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About this paper
Natalia Trukhina, Vadim Vashkelis
The Context Codec framework is presented, formalizing Large Language Model context compression around the explicit preservation of 'semantic commitments' rather than merely token reduction. A diagnostic study demonstrates that this commitment-level approach can achieve perfect critical atom recall (CAR and WAR of 1.00) with moderate token compression (21.8% gain for CCL-Core) in contrast to free prose, which yields higher compression (68.3%) but significant commitment loss (CAR 0.61).
Key insights
- LLM context should be managed and compressed by explicitly preserving 'semantic commitments' rather than solely focusing on token count, enabling verifiable context management and improved system reliability.
- A structured taxonomy of eight semantic compression errors (e.g., Omission, Mutation, Polarity Flip) provides a systematic method for diagnosing and understanding *why* context compression fails beyond simple performance metrics.
- Commitment-level metrics like Critical Atom Recall (CAR) and Weighted Atom Recall (WAR) reveal distinct tradeoffs between different context representations, demonstrating that free prose sacrifices semantic integrity for compactness, while structured formats balance both.
Summary by alphaXiv.
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