Output Token Waste

A UCLA and Northeastern University paper introduces a proxy-based audit that uses an LLM judge to mark removable spans in agent output, then reruns tasks to verify nothing breaks. Across six frontier models and four benchmarks, roughly 18% of output tokens were waste; pruning repetition and filler from Kimi-K3's reasoning on SWE-bench Verified cut 41% of tokens while success held at 89.5% versus 89.0%. The takeaway: cut agent costs by locating waste in your task and harness, not by capping tokens.