AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence

• URL: http://arxiv.org/abs/2602.16873v1
• Date: Wed, 18 Feb 2026 21:00:05 GMT
• Title: AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence
• Authors: Geunbin Yu,
• Abstract summary: AdaptOrch is a formal framework for task-adaptive multi-agent orchestration.<n>Topology-aware orchestration achieves 12-23% improvement over static single-topology baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminishing returns. We argue that orchestration topology -- the structural composition of how multiple agents are coordinated, parallelized, and synthesized -- now dominates system-level performance over individual model capability. We present AdaptOrch, a formal framework for task-adaptive multi-agent orchestration that dynamically selects among four canonical topologies (parallel, sequential, hierarchical, and hybrid) based on task dependency graphs and empirically derived domain characteristics. Our framework introduces three key contributions: (1) a Performance Convergence Scaling Law, formalizing conditions under which orchestration selection outweighs model selection; (2) a Topology Routing Algorithm that maps task decomposition DAGs to optimal orchestration patterns in O(|V| + |E|) time; and (3) an Adaptive Synthesis Protocol with provable termination guarantees and heuristic consistency scoring for parallel agent outputs. We validate AdaptOrch across coding (SWE-bench), reasoning (GPQA), and retrieval-augmented generation tasks, demonstrating that topology-aware orchestration achieves 12-23% improvement over static single-topology baselines, even when using identical underlying models. Our results establish orchestration design as a first-class optimization target independent of model scaling.

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