The Hidden Weakness of Neural Networks: Why Large Context Windows Don’t Work

▤ Summary

This article examines why large context windows in AI models often fail to deliver the expected performance. It focuses on context rot, where models lose accuracy as input length grows, and uses Context Rot Evaluation (CRE) to test retrieval and reasoning across long prompts. The piece compares several recent models and shows that effective context length is often far shorter than the advertised maximum. It also highlights how filler text, duplicate content, and solution-heavy prompts can distort results and reduce model reliability. The main takeaway is that benchmark claims about long-context capability need much stricter validation.

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