arXiv AI
Aug 18
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
arXiv:2608. 16801v1 Announce Type: new Abstract: We study how teams of AI coding agents coordinate while solving programming tasks.
Agensh: Scaling Organizational Intelligence to 1,024 Agents
Agensh is a new multi‑agent harness that eliminates a central orchestrator by letting workers self‑organize through a continuous cooperation loop. The system uses a shared workspace, message interface, and shared context to coordinate tasks, verify results, and merge progress asynchronously. Experiments on ProgramBench and pandoc show that scaling from 1 to 1,024 agents improves test‑pass rates by up to 49% relative, demonstrating that agent count is a viable scaling dimension for complex tasks.
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arXiv AI
Jul 29
OrchBench: Evaluating Multi-Agent Orchestration Plans in Isolation via Deterministic Simulation
arXiv:2607. 25656v1 Announce Type: new Abstract: Complex tasks often decompose into parallelizable yet interdependent subtasks, making orchestration critical to the performance of multi-agent systems (MAS).
arXiv AI
Aug 25
Apodex 1.1: Scaling Agentic Intelligence for Complex Work
arXiv:2608.23283v1 Announce Type: new Abstract: General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sourc...
arXiv AI
Jul 9
Effective Strategies for Asynchronous Software Engineering Agents
arXiv:2603. 21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github.
arXiv AI
Jun 2
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
arXiv AI
Jul 1
ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
arXiv:2606. 31174v1 Announce Type: new Abstract: Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows.
arXiv AI
1d ago
Topological Coherence for Self-evolving Multi-agent Systems
The paper introduces TOCOMAS, a Topology‑Coherent Multi‑Agent System that enforces topological coherence—consistent responsibility, handoff, and memory boundaries—within self‑evolving multi‑agent systems. TOCOMAS grounds task graphs in tool interfaces, groups compatible task nodes into reusable responsibility domains, and derives collaboration and memory visibility rules that respect task dependencies. In experiments on BBEH, WorkBench, SWE‑Bench‑Verified, and CoMemBench, TOCOMAS outperforms baseline methods in task success, verified progress, handoffs, and memory isolation.
arXiv Machine Learning
Jun 2
Multi-Agent Computer Use
arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.
arXiv Machine Learning
Sep 22
Organizational Principles Enable Collective Intelligence in Embodied AI
arXiv:2609.11737v2 Announce Type: replace-cross Abstract: Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artifici...
arXiv Machine Learning
Sep 11
ORCH: Organizational Principles Enable Collective Intelligence in Embodied AI
The paper introduces ORCH, a method that applies human organizational theory to create task‑specific hierarchical structures for large, heterogeneous embodied AI teams. Using ORCH, teams of up to 50 agents across 25 wildfire‑response missions outperformed four existing multi‑agent frameworks, achieving higher mission scores and greater execution efficiency. Both human‑designed and language‑model‑generated ORCH organizations improved performance, with hierarchical organization preserving concurrent activity while coordinating ordered transitions between mission phases.
arXiv AI
Sep 23
Self-Organizing Agent Teams Learn to Reason Together
arXiv:2609.22682v1 Announce Type: new Abstract: Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unkno...
arXiv AI
Jun 9
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.