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

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 perform worse than an exact-count exponentially weighted moving average (EWMA) baseline in compositional accuracy. We identify two linked bottlenecks. Under cumulative-history access, State carry-forward outperforms direct Forecast for all four diagnostic models; frozen-evidence replay links a shared component of this reversal to Forecast-oriented policies retrieving a smaller share of recent evidence. Even with exact historical activity, future-specific updating remains limited, with only GPT-5.5 plus reopened Search slightly surpassing EWMA. Fine-tuning on realised outcomes improves Qwen3-4B's forecast Spearman correlation by 0.105 on held-out fields at later origins, with gains also on change-rich episodes.

Authors 7

Yingqian Wu, Jingcong Liang, Siyuan Wang, Zhenfei Yin, Philip Torr, Junchi Yu, Zhongyu Wei

Specification

Official page: arxiv.org/abs/2609.10092
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
arXiv id: 2609.10092
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
Categories: cs.AI, cs.CL
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
PDF: arxiv.org/pdf/2609.10092
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

6 h ago

Conflicts

None

No models linked to this paper yet.

Authors: Yingqian Wu, Jingcong Liang, Siyuan Wang

As of

Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.

Claim history · Categories

1 claims · 1 propertiesShow all properties

Categoriescategories1

Claim history for Categories
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.AI, cs.CL	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperRAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases
New paper: RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases
arxiv
rss.arxiv.org

Full timeline →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	5 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.