Intelligence Degradation in Long-Context LLMs: Critical Threshold Determination via Natural Length Distribution Analysis

• URL: http://arxiv.org/abs/2601.15300v1
• Date: Wed, 07 Jan 2026 07:56:31 GMT
• Title: Intelligence Degradation in Long-Context LLMs: Critical Threshold Determination via Natural Length Distribution Analysis
• Authors: Weiwei Wang, Jiyong Min, Weijie Zou,
• Abstract summary: Large Language Models (LLMs) exhibit performance degradation when processing contexts approaching certain critical thresholds.<n>This intelligence degradation-defined as over 30% drop in task performance-severely limits long-context applications.<n>This work provides the first systematic characterization of intelligence degradation in open-source Qwen models.
• Score: 2.085792950847639
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Large Language Models (LLMs) exhibit catastrophic performance degradation when processing contexts approaching certain critical thresholds, even when information remains relevant. This intelligence degradation-defined as over 30% drop in task performance-severely limits long-context applications. This degradation shows a common pattern: models maintain strong performance up to a critical threshold, then collapse catastrophically. We term this shallow long-context adaptation-models adapt for short to medium contexts but fail beyond critical thresholds. This paper presents three contributions: (1) Natural Length Distribution Analysis: We use each sample's natural token length without truncation or padding, providing stronger causal evidence that degradation results from context length itself. (2) Critical Threshold Determination: Through experiments on a mixed dataset (1,000 samples covering 5%-95% of context length), we identify the critical threshold for Qwen2.5-7B at 40-50% of maximum context length, where F1 scores drop from 0.55-0.56 to 0.3 (45.5% degradation), using five-method cross-validation. (3) Unified Framework: We consolidate shallow adaptation, explaining degradation patterns and providing a foundation for mitigation strategies. This work provides the first systematic characterization of intelligence degradation in open-source Qwen models, offering practical guidance for deploying LLMs in long-context scenarios.

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