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[2602.21221] Latent Context Compilation: Distilling Long Context into Compact Portable Memory
Summary
The paper introduces Latent Context Compilation, a novel framework that enhances long-context LLM deployment by distilling long contexts into compact, portable memory, improving efficiency and generalization.
Why It Matters
This research addresses critical challenges in deploying long-context language models, particularly the trade-offs between compression and adaptability. By providing a solution that maintains model performance while reducing memory requirements, it has significant implications for AI applications requiring efficient context management.
Key Takeaways
- Latent Context Compilation shifts context processing from adaptation to compilation.
- The framework utilizes a disposable LoRA module to create compact buffer tokens.
- Self-aligned optimization eliminates the need for synthetic context-relevant QA pairs.
- Experiments show a 16x compression ratio while preserving model reasoning capabilities.
- The approach effectively decouples memory density from model parameters.
Computer Science > Machine Learning arXiv:2602.21221 (cs) [Submitted on 31 Jan 2026] Title:Latent Context Compilation: Distilling Long Context into Compact Portable Memory Authors:Zeju Li, Yizhou Zhou, Qiang Xu View a PDF of the paper titled Latent Context Compilation: Distilling Long Context into Compact Portable Memory, by Zeju Li and 2 other authors View PDF HTML (experimental) Abstract:Efficient long-context LLM deployment is stalled by a dichotomy between amortized compression, which struggles with out-of-distribution generalization, and Test-Time Training, which incurs prohibitive synthetic data costs and requires modifying model weights, creating stateful parameters that complicate concurrent serving. We propose Latent Context Compilation, a framework that fundamentally shifts context processing from adaptation to compilation. By utilizing a disposable LoRA module as a compiler, we distill long contexts into compact buffer tokens -- stateless, portable memory artifacts that are plug-and-play compatible with frozen base models. Crucially, we introduce a self-aligned optimization strategy that eliminates the need for synthetic context-relevant QA pairs. By regularizing context reconstruction task with context-agnostic random queries, we force compressed tokens to reside within the model's existing instruction-following manifold. Experiments with Llama-3.1-8B demonstrate that Latent Context Compilation preserves fine-grained details and reasoning capabilities where pri...
