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# Paper: KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
---
type: paper
title: "KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling"
authors: Peng Kuang, Haibo Jin, Xiaoyu Han, Yanli Wang, Xiaopeng Yuan, Ye Yu, Kaidi Xu, Haohan Wang
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2607.09153
code_url:
source: arxiv
collected_at: 2026-07-27
published_at: 2026-07-10
updated_at: 2026-07-10
status: queued
relevance: high
topics:
- agent-evaluation
- coding-agent
- computer-use
- memory
- multi-agent
methods:
-
benchmarks:
-
models:
-
datasets:
- cs.AI
related_concepts:
-
related_jobs:
-
related_experiments:
-
related_projects:
-
collection_score: 17
collection_queries: multi-agent-llm
---
## One-line Takeaway
Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
## Why Collected
- matched queries: multi-agent-llm
- inferred topics: agent-evaluation, coding-agent, computer-use, memory, multi-agent
- arXiv categories: cs.AI
- collection score: 17
## Review Checklist
- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?
## Links
- arXiv: https://arxiv.org/abs/2607.09153