agent/papers/items/2026-2607-09101-multi-agent-llm-collaboration-for-unit-test-generation-via-human-testing-inspire.md

Paper: Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows

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type: paper title: Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows authors: Quanjun Zhang, Ye Shang, Siqi Gu, Jianyi Zhou, Chunrong Fang, Zhenyu Chen, Liang Xiao year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.09101 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
• multi-agent
• planning
• rag
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.SE related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 18 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, multi-agent, planning, rag, reasoning, tool-use, workflow-agent
• arXiv categories: cs.SE
• collection score: 18

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/2607.09101