Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate steps cause severe overthinking that inflates inference cost. KV-cache compression is a common solution, yet existing reasoning-oriented methods apply a uniform policy across the trajectory and judge compression only by what it removes from the cache.
arXiv:2606. 03928v1 Announce Type: new Abstract: Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck.
By Ting-Yun Chang, Harvey Yiyun Fu, Deqing Fu, Chenghao Yang, Jesse Thomason, Robin Jia
arXiv:2510. 08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance.
By Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
Long chain-of-thought (CoT) trajectories in large language model (LLM) reasoning cause severe inference bottlenecks due to rapid key-value (KV) cache growth. Current decoding-time compression methods mitigate this issue via token eviction, but typically assume a uniform budget distribution across all layers and heads.
arXiv:2602. 03203v2 Announce Type: replace-cross Abstract: Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces.
By Zican Dong, Peiyu Liu, Junyi Li, Zhipeng Chen, Han Peng, Shuo Wang, Wayne Xin Zhao
StepKV introduces a step-aware approach to compressing the key-value cache used during large language model inference, treating reasoning steps as primary units of retention rather than individual tokens. By linking cache entries to the steps that generated them and estimating each step’s utility from trajectory signals, StepKV assigns a combined token‑ and step‑level score to guide pruning. Experiments on multi‑hop question answering and long‑horizon web reasoning show that StepKV maintains accuracy even under tight cache budgets, outperforming token‑level baselines that suffer sharp performance drops.
By Boyu Feng, Jiahong Liu, Yifan Li, Wenhao Yu, Zexuan Qiu, Yuliang Sun, Ming Shen, Xiang Li, Quanyu Dai, Irwin King