Multi-Agent Systems and Negotiation

Últimos artículos

• Right Answers, Wrong States: Hidden Information Failures in Multi-Agent Collaboration

  Herun Wan, Jiaying Wu, Minnan Luo, Zihan Ma, Fanxiao Li, Nancy F. Chen, Min-Yen Kan · 2 de octubre de 2026

  Multi-agent systems are often judged by whether they reach the correct answer. This can miss a distinct failure: collaboration may leave behind a corrupted information state even when the immediate decision is correct. We call this an off-query failure. To study this failure in collaborative decisio…

• My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning

  Yihua Zhu, Qianying Liu, Weixu Qiao, Xuan Ren, Weiwei Xu, Wenbo Li, Wei Wang, Ruijia Chen, Xinmiao Luan, Yin Luo, Hao Huang, Xiang Zheng, Hidetoshi Shimodaira · 2 de octubre de 2026

  Agentic reinforcement learning (RL) has emerged as a powerful approach for training large language model agents on multi-step tasks, yet reliance on terminal outcome rewards creates two credit-assignment problems, particularly in long-horizon tasks. First, same-outcome rollout groups provide no lear…

• Beyond Leaderboards: Tokenomics of Agentic Small Language Model Ensembles

  Alexei N. Skurikhin, Emily M. Taylor, Nathan A. DeBardeleben · 2 de octubre de 2026

  As large language models (LLMs) move from standalone assistants into agentic workflows, evaluation must extend beyond scalar leaderboard accuracy to account for operational reliability, cost, latency, and token efficiency. We use an agentic ensemble of small language models (SLMs) with an SLM-judge-…

• LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

  Bingxi Zhao, Lin Geng Foo, Ping Hu, Christian Theobalt, Hossein Rahmani, Jun Liu · 2 de octubre de 2026

  Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways. This survey introduces a unified formal language to systematically categorize these frameworks at three compositional levels: single-agent, tool-based,…

• Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

  Dayu Wang, Yutong Liu, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li · 2 de octubre de 2026

  Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, however, often follow a single-agent paradigm in which one large model interleaves long-horizon reasoning with precise tool o…

• LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing

  Kay K\"ohle, Darko Anicic, Thomas A. Runkler, Ren\'e Graf · 2 de octubre de 2026

  Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable automation systems. LLM-based agents can be deployed in two complementary roles: Offline, they generate deterministic production sequences, reducing progr…

• Understanding Issues, Causes and Solutions in Open-Source LLM-based Multi-Agent Systems

  Asad Ur Rehman, Syed Mohammad Kashif, Ruiyin Li, Peng Liang, Zengyang Li, Arif Ali Khan · 2 de octubre de 2026

  With the advancement of LLM-based multi-agent systems (MAS), an increasing number of opensource projects are adopting multi-agent architectures as the foundation of their core functionality. Although research and practice on MAS have attracted considerable attention, limited studies have explored th…

• Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration

  Xin Heng · 2 de octubre de 2026

  AI agents can each make locally valid decisions yet jointly produce an invalid result. We call this the global coherence problem: a failure of shared state, not merely of model intelligence. Our Observation-Aliasing Impossibility Theorem gives the exact boundary. A policy can guarantee a valid act…

• Counting Moves, Weighing Voices: Bayesian Dialectical Argumentation for Calibrated Multi-LLM Councils under Persistent Adversaries

  Ionel Eduard Stan, Paolo Napoletano · 2 de octubre de 2026

  A multi-LLM \emph{council} lets several large language models (LLMs) deliberate on a question and return an answer together with a confidence estimate. As these systems become increasingly used for reasoning, that confidence should represent a calibrated \emph{probability of being correct}, and the …

• Code Owns the Simulation, Jev Owns the Evaluation

  Yaodong Yang, Hongyao Tang, Yi Ma, Xingyu Fan, Weixun Wang, Jinpeng Li, Tianpei Yang · 2 de octubre de 2026

  Judgment models such as \jev{} return, in a single call and without reasoning text, a probability for each described option. This makes them attractive as an agent's action-selection layer, but it is unclear which decisions they can be trusted with. We test \jev{} on reflection tests, one-shot matri…

• Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

  Yu Luo, Jiamin Jiang, Yimin Zuo, Xidao Wen, Rongchen Gao, Yongqian Sun, Shenglin Zhang, Guiyang Liu, Cheng Zhang, Fang Situ, Qi Zhou, Dan Pei · 2 de octubre de 2026

  Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensure a coherent understanding of the current world. We introduce PoS, an inference-time framework that constructs and continually maintains explicit be…

• Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents

  Ali Atiah Alzahrani · 2 de octubre de 2026

  When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate task score. That score cannot tell whether improvement was attainable, which component lost value, or what the agent's own checks certify. We intro…

• Revision-Aware Independent Agent Graphs for Dynamic Reasoning

  Yan Luo, Selim-Antoine Lali, Jeremy Moebel, Iliass Khoutaibi, Ahmadou Aidara, Mengyu Wang · 2 de octubre de 2026

  Conventional reasoning protocols present a fixed, preselected task, so they cannot test whether an agent propagates relevant updates, preserves unaffected work, or reconstructs a historical task binding. We therefore study \emph{dynamic task routing}, in which an event stream revises task bindings a…

• Auditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay

  Haotian Chen, Bowen Ye, Yuning Zhang, Jingkun Yu · 2 de octubre de 2026

  Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a typed snapshot-settlement contract and audit three distinct properties: order sensitivity, useful progress, and replay consistency. Five settlement po…

• Beyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems

  Shixuan Li, Wei Yang, Peiyu Zhang, Anzhe Cheng, Heng Ping, Paul Bogdan · 2 de octubre de 2026

  Multi-agent communication aims to help agents benefit from one another's information. Yet improvements in system performance leave a fundamental ambiguity: do they reflect effective communication, a favorable agent architecture, or simply additional reasoning? Because communication methods are commo…

• Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization

  Xuehang Guo, Haoyu Wang, Shengyu Chen, Zach Chen, Wei Cheng, Qingyun Wang, Haifeng Chen · 2 de octubre de 2026

  Large language models (LLMs) increasingly construct multi-agent workflows that decompose a complex task and assign specialist agents from a pool. However, building such a workflow well remains challenging: how finely to divide the task, which agent to trust with each subtask, and when to create a ne…

• Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

  J\'er\'emie Lumbroso · 2 de octubre de 2026

  As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a shift CS-education researchers have begun to name. This shift exposes a vocabulary gap: the field asks for "human oversight" without a working disti…

• Finding the Right Fit: Model-Harness Interactions across Agent Tasks

  Yixuan Li, Yiyun Zhou, Yao Long Teng, Fuchao Yang, Yanchen Deng, Zhiyi Lyu, Xuyu Dong, Feng Chen, Bo An · 2 de octubre de 2026

  Choosing an agent system means choosing both a language model and the harness through which it acts. We ask whether a strong model, harness, or pairing stays strong when the setting changes. We evaluate 66 configurations: four configurable harnesses (OpenHands, DeepSeek Harness, PI, and openJiuwen) …

• Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard

  Michael Hardy, Ruhana Azam, Anka Reuel, Mykel Kochenderfer, Sanmi Koyejo · 2 de octubre de 2026

  Agent evaluations are increasingly used to compare LLMs and inform deployment decisions, yet ranks can reflect not only the model but also the effects of the evaluation conditions such as the scaffolds or tasks. This makes reliability claim-dependent: an evaluation that reliably ranks deployed syste…

• Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams

  Sahan Paliskara, Nattaput Namchittai, Andrew Lampinen · 2 de octubre de 2026

  People are increasingly delegating tasks to AI agents, and those agents are increasingly encountering other people's agents over shared resources such as a codebase, a calendar, or a budget. When each agent acts for a different user with different goals, coordination often fails, and the group ends …

• Before Agents Decide: Epistemic Action in LLM-Based Systems

  Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan · 2 de octubre de 2026

  Before a difficult decision, people often act simply to understand the situation better. We turn an object to see another side, place alternatives next to each other, or change one condition and observe what happens. These actions may not complete the task, but they improve the evidence needed for t…

• JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

  Haoyang Su, Weiran Huang · 2 de octubre de 2026

  LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic predictions over finite fields, but require those fields to be specified in advance. This requirement limits autonomous ta…

• Knowing When to Yield: Grounded Arbitration of User Corrections in Text-Based Embodied Agents

  Yezhou Cheng, Runjia Du, Zeming Liu, Hang Lyu, Zehua Yang, Bojun Lin · 2 de octubre de 2026

  How should an embodied agent respond when a person's correction may be wrong? We formulate grounded correction arbitration as a choice among accepting, rejecting, inspecting the world, and asking the speaker. GAVA implements this interface with observation-bounded evidence, legal probes, and a one-s…

• From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution

  Stefan G. Creadore · 2 de octubre de 2026

  Large-language-model agents can propose and execute actions, but proposal, authority, dispatch, verified external effect, and serving promotion are different claims. We present Praxa, an agent harness that represents these states explicitly through deterministic admission, brokered execution, extern…

• LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration

  Shinan Zhang, Tao Zhang, Qihui Zhu, Mengjie Zhang, Dong Jin, Yunpeng Hou, Shuangwu Chen, Xiaobin Tan, Quan Zheng, Jian Yang · 1 de octubre de 2026

  LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and …