Gu, Shane, Lin, Yufeng, Yao, Dixi, Gong, Xuchen, Tan, Chenhao · Nov 1, 2026
TIGER authors report on tailoring research ideas to researcher taste
Markdown · JSON · Original source
The authors of “TIGER: Learning from Researcher Taste to Generate Tailored Research Ideas” report that, in pairwise comparisons, judges more often chose an idea from the TIGER-8B policy matched to them than one from GPT-4.1. The three researchers chose their matched TIGER-8B idea in 56.6%–68.4% of 76 comparisons per researcher. Two PhD judges chose the TIGER-8B idea in 19 of 30 pairs and 17 of 30 pairs. The authors also report that all six directed cross-policy comparisons favored a judge’s matched policy over an off-target policy. They argue that these results support using individual preference feedback to tailor research ideation. Escape Meridian interpretation: the evidence presented here concerns judges’ choices in these comparisons; it does not establish how useful the generated ideas are for conducting research.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 1, 2026 (2026-11-01).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T09:24:20.751Z. First observed: 2026-11-03T09:04:27.620Z. Retrieved: 2026-11-04T01:32:46.386Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The supplied evidence is the page’s abstract; it does not describe how the ideas were generated or how judges assessed them. The reported comparisons show judges’ choices, not whether the ideas are useful for conducting research.
Open question
How well would the reported preference for matched TIGER-8B ideas hold in evaluations beyond the comparisons reported here?
Source-stated
The authors report that three LLM researchers preferred ideas from their corresponding TIGER-8B policy over ideas from GPT-4.1 in 56.6%–68.4% of 76 comparisons per researcher.
the three LLM researchers select the TIGER-8B idea in 56.6%-68.4% of 76 comparisons per researcher.
Source-stated
The authors report that two PhD judges selected the TIGER-8B idea in 19 of 30 pairs and 17 of 30 pairs, respectively.
PhD1 and PhD2 select the TIGER-8B idea in 19 of 30 pairs (63.3%) and 17 of 30 pairs (56.7%), respectively.
Source-stated
The authors say all six directed cross-policy comparisons favored the judge’s matched TIGER-8B policy over an off-target policy.
All six directed cross-policy comparisons likewise favor the judge's matched TIGER-8B policy over an off-target policy
Source-stated
The authors interpret the comparison results as supporting individual preference feedback for tailored research ideation.
supporting the use of individual preference feedback for tailored research ideation.
Escape Meridian interpretation
The reported choices measure preference in comparisons; the abstract does not report whether the ideas are useful for conducting research.
In pairwise comparisons where each judge chooses between an idea generated by its corresponding TIGER-8B policy and one generated by GPT-4.1
Source and contributor context
Gu, Shane, Lin, Yufeng, Yao, Dixi, Gong, Xuchen, Tan, Chenhao · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 0811be1d-f3e4-4186-8676-20a168ff5f1e.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T09:24:20.751Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: c50891ec8b087c651c83c7e539c028aa2d1671baf1ebd4649f9589f0636c18aa. Source text hash: d97f4c383a3c02e6cf354751502b16b7329ada9028bfe59a7a4f8ccec8115396.
Revision and source-check history
Version 1 · 2026-11-10T09:24:20.751Z
TIGER authors report on tailoring research ideas to researcher taste
The authors of “TIGER: Learning from Researcher Taste to Generate Tailored Research Ideas” report that, in pairwise comparisons, judges more often chose an idea from the TIGER-8B policy matched to them than one from GPT-4.1. The three researchers chose their matched TIGER-8B idea in 56.6%–68.4% of 76 comparisons per researcher. Two PhD judges chose the TIGER-8B idea in 19 of 30 pairs and 17 of 30 pairs. The authors also report that all six directed cross-policy comparisons favored a judge’s matched policy over an off-target policy. They argue that these results support using individual preference feedback to tailor research ideation. Escape Meridian interpretation: the evidence presented here concerns judges’ choices in these comparisons; it does not establish how useful the generated ideas are for conducting research.
Original source
cs.AI · Article
Source details
Original source date: Nov 1, 2026 (2026-11-01).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T09:24:20.751Z. First observed: 2026-11-03T09:04:27.620Z. Retrieved: 2026-11-04T01:32:46.386Z.
Limits
The supplied evidence is the page’s abstract; it does not describe how the ideas were generated or how judges assessed them. The reported comparisons show judges’ choices, not whether the ideas are useful for conducting research.
Open question
How well would the reported preference for matched TIGER-8B ideas hold in evaluations beyond the comparisons reported here?
Source-stated
The authors report that three LLM researchers preferred ideas from their corresponding TIGER-8B policy over ideas from GPT-4.1 in 56.6%–68.4% of 76 comparisons per researcher.
the three LLM researchers select the TIGER-8B idea in 56.6%-68.4% of 76 comparisons per researcher.
Source-stated
The authors report that two PhD judges selected the TIGER-8B idea in 19 of 30 pairs and 17 of 30 pairs, respectively.
PhD1 and PhD2 select the TIGER-8B idea in 19 of 30 pairs (63.3%) and 17 of 30 pairs (56.7%), respectively.
Source-stated
The authors say all six directed cross-policy comparisons favored the judge’s matched TIGER-8B policy over an off-target policy.
All six directed cross-policy comparisons likewise favor the judge's matched TIGER-8B policy over an off-target policy
Source-stated
The authors interpret the comparison results as supporting individual preference feedback for tailored research ideation.
supporting the use of individual preference feedback for tailored research ideation.
Escape Meridian interpretation
The reported choices measure preference in comparisons; the abstract does not report whether the ideas are useful for conducting research.
In pairwise comparisons where each judge chooses between an idea generated by its corresponding TIGER-8B policy and one generated by GPT-4.1
Source and contributor context
Gu, Shane, Lin, Yufeng, Yao, Dixi, Gong, Xuchen, Tan, Chenhao · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 0811be1d-f3e4-4186-8676-20a168ff5f1e.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T09:24:20.751Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: c50891ec8b087c651c83c7e539c028aa2d1671baf1ebd4649f9589f0636c18aa. Source text hash: d97f4c383a3c02e6cf354751502b16b7329ada9028bfe59a7a4f8ccec8115396.
uncertain · 2026-11-10T09:24:20.751Z · anthropic/claude-opus-5.5
Publication: 0811be1d-f3e4-4186-8676-20a168ff5f1e
attribution — pass: The results and the conclusion are attributed to the authors of the arXiv paper. The interpretive sentence is clearly labeled as Escape Meridian interpretation. The paper's claims are not presented as independently established facts.
Authors:Shane Gu, Yufeng Lin, Dixi Yao, Xuchen Gong, Chenhao Tan
summary — uncertain: The numbers are faithful to the abstract, but central terms go unexplained for a general reader: TIGER-8B policy, LLM researchers, directed cross-policy comparisons and off-target policy. 'Three LLM researchers' is especially ambiguous: are they simulated researchers or people who study LLMs? The summary also never says plainly what TIGER does, for example a model tuned to an individual's preferences, as the title suggests. Smallest repair: one plain-language sentence on what TIGER is and who the judges are, plus suggested term-library entries for 'policy' and 'LLM researchers'. If the abstract leaves the judges' nature unclear, the reading should say so.
the three LLM researchers select the TIGER-8B idea in 56.6%-68.4% of 76 comparisons per researcher.
date — pass: The original publication date of 2026-11-01 matches the arXiv v1 submission date. It is not the later feed date of 2026-11-03.
[Submitted on 1 Nov 2026]
support — pass: Every source-stated claim matches the abstract verbatim in substance, including the percentages, counts and the cross-policy result. The interpretation that the evidence shows preference choices rather than research usefulness is labeled and conditional, and it preserves the gap between evidence and conclusion. The question about generalization is open-ended and follows from the evidence without assuming an answer.
PhD1 and PhD2 select the TIGER-8B idea in 19 of 30 pairs (63.3%) and 17 of 30 pairs (56.7%), respectively.
media — pass: The selected media is the verified, direct link to the original arXiv abstract page. It is the most direct access to the source.
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A language model with many learned numerical settings, used to process and generate language.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv