IdeaTaste: Do LLMs Share the Research Taste of Human Researchers?
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IdeaTaste is a benchmark that scores LLMs by how closely research proposals they write from a literature snapshot frozen at 2025 match work the same communities published in 2026. Across six language models and four research areas, covering more than 90,000 proposals, LLM agents reached about 26% of human level on citation matching and 18% on idea matching. Their populations cite diversely and cover community tasks and methods, but rarely reach the concrete approaches and research plans the communities pursued. Neither richer field context nor larger populations substantially closed the gap.
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- Primary sourceHugging Face Daily Papers
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IdeaTaste is a benchmark that scores LLMs by how closely research proposals they write from a literature snapshot frozen at 2025 match work the same communities published in 2026. Across six language models and four research areas, covering more than 90,000 proposals, LLM agents reached about 26% of human level on citation matching and 18% on idea matching. Their populations cite diversely and cover community tasks and methods, but rarely reach the concrete approaches and research plans the communities pursued. Neither richer field context nor larger populations substantially closed the gap.
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- 01Who is making each claim?
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- 02What was actually tested?
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- 03Where are the sources?
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- 04What could mislead?
Source claims not independently checked; the benchmark is defined by matching to future publications, which may not fully capture proposal quality.
- 05What is at risk if wrong?
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- 06What would change the view?
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Key findings from the stored brief
- LLM agents reached about 26% of human-community level on citation matching and 18% on idea matching.
- AI agents cover broad community directions and tasks but rarely arrive at concrete approaches or research plans.
- Richer field context and larger agent populations did not substantially close the gap.
- In blinded expert review, AI proposals trailed 2026 papers most in expected success and least in presentation.
Source claims not independently checked; the benchmark is defined by matching to future publications, which may not fully capture proposal quality.
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Evidence & sources
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Why this may matter
The authors argue the main bottleneck in AI research taste is turning directions into concrete approaches rather than finding directions. An open, continuously updated leaderboard lets researchers track proposal quality against future publication cycles.
Limits and open questions
Source claims not independently checked; the benchmark is defined by matching to future publications, which may not fully capture proposal quality.
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