NeuroMAS treats a multi-agent language system as a trainable neural‑network‑like architecture, with large language model agents as nodes and textual signals as edges. The topology defines general information flow, while reinforcement learning determines how agents communicate, specialize, and coordinate, making the system role‑free but structure‑aware. Experiments show NeuroMAS outperforms both inference‑time and trained multi‑agent baselines, and that scaling larger systems is feasible when grown progressively from smaller trained systems.
arXiv:2506. 09046v3 Announce Type: replace-cross Abstract: Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations.
The paper examines when multi‑agent collaboration is beneficial versus single‑agent approaches. It finds that collaboration yields systematic advantages mainly in long‑horizon tasks with sparse dependencies, while single agents perform better in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight multi‑agent mechanism that models collaboration as a dynamically evolving graph, and show that it balances context efficiency and task performance without always improving outcomes as more agents are added.
arXiv:2604. 25917v2 Announce Type: replace Abstract: Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning.
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...
The paper examines when multi‑agent collaboration is truly beneficial as large language models grow more capable. It finds that multi‑agent systems yield systematic advantages mainly for long‑horizon tasks with sparse dependencies, while single‑agent approaches excel in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight, graph‑based collaboration framework that balances context efficiency and performance, demonstrating that adding more agents or deeper recursion does not always improve outcomes.
arXiv:2606. 09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite.
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.
GLOW is a framework that predicts the performance of Agentic Workflows by combining Graph Neural Networks with a graph-oriented Large Language Model. It extracts topology-aware semantic representations from workflow descriptions and fuses them with structural representations via a Transformer-based module, using contrastive learning to enhance discriminative power. Experiments on the FLORA-Bench benchmark show GLOW surpasses existing baselines in accuracy and ranking, and when used in the AFLOW generation system, it cuts optimization time by 98.7% with minimal loss in score.
The paper introduces K‑GAT, a neuro‑symbolic framework that generates multi‑agent collaboration topologies conditioned on external evidence, treating the design as a knowledge‑conditioned structure learning problem. Unlike prior methods that rely mainly on large language model parameters, K‑GAT integrates external evidence directly into autoregressive graph generation, reducing redundant interactions and improving verification in knowledge‑intensive tasks. Experiments on benchmarks such as the expert‑level GPQA dataset show K‑GAT achieving a +15.7% accuracy gain over the LLM‑Debate baseline while using fewer computational tokens.
arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.
arXiv:2606. 15079v1 Announce Type: cross Abstract: Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy.
arXiv:2606. 18837v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks.