Equation 11 · How Benchmark Contamination Actually Works in Agentic Evaluation
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averaged over tasks to produce the benchmark’s headline number. The metric is designed to punish an agent whose competence is real but inconsistent across resampled trials — a stochastic policy with true per-trial success probability p has , which falls quickly as k grows. But a policy whose output on a given task is deterministic — an empty response, always, regardless of sampling — produces the identical transcript on every trial, so
Sources cited in the article section
- [8] Establishing Best Practices for Building Rigorous Agentic Benchmarks ↗
- [7] τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
- [9] Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation ↗
These citations give research context. Read each source to check which claims it supports.
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