Context Discipline and Performance Correlation: Analyzing LLM Performance and Quality Degradation Under Varying Context Lengths

• URL: http://arxiv.org/abs/2601.11564v1
• Date: Thu, 25 Dec 2025 08:37:57 GMT
• Title: Context Discipline and Performance Correlation: Analyzing LLM Performance and Quality Degradation Under Varying Context Lengths
• Abstract summary: Large Language Models (LLMs) have prioritized increasing the maximum context window to facilitate complex, long-form reasoning and document analysis.<n>Managing this expanded context introduces severe computational overhead.<n>This paper investigates the critical trade-off between system performance and model quality when dense transformer architectures are exposed to large volumes of irrelevant and distracting context.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scaling trend in Large Language Models (LLMs) has prioritized increasing the maximum context window to facilitate complex, long-form reasoning and document analysis. However, managing this expanded context introduces severe computational overhead. This paper investigates the critical trade-off between system performance and model quality when dense transformer architectures--specifically Llama-3.1-70B and Qwen1.5-14B--are exposed to large volumes of irrelevant and distracting context. The research identifies a non-linear performance degradation tied to the growth of the Key-Value (KV) cache. Furthermore, an extended analysis of the Mixture-of-Experts (MoE) architecture reveals unique behavioral anomalies at varying context scales, suggesting that architectural benefits may be masked by infrastructure bottlenecks at high token volumes.

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