AI evidence extraction

More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation

Jie Yu, Song Qiu · ArXiv.org · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

Methodology & findings

Study design

Mixed-methods design science approach combining: (1) benchmark construction (TF-Bench) with human annotation and refinement; (2) comparative evaluation of InciteResearch against prompt-based LLM baseline using LLM-as-judge scoring; (3) human expert blind review (2 domain professors on 26 proposals); (4) ablation studies disabling each EVN component; (5) inter-rater reliability analysis using Cohen's weighted kappa coefficient..

Primary method

Design Science Research with cognitive state-machine architecture informed by Socratic method and assumption-breaking hypothesis generation

Main result

The results show substantial gains in novelty and impact over direct prompting baselines, from 3.671 / 3.806 to 4.250 / 4.397 over direct prompting baselines, and the ablation studies further reveal the functional indispensability of each individual operator. Under the condition of domain-unrelated human inspiration, the novelty and impact of InciteResearch are instead higher than under the domain-related condition, increasing to 4.328 / 4.416, while Prompt-based LLM drops to 3.454 / 3.626.

Research paradigm

Design science with human-centered AI collaboration

Author conclusions

The authors conclude: "We present InciteResearch, a multi-agent framework that decomposes the logical chain of Socratic questioning and distributes it across the entire pipeline, aiming to realize the transformation from tacit thinking to explicit scientific ideation... We view this direction as a step toward a new paradigm of human-AI scientific collaboration, in which language models do not function as independent inventors, but as cognitive extensions that enhance the scope, articulation, and depth of human intuition."

Risk of bias

Human annotation bias in TF-Bench construction - examples generated by Grok 4.3 and refined by human annotators may introduce subjective curation bias; LLM-as-judge evaluation bias - using Gemini 3.1 Pro and GPT-5.2 for scoring may bias toward LLM-generated proposals; Limited human expert evaluation (n=2 professors, 26 proposals only) may not be representative; Potential selection bias in domain choice (4 domains selected for coverage); Inconsistency between LLM and human judgment (κ=0.624 indicates moderate, not high agreement); LLM-based evaluation bias (agreement with human experts κ=0.624, moderate vs. inter-human κ=0.748, substantial); Limited human evaluation sample (26 proposals in single domain for blind review); Potential selection bias in benchmark construction (initial examples generated by Grok 4.3 then refined by human annotators); Temperature settings varied across operators, potentially introducing systematic differences in generation patterns; Limited human evaluation sample size (26 proposals from single domain); Single domain expertise for human review (Multimodal Learning for Cancer Prognosis); LLM-based evaluation used as primary metric (models may have systematic biases); Moderate inter-rater agreement between LLM judges and humans (κ = 0.624); Use of Grok 4.3 for initial example generation introduces potential algorithmic bias; No discussion of selection bias in human annotation refinement process; Small number of examples per domain in TF-Bench (10 domain-related + 3 domain-unrelated per domain)

Limitations

The authors state: "However, the InciteResearch is currently limited to lightweight structured interaction and cannot fully capture the richness of long-term human intuition formation, including correction and reflection." Additionally, they note that "N mainly acts as a post hoc structural verifier rather than a fully revision-integrated controller, kept lightweight intentionally to validate the core effectiveness of EVN without requiring continuous human intervention at intermediate nodes."

Open questions raised

Lack of benchmarks designed specifically for tacit-to-explicit research assistance - existing datasets presuppose actionable research questions rather than inchoate intuition fragments
Limited frameworks for treating language models as cognitive extensions rather than independent idea generators
Gap between single-stage explicit problem solving and the earlier stage of problem formulation from vague intuition
Need for long-term human intuition formation that includes iterative correction and reflection cycles
Fully revision-integrated control mechanisms beyond post-hoc necessity checking
Current limitation of lightweight structured interaction that cannot fully capture long-term human intuition formation including correction and reflection

Data: TF-Bench - benchmark constructed for tacit-to-explicit research assistance, containing examples across 4 scientific modes (prediction, discovery, attribution, causality) with domain-related and domain-unrelated ambiguity types. No URL provided for public access in paper.; TF-Bench (benchmark constructed for this research, partition into domain-related and domain-unrelated ambiguity types across four scientific modes: prediction, discovery, attribution, and causality); TF-Bench - benchmark dataset constructed for the study (not explicitly stated as publicly available)Extracted from: pdfAgreement 59%

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