Publication
arXiv
Stage
Preprint
What we read
Summary of the abstract
Authors
Zhihao Zhan, Ting Song, Li Dong, Shaohan Huang, Jianxun Lian, Yan Xia, Furu Wei
Universities and research institutions
Not yet supplied in verified metadata; the Brief does not guess.

What the paper reports

Researchers introduce Agensh, a self-organized multi-agent harness operating without a central orchestrator. Workers form a cooperation loop, claim and self-assign tasks, share findings, and merge progress asynchronously. They test scalability on several tasks, observing improved final pass rates as agents increase.

Why it matters

The work suggests agent count as a scaling dimension for organizational intelligence, highlighting both potential gains and limits when coordination is decentralized.

Agensh proposes a decentralized workflow where autonomous agents repeatedly gather context, claim subtasks, act, and compare results within a shared workspace and context. The study reports performance gains as the number of agents grows, signaling that larger, self-organizing teams can tackle complex problems under latency pressure.

The authors evaluate on five hard tasks andPandoc with GPT-5.6-sol (high), noting improvements in success rates as scale increases, and observe emerging cooperation patterns that stabilize with organization size.

What this does not tell us

Abstract-only scope and preprint status; no empirical validation beyond abstract-level reporting; results are not generalizable beyond the tested tasks and configurations.

Original sources · 1
  1. Agensh: Scaling Organizational Intelligence to 1,024 Agents ↗arXiv · 2026-09-22

Check the original paper for its authors, methods, version and access terms.