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Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization — arXiv:2610.30074

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.30074
License (as archived): CC0-1.0
Archived: 2026-10-22T02:56:26.542Z
SHA-256 of the archived text: 7df8fb326aab6ebfc012aa049f85ec98baa07fcb95129514976fe9cc97afa04e
Stable URL of this page: https://metraipolis.com/documento/dc_c4cfbe3cfcd9

How to cite

Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization — arXiv:2610.30074 — arXiv (en), https://arxiv.org/abs/2610.30074 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c4cfbe3cfcd9 (Metraipolis archive dc_c4cfbe3cfcd9, sha256 7df8fb326aab6ebfc012aa049f85ec98baa07fcb95129514976fe9cc97afa04e)

BibTeX

@misc{dcc4cfbe3cfcd9,
  title = {Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization — arXiv:2610.30074},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.30074},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c4cfbe3cfcd9; sha256 7df8fb326aab6ebfc012aa049f85ec98baa07fcb95129514976fe9cc97afa04e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.30074. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. Independent sets certify compatible edits whose verified gains, measured against the same current solution, add exactly. A coordinator constructs a safe compatible candidate and can also evaluate interacting edits to capture favorable interactions. We prove that an optimal fractional coloring yields a merger that preserves a $1/\chi_f(G)$ fraction of the total verified local improvement, where $\chi_f(G)$ is the graph's fractional chromatic number. This guarantee is tight among mergers that select a subset of the same proposals. Under conditional local-improvement guarantees and a block error-bound condition, repeated collaboration converges geometrically in the objective gap. The rate separates task structure, agent allocation, and computation cost, yielding a linear program for agent assignment and an explicit time-cost trade-off. Code-repair experiments show that verified merging resolves conflicts between individually useful edits that cause naive merging to fail. On larger conflict-free tasks, parallel agents reduce modeled completion time at higher input-token-equivalent cost. On three public road networks, fractional integration retains 922 units of verified graph-cut improvement across 90 paired runs. For route-search agents sharing exclusive access to vehicle routes, fractional scheduling reduces measured wall time by a median of 15% across twelve paired runs, with route-cost changes varying across runs.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI); Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2610.30074 — 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 2026-10-22T02:56:24.519Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).