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Agensh: Scaling Organizational Intelligence to 1,024 Agents

Paper recorded by Signals 4 on 2026-09-22 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.CL · 自然语言处理 · first seen 2026-09-23

Abstract

A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent

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#2 most recent of 227 cs.CL papers we have recorded · ↑ newer: Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory · ↓ older: Detecting GPT-Assisted Writing Using Interpretable Stylometric Feature
Cite this page: Agensh: Scaling Organizational Intelligence to 1,024 Agents: the #2 most recent of 227 cs.CL papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/agensh-scaling-organizational-intelligence-to-1-024-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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