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IdeaAnchor introduces a novel training paradigm that uses structured specifications derived from published papers to teach LLMs how to synthesize literature into viable research directions. By combining anchor-based training with retrieval-augmented generation, the approach demonstrates measurable improvements in ideation quality and functional decomposition of creative synthesis. This work addresses a critical gap in automating the early stages of scientific discovery where existing prompting methods lack sufficient supervision for complex idea formulation.
IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
arXiv:2610.08781v1 Announce Type: new Abstract: Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to perform this form of literature-grounded ideation remains challenging, as existing approaches based on prompting or feedback lack structured supervision for how papers should be synthesized. We introduce IdeaAnchor, a paradigm for training LLMs to perform research ideation using structured specifications as privileged signals. Each IdeaAnchor instance encodes how each input paper should be synthesized into a successful idea, including their functional roles, relationships, and target synthesis criteria. We build this paradigm by mining instances from published papers, capturing how real ideas emerge from prior literature. We then train models via demonstration, self-distillation, and reinforcement learning, and further enhance generation with retrieval at inference time. Experiments show consistent improvements in ideation quality. Our analysis reveals a functional decomposition: anchor-based training strengthens creative synthesis, retrieval enhances detail elaboration, and combining both yields the best performance.
Abstract:Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to perform this form of literature-grounded ideation remains challenging, as existing approaches based on prompting or feedback lack structured supervision for how papers should be synthesized. We introduce IdeaAnchor, a paradigm for training LLMs to perform research ideation using structured specifications as privileged signals. Each IdeaAnchor instance encodes how each input paper should be synthesized into a successful idea, including their functional roles, relationships, and target synthesis criteria. We build this paradigm by mining instances from published papers, capturing how real ideas emerge from prior literature. We then train models via demonstration, self-distillation, and reinforcement learning, and further enhance generation with retrieval at inference time. Experiments show consistent improvements in ideation quality. Our analysis reveals a functional decomposition: anchor-based training strengthens creative synthesis, retrieval enhances detail elaboration, and combining both yields the best performance.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.08781 [cs.CL]
(or arXiv:2610.08781v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.08781 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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