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

arXiv: https://arxiv.org/abs/2602.16873 · HTML: https://mlsanity.com/paper/2602.16873/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 9 of 9)

- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents](https://mlsanity.com/paper/2408.07060/) (2024)
- [Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response](https://mlsanity.com/paper/2511.15755/) (2025)

## Built on by (most cited first, 17 of 17)

- [Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.14892/) (2026)
- [SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System](https://mlsanity.com/paper/2605.09341/) (2026)
- [AWCP: A Workspace Delegation Protocol for Deep-Engagement Collaboration across Remote Agents](https://mlsanity.com/paper/2602.20493/) (2026)
- [Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts](https://mlsanity.com/paper/2604.00901/) (2026)
- [Semantic Early-Stopping for Iterative LLM Agent Loops](https://mlsanity.com/paper/2606.27009/) (2026)
- [Synergy: A Next-Generation General-Purpose Agent for Open Agentic Web](https://mlsanity.com/paper/2603.28428/) (2026)
- [Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction](https://mlsanity.com/paper/2604.27221/) (2026)
- [Dynamic Attentional Context Scoping: Agent-Triggered Focus Sessions for Isolated Per-Agent Steering in Multi-Agent LLM Orchestration](https://mlsanity.com/paper/2604.07911/) (2026)
- [Retrieval-Conditioned Topology Selection with Provable Budget Conservation for Multi-Agent Code Generation](https://mlsanity.com/paper/2605.05657/) (2026)
- [Punctuated Equilibria in Artificial Intelligence: The Institutional Scaling Law and the Speciation of Sovereign AI](https://mlsanity.com/paper/2603.14664/) (2026)
- [Minimal Oversight: Uncertainty-Aware Governance for Delegated AI Systems](https://mlsanity.com/paper/2606.15563/) (2026)
- [A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration](https://mlsanity.com/paper/2609.14413/) (2026)
- [Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum](https://mlsanity.com/paper/2609.18283/) (2026)
- [PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems](https://mlsanity.com/paper/2609.38482/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)
- [WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration](https://mlsanity.com/paper/2610.07860/) (2026)
- [Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes](https://mlsanity.com/paper/2610.17751/) (2026)

## Abstract (arXiv)

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.