The paper introduces RCM, a responsibility-centered coordination mechanism for decentralized multi-agent systems that uses commitments to link task delegation with explicit responsibilities. RCM employs recursive delegation so agents keep upstream responsibilities while locally managing downstream commitments, forming a network of local orchestration. Experiments show RCM outperforms both centralized and other decentralized baselines, improving task performance, responsibility metrics, and failure/harm detection without task-specific training.
Centralized orchestration provides comprehensive multi-agent coordination but creates bottlenecks in scalability, adaptability, and collective performance. Decentralized approaches alleviate these bot...
PANDA is a decentralized architecture for large-scale, fault-tolerant multi-agent systems that enables heterogeneous agents to discover each other's capabilities and self-organize into specialized teams for each task. It decouples collective communication from team communication, allowing agents to participate in multiple teams simultaneously and load-balance tasks across the collective. PANDA supports three planning and execution patterns—star, chain, and mesh—detects and recovers from infrastructure and orchestration failures, and uses a web-of-trust model for governance without a central bottleneck.
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
The paper investigates how large language model agents handle long‑horizon tasks and finds that verification is crucial for success but often occurs too late. It shows that sharing a common context dilutes duty‑relevant information and that simply separating duties among agents is insufficient, as agents can lose sight of the overall goal. To address these issues, the authors propose Goal‑Aware Dynamic Multi‑Agent Collaboration (GAD), which uses an execution graph to organize subtasks, keeps each agent’s context focused on its specific goal, and dynamically adjusts task dependencies and information sharing based on execution evidence, with verification and recovery checks providing feedback for repair.
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.
MA-Mesh is a framework that adds a role‑based self‑healing layer to multi‑agent collaboration patterns. It generates resilience‑aware system specifications and synthesizes them into executable policy code. The framework introduces cross‑agent reflection and role‑based failover to enable agents to evaluate peer performance and dynamically reassign roles, improving task performance and robustness in uncertain environments.
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
Consilience is an inference‑time orchestration framework that steers and certifies communication among multi‑agent large language models in hidden‑profile settings. It summarizes each discussion turn with a compact state of uncertainty, disagreement, evidence gain, redundancy, and premature consensus, then selects a communication intervention (challenge, clarify, seek evidence, or route) and speaker. A round‑wise conformal calibration procedure guarantees that the controller’s proposed action has bounded one‑step regret with high probability, and an acceptance mechanism enforces this guarantee for the executed action. Experiments on HiddenBench‑style tasks show that Consilience improves decision accuracy and communication efficiency over fixed and unstructured protocols, sometimes outperforming a full‑information baseline.
arXiv:2606. 09122v1 Announce Type: cross Abstract: Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures.
Decentralized Multi-Agent Coordination and Communication in Shared Codebases introduces ParallACT, a benchmark for studying how coding agents with independent, human‑driven requests interact in shared repositories. The benchmark includes complementary tasks that require agents to cooperate via a shared interface, and contradictory tasks where agents’ edits conflict, leading to poor merged outcomes. Experiments show that a single‑agent oracle outperforms parallel agents, especially on contradictory tasks, and that merely notifying agents of each other or providing a communication channel only marginally improves performance at a token cost.
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:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require t...