ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
TL;DR
ForeSci is introduced, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence, and agent-based methods improve traceability over Hybrid RAG, while their gains in future-target alignment over native LLMs are modest and task dependent.
Abstract
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, while generation-time retrieval and tools are restricted to the cutoff-aligned evidence package. We evaluate native LLMs, Hybrid RAG, and three research-agent adaptations across four backbones. Agent-based methods improve traceability over Hybrid RAG, while their gains in future-target alignment over native LLMs are modest and task dependent. Diagnostics reveal a recurring evidence-decision decoupling: agents may cite relevant evidence while forecasting the wrong research object. Task-specific checks between evidence and final decision reduce drift and improve future-target alignment. As research agents move from assisting known tasks to shaping open research directions, ForeSci offers a first controlled step toward measuring whether their judgement can be trusted to guide that future.
