# TraceKV

Reasoning vision-language models must hold both multimodal context and a growing reasoning trace in their KV cache, and existing low-rank compression calibrates on static inputs, so its divergence from the dense model grows with generation length. TraceKV instead calibrates on reasoning traces the dense model generates itself, weights each KV state by its sensitivity to policy divergence before the SVD, and allocates ranks globally. Across four models including Qwen2.5-VL-7B, a 70% rank ratio with 4-bit quantization gives 73.41% overall accuracy versus 73.58% for the dense model, with the KV cache reduced 5.64 times.

Reasoning vision-language models must hold both multimodal context and a growing reasoning trace in their KV cache, and existing low-rank compression calibrates on static inputs, so its divergence from the dense model grows with generation length. TraceKV instead calibrates on reasoning traces the dense model generates itself, weights each KV state by its sensitivity to policy divergence before the SVD, and allocates ranks globally. Across four models including Qwen2.5-VL-7B, a 70% rank ratio with 4-bit quantization gives 73.41% overall accuracy versus 73.58% for the dense model, with the KV cache reduced 5.64 times.