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

arXiv: https://arxiv.org/abs/2605.06345 · HTML: https://mlsanity.com/paper/2605.06345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 7 May 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 22 of 22)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://mlsanity.com/paper/2409.04109/) (2024)
- [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://mlsanity.com/paper/2504.03160/) (2025)
- [ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://mlsanity.com/paper/2404.07738/) (2024)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges](https://mlsanity.com/paper/2412.11427/) (2024)
- [Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?](https://mlsanity.com/paper/2502.14297/) (2025)
- [All That Glitters is Not Novel: Plagiarism in AI Generated Research](https://mlsanity.com/paper/2502.16487/) (2025)
- [A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models](https://mlsanity.com/paper/2504.05496/) (2025)
- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent](https://mlsanity.com/paper/2603.01152/) (2026)
- [Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas](https://mlsanity.com/paper/2603.10303/) (2026)
- [Capabilities of GPT-5 across critical domains: Is it the next breakthrough?](https://mlsanity.com/paper/2508.19259/) (2025)
- [MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation](https://mlsanity.com/paper/2509.21978/) (2025)
- [AI Researchers' Views on Automating AI R&D and Intelligence Explosions](https://mlsanity.com/paper/2603.03338/) (2026)
- [System 0: Transforming Artificial Intelligence into a Cognitive Extension](https://mlsanity.com/paper/2506.14376/) (2025)
- [Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines](https://mlsanity.com/paper/2601.09714/) (2025)
- [Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners](https://mlsanity.com/paper/2603.28038/) (2026)
- [SynLang and Symbiotic Epistemology: A Manifesto for Conscious Human-AI Collaboration](https://mlsanity.com/paper/2507.21067/) (2025)
- [From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews](https://mlsanity.com/paper/2512.11661/) (2025)

## Abstract (arXiv)

AI research agents have shown strong potential in automating literature search and manuscript refinement, yet most assume a clear and actionable initial input, operating only after a research question has been made explicit. In contrast, human research often begins with tacit friction, a sense of misalignment before a question can be formed. We introduce InciteResearch, a multi-agent framework designed to make a researcher's implicit understanding explicit, inspectable, and actionable. InciteResearch decomposes the logical chain of Socratic questioning and distributes it across the entire pipeline that: (1) Elicits a structured five-dimensional researcher profile state anchored by specific friction points from vague, even domain-unrelated inputs; (2) Violates hidden assumptions by maximizing the feasibility-novelty product with enforcing a 7-stage causal derivation trace; and (3) check whether the proposed method is a Necessary consequence of the reframed insight. We further introduce TF-Bench, the first benchmark for tacit-to-explicit research assistance that distinguishes domain-related from domain-unrelated inspirations across four scientific modes. On TF-Bench, InciteResearch achieves leapfrogging gains over a prompt-based baseline (novelty/impact from 3.671/3.806 to 4.250/4.397), shifting generated proposals from recombination to architectural insight. Our work demonstrates that AI can serve as an extension of thinking itself, rather than merely automating downstream execution.