@FinanceYF5: Everyone is hyping up the million-token context, but a Prime Intellect engineer spoke the truth: GPT-5.5 has 80% retrieval accuracy at 256k, but when extended to one million, it drops to 36%. The model doesn't fail to hold it—it fails to reason over it—the so-called context rot. Why more...
Summary
A Prime Intellect engineer pointed out that large language models like GPT-5.5 see retrieval accuracy drop from 80% at 256k tokens to 36% at one million tokens, indicating the 'context rot' problem—the model can accommodate but cannot effectively reason over long contexts, posing a challenge to agent applications.
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Cached at: 07/14/26, 06:17 AM
Everyone is hyping up million-token context, but an engineer at Prime Intellect told the truth:
GPT-5.5 has 80% retrieval accuracy at 256k, but when extended to one million, it drops to 36%. The model can take it in, but it can’t reason with it — this is what’s called context rot.
Why larger context can’t save agents: the solution is continual learning + training on your own trajectories + real environment. https://t.co/S6GY2Oyc55
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