LLM context compaction quality degradation curve observed, lacks benchmarks

A user observed that the output quality of LLMs like DeepSeek V4 and Claude Code does not degrade linearly with repeated context compaction. Instead, there appears to be a temporary improvement after the second compaction before a subsequent decline. The user has searched for existing benchmarks measuring this multi-round compaction degradation but found none that specifically address this phenomenon, with existing tests focusing on static input length or single-turn drift. If this "compaction curve" is real, it could inform users when to reset sessions and provide a new dimension for comparing LLM providers, but current major benchmark suites lack this metric. AI

IMPACT Could lead to new methods for evaluating LLM session persistence and inform optimal usage patterns for long-context models.

RANK_REASON User observation and call for community data collection on LLM behavior, not a formal release or research paper.

Read on dev.to — LLM tag →

• BigBench
• Claude Code
• Claude Opus
• Compression Laws for Large Language Models
• Context Rot
• DeepSeek V4
• Gemini
• GPT-5
• MMLU
• RULER

• paper
• other

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