Has Anyone Measured How LLM Output Quality Degrades Across Multiple Compactions?

The Observation

After ~70 sessions with DeepSeek V4 (1M context), I noticed something odd. When Claude Code compacts my session, output quality doesn't just go down linearly. There's a moment — usually after the second compaction — where the model briefly gets better. Then it declines and never recovers.

Maybe I'm imagining it. Maybe it's specific to my model, my prompts, my workflow. But I can't shake the thought: what if context compaction has a curve, and nobody has mapped it?

What I Found (Not Much)

I searched for benchmarks that measure multi-round compaction degradation. Here's what exists:

• 
  RULER: Measures how performance drops as static input grows longer. Nothing about what happens after you compress and re-compress.


• 
  Context Rot (Chroma 2025): 18 models tested, all degrade with more tokens. Again, static.


• 
  Multi-turn evaluation: Tests whether models drift across conversation turns. Doesn't touch compaction.

Parameter compression (pruning, quantization) has well-mapped scaling laws. The Lottery Ticket Hypothesis (ICLR 2019) and Compression Laws for LLMs (2025) tell you exactly where the performance peak sits. Context summarization — the thing that happens every time your agent runs /compact — has no such curve.

Why This Might Matter

If the curve is real, you could:

• Know exactly when to start a fresh session (before the decline hits)


• Compare models on a new dimension: who maintains quality longest across compactions?


• Give LLM providers a concrete target: "your compaction quality drops 20% faster than competitor X"

Right now, none of the major benchmark suites (MMLU, HELM, BigBench, RULER) include a "compaction persistence" metric. If context windows keep growing and sessions keep getting longer, this gap gets bigger every year.

What I'm Asking

I built a tiny monitor ( — 50 lines of Python, 10 benchmark tasks, 0-5 rubric. It's not polished. It's a starting point.

What I'd love:

1. Someone with a Claude Opus / GPT-5 / Gemini account to try reproducing this


2. Feedback on whether the methodology makes sense or is fundamentally flawed


3. If this is a real thing, ideas for how to measure it properly

I don't have the compute or the stats background to do this alone. But if enough people contribute data points across different models, we might find out whether this curve exists — and if it does, maybe it's useful to more people than just me.

References

• Frankle & Carbin, "The Lottery Ticket Hypothesis" (ICLR 2019)


• "Compression Laws for Large Language Models" (2025)


• RULER: What's the Real Context Size of Your Long-Context Language Models? (COLM 2024)


• Chroma Research, "Context Rot" (2025)