RKSC: Reasoning-Aware KV Cache Sharing and Confident Early Exit for Multi-Step LLM Inference

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 when generation confidence is decisive across branches, and (2) it terminates the verification pass at an intermediate layer when per-layer entropy stabilises, using lightweight hooks on the transformer backbone. RSBCM (Reasoning-Selective Block Cache Manager) prevents unbounded cache growth via attention-weighted depth-priority eviction. Across five model families (7B--10B), four benchmarks, and 1,000 evaluated problems, RKSC achieves a mean speedup of $3.008\times$ over the No-KV baseline (peak $3.990\times$), a $1.66\times$ mean improvement over vLLM-equivalent prefix caching, with a CGEE-induced error rate of only $0.37$% (6 errors out of 1,616 verify calls). No fine-tuning or architecture changes are required. Code is available at https://anonymous.4open.science/r/RKSC-9728/.