Author

Toby D. Pilditch

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#artificial intelligence Review Oct 2026

Transect: Retaining Observability for Long-Horizon LLM Agent Evaluations

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 networks. The observability envelop-the range of what evaluators can reliably infer about an agent's behaviours-is therefore narrowing. Language m...

Toby D. Pilditch, Konstantinos Voudouris, Alexander R. Abbas et al. · 0 citations

Preprint Aug 2026

Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations

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 stopping framework that treats evaluation as a sequential measurement problem: keep sampling where uncertainty remains high, and stop where est...

Toby D. Pilditch · 2 citations