CodeComp: Structural KV Cache Compression for Agentic Coding
Agentic code tasks such as fault localization and patch generation require processing long codebases under tight memory constraints, where the Key-Value (KV) cache becomes the primary inference bottleneck. Existing compression methods rely exclusively on attention signals to estimate token importance, systematically discarding structurally critical tokens such as call sites, branch conditions, and assignments that are essential for code understanding. We present CodeComp, a training-free KV cache compression framework that incorporates static program analysis into LLM inference via Code Property Graph priors extracted by Joern. Across bug localization and code generation benchmarks, CodeC...
| Authors | Qiujiang Chen, Jing Xiong, Chenyang Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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