![[2602.16873] AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16873] AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence
Summary
The paper presents AdaptOrch, a framework for task-adaptive multi-agent orchestration that enhances performance by optimizing orchestration topology rather than solely relying on individual model selection.
Why It Matters
As large language models converge in performance, traditional methods of selecting the best model for each task are becoming less effective. AdaptOrch offers a new approach that prioritizes orchestration strategies, potentially leading to significant improvements in multi-agent system performance across various tasks.
Key Takeaways
- AdaptOrch introduces a framework that dynamically selects orchestration topologies based on task characteristics.
- The framework includes a Performance Convergence Scaling Law that emphasizes orchestration over model selection.
- Empirical validation shows 12-23% performance improvement with topology-aware orchestration.
- The Topology Routing Algorithm efficiently maps task dependencies to optimal orchestration patterns.
- Adaptive Synthesis Protocol ensures consistency and termination in parallel agent outputs.
Computer Science > Multiagent Systems arXiv:2602.16873 (cs) [Submitted on 18 Feb 2026] Title:AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence Authors:Geunbin Yu View a PDF of the paper titled AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence, by Geunbin Yu View PDF HTML (experimental) 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...