agent/papers/items/2026-2606-23664-mas-promptbench-when-does-prompt-optimization-improve-multi-agent-llm-systems.md

Paper: MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?

---

type: paper title: "MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?" authors: Juyang Bai, Laixi Shi year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.23664 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-22 updated_at: 2026-06-22 status: queued relevance: high topics:

• agent-evaluation
• multi-agent
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.LG
• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: agentic-ai, multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agentic-ai, multi-agent-llm
• inferred topics: agent-evaluation, multi-agent, workflow-agent
• arXiv categories: cs.LG, cs.MA
• collection score: 16

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/2606.23664