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arxiv: 2604.16723 · v1 · submitted 2026-04-17 · 💻 cs.AI · cs.LG

Recognition: unknown

Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training

Moein Salimi , Babak Hosseini Mohtasham , Amin Aghakasiri , Mahdi Naieni , Amir Hossein Qeysarbeigi , Mohammad Masih Shalchian Nazer , Zahra Azar , Mahdi Jafari Siavoshani
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Mohammad Hossein Rohban
Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:52 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords reinforcement learningscientific ideationmulti-agent rewardreward hackinglarge language modelsidea generationRL post-trainingdebate system
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The pith

A multi-agent debate system supplies robust binary rewards for RL-based scientific idea generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a reinforcement learning framework to train language models for creating scientific ideas. The key innovation is a multi-agent reward system that has agents debate the merits of an idea to decide on a strict yes-or-no reward, avoiding the problems of reward hacking that plague other evaluation methods. Training uses a dataset of problem-solution pairs from recent AI conference papers, and an unbiased version of group relative policy optimization to handle the sparse rewards without favoring longer responses. Experiments show the resulting models produce ideas that experts rate as more novel, feasible, and effective than those from current leading approaches.

Core claim

The paper establishes that a multi-agent reward function based on debate can act as an effective judge for scientific ideas in an RL setting, decoupling methodological validation from implementation details and delivering strict binary rewards that resist reward hacking. Optimized with an unbiased Group Relative Policy Optimization on the ICLR-320 dataset, this leads to LLMs that generate ideas outperforming state-of-the-art baselines according to expert evaluations on novelty, feasibility, and effectiveness.

What carries the argument

The multi-agent reward function, which structures debate among agents to validate ideas and assign binary rewards while remaining independent of specific implementation details.

If this is right

  • Effective RL optimization becomes possible for open-ended tasks with sparse binary rewards.
  • Generated scientific ideas show measurable improvements in quality metrics.
  • Length bias in policy optimization is mitigated through the unbiased variant.
  • The approach scales training for ideation without complex prompting or inefficient architectures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar debate-based rewards could apply to other domains like code generation or creative writing.
  • The framework might reduce the need for extensive human oversight in evaluating AI-generated content.
  • Testing on datasets from other fields could reveal broader applicability of the method.

Load-bearing premise

That the multi-agent debate process yields binary rewards robust to exploitation by the model and that expert human judgments reliably indicate true scientific innovation.

What would settle it

If a follow-up experiment shows that the trained model produces ideas that experts rate no better than baseline models, or if the reward function can be gamed to give high scores to low-quality ideas.

Figures

Figures reproduced from arXiv: 2604.16723 by Amin Aghakasiri, Amir Hossein Qeysarbeigi, Babak Hosseini Mohtasham, Mahdi Jafari Siavoshani, Mahdi Naieni, Moein Salimi, Mohammad Hossein Rohban, Mohammad Masih Shalchian Nazer, Zahra Azar.

Figure 1
Figure 1. Figure 1: Overview of the proposed framework for internalizing scientific reasoning via Re￾inforcement Learning (RL) post-training. The pipeline consists of five distinct stages: (1) Dataset Curation, where research questions and abstracts are extracted from accepted ICLR 2024 papers to form the ICLR-320 dataset; (2) Candidate Generation, utilizing a Qwen2.5 14B policy model to propose research ideas; (3) Multi-Agen… view at source ↗
Figure 2
Figure 2. Figure 2: Ablation of multi-agent Judge architectures. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Precision vs. Recall for Multi-Agent Judge Configurations. We analyze the performance of various agent compositions, numbered 1 through 13. The plot highlights the sensitivity of the reward signal to agent roles: removing the Analyst [1] significantly degrades precision, while removing the Evaluator [10] nearly eliminates recall. Configuration [11] (2 Analysts + 1 Evaluator) represents the optimal trade-of… view at source ↗
Figure 4
Figure 4. Figure 4: The system prompt used to classify whether a paper contains new ideas or is a survey or evaluation [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The user prompt used to classify whether a paper contains new ideas or is a survey or evaluation [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The system prompt used to generate a Research Question from a paper’s title and abstract. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The user prompt used to generate a Research Question from a paper’s title and abstract. System Prompt: Idea Generation Policy You are an expert research collaborator. Your purpose is to refine a general research question into a specific, actionable, and feasible research idea. Your goal is to identify a concrete research gap or a logical next step based on the research question. Your process for the <reaso… view at source ↗
Figure 8
Figure 8. Figure 8: The full system prompt used for the Idea Generation Policy. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The full user prompt used for the Idea Generation Policy. [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The system prompt used for selecting the best idea among N generated ideas. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The user prompt used for selecting the best idea among N generated ideas. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The system prompt used for supervised fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The user prompt used for supervised fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The system prompt used for the Moderator Agent, tasked with guiding the discussion and enforcing methodological alignment. User Prompt: The Moderator Agent {history} Round {round_num} of discussion. Research Question: {rq} Abstract: {abs} Generated Idea: {idea} Lead the experts to focus only on core methodological alignment between Abstract and Generated Idea. Your role: remind everyone of the goal (to de… view at source ↗
Figure 15
Figure 15. Figure 15: The user prompt used for the Moderator Agent, tasked with guiding the discussion and enforcing methodological alignment. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The system prompt used for the Analyst Agent, tasked with identifying core methodological elements and comparing them. User Prompt: The Analyst Agent {history} Research Question: {rq} Abstract: {abs} Generated Idea: {idea} Now, based on the prior discussions and your previous stance, re-evaluate your analysis. Provide your updated opinion explicitly noting any changes or reaffirmations compared to your ea… view at source ↗
Figure 17
Figure 17. Figure 17: The user prompt used for the Analyst Agent, tasked with identifying core methodological elements and comparing them.. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The system prompt used for the Critic Agent, tasked with scrutinizing logic and detecting contradictions. User Prompt: The Critic Agent {history} Research Question: {rq} Abstract: {abs} Generated Idea: {idea} Based on the ongoing discussion and your previous critiques, reassess whether the Generated Idea aligns methodologically with the Abstract. • Identify the Abstract’s and Generated Idea’s methodologic… view at source ↗
Figure 19
Figure 19. Figure 19: The user prompt used for the Critic Agent, tasked with scrutinizing logic and detecting con￾tradictions. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: The system prompt used for the Evaluator Agent, tasked with synthesizing the debate and issuing the final binary reward. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: The user prompt used for the Evaluator Agent, tasked with synthesizing the debate and issuing the final binary reward. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: The full system prompt used for extracting the idea from the output of AI Scientist. [PITH_FULL_IMAGE:figures/full_fig_p028_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: The full user prompt used for extracting the idea from the output of GPT Researcher and AI [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: The system prompt used to evaluate two ideas based on novelty, feasibility, and effectiveness, 29 [PITH_FULL_IMAGE:figures/full_fig_p029_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: The user prompt used to evaluate two ideas based on novelty, feasibility, and effectiveness, [PITH_FULL_IMAGE:figures/full_fig_p030_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: The system prompt used to evaluate a given scientific idea based on novelty, feasibility and [PITH_FULL_IMAGE:figures/full_fig_p031_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: The user prompt used to evaluate a given scientific idea based on novelty, feasibility and effec [PITH_FULL_IMAGE:figures/full_fig_p032_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: The input research question (Sample 1) used to prompt all models for this comparison. [PITH_FULL_IMAGE:figures/full_fig_p033_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Research idea generated for sample 1 by the Unsloth Qwen2.5 14B baseline. [PITH_FULL_IMAGE:figures/full_fig_p033_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Research idea generated for sample 1 by the GPT Researcher agent. [PITH_FULL_IMAGE:figures/full_fig_p033_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Research idea generated for sample 1 by AI Scientist V2. [PITH_FULL_IMAGE:figures/full_fig_p034_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Research idea generated for sample 1 by the Research Agent. [PITH_FULL_IMAGE:figures/full_fig_p034_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Research idea generated for sample 1 by the model Fine-Tuned on ICLR data (SFT). [PITH_FULL_IMAGE:figures/full_fig_p034_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: Research idea generated for sample 1 by LDC. [PITH_FULL_IMAGE:figures/full_fig_p035_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Research idea generated for sample 1 by our method - BoN (10). [PITH_FULL_IMAGE:figures/full_fig_p035_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: The input research question (Sample 2) used to prompt all models for this comparison. [PITH_FULL_IMAGE:figures/full_fig_p036_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Research idea generated for sample 2 by the Unsloth Qwen2.5 14B baseline. [PITH_FULL_IMAGE:figures/full_fig_p036_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: Research idea generated for sample 2 by the GPT Researcher agent. [PITH_FULL_IMAGE:figures/full_fig_p036_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: Research idea generated for sample 2 by AI Scientist V2. [PITH_FULL_IMAGE:figures/full_fig_p036_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: Research idea generated for sample 2 by the Research Agent. [PITH_FULL_IMAGE:figures/full_fig_p037_40.png] view at source ↗
Figure 41
Figure 41. Figure 41: Research idea generated for sample 2 by the model Fine-Tuned on ICLR data (SFT). [PITH_FULL_IMAGE:figures/full_fig_p037_41.png] view at source ↗
Figure 42
Figure 42. Figure 42: Research idea generated for sample 2 by LDC. [PITH_FULL_IMAGE:figures/full_fig_p037_42.png] view at source ↗
Figure 43
Figure 43. Figure 43: Research idea generated for sample 2 by our method - BoN (10). [PITH_FULL_IMAGE:figures/full_fig_p037_43.png] view at source ↗
Figure 44
Figure 44. Figure 44: The input research question (Sample 3) used to prompt all models for this comparison. [PITH_FULL_IMAGE:figures/full_fig_p038_44.png] view at source ↗
Figure 45
Figure 45. Figure 45: Research idea generated for sample 3 by the Unsloth Qwen2.5 14B baseline. [PITH_FULL_IMAGE:figures/full_fig_p038_45.png] view at source ↗
Figure 46
Figure 46. Figure 46: Research idea generated for sample 3 by the GPT Researcher agent. [PITH_FULL_IMAGE:figures/full_fig_p038_46.png] view at source ↗
Figure 47
Figure 47. Figure 47: Research idea generated for sample 3 by AI Scientist V2. [PITH_FULL_IMAGE:figures/full_fig_p039_47.png] view at source ↗
Figure 48
Figure 48. Figure 48: Research idea generated for sample 3 by the Research Agent. [PITH_FULL_IMAGE:figures/full_fig_p039_48.png] view at source ↗
Figure 49
Figure 49. Figure 49: Research idea generated for sample 3 by the model Fine-Tuned on ICLR data (SFT). [PITH_FULL_IMAGE:figures/full_fig_p039_49.png] view at source ↗
Figure 50
Figure 50. Figure 50: Research idea generated for sample 3 by LDC. [PITH_FULL_IMAGE:figures/full_fig_p039_50.png] view at source ↗
Figure 51
Figure 51. Figure 51: Research idea generated for sample 3 by our method - BoN (10). [PITH_FULL_IMAGE:figures/full_fig_p039_51.png] view at source ↗
Figure 52
Figure 52. Figure 52: and [PITH_FULL_IMAGE:figures/full_fig_p040_52.png] view at source ↗
Figure 53
Figure 53. Figure 53: Pairwise Evaluation Scores on NeurIPS 2025 Dataset. Consistent with the ICLR results, our method maintains a lead in Novelty and Effectiveness, while the Base Model and AI Scientist V2 score higher on Feasibility. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_53.png] view at source ↗
read the original abstract

Large Language Models (LLMs) have demonstrated potential in automating scientific ideation, yet current approaches relying on iterative prompting or complex multi-agent architectures often suffer from hallucination or computational inefficiency. A critical bottleneck in applying Reinforcement Learning (RL) to this open-ended domain is reward hacking -- where models exploit imperfect evaluation proxies to maximize scores without producing genuine scientific innovation. To address these limitations, we propose an RL framework explicitly tailored for high-quality scientific idea generation. We propose the first multi-agent reward function designed to serve as a judge, decoupling methodological validation from implementation details while providing strict binary rewards that are robust to reward hacking. To effectively optimize against this sparse signal, we utilize an unbiased variant of Group Relative Policy Optimization to mitigate artificial length bias. We grounded our training in ICLR-320, a curated dataset of problem-solution pairs extracted from ICLR 2024 proceedings. Experiments demonstrate that our framework significantly outperforms state-of-the-art baselines across expert-evaluated metrics of novelty, feasibility, and effectiveness.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 2 minor

Summary. The paper proposes an RL post-training framework for LLM-based scientific ideation that uses a multi-agent debate system to generate strict binary rewards, decoupling methodological validation from implementation details and claiming robustness to reward hacking. It employs an unbiased variant of Group Relative Policy Optimization to optimize against this sparse signal, trains on a curated ICLR-320 dataset of problem-solution pairs from ICLR 2024, and reports significant outperformance over state-of-the-art baselines on expert-evaluated metrics of novelty, feasibility, and effectiveness.

Significance. If the empirical claims hold after proper validation, the work could advance RL applications to open-ended scientific tasks by offering a more reliable alternative to iterative prompting or complex multi-agent setups prone to hallucination. The emphasis on binary rewards from debate and unbiased optimization addresses a recognized bottleneck in reward design for creative generation.

major comments (4)
  1. Abstract: The headline claim of significant outperformance on novelty, feasibility, and effectiveness is presented without any mention of statistical tests, number of expert raters, baseline implementations, or dataset construction details, rendering it impossible to evaluate whether the metrics support the central assertion.
  2. Abstract and reward-function description: The multi-agent reward is stated to deliver 'strict binary rewards that are robust to reward hacking,' yet no formal argument, adversarial attack experiments, or ablation removing the multi-agent component is referenced, which is load-bearing for attributing gains to the proposed framework rather than to the RL setup itself.
  3. Experiments section: Expert human ratings are treated as ground truth for scientific quality, but the manuscript provides no information on blinding, inter-rater reliability statistics, or controls for presentation bias, leaving open the possibility that observed deltas reflect evaluator artifacts rather than genuine ideation improvements.
  4. Optimization section: The 'unbiased variant of Group Relative Policy Optimization' is introduced to mitigate length bias with the sparse binary signal, but no equations, derivation of unbiasedness, or comparison to standard GRPO are supplied, making it unclear whether the optimization is correctly specified for the reward structure.
minor comments (2)
  1. Abstract: Acronyms RL and GRPO appear without initial expansion; the dataset name 'ICLR-320' is introduced without a brief description of its construction or scale.
  2. Overall: Several claims about decoupling validation from implementation would benefit from a concrete example or pseudocode in the method section to improve clarity for readers.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for their constructive comments and recommendations. We address each of the major comments in detail below, indicating the revisions we plan to make to the manuscript.

read point-by-point responses

  1. Referee: Abstract: The headline claim of significant outperformance on novelty, feasibility, and effectiveness is presented without any mention of statistical tests, number of expert raters, baseline implementations, or dataset construction details, rendering it impossible to evaluate whether the metrics support the central assertion.

    Authors: We agree that including these details in the abstract would improve transparency. In the revised manuscript, we will update the abstract to reference the statistical tests conducted, the number of expert raters involved in the evaluation, and key information about the baseline implementations and the construction of the ICLR-320 dataset. These elements are described in the Experiments section, and we will ensure the abstract provides sufficient context for the claims. revision: yes

  2. Referee: Abstract and reward-function description: The multi-agent reward is stated to deliver 'strict binary rewards that are robust to reward hacking,' yet no formal argument, adversarial attack experiments, or ablation removing the multi-agent component is referenced, which is load-bearing for attributing gains to the proposed framework rather than to the RL setup itself.

    Authors: The robustness claim stems from the requirement for multi-agent consensus in generating binary rewards, which we believe makes reward hacking more challenging than with single-agent or scalar reward systems. However, we acknowledge the lack of formal arguments or specific experiments supporting this. We will revise the reward-function description to include a more detailed rationale and add an ablation study comparing the full multi-agent reward system to a single-agent baseline to better attribute the performance gains. revision: partial

  3. Referee: Experiments section: Expert human ratings are treated as ground truth for scientific quality, but the manuscript provides no information on blinding, inter-rater reliability statistics, or controls for presentation bias, leaving open the possibility that observed deltas reflect evaluator artifacts rather than genuine ideation improvements.

    Authors: We recognize the critical need for rigorous human evaluation protocols. The current version of the manuscript does not detail these aspects. In the revision, we will add information on the blinding procedures used, report inter-rater reliability statistics such as agreement rates or kappa coefficients, and describe controls implemented to mitigate presentation bias, such as standardized formatting of ideas presented to raters. revision: yes

  4. Referee: Optimization section: The 'unbiased variant of Group Relative Policy Optimization' is introduced to mitigate length bias with the sparse binary signal, but no equations, derivation of unbiasedness, or comparison to standard GRPO are supplied, making it unclear whether the optimization is correctly specified for the reward structure.

    Authors: We agree that the optimization method requires a more formal presentation. We will expand the Optimization section in the revised manuscript to include the relevant equations for the unbiased GRPO variant, provide a derivation of its unbiasedness property in the context of sparse binary rewards, and include a direct comparison to the standard GRPO algorithm to clarify its specification and advantages for this task. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical outperformance claims rely on external dataset and baselines

full rationale

The paper describes an RL post-training framework with a proposed multi-agent reward function, trained on the external ICLR-320 dataset extracted from ICLR 2024 proceedings, and evaluated via expert human ratings against state-of-the-art baselines. No equations, derivations, or self-citations are referenced in the abstract or provided text that would reduce any claimed result (such as novelty/feasibility/effectiveness scores) to a fitted parameter or prior input by construction. The central results are presented as empirical comparisons to independent benchmarks rather than tautological redefinitions or self-referential predictions. This matches the default expectation for non-circular empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited to the core domain assumption required by the reward design; no free parameters or invented entities are explicitly introduced in the provided text.

axioms (1)
  • domain assumption A multi-agent debate process can produce strict binary rewards for scientific ideas that are robust against reward hacking.
    This premise underpins the entire reward function and the decision to use binary signals instead of continuous scores.

pith-pipeline@v0.9.0 · 5524 in / 1294 out tokens · 83656 ms · 2026-05-10T07:52:27.339347+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

49 extracted references · 2 canonical work pages · cited by 1 Pith paper

  1. [1]

    Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, and Lidong Bing

    URLhttps://doi.org/10.48550/arXiv.2503.19257. Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, and Lidong Bing. Chain of ideas: Revolutionizing research via novel idea development with LLM agents. In Christos Christodoulopou- los, Tanmoy Chakraborty...

    work page doi:10.48550/arxiv.2503.19257 2025

  2. [2]

    Understanding R1-Zero-Like Training: A Critical Perspective

    URLhttps://doi.org/10.48550/arXiv.2503.20783. 11 Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The AI scientist: Towards fully automated open-ended scientific discovery.arXiv preprint arXiv:2408.06292, 2024. URL https://doi.org/10.48550/arXiv.2408.06292. Maxime Robeyns and Laurence Aitchison. Improving llm-generated cod...

    work page Pith review doi:10.48550/arxiv.2503.20783 2024

  3. [3]

    Configuration [11] (2 Analysts + 1 Evaluator) represents the optimal trade-off chosen for the main pipeline, achieving perfect precision (1.0) with robust recall (0.300)

    nearly eliminates recall. Configuration [11] (2 Analysts + 1 Evaluator) represents the optimal trade-off chosen for the main pipeline, achieving perfect precision (1.0) with robust recall (0.300). 17 F Prompts System Prompt: Survey Classifier You are an expert research assistant. Your task is to determine if a research paper is a SUR- VEY/REVIEW paper, a ...

  4. [4]

    paper_type

    If the paper mainly reviews, surveys, or summarizes existing work then it is a SURVEY ("paper_type": "survey")

  5. [5]

    paper_type

    If the paper introduces a new method, model, algorithm, framework, or experimental setup then it is a NEW IDEA ("paper_type": "new_idea")

  6. [6]

    paper_type

    If the paper does not introduce a new method but instead focuses on evaluating, testing, bench- marking, or stress-testing existing methods on certain tasks or datasets, then it is an EVALUATION paper ("paper_type": "evaluation"). Output format:

  7. [7]

    First write your reasoning step by step

  8. [8]

    paper_type

    Then give the final answer in strict JSON format, for example: {"paper_type": "survey"} {"paper_type": "new_idea"} {"paper_type": "evaluation"} Don’t forget to tell your reasoning. Figure 4: The system prompt used to classify whether a paper contains new ideas or is a survey or evaluation paper. User Prompt: Survey Classifier Here is the title of the pape...

  9. [9]

    Core Problem: [Synthesize the main challenge]

  10. [10]

    Gap/Opportunity: [State the specific gap inferred from the question]

  11. [11]

    ‘json). JSON SCHEMA: {

    Bridge: [Explain how your idea connects the gap to a solution] </reasoning> <answer> [Your specific and actionable research idea, focusing on the proposed method] </answer> Figure 8: The full system prompt used for the Idea Generation Policy. 20 User Prompt: Idea Generation Policy Research Question: {research_question} Based on the instructions, please sy...

  12. [12]

    If any participant attempts this, issue a clear warning

    The discussion mustnotcompare the Abstract and the Generated Idea based on data pipelines or evaluation setups. If any participant attempts this, issue a clear warning

  13. [13]

    Ensure they compare concrete components — algorithms, frameworks — and that they do not compare the datasets and evaluation process

  14. [14]

    If the Generated Idea contains placeholder text or fails to propose a substantial or meaningful solution to the stated Research Question, immediately halt the discussion and notify all partici- pants

  15. [15]

    You act as a moderator — your job is to guide and manage the conversation ensuring it stays focused and methodologically sound

    You arenota participant in the discussion. You act as a moderator — your job is to guide and manage the conversation ensuring it stays focused and methodologically sound. Figure 14: The system prompt used for theModerator Agent, tasked with guiding the discussion and enforcing methodological alignment. User Prompt: The Moderator Agent {history} Round{roun...

  16. [16]

    – If your current assessment differs from your prior view explicitly explain why your opinion evolved

    Carefullyreviewthepreviousdiscussionsandyourownearlieropinionstoensurecontinuityandconsistency in reasoning. – If your current assessment differs from your prior view explicitly explain why your opinion evolved

  17. [17]

    Identify the core methodological elements and novel contributions in the Abstract — such as models, algorithms, architectures, and assumptions

  18. [18]

    Compare each core methodological or conceptual element of the Abstract against the Generated Idea

  19. [19]

    Highlight any misalignment or omission, such as when a critical component from the Abstract is absent, replaced, or contradicted in the Generated Idea

  20. [20]

    Pay extra attention to specific innovations or contributions rather than general context, background, or motivation

  21. [21]

    – Provide analytical reasoning when commenting, always grounded in methodological evidence

    Engage with other persons by commenting on their observations: – You may agree, partially agree, or respectfully disagree. – Provide analytical reasoning when commenting, always grounded in methodological evidence. – If another person’s reasoning is unclear, inconsistent or off-topic, politely request clarification. Your Output Should Include: •A clear li...

  22. [22]

    – Ensure consistency with your earlier assessments, or clearly explain any evolution in your stance

    Review prior discussions and your own previous opinions before providing a new one. – Ensure consistency with your earlier assessments, or clearly explain any evolution in your stance

  23. [23]

    – Do not consider datasets, data pipelines, evaluation setups, or metrics for comparison

    IdentifytheAbstract’scoremethodologicalassumptionsandapproach—includingtheoreticalfoundations, modeling frameworks, or algorithmic strategies. – Do not consider datasets, data pipelines, evaluation setups, or metrics for comparison

  24. [24]

    – Explicitly point out contradictions or incompatible assumptions

    Compare these elements with the Generated Idea: – Highlight any missing, altered, or replaced methodological aspects. – Explicitly point out contradictions or incompatible assumptions

  25. [25]

    – Provide concise, evidence-based commentary that either supports or challenges their reasoning

    Engage with other agents constructively: – You may agree, partially agree, or respectfully disagree with their analysis. – Provide concise, evidence-based commentary that either supports or challenges their reasoning. – If another participant’s comment is vague or off-topic, request clarification politely. Your Output Should Include: •A list or brief summ...

  26. [26]

    – Briefly capture the essence of their arguments and reasoning

    Begin by reviewing and summarizing the prior discussion among people (Analyst, Critic, Moder- ator, etc.). – Briefly capture the essence of their arguments and reasoning. – Consider the reasoning and arguments made by all participants (Analyst, Critic, Moderator, etc.). – Extract each participant’s individual opinion (e.g., match or not match) and reasoni...

  27. [27]

    – Ignore datasets, metrics, or evaluation setups as comparison factors

    After summarizing, perform your own evaluation: – Focus solely onmethodologyandcore contributions. – Ignore datasets, metrics, or evaluation setups as comparison factors

  28. [28]

    Determine alignment: Decide whether the Abstract and the Generated Idea express essentially the same core methodological logic and contributions

  29. [29]

    Provide a final, concise judgment reflecting both the discussion summary and your reasoning

  30. [30]

    ‘json {

    Justify your conclusion: Provide a brief, evidence-based explanation that reflects the entire dis- cussion history. Output Format:After writing the summarization and your reasoning, return your answer strictly in the following JSON format: <summarization> Your summarization </summarization> <reasoning> Your reasoning </reasoning> “‘json { "reason": "short...

  31. [31]

    •Extract each participant’s stance (match or not match) and key reasoning

    First summarize the discussion among participants and write it down. •Extract each participant’s stance (match or not match) and key reasoning. •State whether they generally agree or disagree

  32. [32]

    reason":

    Then, make your own evaluation strictly based onmethodologyandcore contributionsand write your reasoning. •Ignore dataset, metrics, and evaluation setups. •If the Generated Idea captures themain ideaandcentral logicof the Abstract, even with small or secondary differences, mark it as a match. •Mark it as not a match if it diverges from or contradicts the ...

  33. [34]

    Provide thorough reasoning explaining how the methods compare on each criterion before indicating which method is superior

    Two proposed methods (Method A and Method B) to address that question Your role is to compare these methods based on three criteria:Novelty,Feasibility, andEffectiveness. Provide thorough reasoning explaining how the methods compare on each criterion before indicating which method is superior. Evaluation Criteria

  34. [35]

    •Task:Compare which method demonstrates greater originality, introduces more innovative concepts, or combines ideas in more unprecedented ways

    Novelty •Definition:The degree to which the proposed method introduces new concepts, approaches, or perspec- tives that differ from existing work in the field. •Task:Compare which method demonstrates greater originality, introduces more innovative concepts, or combines ideas in more unprecedented ways

  35. [36]

    •Task:Comparewhichmethodismorepracticaltoimplement, requiresfewerresources, facesfewertechnical barriers, or has a more realistic timeline

    Feasibility •Definition:The practical viability of implementing the proposed method given current technological capabilities, resource requirements, time constraints, and technical complexity. •Task:Comparewhichmethodismorepracticaltoimplement, requiresfewerresources, facesfewertechnical barriers, or has a more realistic timeline

  36. [37]

    A"- Method A is clearly better •

    Effectiveness •Definition:The expected capability of the method to adequately address the research question and produce meaningful, reliable results that advance scientific understanding. •Task:Compare which method better addresses the research question, is more likely to produce robust results, or has stronger scientific methodology. Comparison Values Fo...

  37. [38]

    A scientific research question

  38. [39]

    Provide thorough reasoning for each criterion before assigning scores

    A proposed method or idea to address that question Your role is to critically assess the proposed method based on three criteria:Novelty,Feasibility, and Effectiveness. Provide thorough reasoning for each criterion before assigning scores. Evaluation Criteria

  39. [40]

    •5 (Highly Novel):Introduces groundbreaking concepts or paradigm-shifting approaches not previously explored

    Novelty (1-5) •Definition:The degree to which the proposed method introduces new concepts, approaches, or perspectives that differ from existing work in the field. •5 (Highly Novel):Introduces groundbreaking concepts or paradigm-shifting approaches not previously explored. •4 (Novel):Presents fresh perspectives or modifications that significantly advance ...

  40. [41]

    •5 (Highly Feasible):Can be readily implemented with existing resources and technology

    Feasibility (1-5) •Definition:The practical viability of implementing the proposed method given current technological capabilities and resources. •5 (Highly Feasible):Can be readily implemented with existing resources and technology. •4 (Feasible):Implementation is practical with reasonable effort. •3 (Moderately Feasible):Presents notable implementation ...

  41. [42]

    novelty": <1-5>,

    Effectiveness (1-5) •Definition:The expected capability of the method to adequately address the research question. •5 (Highly Effective):Directly and comprehensively addresses all aspects of the research question. •4 (Effective):Addresses the core research question well. •3 (Moderately Effective):Partially addresses the research question. •2 (Minimally Ef...

  42. [43]

    Train an LLM to simulate neural activity patterns inspired by the holonomic brain theory, focusing on distributed memory and interference patterns

  43. [44]

    Incorporate concreteness scores for input and output text segments, ensuring the model penalizes responses that deviate from human-like semantic coherence

  44. [45]

    Use Representational Similarity Analysis (RSA) to compare the model’s semantic representations with those of humans, as established in Related Paper #6

  45. [46]

    Implement a scoring mechanism that detects deviations in neural activity patterns simulated by the LLM compared to those observed in human brains, guided by Related Papers #7 and #9

  46. [47]

    Develop a threshold-based system that flags potential hallucinations when the deviation exceeds predefined thresholds

  47. [48]

    Use this flagged data to iteratively refine the model’s training, enhancing its ability to mitigate hallucina- tions over time

  48. [49]

    Evaluate the effectiveness of our method using qualitative and quantitative assessments, including human judgment and benchmark datasets

  49. [50]

    What supporting evidence leads to this conclusion?

    Ensure the method’s generalizability by testing it across various types of LLMs and application domains. Figure 32: Research idea generated for sample 1 by the Research Agent. Example 1: Generated Idea from SFT A framework that monitors the evolution of intermediate representations throughout model generations by computing the KL divergence between distri...

    2024