{"slug": "fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and", "title": "FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds", "summary": "A paper posted to arXiv as 2610.22561v1 introduces FRACTAL, a self-similar multi-agent architecture in which the same five-component pattern — Orchestrator, Ensemble, Knowledge Ledger, Decision Maker and Meta-Observer — recurs at every level of a recursive goal-decomposition tree. The authors report four formal contributions: a communication bound showing intra-node cost is linear in ensemble size and round count (O(kr)) under a two-phase blind-proposal/stress-test protocol versus quadratic cost for free-form debate, a two-mode orchestration criterion using a measurable diversity-saturation threshold, a supermajority convergence guarantee with explicit probability bounds and a formal depth bound, and a Bayesian inter-layer escalation mechanism unifying hysteresis and contract-breach signaling in one posterior update. The paper states it reports no quantitative performance claims, illustrates the architecture only qualitatively through a worked example and a multi-layer case study, and leaves empirical validation of comparative performance to future work.", "body_md": "arXiv:2610.22561v1 Announce Type: new \nAbstract: We present FRACTAL, a self-similar multi-agent architecture for recursive goal decomposition, in which the same five-component organizational pattern -- Orchestrator, Ensemble, Knowledge Ledger, Decision Maker, Meta-Observer -- recurs at every level of a decomposition tree. We make four formal contributions: (1) a communication bound showing intra-node cost is linear in ensemble size and round count (O(kr)) under a two-phase blind-proposal/stress-test protocol, versus quadratic cost for free-form debate, with an explicit, stated assumption on fixed evaluator count; (2) a two-mode orchestration criterion separating generative and evaluative phases via a measurable diversity-saturation threshold; (3) a supermajority convergence guarantee with explicit probability bounds at stated agreement levels, and a formal depth bound under an operationalized goal-complexity measure; (4) a Bayesian inter-layer escalation mechanism unifying hysteresis and contract-breach signaling under one posterior update. We illustrate the architecture's execution qualitatively on a worked example and a multi-layer case study; we do not report quantitative performance claims in this paper, and we explicitly discuss the formal and inductive-bias limitations of the model, including an unimplemented but architecture-compatible direction toward bounded self-improvement. We use \"fractal\" as an architectural metaphor for self-similarity, not a measured mathematical property. Empirical validation of the architecture's comparative performance is left to future work.", "url": "https://wpnews.pro/news/fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and", "canonical_source": "https://arxiv.org/abs/2610.22561", "published_at": "2026-10-19 04:00:00+00:00", "updated_at": "2026-10-19 04:18:38.143772+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-research", "large-language-models"], "entities": ["FRACTAL", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and", "markdown": "https://wpnews.pro/news/fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and.md", "text": "https://wpnews.pro/news/fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and.txt", "jsonld": "https://wpnews.pro/news/fractal-a-self-similar-multi-agent-orchestration-architecture-with-formal-and.jsonld"}}