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cs.AI2026
Transect: Retaining Observability for Long-Horizon LLM Agent Evaluations
Toby D. Pilditch, Konstantinos Voudouris, Alexandra Abbas +1
Frontier AI evaluations increasingly use open-ended, agentic, long-horizon tasks whose transcripts can span hundreds of pages of outputs and actions from complex multi-agent networ…
cs.AI2026
Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
Toby D. Pilditch
LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a precision-based adaptive sto…
cs.AI2026
Can AI agents conduct open-ended AI research? Early evidence from two case studies
Peter Kirgis, Sayash Kapoor, Andrew Schwartz +21
Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations eithe…