agent/papers/items/2026-2606-01199-can-llm-agents-sustain-long-horizon-organizational-dynamics.md

Paper: Can LLM Agents Sustain Long-Horizon Organizational Dynamics?

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type: paper title: Can LLM Agents Sustain Long-Horizon Organizational Dynamics? authors: Xuancheng Zhu, Yang Yue, Shuaibing Wan, Zihan Dou, Xiaohan Zhang, Yongrui Liu, Guoshun Nan year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.01199 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-31 updated_at: 2026-05-31 status: queued relevance: high topics:

• agent-evaluation
• memory
• multi-agent
• planning
• rag
• workflow-agent
• world-model methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 22 collection_queries: language-agent, planning-agent

One-line Takeaway

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

Why Collected

• matched queries: language-agent, planning-agent
• inferred topics: agent-evaluation, memory, multi-agent, planning, rag, workflow-agent, world-model
• arXiv categories: cs.AI
• collection score: 22

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/2606.01199