ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning
Recent progress in LLM reasoning has increasingly shifted from single-pass generation to explicit search over intermediate reasoning states. Tree-of-Thoughts (ToT) organizes inference to tree-structured search with branching and backtracking, but it substantially amplifies the Key--Value (KV) cache: retaining KV states for a frontier of partial trajectories quickly becomes a memory bottleneck that limits throughput and constrains search depth and width under fixed hardware budgets. We address this challenge by observing that KV reuse in ToT-style inference is governed by search dynamics: near-term decoding depends primarily on the active branch and its ancestors, whereas inactive subtrees...
| Authors | Yeqiu Chen, Ziyan Liu, Zhenxin Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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