TIGER: Learning from Researcher Taste to Generate Tailored Research Ideas

Abstract:In pairwise comparisons where each judge chooses between an idea generated by its corresponding TIGER-8B policy and one generated by GPT-4.1, the three LLM researchers select the TIGER-8B idea in 56.6%-68.4% of 76 comparisons per researcher. PhD1 and PhD2 select the TIGER-8B idea in 19 of 30 pairs (63.3%) and 17 of 30 pairs (56.7%), respectively. All six directed cross-policy comparisons likewise favor the judge's matched TIGER-8B policy over an off-target policy, supporting the use of individual preference feedback for tailored research ideation.

Artificial Intelligence (cs.AI)

arXiv:2611.02234 [cs.AI]

(or arXiv:2611.02234v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.02234 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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