The paper investigates zero‑shot coordination (ZSC) among large language model (LLM) agents, a scenario where independently developed agents must coordinate without prior shared scaffolding. Experiments in minimal, controlled environments reveal that current frontier LLMs struggle to coordinate, mainly due to limited understanding of the coordination problem and weak reasoning about partners’ beliefs. The authors introduce Coordination‑Friendly Definitions (CFDs) as a principled, automatically discoverable approach to improve robust coordination among LLM agents.
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
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...
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
arXiv:2510. 19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and solve them autonomously.
The paper introduces the Symbolic Guidance Taxonomy (SGT) to regulate the autonomy of large language model agents in distributed multi‑agent coordination. By providing symbolic guidance—ranging from open‑ended reasoning to fully prescriptive algorithmic steps—the authors find that intermediate levels of autonomy consistently outperform both unguided agents and fully prescriptive specifications. These results suggest that carefully controlling agent autonomy is a crucial design principle for effective LLM‑based coordination.
arXiv:2607. 25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol).
arXiv:2605. 15207v2 Announce Type: replace Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines.
STRIATUM-CTF is a modular agentic framework that uses the Model Context Protocol to mediate between large language model reasoning and the execution of offensive‑security tools. By exposing tools as strictly typed, schema‑validated functions, the protocol rejects invalid tool calls and returns structured observations that the agent can use to self‑correct. In controlled experiments the system achieved 77.8–86.7% success across 15 CTF challenges, and in a live multi‑team CTF at Florida State University it placed first among more than 30 teams.
MASBench is a benchmark for evaluating large language model (LLM)-based multi‑agent systems (MAS) under partial observability, a realistic constraint where agents only see limited environmental information. The benchmark is structured into three progressive task categories—Reasoning, Scheduling, and Game—and assesses three collaboration mechanisms: Protocol, Memory, and Routing. It provides deterministic evaluation metrics such as performance score, communication cost, and cost effectiveness, and includes empirical results across various LLM backbones to guide effective MAS design.
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.
arXiv:2604. 01687v3 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address.
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.