Classifier Context Rot: Monitor Performance Degrades with Context Length

Monitoring coding agents for dangerous behavior using language models requires classifying transcripts that often exceed 500K tokens, but prior agent monitoring benchmarks rarely contain transcripts longer than 100K tokens. We show that when used as classifiers, current frontier models fail to notice dangerous actions often in longer transcripts. In particular, on MonitorBench, Opus 4.6, GPT 5.4, and Gemini 3.1 miss these actions 2x to 30x often when we prepend 800K tokens of benign activity than when we use the original transcripts.