ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment

Qiuyu Tian, Haojie Yin, Yingce Xia, Youyong Kong, Zequn Liu

ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment: 1 upvotes on Hugging Face Daily Papers, #49 of 50 papers on 2026-06-05. Day-by-day upvote history.

AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence. ForeSci contains 500 tasks across four fast-moving AI domains and four decision families. Each task is paired with a cutoff-aligned offline knowledge base; post-cutoff papers are hidden during generation and used only for validation. To avoid random future-event prediction, tasks are derived from pre-cutoff taxonomy branches and evidence signals, and answer-generation backbones are selected to precede the task cutoffs. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Results show that explicit evidence organization improves traceability and factual support, but gains depend strongly on the decision family. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. ForeSci turns forward-looking AI research judgement into a controlled benchmark for evaluating research agents as decision-making systems.

Paper page on Hugging Face · arXiv

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