Beyond Static Leaderboards — Predictive Validity for LLM Agent Evalua… | SynapWeave
Three papers landed today, all pointing at the same gap: static benchmarks don't predict production behavior for LLM agents. One paper proves it with data, another builds a calibration method for high-stakes domains, and the third proposes a spatial tool-use paradigm that sidesteps the whole eval problem. The common thread is that deployment reality is more complex than any leaderboard captures. ▶ Key takeaways Static leaderboards miss 60%+ of deployment-critical dimensions. Teams should build custom eval suites that mirror real workload conditions, not rely on public benchmark ranks. Aggregate hallucination rates (~52%) are misleading for compliance. Teams must audit by error type and severity, not by average, to make safe deployment decisions. Static VLMs fail at continuous 3D reasoning. Teams building spatial agents must add a stateful spatial memory layer, not rely on per-frame inference. 📊 Beyond Static Leaderboards — Predictive Validity for LLM Agent Evaluation 사실 요약 A paper...