Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression

Researchers introduce KVFundaBench to expose a critical gap in KV cache compression evaluation: while retrieval tasks remain robust under compression, reasoning tasks degrade severely due to disrupted Chain-of-Thought coherence. They propose ShotKV, which preserves semantic integrity by treating few-shot examples as indivisible units, achieving 9-18% accuracy improvements on long-context tasks while reducing latency by 11%.

The paper addresses a fundamental blind spot in how the AI community evaluates language model optimization techniques. KV cache compression has become standard practice for efficient inference, but existing benchmarks predominantly test sparse retrieval scenarios that mask failures in complex reasoning. This distinction carries significant technical weight: retrieval tasks tolerate fragmented information, while reasoning chains require unbroken semantic continuity to maintain logical coherence.

The emergence of this problem reflects the field's evolution toward reasoning-focused models like DeepSeek-R1. As models become more sophisticated, evaluation methodologies must similarly advance to capture task-specific degradation patterns. The authors demonstrate that aggressive compression disrupts Chain-of-Thought links by fragmenting the semantic context that enables step-by-step reasoning, a failure mode invisible to traditional benchmarks.

ShotKV's solution proves elegant: explicitly separating prefill and decoding phases to protect few-shot examples as atomic semantic units. This targeted preservation strategy avoids the computational overhead of protecting entire contexts while maintaining reasoning integrity. The 11% latency improvement over unoptimized inference suggests practical deployment viability without accuracy sacrifice.

For the broader AI infrastructure ecosystem, this research signals that optimization techniques require task-aware validation. Teams deploying inference systems must now consider whether their compression strategies preserve reasoning capability, not just retrieval performance. The work establishes a new evaluation standard that others will likely adopt, particularly as reasoning becomes central to LLM applications.

• →KV cache compression degrades reasoning tasks far more than retrieval tasks due to disrupted Chain-of-Thought coherence
• →ShotKV preserves few-shot examples as indivisible semantic units, achieving 9-18% accuracy improvements on long-context generation
• →Current evaluation benchmarks mask reasoning degradation by over-indexing on sparse retrieval performance
• →Task-dependent degradation suggests compression strategies must be evaluated across diverse workloads, not generic benchmarks
• →The approach delivers 11% latency reduction compared to full cache inference while maintaining semantic integrity