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Distill, Debate, Discover: Grounding Multi-Agent Research Ideation in Graph-Distilled Insights

Qianran Ma, Muxin Liu, Grace Kim, Xiao Liang, Chris Callison-Burch

arXiv:2610.33366v1cs.CL

TL;DR

Initial research ideas can shape subsequent outcomes, but equipping debate agents with distinct, meaningful expertise is a challenge. INSIGHTAGENTS distills literature-based insights into agent perspectives that guide collaborative proposal development. Across 100 NLP topics, it achieves the highest average Elo among compared methods in both knowledge settings under both judges.

  • Problem

    Initial research ideas can shape subsequent research outcomes, motivating literature-grounded research idea generation through multi-agent collaboration.

    Source: §1 · p. 1

  • Method

    INSIGHTAGENTS distills insights from related-paper graphs to initialize expert agents, who identify a problem and propose, challenge, and refine approaches through debate.

    Source: §1 · pp. 1–2

  • Results

    INSIGHTAGENTS achieves the highest average Elo among compared methods under both judges in both online and offline settings on 100 NLP topics.

    Source: §7 · p. 8

  • Takeaways & Limitations

    The findings suggest that how agents acquire and use scientific knowledge is an important consideration in automated research.

    Source: §7 · p. 8

    Evaluation covers only NLP topics, and the framework’s reliance on a predefined topic and existing literature limits its ability to independently discover emerging or entirely new directions.

    Source: Limitations · p. 9

Abstract

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Large language models (LLMs) are increasingly used to support research idea generation and scientific discovery. Multi-agent debate offers a collaborative approach to this task by bringing together diverse perspectives to develop research ideas. However, a key challenge is how to equip agents with distinct and meaningful expertise to guide their contributions. Inspired by how researchers draw on accumulated research experience during collaboration, we introduce InsightAgents, a framework that constructs literature-grounded agent expertise by distilling insights from graphs of related papers. Guided by insights that trace evolving research approaches, reveal underlying assumptions, and identify unresolved challenges, within a line of research, agents jointly identify a research problem and propose, challenge, and refine potential approaches through debate to develop a research proposal. We evaluate InsightAgents against three research idea generation frameworks on 100 NLP research topics through pairwise comparisons by two LLM judges. In both online and offline knowledge settings, InsightAgents achieves the highest average score across the five evaluation dimensions under both judges.

1 Introduction

INSIGHTAGENTS grounds multi-agent research ideation in insights distilled from related literature. Agents use distinct research perspectives to develop proposals through debate, and the framework earns the highest average scores among the compared methods in its evaluation.

1 Introduction

  • The work focuses on literature-grounded idea generation through multi-agent collaboration because initial research ideas can shape subsequent research outcomes.Source: §1 · p. 1
  • INSIGHTAGENTS distills research insights from related-paper graphs to initialize expert agents that identify a problem and propose, challenge, and refine approaches through debate.A moderator coordinates discussion, and a critic gives feedback on draft proposals.Source: §1 · p. 1, Figure 1 · p. 2
  • On 100 NLP topics, INSIGHTAGENTS achieves the highest average score among compared methods under both judges across novelty, significance, feasibility, clarity, and expected effectiveness.The evaluation uses pairwise comparisons.Source: §1 · p. 2
  • A research insight synthesizes how a line of research evolves, including its approaches, assumptions, supporting findings, and unresolved tensions.The insights and their supporting findings are distilled from a topic-specific paper graph.Source: §1 · pp. 1–2

2 Related Work

Prior systems ground idea generation in retrieved literature or structured research relationships, while multi-agent frameworks use several forms of agent diversity. INSIGHTAGENTS distinguishes itself by grounding debate in insights from different research lines.

2 Related Work

  • Retrieval-based systems use relevant papers, while chain- or graph-based methods capture research developments; the cited discussion contrasts their uses of structure with INSIGHTAGENTS’ insight-grounded debate.Source: §2 · p. 2
  • INSIGHTAGENTS grounds multi-agent debate in insights distilled from distinct research lines, allowing those perspectives to interact during idea development.Source: §2 · pp. 2–3
  • Table 1 compares frameworks by topic control, literature graphs, insight extraction, debate, expertise source, and documented support or official code.Shaded framework names identify the three experimental baselines.Source: Table 1 · p. 3
  • Existing collaboration frameworks vary agents through roles, personas, profiles, or knowledge backgrounds, but the paper says these do not provide topic-specific expertise grounded in prior findings and assumptions.Source: §2 · p. 3

3 Method

INSIGHTAGENTS has two components: graph-based expertise construction and multi-agent debate. It distills insights from related papers into distinct agent perspectives, which guide problem selection, proposal development, and final synthesis.

3 Method

  • The framework combines graph-based expertise construction with multi-agent debate to turn literature-derived insights into research proposals.Source: §3 · p. 3

3.1 Constructing Agent Expertise

  • The paper graph starts from a user-specified or topic-retrieved seed, expands through Semantic Scholar recommendations, selects relevant papers with an LLM, and orients links from older to newer papers.Expansion proceeds recursively for three rounds, up to three levels beyond the seed.Source: §3.1 · p. 3
  • An LLM groups papers into three distinct research lines and synthesizes each line’s recurring challenges, evolving approaches, assumptions, and unresolved tensions into an insight.Each line is supported by at least two papers, with findings linked to source papers.Source: §3.1 · pp. 3–4
  • Each expert is initialized with one insight, its findings, and source-paper titles and abstracts, grounding its perspective in specific methods, benchmarks, and results.Source: §3.1 · p. 4

3.2 Research Idea Generation through Multi-Agent Debate

  • Experts propose problems, and the moderator consolidates three anonymous candidates for independent ranking; Borda scores determine the selected problem.First, second, and third place receive 3, 2, and 1 points, respectively.Source: §3.2 · p. 4
  • Experts develop and refine a shared proposal through proposal, refinement, and convergence rounds, with the critic reviewing drafts and experts addressing feedback.The moderator combines complementary approaches, updates the draft, and tracks resolved and unresolved issues.Source: §3.2 · p. 4
  • The moderator’s final synthesis retains the selected problem, incorporates agreed methods, reports unresolved concerns as limitations, and specifies the experiment, anticipated novelty, and potential impact.Source: §3.2 · p. 4

4 Experimental Setup

The evaluation uses 100 NLP topics in two knowledge settings and compares INSIGHTAGENTS with three baselines. Pairwise LLM judgments assess five dimensions, with a separate human–LLM agreement study on a subset of topics.

4.1 Pipeline Setup

  • Both knowledge settings use the same insight distillation, expertise formation, and debate procedures, but differ in knowledge sources and graph retrieval and expansion.The online setting dynamically builds a paper graph; the offline setting maps contribution nodes in a preconstructed graph to source papers.Source: §4.1 · pp. 4–5
  • The online and offline configurations are not a controlled comparison of knowledge sources because their corpora, graph construction, edge semantics, and seed selection differ.Source: §4.1 · p. 5
  • The 100 evaluation topics are randomly selected from a pool derived from 2025 ACL, EMNLP, NAACL, and COLING proceedings and Findings volumes, spanning 20 research areas.Source: §4.1 · p. 5

4.2 Baselines

  • The three baselines are ResearchAgent, AI Scientist-v2, and Chain of Ideas; other multi-agent frameworks are excluded because they do not meet the given-topic and public-code criteria.All methods use the same topics and generation model, but generation costs are not controlled.Source: §4.2 · p. 5

4.3 Evaluation Protocol

  • Two LLM judges compare every method pair on novelty, significance, feasibility, clarity, and expected effectiveness, evaluating each pair in both presentation orders.Source: §4.3 · p. 5
  • Two NLP researchers independently rank proposals on 10 topics across the same five dimensions, enabling measurement of interannotator and human–LLM agreement.Source: §4.3 · p. 5

5 Results

Across both knowledge settings and both LLM judges, INSIGHTAGENTS leads in average Elo, with strong rankings in novelty, significance, and expected effectiveness. Human ratings on a 10-topic subset support the main pattern, while feasibility and clarity show trade-offs.

Main results

  • INSIGHTAGENTS achieves the highest average Elo in all four knowledge-setting and judge combinations, and both judges rank it first in novelty, significance, and expected effectiveness across settings.AI Scientist-v2 ranks second in average Elo.Source: §5 · p. 6
  • Feasibility is the main trade-off: AI Scientist-v2 ranks first and INSIGHTAGENTS second across settings and judges, while clarity rankings vary by judge.GPT-5.6 Sol ranks INSIGHTAGENTS first in clarity; Claude Opus 4.8 favors AI Scientist-v2.Source: §5 · p. 6

Human–LLM agreement

  • On 10 human-evaluated online topics, INSIGHTAGENTS has the highest average Elo, with its largest margins over AI Scientist-v2 in novelty, significance, and expected effectiveness.The methods receive similar human ratings for clarity and feasibility; INSIGHTAGENTS is slightly ahead on clarity and AI Scientist-v2 on feasibility.Source: §5 · p. 6
  • Human–LLM comparisons show 90.0–92.7% non-contradiction, while exact agreement is 63.7–65.0%; most disagreements involve ties rather than opposite preferences.Human annotators likewise show more tie–preference disagreements (46.0%) than exact agreement (44.0%).Source: §5 · p. 6

6 Analysis

Ablations associate insight distillation, retrieval, and especially multi-agent debate with benefits to proposal quality, while evidence analysis shows experts contribute distinct literature and judges favor different proposal properties. Case studies illustrate complementary insights being combined and refined through debate.

6.1 Ablation Study

  • Removing insight distillation or retrieval reduces novelty and significance without reducing effectiveness, while simpler proposals score slightly higher on feasibility and clarity.The ablations propose 8.6 and 7.5 experimental components versus 9.1 for INSIGHTAGENTS; without retrieval, proposals cite 1.9 works from 2021 on average, versus 3.6 from 2026 for INSIGHTAGENTS, and can score close to the full system despite weaker grounding.Source: §6.1 · pp. 6–7
  • Removing debate produces the largest overall performance drop (33.0), especially in novelty (19.2) and expected effectiveness (11.7), while feasibility remains nearly unchanged (51.7).The authors suggest multi-agent discussion and iterative refinement help develop more novel and potentially effective proposals.Source: §6.1 · p. 7

6.2 Generation Process Analysis

  • Across 100 online topics, 289 of 300 expert pairs share no supporting papers, and 299 of 300 first-round proposals cite at least one paper from their expert’s support set.In 67 topics, the final proposal’s field references draw on at least two experts.Source: §6.2 · p. 7
  • For personalized dialogue with evolving memory, three distilled insights address memory-use evaluation, evolving memory representation, and regulating personalization.In the debate example, experts turn a loosely defined influence budget into four discrete influence levels, lifecycle constraints, and interventions applied to a shared candidate pool before generation.Source: §6.2 · pp. 7–8

6.3 Comparison with Other Frameworks

  • Judges’ explanations favor INSIGHTAGENTS on originality and evidence across all three baselines (81.9–98.9%), but favor AI Scientist-v2 on executability in 80.1% of relevant rationales.Judges cite the scale of proposed studies, human annotation, and custom experimental setups as executability obstacles.Source: §6.3 · p. 8

7 Conclusion

INSIGHTAGENTS builds agent expertise from insights distilled across related papers, giving agents distinct research perspectives grounded in supporting findings. On 100 NLP topics, it achieves the highest average Elo among compared methods in both knowledge settings and under both LLM judges.

7 Conclusion

  • INSIGHTAGENTS constructs distinct agent expertise from insights distilled across related papers and their supporting findings.The framework uses multi-agent debate for research idea generation.Source: §7 · p. 8
  • On 100 NLP research topics, INSIGHTAGENTS achieves the highest average Elo among compared methods under both LLM judges in online and offline knowledge settings.Source: §7 · p. 8
  • The findings suggest that how agents acquire and use scientific knowledge is an important consideration in automated research.Literature-grounded expert debate could serve as an ideation component in larger automated research workflows.Source: §7 · p. 8

Limitations

The evaluation has three main boundaries: it covers NLP topics, assesses proposals before experimental execution, and relies primarily on LLM judges with human evaluation on a small subset.

Limitations

  • Applicability beyond NLP remains untested, and starting from predefined topics and existing literature limits independent discovery of emerging or entirely new directions.The framework does not execute proposed experiments or incorporate experimental feedback, so the evaluation measures idea quality before execution rather than whether proposals yield meaningful findings.Source: Limitations · p. 9
  • Evaluation relies primarily on LLM judges; human evaluation covers only a small subset, and LLM assessments may not fully reflect expert judgments of research quality.Source: Limitations · p. 9

Ethical considerations

The paper describes safeguards and intended use for LLM-generated research proposals, details how its NLP topic pool was selected, and cautions against attributing online–offline differences to knowledge source alone.

Ethical considerations

  • LLM-generated proposals may contain unsupported claims, inaccurate interpretations, or non-novel ideas; they are experimentally unvalidated and require expert review before use.The authors report separate results from two LLM judges and compare their assessments with human rankings on a subset to assess potential judge bias.Source: Ethical considerations · p. 9
  • Automated idea generation could enable large-scale production of unvetted proposals or submissions; the authors intend the system to support researchers, not replace human judgment or scientific validation.Source: Ethical considerations · p. 9

A Topic Selection

  • The topic pool starts with 8,207 eligible ACL Anthology papers, uniformly samples 600, and uses research-area labels to inspect coverage without imposing area quotas.Demonstrations, tutorials, student research workshops, and industry tracks are excluded; abstracts must contain at least 200 characters.Source: A Topic Selection · p. 12
  • Topic generation and deduplication leave 556 unique candidate topics from the sample: 14 surveys or resource papers are rejected, then 30 near-duplicates are removed.A topic is removed when its cosine similarity to a previously retained topic exceeds 0.85.Source: A Topic Selection · p. 12
  • The evaluation randomly samples 100 topics from the deduplicated pool, spanning 20 of 22 ACL Rolling Review areas, with no area contributing more than 12 topics.Source: A Topic Selection · p. 12

B Offline Knowledge Setting

  • The offline setting uses a local 230,454-paper corpus and changes graph retrieval while leaving insight distillation, expertise formation, and debate unchanged.Offline graphs average 38.0 papers versus 43.4 online, and the settings select the same seed for 9 of 100 topics; differences cannot be attributed to knowledge source alone because corpus, edge relation, and paper evidence also differ.Source: B Offline Knowledge Setting · pp. 12–13

C Implementation Details

The appendix reports direct comparisons, feasibility rationales, implementation costs, and baseline configurations. INSIGHTAGENTS scores above the baselines overall, with feasibility the main trade-off against AI Scientist-v2.

C Implementation Details

  • The graph starts from a selected seed, expands up to three levels, and has an upper bound of 51 papers; one extraction call produces three insights, each supported by at least two papers.The configured output allowances are 8,000 tokens for insight extraction, 2,500 per expert speech, 4,000 per critic review, and 6,000 for the final rewrite.Source: C Implementation Details · p. 13
  • The online system averages 42.5 model calls, $0.104, and 7.1 minutes per proposal; its reported cost uses GPT-5.6 Luna prices and includes Semantic Scholar API latency in time.Source: C Implementation Details · p. 13, Table 5 · p. 13
  • INSIGHTAGENTS costs about as much as ResearchAgent, 1.6 times as much as Chain of Ideas, and over 30 times the AI Scientist-v2 idea-generation stage; cost alone does not explain ranking.AI Scientist-v2 has the second-highest average Elo despite lower cost, while ResearchAgent ranks third despite costing slightly more than INSIGHTAGENTS.Source: C Implementation Details · p. 13, Table 6 · p. 13

D Baseline Configurations

  • Baselines use official implementations with GPT-5.6 Luna, but adaptations include default temperatures, different output limits, and adding topic text to ResearchAgent’s problem prompts.ResearchAgent’s output limit is raised from 1,000 to 4,096 tokens; AI Scientist-v2 and Chain of Ideas retain limits of 4,096 and 4,000 tokens per call, respectively.Source: D Baseline Configurations · p. 13, Table 7 · p. 14

E.1 Detailed results and direct comparison

  • 70.8% online and 73.8% offline under GPT-5.6 Sol, and 61.5% online and 63.3% offline under Claude Opus 4.8, are INSIGHTAGENTS’ average direct-comparison scores against AI Scientist-v2; all four confidence intervals exceed 50%.Against Chain of Ideas and ResearchAgent, scores exceed 68% on every dimension under both judges, with average scores from 86.8% to 92.8%. Novelty, significance, and expected effectiveness remain above 74%; clarity is the only dimension where the judges favor different methods in both settings.Source: §E.1 · p. 14

E.2 Feasibility trade-off

  • AI Scientist-v2 ranks above INSIGHTAGENTS on feasibility across both settings and judges; its winning rationales often mention simple or standard resources, while INSIGHTAGENTS rationales often mention human annotation and multiple components.Claude Opus 4.8 rationales also highlight preregistration and audits; compute concerns appear in fewer than 10%, and only one of 270 rationales explicitly calls INSIGHTAGENTS infeasible, impossible, or impractical.Source: §E.2 · p. 14
  • The authors suggest the feasibility gap may relate more to proposed-study scope and validation requirements than computational demands, but keyword matches do not establish what determines judges’ preferences.Source: §E.2 · pp. 14–15

E.3 LLM Judge Reasoning Analysis

The appendix examines judge-rationale coding and evaluation reliability through order consistency, human agreement, dimension-specific patterns, and proposal length; it also gives the human annotation protocol and an example distilled insight.

E.3 LLM Judge Reasoning Analysis

  • The analysis covers 6,000 online explanations comparing INSIGHTAGENTS with a baseline; when told the proposal’s identity, labeling runs agreed on 97.7% of property labels, and no Figure 6 share changed by more than 3.0 points.Source: §E.3 · p. 15
  • Execution barriers were cited more often for INSIGHTAGENTS than the baseline, with the largest gaps for human annotation (187 vs. 29) and custom apparatus (131 vs. 13).Reported shares were scale, data access, or integration (76.1%), human annotation (52.8%), custom apparatus (39.8%), and an unsolved prerequisite (34.6%). Against Chain of Ideas and ResearchAgent, the direction reverses, with every barrier type cited more often against the baseline.Source: §E.3 · p. 15, Figure 7 · p. 16

E.4.1 Consistency within and across LLM judges

  • On novelty, feasibility, and expected effectiveness, within-judge decisions remain unchanged in at least 89.0% of comparisons for GPT-5.6 Sol and 80.3% for Claude Opus 4.8; cross-judge agreement is at least 83.1%.Claude Opus 4.8 has lower order consistency on significance and clarity (72.3–76.0%), while cross-judge agreement on those dimensions is 68.9–72.5%.Source: §E.4.1 · p. 17, Table 12 · p. 19

E.4.2 Agreement with human annotators

  • Human–LLM non-contradiction is 90.0% for GPT-5.6 Sol and 92.7% for Claude Opus 4.8; exact agreement is lower, at 63.7% and 65.0%, respectively.The human–human and LLM–LLM exact-agreement rates are 44.0% and 82.0%; the authors suggest many human–LLM disagreements involve ties rather than opposing preferences.Source: §E.4.2 · p. 17

E.4.3 Agreement by dimension

  • 73.3–78.3% exact human–LLM agreement on novelty contrasts with 48.3–50.0% on clarity; non-contradiction remains at least 83.3% across dimensions, which each have 30 instances.Annotator 1 returns ties in 30–83% of comparisons, versus at most 17% for the other judges; feasibility shows more baseline preferences and more opposing judgments than novelty.Source: §E.4.3 · p. 17, Figure 8 · p. 18

E.4.4 Proposal length

  • Longer proposals win only 18.1–46.5% of decided judgments across dimensions, settings, judges, and six method pairs; INSIGHTAGENTS’ average score has weak correlation with length differences against AI Scientist-v2 (r = 0.06–0.21).The comparison includes 931-word AI Scientist-v2 proposals, 1,297 online and 1,281 offline INSIGHTAGENTS proposals, 2,175-word Chain of Ideas proposals, and 3,274-word ResearchAgent proposals.Source: §E.4.4 · pp. 18–19, Table 13 · p. 19

F Human Annotation Protocol

  • Two NLP researchers independently ranked four proposals on five dimensions for 10 familiar online topics, with identities hidden, positions randomized, and ties allowed.For agreement analysis, the rankings yield 150 INSIGHTAGENTS–baseline topic–dimension–pair instances.Source: F Human Annotation Protocol · p. 19

G.1 Insights and supporting findings

  • Expert B’s evolving-memory insight traces a shift from profile-memory graphs toward temporal state, multi-round retrieval, selective memory types, and lifecycle-aware relations, alongside growing maintenance and retrieval complexity.Its supporting findings include EMIR2’s reported relative improvements exceeding 12% in some LoCoMo and MemConflict categories [P9].Source: §G.1 · p. 20, Table 16 · p. 21

G.2 Final research proposal

The proposal studies lifecycle-aware control of how memory changes personalized recommendations and plans, using auditable interventions and a no-memory comparison. Debate makes the influence policy testable and defers longitudinal authority decay beyond the proposed primary study.

G.2 Final research proposal

  • The system represents memory lifecycle states and provenance, then selects an action and one of four influence levels before a generator verbalizes the resulting structured representation.Permitted influence ranges from preventing memory-driven changes to filtering options or materially changing a plan when evidence is a confirmed current constraint.Source: §G.2 · p. 22
  • The primary study compares memory-control systems on recommendation, planning, and decision-support cases, with controlled lifecycle conditions and a natural-data component subject to an audit.The natural component is exploratory rather than representative if an audit finds too few suitable examples.Source: §G.2 · p. 22
  • The preregistered success criterion requires at least a 0.10-point gain in useful influence without more than a 0.05-point rise in harmful influence.Three blinded raters judge held-out outputs; the complete method is compared with matched controls, with intervals accounting for case and rater clustering.Source: §G.2 · p. 22
  • A supporting result must show higher useful and lower harmful behavioral change at matched abstention and average influence; a null result applies only to this controller, benchmark, and task domain.Lifecycle ablations are expected to fail especially on supersession, contextual coexistence, and contradiction cases.Source: §G.2 · p. 22
  • The proposal centers memory’s permitted behavioral influence, using lifecycle-aware evidence, discrete controls, and a no-memory shadow response to distinguish justified changes from silent adaptation.It treats justified no-change as an outcome and controls for gains from retrieving more evidence, abstaining more often, or changing prompt format.Source: §G.2 · p. 22

G.3 Debate excerpts and proposal revisions

  • In the initial debate, Experts A and C proposed counterfactual comparison and limiting uncertain-memory influence, while Expert B argued lifecycle state also matters.Their proposals emphasized different aspects despite both invoking counterfactual comparisons.Source: §G.3 · p. 23, Table 19 · p. 23
  • Debate made the influence budget operational: experts specified four discrete levels, constraints, shared structured representations, matched candidate pools, and deterministic decoding.These refinements restricted generation to the post-intervention representation, making control more explicit and testable.Source: §G.3 · pp. 23–24, Table 20 · p. 24
  • The experts removed authority decay from the primary study after the critic questioned whether the case-based benchmark could evaluate its longitudinal mechanism; it remains future work.The proposal likewise identifies longitudinal authority decay and recommendation-induced self-confirmation as possible follow-up studies, not present claims.Source: §G.2 · p. 22, §G.3 · p. 24, Table 21 · p. 24

H Prompt Templates

The appendix presents prompts in execution order, from expertise construction and agent setup through problem selection, proposal debate, final synthesis, and pairwise evaluation. It specifies how graph-derived insights initialize distinct experts and how the resulting proposals are judged.

H Prompt Templates

  • Prompt groups follow execution order, covering expertise construction, agent initialization, problem selection, proposal debate, final synthesis, and pairwise evaluation.Table 22 summarizes prompt groups and their inputs and outputs.Source: H Prompt Templates · p. 25, Table 22 · p. 26

H.1 Expertise construction

  • Expertise construction filters related-paper recommendations, organizes graph papers into distinct research lines, and distills three insights with supporting findings.The prompt asks each insight to describe a line’s recurring problem, evolving approaches, assumptions, and unresolved frontier rather than prescribe a reusable move.Source: §H.1 · pp. 26–27

H.2 Agent setup

  • Each expert receives an insight, supporting findings, source-paper metadata, and abstracts, which provide a distinct perspective while allowing broader field knowledge.The prompt notes that only abstracts, not full papers, are available.Source: §H.2 · p. 28, Table 26 · p. 29

H.3 Stage 1: Problem selection

H.4 Stage 2: Proposal development and debate

  • During proposal development, a moderator integrates expert proposals into one coherent idea without stapling them together, keeping one primary study and treating other tests as extensions.The appendix also lists first-draft, independent-review, revision, and discussion-summary prompt groups.Source: §H.4 · p. 31, Table 34 · p. 32, Table 35 · p. 32

H.5 Stage 3: Final synthesis

  • The prompt directory includes a distinct Stage 3 final-proposal synthesis group.Source: Table 38 · p. 34

H.6 Idea evaluation

  • Pairwise evaluation asks judges to compare ideas separately on novelty, significance, feasibility, clarity, and effectiveness.Effectiveness concerns whether the mechanism and evaluation credibly deliver the objective and support justified knowledge, including informative hypothesis refutation.Source: §H.6 · p. 34, Table 39 · p. 35

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