RKSC: Reasoning-Aware KV Cache Sharing and Confident Early Exit for Multi-Step LLM Inference
arXiv:2606.09937v1 Announce Type: cross Abstract: We introduce RKSC (Reasoning-Aware KV Cache Sharing), a training-free inference framework that eliminates two structural redundancies in multi-branch LLM reasoning pipelines. ASKS (Attention-Similarity KV Sharing) computes the prefix KV cache once and broadcasts it to all semantically similar branches via hidden-state cosine similarity, strictly generalising the token-exact prefix caching used by vLLM and SGLang. CGEE (Confidence-Gated Early Exit) applies two complementary exit mechanisms: (1) it skips the verification forward pass entirely whe
The increasing computational demands of large language models, especially in multi-step reasoning, are driving innovation in inference efficiency to reduce costs and latency.
This development offers a significant improvement in the efficiency of LLM inference, directly impacting the scalability and economic viability of deploying advanced AI applications.
New techniques like RKSC make multi-branch LLM reasoning pipelines more efficient and cost-effective by reducing redundant computations and enabling early exits, thus accelerating inference speeds.
- · AI developers
- · Cloud providers
- · SaaS companies leveraging LLMs
- · Less efficient inference frameworks
- · Organizations with high LLM inference costs
Reduced operational costs for AI companies and increased throughput for LLM-powered services.
Faster development cycles and deployment of more complex, multi-step AI agents and applications.
Broader adoption of sophisticated AI reasoning in everyday applications as the computational barrier decreases.
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