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AI models fail to detect danger in long transcripts

By PulseAugur Editorial · [1 sources] ·

A new paper reveals that leading AI models like Opus 4.6, GPT 5.4, and Gemini 3.1 exhibit significant performance degradation when classifying long transcripts, a crucial task for monitoring coding agents. These models miss subtly dangerous actions much more frequently in transcripts exceeding 800,000 tokens compared to shorter ones. While prompting techniques can partially mitigate this issue, further post-training improvements are likely necessary to ensure reliable monitoring in long-context scenarios. AI

IMPACT Leading AI models struggle with long contexts, potentially overestimating their safety monitoring capabilities and requiring new training or prompting strategies.

RANK_REASON The cluster contains an academic paper detailing a new finding about AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models fail to detect danger in long transcripts

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The cluster contains an academic paper detailing a new finding about AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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High
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136 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Fabien Roger ·

    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 …

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