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AutoSwarm: Automating Agent Swarms — arXiv:2610.33048

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.33048
License (as archived): CC0-1.0
Archived: 2026-10-27T09:18:51.692Z
SHA-256 of the archived text: da8198bfd48e9182e992999205f32329d6e59e5151ec2dd8a52d2facf31536a7
Stable URL of this page: https://metraipolis.com/documento/dc_c8bcdbdba785

How to cite

AutoSwarm: Automating Agent Swarms — arXiv:2610.33048 — arXiv (en), https://arxiv.org/abs/2610.33048 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8bcdbdba785 (Metraipolis archive dc_c8bcdbdba785, sha256 da8198bfd48e9182e992999205f32329d6e59e5151ec2dd8a52d2facf31536a7)

BibTeX

@misc{dcc8bcdbdba785,
  title = {AutoSwarm: Automating Agent Swarms — arXiv:2610.33048},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.33048},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8bcdbdba785; sha256 da8198bfd48e9182e992999205f32329d6e59e5151ec2dd8a52d2facf31536a7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.33048. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language model (LLM) agents increasingly automate complex tasks, with multi-agent systems seeking further performance gains through specialization and collaboration. However, distributing work across agents does not automatically yield complementary contributions, and inter-agent dependencies can turn local errors into task failure. To address these challenges, we propose AutoSwarm, a multi-agent framework for collaborative problem solving. Collaboration formation jointly defines responsibility boundaries and conditions for acceptance and downstream use, grounding capability allocation in contributions that other agents can assess and integrate toward a shared objective. Execution coordination couples agents' local progress through a shared state, using dependencies and supervisory relationships to govern agent activation, result handoffs, and integration while adapting subsequent actions to review feedback and emerging blockers. Experiments spanning long-horizon programming, interactive planning, and professional financial analysis demonstrate AutoSwarm's advantages over single-agent baselines and existing multi-agent frameworks. Source code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.33048 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T09:18:21.067Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).