Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
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90
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100
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67
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TL;DR - A framework evaluating seven frontier models on 36 long-horizon R&D tasks finds that current agents behave more like engineering optimizers than autonomous researchers. They implement practical solutions but remain inconsistent and rarely produce genuinely novel methods.
- Rule-based metrics examine Solution Framing, Execution, and Feedback Control within each run.
- Controlled comparisons assess whether agents reuse experience effectively within and across tasks.
- Similar final scores can conceal different process bottlenecks, while prior experience may help or mislead later decisions.
- Agent stability depends partly on harness design, suggesting improvements to training, inference strategies, and experience management.
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Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Public signals
Hugging Face upvotes 58
TL;DR - A framework evaluating seven frontier models on 36 long-horizon R&D tasks finds that current agents behave more like engineering optimizers than autonomous researchers. They implement practical solutions but remain inconsistent and rarely produce genuinely novel methods.
- Rule-based metrics examine Solution Framing, Execution, and Feedback Control within each run.
- Controlled comparisons assess whether agents reuse experience effectively within and across tasks.
- Similar final scores can conceal different process bottlenecks, while prior experience may help or mislead later decisions.
- Agent stability depends partly on harness design, suggesting improvements to training, inference strategies, and experience management.