# 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
