FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights

Autonomous AI agents powered by large language models (LLMs) are increasingly capable of running a full cycle of scientific research, yet we still lack reliable ways to verify that their discoveries are correct. Because novel findings demand costly real-world validation, existing benchmarks fall back on LLM-as-judge scoring of generated papers or single leaderboard metrics, both coarse proxies for scientific reasoning. We introduce FIRE-BENCH (Full-cycle Insight Rediscovery Evaluation), which instead asks agents to rediscover established, verifiable findings from recent, high-impact machine learning research. Given only a high-level research question from a published study, an agent must independently design experiments, run them, and draw evidence-backed conclusions, scored against the study's documented findings. Across state-of-the-art agents with frontier backbones such as gpt-5, even the strongest reaches limited rediscovery success (<50 F1), with high run-to-run variance and recurring failures in experimental design, execution, and evidence-based reasoning. Beyond diagnosing current systems, FIRE-BENCH shows that open-ended discovery can be evaluated rigorously and verifiably, laying a foundation for building reliable environments that improve agents.