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

TL;DR

AdaptOrch introduces a formal framework for dynamically selecting multi-agent orchestration topologies based on task characteristics, significantly improving performance over static approaches in various NLP tasks.

Contribution

It presents a novel formal framework with a performance convergence law, an efficient topology routing algorithm, and an adaptive synthesis protocol for task-specific multi-agent orchestration.

Findings

Topology-aware orchestration improves performance by 12-23%.

Dynamic orchestration outperforms static baselines across tasks.

Orchestration design is a key system-level optimization independent of model scaling.

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.