arXiv Artificial Intelligence

RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases

2 hours ago 11.09.2026 07:00 👁 2 views
RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases

Quick summary

arXiv:2609.10092v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate because reviews and research ideas lack uniquely verifiable outcomes. We introduce Research Attention Prediction (RAP), a rolling benchmark covering 278 AI/ML fields and 1,390 episodes. At each cut-off, an LLM agent searches a temporally restricted arXiv corpus and predicts the next six months' paper shares across eight frozen research directions. Search generally helps, but all four diagnostic models

Key takeaways

  • arXiv:2609.10092v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate because reviews and research ideas lack uniquely verifiable outcomes.
  • We introduce Research Attention Prediction (RAP), a rolling benchmark covering 278 AI/ML fields and 1,390 episodes.
  • At each cut-off, an LLM agent searches a temporally restricted arXiv corpus and predicts the next six months' paper shares across eight frozen research directions.

Why it matters

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Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗