agent/papers/items/2026-2606-20629-specialize-roles-mix-deployments-pushing-the-cost-accuracy-frontier-of-llm-agent.md

Paper: Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams

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type: paper title: "Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams" authors: Yinsicheng Jiang, Liang Cheng, Yeqi Huang, Yufan Zhao, Zhan Lu, Li Dong, Wenda Li, Edoardo Ponti, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.20629 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-28 updated_at: 2026-05-28 status: queued relevance: high topics:

• agent-evaluation
• computer-use
• planning
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.MA
• cs.AI
• cs.LG related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 19 collection_queries: agent-evaluation

One-line Takeaway

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

Why Collected

• matched queries: agent-evaluation
• inferred topics: agent-evaluation, computer-use, planning, reasoning, tool-use, workflow-agent
• arXiv categories: cs.MA, cs.AI, cs.LG
• collection score: 19

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/2606.20629