arXiv AI
Jul 17
Provable Coordination for LLM Agents via Message Sequence Charts
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination
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.
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arXiv AI
1d ago
Zero-Shot Coordination among LLM Agents
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 AI
Oct 19
Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides
Structured Authority Injection (SAI) is a protocol that enables persistent human control over large language model (LLM) agents by routing operator corrections through typed, schema‑validated Model Context Protocol (MCP) tool calls and maintaining a ranked authority context in each prompt. It defines three override classes—Constraint, Priority, and Strategy—each with distinct persistence semantics and a strict precedence order enforced via a directed authority channel. Evaluations across nine LLMs and eight scenarios show reduced ambiguous directive interpretation, mitigated authority decay, and lower prompt overhead in the strongest models, though benefits vary by model.
arXiv AI
Aug 28
From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities
The paper introduces a new benchmark that evaluates large language models (LLMs) on their agentic mathematical reasoning rather than just final answers. It aligns problem‑solving behaviors with a taxonomy of reusable mathematical atomic capabilities and includes planning, action, and feedback tasks in both textual and multimodal settings. Experiments show that models with similar end‑to‑end accuracy can have very different agentic profiles, highlighting the importance of process‑level evaluation.
arXiv AI
Jul 21
OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
arXiv:2505. 11765v5 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications.
arXiv AI
Oct 15
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes
The paper introduces Degrees of Freedom (DoF), a continuous framework that measures how much decision-making is delegated to agents in large language model–based multi‑agent systems. By varying DoF, the authors explore a spectrum between fully centralized and fully decentralized coordination, finding that intermediate settings often outperform both extremes across several task domains. The study shows that task characteristics and retained experience influence how coordination granularity affects performance, offering empirical guidance for designing hybrid coordination regimes.
arXiv AI
Oct 27
Agentic AI Empowered Intent-Based Networking for 6G
arXiv:2601.06640v2 Announce Type: replace Abstract: The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level o...
arXiv AI
Sep 10
Many-Tier Instruction Hierarchy in LLM Agents
The paper introduces Many-Tier Instruction Hierarchy (ManyIH), a new framework for resolving conflicts among instructions with arbitrarily many privilege levels in large language model agents. It presents ManyIH-Bench, a benchmark featuring 853 agentic tasks that require navigating up to 12 levels of conflicting instructions across 46 real-world agents. Experiments show current models achieve only about 40% accuracy when instruction conflict scales, highlighting a gap in fine-grained, scalable conflict resolution.
arXiv AI
1d ago
MASBench: Benchmarking LLM-based Multi-Agent Collaboration under Partial Observability
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 AI
Aug 19
DeAR: Decentralized Agentic Reasoning via Capability Grounding and Collaborative Thought Navigation
DeAR (Decentralized Agentic Reasoning) is a new framework that replaces centralized protocols with autonomous peer‑to‑peer collaboration for agentic reasoning. It introduces three key mechanisms: decentralized capability grounding for agent specialization, thought map navigation for targeted peer interactions, and topology updates for adaptive error correction. Across nine multimodal reasoning and text‑based QA benchmarks, DeAR consistently outperforms recent baseline methods, demonstrating that decentralized and adaptive collaboration improves accuracy in knowledge‑intensive reasoning tasks.
arXiv AI
Oct 2
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
arXiv AI
Jun 29
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.