The paper introduces DHCG, a framework that dynamically constructs hierarchical collaboration graphs for large language model–based multi‑agent systems. DHCG coordinates Planner, Worker, and Generator modules to adaptively determine the composition and scale of agents during execution, guided by feedback and action‑aware preference optimization. Experiments on code generation, mathematical reasoning, and domain‑specific tasks show DHCG surpasses static and dynamic baselines, improving performance by 2.77–8.02 points and achieving a 13.06‑point gain over single‑agent baselines.
arXiv:2610.08155v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by en...
WorkflowOps is a multi‑agent workflow orchestration framework that learns collaboration priors from historical workflows. It uses a transition probability matrix to guide DAG construction, a sufficiency‑driven loop to create new agents on demand via an LLM, and a layered semantic matching strategy to reduce LLM calls. Experiments show that WorkflowOps improves end‑to‑end pass rates, especially on structured, decomposable tasks.
arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
arXiv:2606. 16328v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows.
The paper introduces a method to improve test-time scaling (TTS) for large language models by using multi-agent systems (MAS) to split long reasoning chains into manageable contexts. A new dataset, M500, containing 500 multi-agent collaborative reasoning traces, is used to fine‑tune open‑source models, enabling them to learn collaborative patterns and outperform their base versions. An adaptive scaling strategy with a "CEO" agent is proposed to dynamically guide reasoning depth, and experiments in the AgentVerse framework confirm the effectiveness of the approach.
arXiv:2410.19245v3 Announce Type: replace-cross Abstract: With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across...
Unified-MAS is a two-stage framework that decouples node implementation from orchestration in Automatic Multi-Agent Systems. It first searches external knowledge to synthesize domain‑specific node blueprints, then uses a perplexity‑guided reward to optimize bottleneck nodes. Experiments across four specialized domains show that adding Unified-MAS to existing baselines improves performance‑cost trade‑offs by up to 14.2% while lowering costs.
arXiv:2606. 13003v1 Announce Type: new Abstract: Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed decision-making.
arXiv:2608.25992v2 Announce Type: replace Abstract: Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative re...
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
arXiv:2608. 04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents.