🤖 AI Summary
This work addresses the lack of explicit preservation and verifiability of semantic commitments—such as goals, constraints, and decisions—in existing context compression methods for large language models. The authors propose the Context Codec framework, which models dialogue states as typed, source-anchored semantic atoms and decomposes compression into five modular stages: extraction, normalization, representation, rendering, and verification. To evaluate fidelity, they introduce novel metrics including semantic compression error taxonomy and critical atom recall, alongside a conservative fallback strategy. They also design the Context Compression Language (CCL), a compact, human-readable, and auditable format, with CCL-Core striking a balance between structured text and JSON to achieve superior performance in terms of size, risk mitigation, and verifiability.
📝 Abstract
LLM context is not just tokens; it is a set of commitments. Long-running conversations accumulate goals, constraints, decisions, preferences, tool results, retrieved evidence, artifacts, and safety boundaries that future responses must preserve. Existing context-management methods reduce length through truncation, retrieval, summarization, memory systems, or token-level prompt compression, but they rarely specify which semantic commitments must survive compression or how their preservation should be measured. We propose Context Codec, a commitment-level framework for compressing prompts and chat histories.
Context Codec represents dialogue state as typed, source-grounded semantic atoms with canonical identity, equivalence, conflict, confidence, risk, and evidence spans. It separates five concerns - extraction, normalization, representation, rendering, and verification - and introduces metrics for Critical Atom Recall, Weighted Atom Recall, Commitment Density, and round-trip recoverability. It also defines a taxonomy of semantic compression errors, a concrete normalization procedure, conservative fallback rules for low-confidence and safety-critical atoms, and Context Compression Language (CCL), an ASCII-first compact rendering of canonical JSON atoms. In a small diagnostic study, CCL-Core occupies a useful middle ground between structured prose and JSON: more explicit and auditable than prose, usually more compact than JSON, and less risky than heavily minified notation. The result is not a claim that shorthand solves compression, but a framework for making context compression verifiable: compress the conversation, keep the commitments.