination -> vinyl ether -> Claisen) : PASSED
Role of NaHC03 as neutralizing base (sul fenic acid) PASSED
Internal consistency of proposed names vs. drawn skeleton: FASSED
TUPAC audit:
parent-chain selection ion and 1owest-locams C-C (REQUIRED): SKIPPED
QAIR Error Note
Missed mandatory audit. QAIR should have enforced:
Parent-chain audit (aldehyde rule): chain includes C=O and is maximized;
Lowest-locant audit (C=C) within that parent chain;
[3.3] mapping check to determine which branch becomes part of the main chain. Skipping these allowed self-consistent but wrong pent narrative pass.
Observed plateau: QAIR converged self-consistent pent-chain narrative and terminated without running the naming audit that would force re-evaluation the parent chain under aldehyde rules.
Conclusion kept by QAIR:
"... The major product is 4, 4--dimethyyleent---nna <answer> \boxed (4, d-dimttyyyll wwwetttyyyyytttteeeeeeee -pent-5-enal) </answer>*
A.7 HSR/QAIR SUCCESS CASE ANALYSIS
This section highlights that HSR and QAIR independently correct reasoning errors when Monitor-Ree RAG not triggered. Each subsection provides real HLE question shows how HSR and QAIR independently correct the Bascline solvers errors without retrieval.
We present five representative success cases covering the main types that HSR and QAIR are de- signed repair: (i) missing or inconsistent reasoning steps, (ii) arithmetic or numerical inconsistencies, (iii) methodological misapplication, and (iv) insufficient peer-evidence consolidation.
A.7.1 TYPE REPAIRING MISSING OR INCONSISTENT REASONING STEPS
Case 1: Pond predator reasoning
HIEE Question: All ponds are cement boxes (2 ft deep; 10 or 30 square). Half were built one year ago and half five years ago. Older ponds now host established insect communities. With single emergence trap, choose the pond posing the greatest medical threat.
29

---

Published as a conference paper at ICLR 2026
Answer Choices (A-E):
A. 10 square, year old.
B. There will be no difference between ponds.
C. 30 square, one year old.
10 square, five years old.
30 square, five years old.
Baseline Trajectory (Incorrect)
Baseline summary. The Baseline solver focuses on temperature rather than predators:
"Older ponds have more insects, so they host more mosquitoes. Cooling is slower in small ponds; hence the 10-ft, 1-year pond poses the greatest threat."
Baseline Error Note
Missing predator chain. The solution ignores the explicit cue that older ponds already host insect communities (including predators), so young ponds are more dangerous. Irrelevant heuristic. It applies small pond warms faster temperature heuristic that is not meaningful for uniform 2-ft cement boxes.
HSR (Hierarchical Solution Refinement)
Reconstructed causal chain. HSR supplies the missing ecological steps:
Young ponds lack established predators; older ponds have predator communities.
Larger surface area provides more larval habitat (30 ft: 302 = 900 ft?, vs. 102 ft))
Uniform depth and cement walls remove the small warms faster argument.
HSR anchor:
"Young predator-free. Larger surface -> egg-laying substrate. The 30-ft pond provides 9x the larval habitat, the 30-ft, !-year pond poses the greatest medical threat."
QAIR (Quality-Aware Iterative Review)
Checks logged as performed:
Performed:
Predator-control logic (young vs old ponds)
PASS
Surface-ares calculation. (900 100)
PASS
Tenperatuure/strueuure audit for cement tanks
PASS
Outcome. HSR+QAIR repair the missing predator/surface-arca reasoning and flip the answer from incorrect 10-ft pond the correct 30-ft, 1-year pond (C).
Final: choose the young 20-ft pond; predators colonize older ponds, and 9x surface area increases larval habitat <answer> Vboxed </)<//nser>>>
Case 2: Predator-vision framing
Twenty untrained undergraduates rank visual similarity of Bombus species from field photos; researcher clusters mimicry syndromes based that consensus, this approach valid for the ecological function of mimicry?
HLE Question:
30

---

Published as a conference paper at ICLR 2026
Baseline Trajectory (Incorrect)
Baseline summary.
"Human perception is close to predator perception, the method is acceptable for evaluating mimicry."
Baseline Error Note
Missing ecological reasoning. The solver assumes human and predator perception are inter- changeable, ignoring UV/tetrcchromttie vision in predators and the role of learned avoidance. It also neglects motion and background cues important for aposematic displays.
HSR (Hierarchical Solution Refinement)
Repaired reasoning. HSR inserts the missing ecological context:
Many predators see UV or have different colour channels than humans.
Predator avoidance is learned; untrained students lack aversive conditioning.
Static photographs omit motion and environmental background.
HSR anchor:
"Undergraduate rankings can be useful starting point for hypotheses, but ecological validity requires predator-vision models, multispectral imaging, or behavioural assays. Thus the method is only partially valid."
QAIR (Quality-ware Iterative Review)
Checks logged as performed:
Performed:
Partial -utillttllll -utility acknowledgement (hypothesis generation)
PASS
Need for predator-vision. behavloural evidence PASS
Outcome. HSR+QAIR convert an overconfident fully valid judgment into a qualified partially valid conclusion, correctly reflecting the ecological limitations of human-based similarity rankings.
Final: "Human rankings generate hypotheses; ecological validity still requi res predator-visiin sion tests." Kanswer>Partially validk/answer>
A.7.2 TYPE 2: RESOLVING ARITHMETIC AND NUMERICAL INCONSISTENCIES
Case 3: Ozonolysis atom tracking
HLE Question: Follow w--buuene-..4deee (11) through TES protection, ozonolysis, MeNO, addition (nitroolefin 10), HayashiJrgensen addition (7), Nef oxidation (14), and final ring formation to multifidene (1). How many of Iss carbons reach multifidene (1)? How many oxygens from 11 are in 14? How many nitrogens from appear 10?
Baseline Trajectory (Incorrect)
Baseline summary. The Baseline solver implicitly treats compound 11 as if both TES-bearing fragments and nitrogen from 7 survive into later intermediates, leading to an overcount of carbons, oxygens, and N-origin.
31

---

Published as a conference paper at ICLR 2026
Baseline Error Note
Atom-count inconsistency. Ozonolysis treated as appology-preserving rather than fragmenting 11 into two C2 pieces, and nitrogen in 10 is incorrectly traced back to instead of MeNO,.
HSR (Hierarchical Solution Refinement)
Tragment-by-fragmmee repair.
Ozonolysis splits 11 into two C2 fragments; only one TES-bearing fragment is carried forward 11 >> 12 + 10 + 13 - 14
Nitroolefin 10 is formed before adduct 7; its nitrogen comes from MeNO not 7.
Each molecule of 14 therefore inherits two carbons and one oxygen from 11, and zero nitrogens
from 7.
HSR anchor:
"After 03 cleavage, only one TES TES-bearing fragment enters the sequence -2 -> 10 -> 13 -> 14 -> 1. Nitrogen in 10 is introduced by MeNO2, not adduct 7. Thus each 14 carries 2 carbons and 1 oxygen from 11, and 0 nitrogens from 7."
QAIR (Quality-Awark Iterative Review)
Checks logged as performed:
Per formed:
Fragment sketch and mapping 11 -> 14 Source of nitrogen in 10 (MeN02 vs 7)
PASS
PASS
Final tally verification.
PASS
Outcome. HSR+QAIR resolve the numerical inconsistency and yield the correct atom-count triple (2, 1,0).
Final: "Correct tally: carbons, oxygen, 0 nitrogens." <answer>2, 1, 0</answer>
A.7.3 TYPE 3: CORRECTING METHODOLOGICAL ERRORS
Case 4: Jensen-guided allocation
HLE Question: Given a strictly concave convex benefit function s(r;) and total resources R. several statements relate to when a fair (equal split) vs. extreme (all-or-nothing) allocation is optimal. Which option lists all true statements?
Answer Choices (A-P):
[1]
B. [2]
C. [3]
[4]
E. [1, 2]
F. [1,3]
G. [1, 4]
H. [2
[]],4]
32

---

Published as a conference paper at ICLR 2026
J. [3,4]
K. [1, 2, 3]
2,4]
M. [1, 3,
N. [2,3,4]
O. 21,
P. [5]
Baseline Trajectory (Incorrect)
Baseline summary.
"If is strictly increasing, the fair allocation is opt imal. Therefore multiple statements about fair splitting are accepted together."
Baseline Error Note
Method choice error. The solver conflates monotonicity with curvature, using informal heuris- ties instead of principled method such as Jensens inequality plus counterexamples.
HSR (Hierarchical Solution Refinement)
Methodological repair. HSR replaces heuristics with a standard functional-analytic argument: For concave Jensens inequality implies that the equal split maximizes Σ; s(r;) given Σ, Ti
R.
For convex extreme allocations can be optimal.
Explicit counterexamples are constructed show why statements 13 are not necessarily true.
HSR anchor:
"Apply Jensen: for concave s, sum s i) n s (R/n), with equality at the fair split. For convex 8, extremes dominate. We build explicit counterexamples for Statements 13, leaving only Statement true."
QAIR (Quality-Aware Iterative Review)
Checks logged as performed:
Performed;
Jensen inequality citation and
PASS PASS
Counterexample construction for 13 Mapping suzviving statement -> option
FASS
Final:
"Only Stazement survives; therefore option D."
<answer>\bokee (//
Outcome. HSR+QAIR correct the methodological choice, moving from heuristic monotonicity
reasoning to Jensen-based analysis that selects the correct option D.
Final: "Only Stazement survives; therefore option D."
<answer> bboxed (D) //aswwr>>
A.7.4 TYPE 4: PEER-EVIDENCE CONSOLIDATION AND STABILIZATION
Case 5: Camera-trap augmentation
33

---

Published as a conference paper at ICLR 2026
HLE Question: A camera trap monitors 7 ravine using 50 mm lens f/4, ISO 190, and 1/500 with fixed infrared (IR) lighting. Training data come from colourful GBIF photos. Which augmentation is most important for deployment accuracy?
Baseline Trajectory (Incorrect)
The Baseline solver focuses on optical artifacts alone and proposes blur-based augmentations (Gaussian or motion blur). without addressing the RGB + IR domain shift between GBIF training images and monochrome deployment images.
Baseline summary.
Baseline Error Note
Peer evidence under-used. Peer arguments about depth of field and motion blur are considered in isolation, and the much larger spectral mismatch (colour vs. infrared) is not treated as the dominant shift.
HSR (Hierarchical Solution Refinement)
Peer Evidence Integration
Peer A (Gaussian blur). Provides depth-of- field estimates (=: 3.04.2 m sharp zone at f/4) and argues for Gaussian blur mimic defocus.
Peer B (motion blur). Argues that animal motion can still cause blur 1/500 and advocates motion-blur augmentation.
HSR re-evaluates these contributions and contrasts them with the RGB KG domain gap:
Training images: colourful RGB web photos.
Deployment images: IR-illuminated, often monochrome or strongly colour-suppressed.
Optical blur differences are smaller than spectral differences.
HSR anchor:
"Infrared camera-trap images are colour-deficient, whereas GBIF training images are colourful RGB. Augmentations that disrupt colour patterns, such colour noise (option J), force the model to rely shape/texture rather than hue and directly address the dominant RGB->IR donain shift."
QAIR (Quality-Aware Iterative Review)
Checks logged as performed:
Performed:
DOF and mot motionbluu calculations acknowledged ft. ident fi cati Justification of colour-noise as dominant fix
PASS
RGB->
PASS
PASS
Outcome. HSR+QAIR consolidate peer evidence, keep the blur calculations explicit, but ultimately choose colour-noise augmentation (J) as the most important transformation for bridging the training- to-deployment domain gap.
Final: "Colour noise (J) corrects the major RGB->IR ciscrepancy." <answer> bboxed (J]s/answer>> answer>
A.8 REASONING-PROCTE REPAIR CASE ANALYSIS
process error, HSR (Hierarchical Solution Refinement) and QAIR (Quality-Aware Iterative Review) jointly provide the full correction mechanism. They repair reasoning defects by (i) reconstructing incomplete partially incorrect causal chains, (ii) correcting invalid logical inferences
For reasoning- sessoning-process
34

---

Published as a conference paper at ICLR 2026
when all relevant facts already present, and (iii) detecting and resolving subtle inconsistencies introduced during refinement.
A.8.1 CASE 1: HSR CHAIN RECONSTRUCTION
HLEE Question: An elderly patient treated for insomnia and seizures was switched among several antiepileptic drugs. After the third drug was started, the patient developed severe skin blistering. Which specific biochemical reaction initiated the cascade leading to this blistering reaction?
Baseline Trajectory (Incorrect)
Baseline summary.
"The deve' opment of skin blisters conaistent with SCAR such SJS. The specific biochemical reaction ... involves metabolism of lamotrigine
by cytochrome 2450 enzymes (particularly CYP3А4) to form reactive arene oxide on its dichlorophenyl ring."
Baseline Error Note
Incomplete causal chain. The baseline names lamotrigine oxidation, but omits (i) valproate- mediated UGT inhibition, (ii) the forced diversion into minor P450 route, and (iii) the down- stream immune cascade responsible SJS/TEN lesions.
HSR (Hierarchical Solution Refinement)
Reconstructed mechanism. HSR completes the causal chain:
UGT blockade. Coadministration of valproic acid inhibits lamotrigine glucuronidation A4/GT247))2 removing its main detox pathway.
Forced oxidation. Lamotrigine is diverted toward eytochrome P450-mediated bioactivation (primarily CYP2A6/CYP2D6) to a reactive arene oxide.
Immune trigger. When epoxide hydrolase and glutathione detox pathways saturated, oxide accumulates, haptenizes keratinocyte proteins, and initiates the SJS/TEN immune cascade.
HSR anchor:
"Valproate shuts down UGT glucuronidationn so lamotrigine is oxidised
by CYP2A6/2D6 to a reactive arene-oxide. epoxide hydrolasc/GSH saturated, the arene-oxide binds keratinocyte proteins, forming the hapten that initiates the SJS/TEN immune cascade."
QAIR (Quality-Aware Iterative Review)
Checks logged as performed:
Performed:
Mechanism chain completeness
PASS
Cclinical-sequeert summary
PASS
Requested clarifications:
Acknowledge CYP2A6/2D6 key minor -inorrypthway enzymes
Trim tangential discussion; focus initiating oxidation
Outcome. After HSR incorporates the enzyme clarifications and removes tangential details, QAIR accepts the refined causal narrative as complete and mechanistically consistent.
35

---

Published as a conference paper at ICLR 2026
Final: <answer>
Cytochrone 2450mediated oxidation lamotrigine (after valproate blocks UGTIA4/UGT2B7) to reactive arene oxi that overwhelms detox pathways and triggers SJS/TEN blistering. </answer>
A.8.2 CASE 2: HSR INFERENCE CORRECTION
HLE Question: A hypothetical population is infinitely large, randomly mating, diploid, and experi- ences no mutation, migration, selection; all genotypes have equal fitness and reproduce discrete generations. A quantitative trait on an autosomal chromosome has broad-sense heritability H = 0.7, and researcher measures all individuals in the western and eastern halves of the region single time point. Which statements must always be true?
Answer Choices (A-P):
A. only
B. only
C. only
D. 4 only
E. and only
F. 1 and only
G. and only
H. 2 and only
I. 2 and 4 only
J. and 4 only
K. 2, 3, and only
L. 1,3, and 4 only
M. 1, 2, and 4 only
2, and only
O. 11, 3, and
P. None of the options must always be true
Baseline Trajectory (Incorrect)
Baseline summary.
"With random mating, no spatial structure, and an infinite population, the genetic composition is uniform, leading to no substantial geographic
variation in phenotype. ... Thus, statements 1, 3, and 4 must alwa be true, so choose option [1,3,4].*
Baseline Error Note
Facts correct, inference wrong. The baseline equates uniform genetics with uniform phenotype. ignoring that 2 0.7 implies 30differ even in an infinite population.
36

---

Published as a conference paper at ICLR 2026
HSR (Hierarchical Solution Refinement)
Repaired reasoning. HSR separates givens from conclusions:
No selection, mutation, gene flow + statement is necessarily true (no selection the measured trait).
Infinite size and mutation/gene flow remove drift and differentiation, but !f < leaves 30% environmental variance, so west-eas mean phenotypes need not be identical; statement not forced.
With no mutation and no gene flow, the conditions future speciation are absent, so statement is necessarily true.
HSR anchor:
"Even in an infinite EW population, H = 0.7 still leaves 30 environnental variance, so West ve East moans can differ. Therefore only statements and are forced; select option F."
QAIR (Quality-Aaar Iterative Review)
Checks logged as performed:
Performede
Reasoning-rule validity
PASS
Weighting sanity check (genetic ve env) Multi-evidence reconciliation
PASS
PASS
Notes:
Make explicit that no mutation/gene 2low prevents speciation
Highlight that H-2-0.7 -> 308 envi ronmentaatall variance
Outcome. QAIR confirms that the corrected solution uses heritability and population-genetic assumptions appropriately, and that only statements and 3 must hold.
Final: "Only statements 1 and are forced; therefore option F. <answer> (+)
A.8.3 CASE 3: QAIR CONSISTENCY DETECTION
HLE Question: Five alien species differ in external traits such as setae, color, claws, simple eyes, and antenna morphology. Using these characters, construct trait-difference matrix and infer the most parsimonious phylogeny Which tree (in numeral notation, grouping sister taxa with parentheses) is most parsimonious?
Answer Choices (A-E):
A. ((()(4.))).3)55))
B. (3,)),(),(1,5)))
C. (3.(())))))
D. ((1.4),5) (2.3)
E. ((3).)
Baseline Trajectory (Incorrect)
Baseline summary.
37

---

Published as a conference paper at ICLR 2026
"Tree A ... changes Trec 6 changes Tree changes.
Tree с has the Lowest parsimony score of 6, making it the most parsimonious phylogeny."
Baseline Error Note
Unsupported conclusion. The baseline asserts that Tree C has 6 steps and is most parsimonious, but does not show the underlying trait matrix per-tree Fitch step counts, the result is unverifiable.
HSR (Hierarchical Solution Refinement)
First-pass refinement (still incomplete). HSR attempts to fill missing structure:
Introduces a binary trait matrix for the five morphological characters (texture, simple eye, antennae, serration, claws).
Claims updated parsimony scores, still reporting Tree C with 6 changes and alternatives with higher scores.
Residual HSR Error
Still incomplete. The initial HSR pass mis-codes certain character states (e.g., species antennae) and fails to provide explicit Fitch step-by-step counts, leaving the parsimony claim unverifiable.
QAIR (Quality-Awail Iterative Review)
QAIR detection and enforced repair.
Performed:
Consistency audit
FAIL FAIL FAIL
character-cod ng check
Per-tree step count audit
Action required:
Provide a corrected trait matrix
Add explicit Fitch step counts for every topology
QAIR Error Note
Unverifiable reasoning. QAIR determines that the refinement still lacks correct trait matrix and per-tree Fitch counts, meaning the claim that "Tree C has changes" not yet logically or empirically supported. QAIR therefore blocks finalization and demands explicit reconstruction.
Following QAIRs requirements, HSR revises the solution:
Cleans the trait table for all five characters across taxa.
Runs explicit Fitch parsimony evaluation, showing: Tree C has changes; Trees and have 7; others >
Attaches the per-tree step count so that the "6 vs. 7" claim auditable.
QAIR-approved summary:
"Trait table cleaned (texture, simple eye, antennae, serration, claws).
Pitch eva" uation shows Tree C changes; Trees A and B. others > 7.
Therefore Tzee C is the unique most parsimonious topology.
Outcome. Only after the corrected matrix and full Fitch counts are provided does QAIR allow the solution to finalize.
38

---

Published as a conference paper at ICLR 2026
Final: "Tree is the unique parsimonious tree with changes. <answer> CCI
A.9
COMPONENT-ERROR MAPPING
Table A3: Mapping between error types and repairing modules
Error Type
Monitor-RAG
HSR
QAIR
Knowledge insufficiency
Primary fix Flags uncertainty
Reasoning-chain
Primary fix Targeted correction
Secondary consistency check
Numerical arithmetic
Re-verification
Methodological
Sometimes helpful
Strong fix
Filter for incorrect revisions Final quality check
Overconfidence incomplete justification
A.10
ADDITIONAL BENCHMARK RESULTS
Table A4: Benchmark comparison HLE Bio/Chem (149 problems; 03-mini judge)
Model
Acc (%)
LLMs
DeepSeek V3.1 (Non-Think)
6.71
Deekseek R1
10.74
Qwen3 235B A22B
15.38
LLM with Tools
Deekseek RI with Browsing
16.82
DeepSeek V3.1 with Browsing
11.21
Doubao with Browsing
11.21
Agents Kimi Researcher
9.35
A.11 ADDITIONAL EXPERIMENTS WITH GENERIC MIXED-DOMAN RETRIEVAL CORPUS
To examine whether the improvements of Eigen-Agent primarily arise from its reasoning mechanisms rather than domain alignment between the retrieval corpus and the evaluation benchmarks, performed additional experiments using a generic mixed-domaie retrieval corpus, along with configuration that disables retrieval entirely.
The generic retrieval corpus was constructed by sampling 14,731 post-2020 papers from the Hugging- Face arxiv sexii--pppers rs dataset?. which spans 148 subject areas. For each paper, we extracted concise knowledge snippets from the abstract using unified prompt template (shown below). The resulting corpus intentionally heterogeneous and contains no explicit alignment with the biology or chemistry domains of the evaluation benchmarks.
Prompt of Generie Corpus Construction
You are an expert assistant helping extract high-level claims from scientific paper abstracts. Task: Given scientific abstract, extract ONLY the statements, insights, or claims explicitly present the abstract itself. Do NOT invent, guess, or add any extra content that is not written in the abstract. Only include knowledge points that are clearly stated. Do NOT include:
The method proposed by the paper (e.g., We propose, Our model)
pappas
39

---

Published as a conference paper at ICLR 2026
Implementation details experimental setup
Numerical results performance comparisons
Dataset descriptions
Any statements uniquely tied to this specific papers approach, model, results
Anything not explicitly stated in the abstract
DO include:
Background claims
Scientific facts and relationships
Observations, hypotheses, or general principles explicitly mentioned
Statements about phenomena, mechanisms, trends, limitations, open problems explicitly mentioned
Additional requirements:
Do NOT repeat the same claim. Make each claim distinct.
If multiple sentences convey the same insight, combine them into single concise claim.
If there are NO claims in the abstract, output exactly: { "cllaims": UI
Important formatting rule: Always output JSON only, with this exact shape:
"claims":
"claim 1", "claim 2",
Do NOT output any extra explanation or text outside the JSON object.
We evaluated Eigen-Agent under two configurations: no-RAG setting where only HSR and QAIR operate, and retrieval over the generic mixed-domain corpus. As shown in Table A5, Eigen-Agent retains strong performance even when retrieval is entirely disabled, indicating that the iterative correction mechanisms in HSR and the quality gating in QAIR contribute significantly When supplied with generic mixed mixed-domain corpus, the system achieves substantial improvements, suggesting that Eigen-Agents mechanisms robust to corpus heterogeneity and noise. Retrieval over the Bio/Chem corpus provides an additional but modest improvement, which is consistent with the expected benefits of specialized background knowledge. Together, these findings confirm that the performance gains of Eigen-Agent are driven primarily by its architectural design and not by privileged domain-specific retrieval.
overall accuracy.
Table A5: Evaluation of Eigen-Agent under no-retrieval, generic mixed-domain, and Bio/Chem
retrieval corpora.
Configuration
Acc (%)
Baseline
25.3%
Baseline + HSR/QAIR (No RAG)
34.5%
Eigen-Agent (Generic Mixed-Domain Corpus)
45.6%
Eigen-Agent (Bio/Chem Domain Corpus)
48.3%
40

---

Published as a conference paper at ICLR 2026
A.12 PSEUDO-CODE
Algorithm Eigen-Agent: High-level Workflow
Require: Query g, config (LLM & retriever), #proposers K (c.g., 5), QAIR threshold 7, max rounds Tmax
Ensure: Final solution 8*
1: Init: set LLM, RETRIEVER, and tool endpoints from C
2:
Proposer generates initial solutions
3: for all E (1....: K} parallel do
4: Sli] + GENERATE(()
> initial solution generation
5:
end for
6:
Local correction (optional, per-candidate)
7: for all E (1....: K} parallel do
8: C(i] + CORRECTOR(S(i)
b targeted fixes without cross-solution access
9:
end for
10:
Hierarchical Solution Refinement (HSR)
11: R + Ø
12: for a E {1....,K* K} do
rotate anchors
13: A Claj, Ref C3 {C[4]}
14: R[aa + REFINE(A, Ref)
apply peer-informed repairs: logic, numeric, method, expression
end for
16:
Quality-Aware Iterative Reasoning (QAIR)
17:
< 0. p I
while < Tmax
(parallel) for each E P: (qlogic quns: qesps suggestion) < EVALUATOR(S) 20: acore(s) + 0.2 Qloic + 0.6 + 0.2 Geep
19:
21:
Pass {s P score(s) T): Fail + P) Pass
22:
if Fail then break
end
24:
(parallel) for each E Fail: ă 4- CORRECTOR(S, (
25: P - Pass U [$ Fail]; 8 ++++1
26: end while
Rank & select
28:
8" + MANKER.SELECTTE
b c.g., composite score or pairwise compare
29:
return 8*
Subroutines used in LLM generation process
30: function MONITOR-BASER RAG(()
31:
cla: INITCONTEXT(2)
32:
while not DONE(ctx) do
33:
ctx: <- LLL..NEXT MONITOR (ctt))ooo((())))))))))))))))))>) = then
34:
> detect uncertaintylnnsffiicnnny on-stream > minimal, targeted keywords
35:
qry < QUERIER (cfz)
36:
docs <- RETRIEVER (gry)
37:
ctr INIECTOR (ctar, docs)
compress & integrate evidence seamlessly
end if end while
39:
40: return TRACETOSOOLUTIONCC)
41:
end function
A.13 USAGE OF LANGUAGE MODELS
We utilized large language model (LLM) to aid in the preparation of this manuscript. Its was limited to editorial tasks, including proofreading for typographical errors, correcting grammar, and improving the clarity and readability of the text.
41


Reviews:
[{"review_id": "w6R8Li9ovc", "reviewer": "Reviewer_h3qW", "summary": "This paper presents EIGEN-1, a new multi-agent system for scientific reasoning. It tries to fix two big problems: 1) RAG calls breaking the flow of thought (the \"tool tax\") and 2) multi-agent groups getting bogged down by bad ideas. The main ideas are an \"implicit\" RAG that injects knowledge when the model seems confused and a \"hierarchical\" refinement process where solutions are repaired by peers instead of just averaged. The system gets SOTA results on HLE Bio/Chem Gold, hitting 48.3% and beating strong models by a lot , all while using way fewer tokens.", "questions": "Your system has a lot of parts. Can you say more about how the Monitor-RAG and the HSR/QAIR parts work together? Does the implicit RAG just make the initial solutions better for HSR, or are the improvements from these two modules mostly separate?\n\nYou show that reasoning errors are the biggest problem (92.78% of failures ). The Monitor-RAG seems to help with knowledge errors, but how does your system fix pure logical mistakes?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper offers clever fixes for real problems in agent reasoning. The \"Monitor-based RAG\" is a great idea; it avoids stopping the reasoning process just to look something up, which clearly saves a lot of compute (53.5% token reduction ). The hierarchical refinement (HSR/QAIR) is also a strong point. Making agents repair each other's work instead of just voting makes a lot of sense and seems to work well based on the ablations. The SOTA results on a tough benchmark like HLE are impressive. I also liked the extra analyses, like the component breakdowns and the look at diversity vs. consensus, which back up the main claims.", "weaknesses": "My main concern is that this framework is very complex. There are a lot of moving parts (Monitor, Querier, Injector, HSR, QAIR, etc. ) and it's not obvious how sensitive it is to tuning. The ablation justifies each part, but it's hard to tell how they interact. For instance, does HSR only work well because the Monitor-RAG gives it better solutions to start with, or are those two gains separate? Also, the error analysis is interesting, but the appendix case studies mostly show HSR/QAIR failing to fix the problems. It would be good to see a more direct link between the new components and the specific errors they claim to fix.", "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": "HP8vYmQpiO", "reviewer": "Reviewer_iFym", "summary": "This paper presents EIGEN-1, a novel framework for scientific reasoning that unifies implicit retrieval with structured multi-agent refinement. The authors aim to address two major challenges in retrieval-augmented reasoning systems: the fragmentation of reasoning caused by explicit retrieval—referred to as the “tool tax”—and the dilution of strong candidate solutions through democratic multi-agent aggregation.\n\nOn the Humanity’s Last Exam (HLE) Bio/Chem Gold benchmark, EIGEN-1 achieves 48.3% accuracy, outperforming the strongest baseline model, SciMaster, by 13.4 points while reducing token usage by 53.5% and reasoning steps by 43.7%. Further evaluations on SuperGPQA and TRQA confirm its robustness. The paper also provides insightful analyses, including a quantitative study of the tool tax and the diversity–consensus trade-off in retrieval versus reasoning tasks.", "questions": "1. How sensitive is EIGEN-1 to prompt variations? Have you observed significant degradation when the prompts for the Monitor, Querier, or QAIR modules are paraphrased or shortened?\n\n2. Since the method is described as model-agnostic, could you report or estimate its performance when using a different base model (e.g., GPT-4, Claude 3, or Gemini 2.5)? Does the Monitor module’s uncertainty detection generalize across architectures?\n\n3. QAIR and the o3-mini judge play central roles in assessment. How robust are the results if these evaluators are replaced by another LLM or a human scoring subset?\n\n4. Do you expect similar gains in domains that require abstract reasoning but less factual grounding (e.g., logic puzzles, programming tasks)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The integration of implicit retrieval and hierarchical refinement is novel, bridging retrieval efficiency and collaborative reasoning in a unified system.\n\n2. The modular design (Monitor-Querier-Injector-HSR-QAIR) is well-justified and empirically validated through controlled ablations and analyses.\n\n3. The paper is clearly organized with intuitive visuals and detailed appendices (e.g., Algorithm 1).\n\n4. The quantitative treatment of tool tax offers a new analytical lens for reasoning system efficiency, which can influence future research directions.\n\n5. Strong benchmarks, token-efficiency metrics, and error-type breakdowns demonstrate robustness and depth of evaluation.", "weaknesses": "1. Lack of prompt sensitivity analysis.\nAlthough the paper provides detailed templates for each component (Monitor, Querier, Injector, HSR, QAIR), it does not evaluate how sensitive the framework’s performance is to prompt wording, structure, or length. Since EIGEN-1 heavily relies on prompt-driven modular coordination, empirical evidence on prompt robustness would strengthen the paper’s claims of generality.\n\n2. Limited exploration of base-model dependence.\nAll experiments are conducted on DeepSeek-V3.1 as the underlying model. The authors claim that Monitor-based RAG is “model-agnostic,” but they do not test the framework on other LLMs such as GPT-4.1 or Claude Opus. It remains unclear how performance, retrieval triggering frequency, or refinement behavior change when the base model’s reasoning or linguistic style differs.\n\n3. Potential evaluator bias.\nThe QAIR and automatic judging components rely on LLM-based evaluators (e.g., o3-mini). Without cross-model validation, the risk of evaluator–solver coupling remains unquantified.\n\n4. Domain focus.\nThe benchmarks are concentrated in biology and chemistry. Additional experiments in other reasoning domains (e.g., physics, math) would better establish the framework’s general applicability.", "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": "t9Zr6BA0TC", "reviewer": "Reviewer_nD2g", "summary": "This paper presents EIGEN-1, a multi-agent framework for scientific reasoning that integrates three modules:\n(1) Monitor-based RAG, an implicit retrieval mechanism that adaptively augments reasoning without explicit tool calls;\n(2) Hierarchical Solution Refinement (HSR), which coordinates agents through an anchor–reference structure for iterative correction; and\n(3) Quality-Aware Iterative Reasoning (QAIR), a quality-gated refinement loop.\nThe framework is evaluated on HLE Bio/Chem, SuperGPQA, and TRQA datasets, showing clear gains over strong LLM baselines.\nThe paper is clearly written, well-structured, and the empirical results are strong.", "questions": "Refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Comprehensive System Design:\nThe integration of retrieval, multi-agent collaboration, and quality-aware refinement forms a coherent and practically useful system.The proposed orchestration shows engineering sophistication and appears well implemented.\n\n2.Strong Empirical Results:\nAcross multiple benchmarks, EIGEN-1 demonstrates consistent improvements in accuracy and efficiency.The ablation analyses (e.g., removing Monitor or QAIR) are detailed and convincing.\n\n3.Deployment Practicality:\nSince the framework operates over frozen LLMs without training, it can be directly applied to existing models, which increases its practical relevance.", "weaknesses": "Domain-specific RAG corpus and evaluation validity\n\nThe paper’s most significant issue lies in how the retrieval corpus was constructed and evaluated.The authors build a biology/chemistry-specific RAG database from domain papers (Appendix A.1) and test primarily on HLE Bio/Chem and SuperGPQA Bio datasets.\nHowever, the HLE benchmark itself explicitly states that:\n“Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval.”\n\nThis creates a conceptual inconsistency: the proposed system demonstrates large gains precisely on a dataset that was designed to minimize the benefits of retrieval. Even if the database does not contain direct answers, its domain overlap likely gives the model privileged access to related context or background information. Thus, the reported improvements may reflect content alignment rather than genuine reasoning enhancement.\n\nI strongly recommend the authors to: Report results using a generic, mixed-domain corpus (e.g., Wikipedia or full arXiv); Or evaluate cross-domain performance, e.g., using the Bio/Chem corpus for non-scientific HLE subsets. These analyses would clarify whether EIGEN-1’s gains stem from its mechanism or its domain priors.", "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)