id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7c1861f9290144204b1de13a9cb05b4c0c3e0db37d6610f72e07bcdd20446c77 | [
"arxiv",
"semantic_scholar"
] | TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization | Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those preferences. We present TO-Agents, a multi-agent AI framework that conn... | [
"Isabella A. Stewart",
"Hongrui Chen",
"Faez Ahmed"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.21622 | https://arxiv.org/pdf/2605.21622v1 | 2605.21622 | null | 0 | 0 | false | null | null | 0.35 |
de557b2ce0d60421f5c4aa6a1c64b0bb0bb9d777d12626419e59e6ae3b6df601 | [
"arxiv",
"semantic_scholar"
] | From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA) | Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-agent reference architecture enabling hig... | [
"Binghan Wu",
"Shoufeng Wang",
"Yunxin Liu",
"Ya-Qin Zhang",
"Joseph Sifakis",
"Ye Ouyang"
] | [
"cs.AI",
"cs.NI"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20608 | https://arxiv.org/pdf/2605.20608v1 | 2605.20608 | 10.1109/LNET.2026.3693226 | 0 | 0 | false | null | IEEE Networking Letters | 0.55 |
2bc6115a3e47a32f10a28b6777dedacd7924f55404133735a9d5b73f2e64f6db | [
"arxiv",
"semantic_scholar"
] | MemGym: a Long-Horizon Memory Environment for LLM Agents | Memory is a central capability for LLM agents operating across long-horizon tasks. Existing memory benchmarks predominantly evaluate retention of personalized information in multi-turn chat scenarios, overlooking the dynamic memory formation that occurs during extended agent execution. Consequently, the memory systems ... | [
"Wujiang Xu",
"Yu Wang",
"Kai Mei",
"Kaiqu Liang",
"Zhenting Wang",
"Mingyu Jin",
"Han Zhang",
"Shi-Xiong Zhang",
"Wenyue Hua",
"Sambit Sahu",
"Dimitris N. Metaxas"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20833 | https://arxiv.org/pdf/2605.20833v1 | 2605.20833 | null | 0 | 0 | false | null | null | 0.35 |
5a3f2686f860fd6c68f956ce490947a40a43f4b8d401c914e3c12963fb3c1f3f | [
"arxiv",
"semantic_scholar"
] | CASPIAN: Online Detection and Attribution of Cascade Attacks in LLM Multi-Agent Systems via Cross-Channel Causal Monitoring | Cascade attacks in LLM multi-agent systems (MAS) arise when adversarial influence propagates across agents and leads to escalated system-level failures through complex agent interactions. Detecting such cascades is challenging, as their signals are distributed, tightly coupled across interaction channels, and often app... | [
"Kavana Venkatesh",
"Jafar Isbarov",
"Saad Amin",
"Murat Kantarcioglu",
"Jiaming Cui"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.19240 | https://arxiv.org/pdf/2605.19240v1 | 2605.19240 | null | 0 | 0 | true | https://github.com/caspian-detector/caspian | null | 0.65 |
ef0ec7457174e454cb2e39324d3814496beadcb5663ec786a17d1b7eaf151c74 | [
"arxiv",
"semantic_scholar"
] | Multi-agent Collaboration with State Management | Recent advances in multi-agent systems have shown great potential for solving complex tasks. However, when multiple agents edit a shared codebase concurrently, their changes can silently conflict and inconsistent views lead to integration failures. Existing multi-agent systems address this through workspace isolation (... | [
"Mengyang Liu",
"Taozhi Chen",
"Zhenhua Xu",
"Xue Jiang",
"Yihong Dong"
] | [
"cs.MA",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SE"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.20563 | https://arxiv.org/pdf/2605.20563v1 | 2605.20563 | null | 0 | 0 | false | null | null | 0.35 |
c39ea1406741a7a1de6a94cb686478588f16a41a9f778225b485d9756bde1543 | [
"arxiv",
"semantic_scholar"
] | EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design | Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation. We introduce a benchmark suite with three evaluation dimensions: (1) a workflow benc... | [
"Gioele Molinari",
"Florian Felten",
"Soheyl Massoudi",
"Mark Fuge"
] | [
"cs.AI",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.19743 | https://arxiv.org/pdf/2605.19743v2 | 2605.19743 | null | 0 | 0 | true | null | null | 0.65 |
9e1ff3183cd1fe9e45dde36c5e042e33a209985747d984f82ddf80b2dba8172f | [
"arxiv",
"semantic_scholar"
] | A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents | Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first-class architectural object. This paper names that boundary the stochastic-deterministic boundary (SDB): a four-part contract among a proposer, verifier, commit step, ... | [
"Vasundra Srinivasan"
] | [
"cs.AI",
"cs.SE"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.20173 | https://arxiv.org/pdf/2605.20173v1 | 2605.20173 | null | 0 | 0 | true | https://github.com/vasundras/agent-runtime-patterns | null | 0.65 |
a9fd92403376284f90d4f45da3b75461842a9160cc00aa9cc2905f5c7b2446cf | [
"arxiv",
"semantic_scholar"
] | ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning | Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video understanding. However, existing native-RL methods dispatch tool calls sequentially (i.e., one per turn): a single wrong crop propagates errors wi... | [
"Zuhao Yang",
"Kaichen Zhang",
"Sudong Wang",
"Keming Wu",
"Zhongyu Yang",
"Bo Li",
"Xiaojuan Qi",
"Shijian Lu",
"Xingxuan Li",
"Lidong Bing"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.20342 | https://arxiv.org/pdf/2605.20342v2 | 2605.20342 | null | 1 | 0 | false | null | null | 0.35 |
c14a952a4a42efb330843fb7d5cdae09ff8aa24c1ce0c2ea16595a1712a5037c | [
"arxiv",
"semantic_scholar"
] | Mix-Quant: Quantized Prefilling, Precise Decoding for Agentic LLMs | LLM agents have recently emerged as a powerful paradigm for solving complex tasks through planning, tool use, memory retrieval, and multi-step interaction. However, these agentic workflows often introduce substantial input-side overhead, making the compute-intensive prefilling stage a key bottleneck in long-context, mu... | [
"Haiquan Lu",
"Zigeng Chen",
"Gongfan Fang",
"Xinyin Ma",
"Xinchao Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.20315 | https://arxiv.org/pdf/2605.20315v1 | 2605.20315 | null | 0 | 0 | false | null | null | 0.35 |
6d565b4c9ea15c8d07c6fd6209eb28aa2ce85af8087235e3edffaceb5324ff51 | [
"arxiv",
"semantic_scholar"
] | APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents | LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at... | [
"Quan Zheng",
"Yan Gao",
"Shaobin He",
"Haoxiang Guan",
"Yuanhe Tian",
"Jie Feng",
"Ming Wang",
"Shuxin Zheng",
"Zhen Liu"
] | [
"cs.MA",
"cs.CY"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.27419 | https://arxiv.org/pdf/2605.27419v1 | 2605.27419 | null | 0 | 0 | false | null | null | 0.35 |
4e93119299b2bde502e7c5226ecef80199853597790c03ebe91e5e88702c00a5 | [
"arxiv",
"semantic_scholar"
] | Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling | LLM-based multi-agent systems (MAS) have demonstrated strong reasoning and decision-making capabilities that consistently surpass those of single LLM agents. However, their performance often suffers from naive aggregation mechanisms that assume uniformly cooperative interactions. Upon close inspection, we observe that ... | [
"Longgang He",
"Longzhu He",
"Daojing He",
"Chaozhuo Li"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.19418 | https://arxiv.org/pdf/2605.19418v1 | 2605.19418 | null | 0 | 0 | false | null | null | 0.35 |
fc6cac82eafe90e40fa6277d56461957ed8237b6d6afb421a66e82150a939b23 | [
"arxiv",
"semantic_scholar"
] | Code as Agent Harness | Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoni... | [
"Xuying Ning",
"Katherine Tieu",
"Dongqi Fu",
"Tianxin Wei",
"Zihao Li",
"Yuanchen Bei",
"Jiaru Zou",
"Mengting Ai",
"Zhining Liu",
"Ting-Wei Li",
"Lingjie Chen",
"Yanjun Zhao",
"Ke Yang",
"Bingxuan Li",
"Cheng Qian",
"Gaotang Li",
"Xiao Lin",
"Zhichen Zeng",
"Ruizhong Qiu",
"S... | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18747 | https://arxiv.org/pdf/2605.18747v1 | 2605.18747 | null | 3 | 0 | true | https://github.com/YennNing/Awesome-Code-as-Agent-Harness-Papers | null | 0.65 |
de5d4ba67a1868992899afe32f4bffff0b1a56682bdd6953bcc6e4e36d33e01b | [
"arxiv",
"semantic_scholar"
] | PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows | Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform single-prompt baselines, but they remain difficult to debug and refine. Failures can originate from subtle errors in intermediate outputs that propagate to downstream nodes, requiring developers to inspect long traces ... | [
"Kazuki Kawamura",
"Satoshi Waki",
"Kei Tateno"
] | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.SE"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18032 | https://arxiv.org/pdf/2605.18032v1 | 2605.18032 | null | 0 | 0 | false | null | null | 0.35 |
9d6beb984b78c83f21788c676031df5c83b5e0d183528dbf74c0806cce62517f | [
"arxiv",
"semantic_scholar"
] | Sequential Consensus for Multi-Agent LLM Debates: A Wald-SPRT compute governor with calibration-based failure detection | Multi-agent LLM debate improves factuality and reasoning, but most recipes pick a fixed round count, over-spending on easy items and under-spending on hard ones. We adapt Wald's Sequential Probability Ratio Test (SPRT) as a plug-in compute governor for LLM debates. After each round, an LLM judge emits a [0,1] consensus... | [
"Andrea Morandi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.19193 | https://arxiv.org/pdf/2605.19193v1 | 2605.19193 | null | 0 | 0 | false | null | null | 0.35 |
5111e7da38474f17944b806c2c9e8887d06ed088809c1ce76b0512b4cf9023d5 | [
"arxiv",
"semantic_scholar"
] | Harnessing LLM Agents with Skill Programs | Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizon tasks. However, such lessons are often encoded as textual guidance that remains largely advisory, lacking explicit mechanisms for when and how to intervene in the age... | [
"Hongjun Liu",
"Yifei Ming",
"Shafiq Joty",
"Chen Zhao"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.17734 | https://arxiv.org/pdf/2605.17734v1 | 2605.17734 | null | 1 | 0 | false | null | null | 0.35 |
c1b2aaf032e7abb83c3247cb58d55608c964cdcc523e1fd00b81ac35851ddd9e | [
"arxiv",
"semantic_scholar"
] | LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning | Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information. To address this, we propose LLM-driven Multi-Agent Communication (LMAC), which leverag... | [
"Sangjun Bae",
"Yisak Park",
"Sanghyeon Lee",
"Seungyul Han"
] | [
"cs.AI",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18077 | https://arxiv.org/pdf/2605.18077v2 | 2605.18077 | null | 1 | 0 | false | null | null | 0.35 |
636f8f454c78b86eab408aca1a28299e02b0daa322dd64a94d5b9cd531ab8603 | [
"arxiv",
"semantic_scholar"
] | PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence | Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local agent's expertise to remote agents more... | [
"Zile Wang",
"Qianli Liu",
"Kaibin Guo",
"Haodong Wang",
"Jian Lin",
"Zicong Hong",
"Song Guo"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18067 | https://arxiv.org/pdf/2605.18067v1 | 2605.18067 | null | 0 | 0 | false | null | null | 0.35 |
1355e0aba3dbd9f6a4388cb534677a790f575edfe2e4b6a1d22ab2e486bbdf18 | [
"arxiv",
"semantic_scholar"
] | Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment | This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for deployed LLM agents -- a structural consequence of how agent execution works, not a contingent limitation of current systems. The three dimensions that jointly constit... | [
"S. Bensalem",
"Y. Dong",
"M. Franzle",
"X. Huang",
"J. Kroger",
"D. Nickovic",
"A. Nouri",
"R. Roy",
"C. Wu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18672 | https://arxiv.org/pdf/2605.18672v1 | 2605.18672 | null | 0 | 0 | false | null | null | 0.35 |
57b034743e4188b6f0183e6e991d6605f88fc1096e1bc3a2f5e36aaf950cc145 | [
"arxiv",
"semantic_scholar"
] | Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents? | Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, and synthesis. Their growing role demands scalable, reliable evaluation, positioning LLM-as-judge as a supervision paradigm for assessing factual accuracy, evidence use, ... | [
"Leyao Wang",
"Yanan He",
"Peng Chen",
"Asaf Yehudai",
"Yixin Liu",
"Rex Ying",
"Michal Shmueli-Scheuer",
"Arman Cohan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.19196 | https://arxiv.org/pdf/2605.19196v1 | 2605.19196 | null | 1 | 0 | false | null | null | 0.35 |
7294407b9c92bd8bcd0c1108288b8c212007d67d15609d091b1578a5f23f23e4 | [
"arxiv",
"semantic_scholar"
] | VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems | Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agents remain a key bottleneck to system-level reliability. Automatic failure attribution is therefore critical, but existing approaches, such as direct prediction of agent-error pairs and agent-first failure attribution, r... | [
"Hezhe Qiao",
"Hanghang Tong",
"Ee-Peng Lim",
"Bing Liu",
"Guansong Pang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17467 | https://arxiv.org/pdf/2605.17467v1 | 2605.17467 | null | 0 | 0 | true | null | null | 0.65 |
7b8e0ba5a4867f9ca24596343dff2174443b65b1115466a5727eca83762168de | [
"arxiv",
"semantic_scholar"
] | MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation | Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks assign tasks based on predefined roles without considering whether an agent can accurately assess its own competence boundaries, leading to overconfident execution on tas... | [
"Chenyu Wang",
"Yang Shu"
] | [
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17292 | https://arxiv.org/pdf/2605.17292v1 | 2605.17292 | null | 0 | 0 | false | null | null | 0.35 |
6656e88d69f28a5156dbb8e96e7be5ae3cec2d68529b3cb752ede5041fb4ffe4 | [
"arxiv",
"semantic_scholar"
] | Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback | Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses implicitly assume that tool feedback is trustworthy once a tool has been selected. We study a different failure mode, cognitive poisoning, in which a malicious tool behaves... | [
"Lecheng Yan",
"Ruizhe Li",
"Xicheng Han",
"Wenxi Li",
"Binwu Wang",
"Longyue Wang",
"Chenyang Lyu",
"Guanhua Chen"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17453 | https://arxiv.org/pdf/2605.17453v1 | 2605.17453 | null | 0 | 0 | false | null | null | 0.35 |
8e529ead37b8ebc3c3d4039f1b84e721f6bac0019bfd481a14fb32e07d35f8fc | [
"arxiv",
"semantic_scholar"
] | Learning Transferable Topology Priors for Multi-Agent LLM Collaboration Across Domains | Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through structured communication. However, existing topology-evolution methods typically construct or optimize a collaboration topology for each query from scratch, leading to substa... | [
"Taolin Zhang",
"Zijie Zhou",
"Jiuheng Wan",
"Tingyuan Hu",
"Chengyu Wang",
"Xiaofeng He",
"Richang Hong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17359 | https://arxiv.org/pdf/2605.17359v1 | 2605.17359 | null | 0 | 0 | false | null | null | 0.35 |
5549d89545f6aa150768a9f0cc4f081c5451a3c0d78117c094c2927309e753e2 | [
"arxiv",
"semantic_scholar"
] | Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces | The deployment of Large Language Models (LLMs) as autonomous economic agents introduces systemic risks that extend beyond individual capability failures. As agents transition to directly interacting with marketplaces, their collective behavior can amplify volatility and mask deception at scale. We introduce the Agent B... | [
"Seth Karten",
"Cameron Crow",
"Chi Jin"
] | [
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17698 | https://arxiv.org/pdf/2605.17698v1 | 2605.17698 | null | 0 | 0 | false | null | null | 0.35 |
86030df69f07adcaa0f8cc28c3d3a5dbcde90b9e3e495f30e2c4172a289c4fe9 | [
"arxiv",
"semantic_scholar"
] | Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution | Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall efficiency, existing methods often rely on aggressive graph evolution among agents (e.g., node or edge pruning), which risks prematurely discarding valuable agents due to... | [
"Taolin Zhang",
"Pukun Zhao",
"Qizhou Chen",
"Jiuheng Wan",
"Chen Chen",
"Xiaofeng He",
"Chengyu Wang",
"Richang Hong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17348 | https://arxiv.org/pdf/2605.17348v1 | 2605.17348 | null | 0 | 0 | false | null | null | 0.35 |
682cfebab4f97c339cacc339b6f0475bcd7ae0c72acdbfae410a7b4164ce7175 | [
"arxiv",
"semantic_scholar"
] | RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis | We present RooAgent as a natural-language interface for Root-based high energy physics data analysis. The package provides physics analysis functions as tools that an LLM agent invokes in response to plain-language prompts. Two operating modes are supported: a LangGraph-based agent compatible with OpenAI's GPT-4.1 via ... | [
"Aman Desai"
] | [
"hep-ph"
] | [
"Physics"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17318 | https://arxiv.org/pdf/2605.17318v2 | 2605.17318 | null | 0 | 0 | true | https://github.com/amanmdesai/RooAgent | null | 0.65 |
42b07c895e4407830b29fbbde66de3c2bbdab59b321ee950ab51fc344358d89d | [
"arxiv",
"semantic_scholar"
] | AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering | Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucinations and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMA... | [
"Taolin Zhang",
"Dongyang Li",
"Chen Chen",
"Qizhou Chen",
"Jiuheng Wan",
"Xiaofeng He",
"Chengyu Wang",
"Richang Hong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17352 | https://arxiv.org/pdf/2605.17352v1 | 2605.17352 | null | 0 | 0 | false | null | null | 0.35 |
8c1300afc52492790790c1c2cc4f0302d280a9b1a3f051775269ae35c2ca5c47 | [
"arxiv",
"semantic_scholar"
] | Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework | The rapid evolution of Large Language Model (LLM) agents has produced diverse interaction paradigms, yet few production systems integrate multiple paradigms within a unified architecture. This paper presents a systematic analysis of three principal agent interaction paradigms, including Multi-Agent Orchestration (Gener... | [
"Xiaohua Wang",
"Chao Han",
"Kai Yu",
"XiaoLiang Xu",
"Liang Wang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-16T00:00:00 | https://arxiv.org/abs/2605.16821 | https://arxiv.org/pdf/2605.16821v1 | 2605.16821 | null | 0 | 0 | true | null | null | 0.65 |
e17ebd701cb2661353c7939c1412ad26fcd7243c47aee4533682ab79ee390a92 | [
"arxiv",
"semantic_scholar"
] | S-Bus: Automatic Read-Set Reconstruction for Multi-Agent LLM State Coordination | We address concurrency control for LLM agents sharing mutable state over HTTP, where agents cannot be modified to declare read sets. S-Bus is an HTTP middleware whose central mechanism, a server-side DeliveryLog, reconstructs each agent's read set at commit time from observed HTTP GET traffic. The consistency property ... | [
"Sajjad Khan"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-16T00:00:00 | https://arxiv.org/abs/2605.17076 | https://arxiv.org/pdf/2605.17076v2 | 2605.17076 | null | 1 | 0 | true | https://github.com/sajjadanwar0/sbus | null | 0.65 |
77880cdb6661616827844a3339e2a112023dc9fe2eec013b5e080da9dc1984fe | [
"arxiv",
"semantic_scholar"
] | NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning | Multi-agent language systems are often built as hand-designed workflows, where agents are assigned semantic roles and communication protocols are specified in advance. We propose NeuroMAS, a method that first treats a multi-agent language system as a trainable and scalable neural-network-like architecture with LLM agen... | [
"Haoran Lu",
"Luyang Fang",
"Wenxuan Zhong",
"Ping Ma"
] | [
"cs.AI",
"cs.MA",
"stat.ME",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-16T00:00:00 | https://arxiv.org/abs/2605.16757 | https://arxiv.org/pdf/2605.16757v1 | 2605.16757 | null | 0 | 0 | false | null | null | 0.35 |
9cc54f12e19ad8542c56882b77994e7ca3fddbd740c697f1042153e4fc171235 | [
"arxiv",
"semantic_scholar"
] | BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks | Large language model (LLM) agents are increasingly capable of automating components of machine learning development, yet existing biomedical benchmarks mainly focus on question answering, reasoning, and tool usage, or evaluate only narrow aspects of biomedical ML coding. We present BioXArena, a biomedical machine learn... | [
"Loka Li",
"Duzhen Zhang",
"Xingbo Du",
"Leonard Song",
"Zixiao Wang",
"Assanali Aukenov",
"Noel Thomas",
"Shakhnazar Sailaukan",
"Yonghan Yang",
"Feilong Chen",
"Jiahua Dong",
"Kun Zhang",
"Bin Zhang",
"Le Song"
] | [
"cs.CE"
] | [
"Computer Science"
] | 2026-05-15T00:00:00 | https://arxiv.org/abs/2605.15766 | https://arxiv.org/pdf/2605.15766v1 | 2605.15766 | null | 0 | 0 | false | null | null | 0.35 |
1acdb65f7a03f600cf2bcb3dee403ba62d003c11e4ac08400f4c507124b1f306 | [
"arxiv",
"semantic_scholar"
] | Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining | We introduce \textsc{Cattle Trade, a multi-agent benchmark for evaluating large language models (LLMs) as agents in strategic reasoning under imperfect information, adversarial interaction, and resource constraints. The benchmark combines auctions, hidden-offer trade challenges (TCs), bargaining, bluffing, opponent mod... | [
"Robert Müller",
"Clemens Müller"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14537 | https://arxiv.org/pdf/2605.14537v1 | 2605.14537 | null | 0 | 0 | false | null | null | 0.35 |
39fc5b80727965d7820af2d4ed4ef7eb7d21beeef498519355275c2dc873662e | [
"arxiv",
"semantic_scholar"
] | Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems | LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, tools, and environments. Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination... | [
"Shihao Qi",
"Jie Ma",
"Rui Xing",
"Wei Guo",
"Xiao Huang",
"Zhitao Gao",
"Jianhao Deng",
"Jun Liu",
"Lingling Zhang",
"Bifan Wei",
"Boqian Yang",
"Pinghui Wang",
"Jianwen Sun",
"Jing Tao",
"Yaqiang Wu",
"Hui Liu",
"Yu Yao",
"Tongliang Liu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14892 | https://arxiv.org/pdf/2605.14892v2 | 2605.14892 | null | 0 | 0 | false | null | null | 0.35 |
a72fe7030dd79e5175761308fd3ad74a1dcf2de4decccbbda5112fe516d60e48 | [
"arxiv",
"semantic_scholar"
] | Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs | Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. Under these semantics, LLM decoding is blocked until each function call completes, resulting in increasing end-to-end latency. In this work, we introduce AsyncFC, a pure e... | [
"Guangyu Feng",
"Huanzhi Mao",
"Prabal Dutta",
"Joseph E. Gonzalez"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.15077 | https://arxiv.org/pdf/2605.15077v1 | 2605.15077 | null | 1 | 0 | false | null | null | 0.35 |
bd2fc440df137d37625dedc149737ba12ef155263197653bf40604ce2af6999a | [
"arxiv",
"semantic_scholar"
] | Making OpenAPI Documentation Agent-Ready: Detecting Documentation and REST Smells with a Multi-Agent LLM System | The growing adoption of AI agents and the Model Context Protocol (MCP) has motivated organizations to expose existing REST APIs as agent-consumable tools. In our industrial context, this initiative targeted an ecosystem of 16 production APIs comprising approximately 600 endpoints. Although these APIs were stable and wi... | [
"Rayfran Rocha Lima",
"Davi G. Assunção Pinheiro",
"Thiago Medeiros de Menezes"
] | [
"cs.SE"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14312 | https://arxiv.org/pdf/2605.14312v1 | 2605.14312 | null | 0 | 0 | false | null | null | 0.35 |
53f486dd8cac88c931604833d0c7173ddd9f57d2f1763eddf4139a42647cbed0 | [
"arxiv",
"semantic_scholar"
] | From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents | Voice agents increasingly require reliable tool use from speech, whereas prominent tool-calling benchmarks remain text-based. We study whether verified text benchmarks can be converted into controlled audio-based tool calling evaluations without re-annotating the tool schema and gold labels. Our dataset-agnostic framew... | [
"Md Tahmid Rahman Laskar",
"Xue-Yong Fu",
"Seyyed Saeed Sarfjoo",
"Quinten McNamara",
"Jonas Robertson",
"Shashi Bhushan TN"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.15104 | https://arxiv.org/pdf/2605.15104v2 | 2605.15104 | null | 0 | 0 | true | null | null | 0.65 |
2d9e60909ce1e6a4d179789e5bc87074612d4553d2fed2ea6a2da13f1847efdb | [
"arxiv",
"semantic_scholar"
] | Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents | Tool-augmented LLM agents increasingly access the same tool type through multiple functionally equivalent providers, such as web-search APIs, retrievers, or LLM backends exposed behind a shared interface. This creates a provider-routing problem under runtime load: the router must choose among providers that differ in l... | [
"Kexin Chu",
"Dawei Xiang",
"Wei Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14241 | https://arxiv.org/pdf/2605.14241v2 | 2605.14241 | null | 0 | 0 | false | null | null | 0.35 |
40038dc22a839241eea9fc0e1f4a884c0d4aeacd0d35034e7aaee8b4d644c83d | [
"arxiv",
"semantic_scholar"
] | GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations | Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems that extract, retrieve, and apply information across long-running conversations. However, both existing memory systems and benchmarks are built around the dyadic, single... | [
"Jingbo Yang",
"Kwei-Herng Lai",
"Xiaowen Wang",
"Shiyu Chang",
"Yaar Harari",
"Evgeniy Gabrilovich"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14498 | https://arxiv.org/pdf/2605.14498v2 | 2605.14498 | null | 2 | 0 | false | null | null | 0.35 |
a71c0b15a0e23ab9070dbb9610ddc06c438e402963bafafb7422c98ff16cc147 | [
"arxiv",
"semantic_scholar"
] | Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence | Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing AI agents effectively support individual tasks, their implementation remains fragmented and inefficient for handling the complexity of educational institutions. This h... | [
"Vidya K Sudarshan",
"Anushka Sisodia",
"Reshma A Ramachandra",
"Sia Batra",
"Josephine Chong Leng Leng"
] | [
"cs.AI",
"cs.CY"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14266 | https://arxiv.org/pdf/2605.14266v1 | 2605.14266 | null | 0 | 0 | false | null | null | 0.35 |
70a89ae4fff390538074a3884ab586529ef725c8300b2179cdbef8d8700f97c2 | [
"arxiv",
"semantic_scholar"
] | Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling | There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applications where the agent needs to interact with a person, real-time low-latency responsiveness is required; for example, with voice-controlled applications, under 1 second ... | [
"Coleman Hooper",
"Minwoo Kang",
"Suhong Moon",
"Nicholas Lee",
"Eric Wen",
"John Wawrzynek",
"Michael W. Mahoney",
"Yakun Sophia Shao",
"Amir Gholami",
"Kurt Keutzer"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13360 | https://arxiv.org/pdf/2605.13360v2 | 2605.13360 | null | 0 | 0 | false | null | null | 0.35 |
271a062541240609a96c3c31a77d6c3f8cfcf8d59cc7376dabf2253de902e3a8 | [
"arxiv",
"semantic_scholar"
] | Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning | Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse modalities. As these systems continue to expand in scale and functionality, investigating their potential vulnerabilities has become increasingly important. However, e... | [
"Hao Zhou",
"Tiru Wu",
"Yan Jiang",
"Wanqi Zhou",
"Junxing Hu",
"Ai Han"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13213 | https://arxiv.org/pdf/2605.13213v1 | 2605.13213 | null | 0 | 0 | false | null | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026 | 0.55 |
576236b235ac67e87fabfc03c3974b67e62badf3a38a122e9efabec2b73d9a51 | [
"arxiv",
"semantic_scholar"
] | MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters | Large Language Models (LLMs) have become increasingly prevalent in cloud-based platforms, propelled by the introduction of AI-based consumer and enterprise services. LLM inference requests in particular account for up to 90% of total LLM lifecycle energy use, dwarfing training energy costs. The rising volume of LLM inf... | [
"H. Moore",
"S. Qi",
"D. Milojicic",
"C. Bash",
"S. Pasricha"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13496 | https://arxiv.org/pdf/2605.13496v1 | 2605.13496 | null | 0 | 0 | false | null | null | 0.35 |
deacc5441d8c0e0106e68ae992cca8c4728f848fa3f51aae33bce8688394458c | [
"arxiv",
"semantic_scholar"
] | Reinforced Collaboration in Multi-Agent Flow Networks | Multi-agent systems provide a powerful way to extend large language models (LLMs) by decomposing a complex task into specialized subtasks handled by different agents. However, their performance is often hindered by error propagation, arising from suboptimal workflow design or inaccurate agent outputs, which can propaga... | [
"Zheng Wang",
"Yuang Liu",
"Yangkai Ding"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.12943 | https://arxiv.org/pdf/2605.12943v1 | 2605.12943 | null | 0 | 0 | true | https://github.com/openJiuwen-ai/agent-store/tree/main/community/mango | null | 0.65 |
3039edee21038e7a4199d7dc0a8ec7d4566194277171a50ce555bfc9dbef85a3 | [
"arxiv",
"semantic_scholar"
] | Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR) | Fast Healthcare Interoperability Resources (FHIR) is the dominant standard for interoperable exchange of healthcare data. In FHIR, electronic health records form a directed graph of resources. Answering clinically meaningful questions over FHIR requires agents to perform multi-step reasoning, filtering, and aggregation... | [
"Marius S. Knorr",
"Robert Müller",
"Jan P. Bremer",
"Nils Schweingruber"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.14126 | https://arxiv.org/pdf/2605.14126v1 | 2605.14126 | null | 0 | 0 | false | null | null | 0.35 |
4627974c6c6f0f12ce52a9f283a15c50c8ad8544bbc75ca04aec56edc2b0b8a0 | [
"arxiv",
"semantic_scholar"
] | Collaborating in Multi-Armed Bandits with Strategic Agents | We study collaborative learning in multi-agent Bayesian bandit problems, where strategic agents collectively solve the same bandit instance. While multiple agents can accelerate learning by sharing information, strategic agents might prefer to free-ride and avoid exploration. We consider a setting with persistent agent... | [
"Idan Barnea",
"Ofir Schlisselberg",
"Yishay Mansour"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13145 | https://arxiv.org/pdf/2605.13145v1 | 2605.13145 | null | 0 | 0 | false | null | null | 0.35 |
8b6ed9395f084e06396a9ee82d752cc62472b282b2acd83cf88e624d6c210de1 | [
"arxiv",
"semantic_scholar"
] | Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems | This paper studies multi-agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a local categorical policy, so feasible joint actions form a partition matroid over agent-action pairs. Classical multilinear extensions use in... | [
"Jing Liu",
"Yangyang Yang",
"Luca Ballotta",
"Fangfei Li",
"Yang Tang",
"Ruggero Carli"
] | [
"eess.SY"
] | [
"Engineering",
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13269 | https://arxiv.org/pdf/2605.13269v1 | 2605.13269 | null | 0 | 0 | false | null | null | 0.35 |
4022879579a0f5f894d699fd213321925bc0a394816d75e172e552618923b4ee | [
"arxiv",
"semantic_scholar"
] | TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate | Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation. It is also a canonical testbed for agentic language models, requiring multi-turn interaction under hidden preferences, strategic communication, and binding constraints. These properties make... | [
"Erica Zhang",
"Fangzhao Zhang",
"Aneesh Pappu",
"Batu El",
"Jose Blanchet",
"Susan Athey",
"Jiashuo Liu",
"James Zou"
] | [
"cs.GT",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13909 | https://arxiv.org/pdf/2605.13909v1 | 2605.13909 | null | 0 | 0 | false | null | null | 0.35 |
82eb7e6fcce27ec07b72905f149571ae672b0d49474d3324e7e217cbc8895e0c | [
"arxiv",
"semantic_scholar"
] | LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents | We propose a personal-LLM exchange (LLM-X), a scalable negotiation-oriented environment that enables direct, structured communication across populations of personal agents (LLMs), each representing an individual user. Unlike existing tool-centric protocols that focus on agent-API interaction, LLM-X introduces a message... | [
"Giuliano Lorenzoni",
"Paulo Alencar",
"Donald Cowan"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11376 | https://arxiv.org/pdf/2605.11376v1 | 2605.11376 | 10.1145/3786167.3788429 | 0 | 0 | false | null | null | 0.35 |
cbac687f7ad0f6f12cae28b4df6620b643eb2f3af4b01794c9175a3ab6f426fd | [
"arxiv",
"semantic_scholar"
] | Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations | Operational disaster response goes beyond damage assessment, requiring responders to integrate multi-sensor signals, reason over road networks, populations and key facilities, plan evacuations, and produce actionable reports. However, prior work largely isolates remote-sensing perception or evaluates generic tool use, ... | [
"Junjue Wang",
"Weihao Xuan",
"Heli Qi",
"Pengyu Dai",
"Kunyi Liu",
"Hongruixuan Chen",
"Zhuo Zheng",
"Junshi Xia",
"Stefano Ermon",
"Naoto Yokoya"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11633 | https://arxiv.org/pdf/2605.11633v1 | 2605.11633 | null | 0 | 0 | false | null | null | 0.35 |
d2e7284e3e15c93303ad4effeb4c0695ce0da65b310f2aab23d9a5d2ecedd63e | [
"arxiv",
"semantic_scholar"
] | FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems | Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts into subtasks, roles, dependencies, and routing paths. This flexibility enables adaptive coordination, but exposes an attack surface in workflow formation: prompts can sha... | [
"Fanxiao Li",
"Jiaying Wu",
"Tingchao Fu",
"Natasha Jaques",
"Wei Zhou",
"Min-Yen Kan"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11514 | https://arxiv.org/pdf/2605.11514v1 | 2605.11514 | null | 2 | 0 | false | null | null | 0.35 |
02d238ce4bcdc8b7ec6b67af51f5eb81187d1e2214fcf32741eee6e89cef6cda | [
"arxiv",
"semantic_scholar"
] | Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies | Practitioners deploying multi-agent large language model (LLM) systems must currently choose between communication topologies such as chain, star, mesh, and richer variants without any pre-inference diagnostic for which topology will amplify drift, converge to consensus, or remain robust under perturbation. Existing ev... | [
"Ethan Parks",
"Dalal Alharthi"
] | [
"cs.MA",
"cs.AI",
"cs.LG",
"cs.SI",
"math.SP"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11453 | https://arxiv.org/pdf/2605.11453v2 | 2605.11453 | null | 0 | 0 | false | null | null | 0.35 |
cfde58fb27f5fd2fc739c3796209fca567a5d181c0beebdec8183ac47cb52025 | [
"arxiv",
"semantic_scholar"
] | Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations | Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems, like multiple arms or vehicles, to coordinate through imitation learning is hindered by a fundamental data bottleneck: as the joint state-action space grows exponentia... | [
"Lasse Peters",
"Laura Ferranti",
"Andrea Bajcsy",
"Javier Alonso-Mora"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11485 | https://arxiv.org/pdf/2605.11485v2 | 2605.11485 | null | 0 | 0 | false | null | null | 0.35 |
fd223853382fed2eb9a07980f3b2f3e60c0c80bf4ac153dd2da3f12e23e8eebe | [
"arxiv",
"semantic_scholar"
] | No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agents | Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-horizon tasks, as they often produce policy violations, tool hallucinations, and misaligned actions, which greatly impedes their real-world d... | [
"Zixu Yang",
"Hang Zheng",
"Nan Jiang",
"Zhiyang Tang",
"Situo Zhang",
"Xiaobao Wu",
"Lu Chen",
"Kai Yu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.12240 | https://arxiv.org/pdf/2605.12240v1 | 2605.12240 | null | 0 | 0 | false | null | null | 0.35 |
577e461f8790899ce88458cd0ef2d2ebbb0a20b72d39614dfe711cb8da73d245 | [
"arxiv",
"semantic_scholar"
] | CTFusion: A CTF-based Benchmark for LLM Agent Evaluation | Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent application. To evaluate such agents, researchers widely adopt Capture The Flag (CTF) benchmarks. However, current CTF benchmarks reuse existing challenges, which exposes ... | [
"Dongjun Lee",
"Ga-eun Bae",
"Insu Yun"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11504 | https://arxiv.org/pdf/2605.11504v1 | 2605.11504 | null | 1 | 0 | true | null | null | 0.65 |
01ed7cb89353aab6eae6be4038ee642394bef0132974dc076a28c6841a52a4d8 | [
"arxiv",
"semantic_scholar"
] | AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation | Visual anomaly detection (VAD) is crucial in many real-world fields, such as industrial inspection, medical imaging, infrastructure monitoring, and remote sensing. However, the specific anomaly definitions, data modalities, and annotation standards across different domains make it difficult to transfer single-domain tr... | [
"Xi Jiang",
"Yinjie Zhao",
"Zesheng Yang",
"Feng Zheng"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10397 | https://arxiv.org/pdf/2605.10397v1 | 2605.10397 | null | 0 | 0 | true | https://github.com/jam-cc/AnomalyClaw | null | 0.65 |
deb049020bdf018c6efc575012cff59013a0f8a9b412f684cb967f0bcd9ab7f8 | [
"arxiv",
"semantic_scholar"
] | OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents | Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks. In deployed settings where agents repeatedly handle related multi-step tasks, small action-selection errors can accumulate into wasted tool calls, latency, and reduced reliability. Despite this... | [
"Sheldon Yu",
"Junda Wu",
"Xintong Li",
"Nikki Lijing Kuang",
"Sizhe Zhou",
"Tong Yu",
"Jiawei Han",
"Jingbo Shang",
"Julian McAuley"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.11169 | https://arxiv.org/pdf/2605.11169v1 | 2605.11169 | null | 0 | 0 | false | null | null | 0.35 |
b5e7edd8fd8732c151b6a618bb97b8dcc16346b08e9c702a8e16fdf0699ccdb9 | [
"arxiv",
"semantic_scholar"
] | Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems | Modern LLM based agents are no longer passive text generators. They read repositories, call tools, browse the web, execute code, maintain memory, communicate with other agents, and act through long horizon workflows. This shift moves the unit of safety. A system may produce a compliant final answer while leaking privat... | [
"Tianxiao Li",
"Yixing Ma",
"Haiquan Wen",
"Zhenglin Huang",
"Qianyu Zhou",
"Zeyu Fu",
"Guangliang Cheng"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10481 | https://arxiv.org/pdf/2605.10481v1 | 2605.10481 | null | 0 | 0 | false | null | null | 0.35 |
24b643bc218338eb499555efe3b153632be2498cae7379795f5c2dd7c9931ec7 | [
"arxiv",
"semantic_scholar"
] | RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents | LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduce this setting to reranking small shortlisted candidate sets or judge reports mainly by semantic plausibility. We introduce Recommendation A... | [
"Imad Aouali",
"Flavian Vasile",
"Otmane Sakhi",
"Alexandre Gilotte",
"Benjamin Heymann"
] | [
"cs.IR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.18805 | https://arxiv.org/pdf/2605.18805v1 | 2605.18805 | null | 0 | 0 | false | null | null | 0.35 |
81552708381cc2572b9140dc5244c9b85e6ea83c153011b5c9d60bd9bcb0c4db | [
"arxiv",
"semantic_scholar"
] | AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks | Building effective clinical decision support systems requires the synthesis of complex heterogeneous multimodal data. Such modalities include temporal electronic health records data, medical images, radiology reports, and clinical notes. Large language model (LLM)-based agents have shown impressive performance in vario... | [
"Baraa Al Jorf",
"Farah E. Shamout"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10286 | https://arxiv.org/pdf/2605.10286v1 | 2605.10286 | null | 0 | 0 | false | null | null | 0.35 |
9343e5bf88ac1848c0470ca375df1914687790e5679e483674f76bec05f3803b | [
"arxiv",
"semantic_scholar"
] | Collective Alignment in LLM Multi-Agent Systems: Disentangling Bias from Cooperation via Statistical Physics | We investigate the emergent collective dynamics of LLM-based multi-agent systems on a 2D square lattice and present a model-agnostic statistical-physics method to disentangle social conformity from intrinsic bias, compute critical exponents, and probe the collective behavior and possible phase transitions of multi-agen... | [
"Cristiano De Nobili"
] | [
"cond-mat.stat-mech",
"cs.CL",
"cs.MA",
"physics.soc-ph"
] | [
"Physics",
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10528 | https://arxiv.org/pdf/2605.10528v1 | 2605.10528 | null | 0 | 0 | false | null | null | 0.35 |
c4ce8d2d143644c4667df938fc9028c48848551d88c883ec6e725a3e80de4ca7 | [
"arxiv",
"semantic_scholar"
] | Control Charts for Multi-agent Systems | Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal restrictions in open ended, multi-agent environments. Current methods for monitoring the dynamics of open-ended multi-agent systems are limited to qualitative inspection... | [
"Hayden Helm",
"Carey Priebe",
"Brandon Duderstadt"
] | [
"cs.MA",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.11135 | https://arxiv.org/pdf/2605.11135v1 | 2605.11135 | null | 1 | 0 | false | null | null | 0.35 |
99e1d26655b759cda4d62b2e3bdcf6efddae87338230ac3c3c03591eb0b3900b | [
"arxiv",
"semantic_scholar"
] | TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents | Multimodal large language models increasingly solve vision-centric tasks by calling external tools for visual inspection, OCR, retrieval, calculation, and multi-step reasoning. Current tool-using agents usually expose the executed tool trajectory and the final answer, but they rarely specify which tool observation supp... | [
"Bihui Yu",
"Caijun Jia",
"Jing Chi",
"Xiaohan Liu",
"Yining Wang",
"He Bai",
"Yuchen Liu",
"Jingxuan Wei",
"Junnan Zhu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.09934 | https://arxiv.org/pdf/2605.09934v1 | 2605.09934 | null | 0 | 0 | false | null | null | 0.35 |
a2a46ddccd7f4791969aa3c599d76ed541b12581c6cb1048267bba54b9028dfb | [
"arxiv",
"semantic_scholar"
] | PRISM: Generation-Time Detection and Mitigation of Secret Leakage in Multi-Agent LLM Pipelines | Multi-agent LLM systems introduce a security risk in which sensitive information accessed by one agent can propagate through shared context and reappear in downstream outputs, even without explicit adversarial intent. We formalise this phenomenon as propagation amplification, where leakage risk increases across agent b... | [
"Riya Tapwal",
"Abhishek Kumar",
"Carsten Maple"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10614 | https://arxiv.org/pdf/2605.10614v1 | 2605.10614 | null | 0 | 0 | false | null | null | 0.35 |
3f6dac0296b9761823c1e70d7524c0e9ab0cd55d4d00f7691c95cce392ab5f3c | [
"arxiv",
"semantic_scholar"
] | Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems | As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD paradigms. This paper identifies five fundamental architectural mismatches between conventional APIs and autonomous agent requirements: exact-identifier dependence, re... | [
"Kai Pan"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10555 | https://arxiv.org/pdf/2605.10555v1 | 2605.10555 | null | 0 | 0 | false | null | null | 0.35 |
c6895095b32772656457af695ee443d710d744382abfe87788e766b26b9dd727 | [
"arxiv",
"semantic_scholar"
] | LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments | The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. Existing benchmarks either evaluat... | [
"Chiyu Zhang",
"Huiqin Yang",
"Bendong Jiang",
"Xiaolei Zhang",
"Yiran Zhao",
"Ruyi Chen",
"Lu Zhou",
"Xiaogang Xu",
"Jiafei Wu",
"Liming Fang",
"Zhe Liu"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10779 | https://arxiv.org/pdf/2605.10779v1 | 2605.10779 | null | 0 | 0 | false | null | null | 0.35 |
dae0121a44690a3903617e80bf7b7cc778c6e5eb1fc8154a21a69ceadd22e36d | [
"arxiv",
"semantic_scholar"
] | LLM Agents Already Know When to Call Tools -- Even Without Reasoning | Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically studies when a tool call is actually needed. We propose When2Tool, a benchmark of 18 environments (15 single-hop, 3 multi-ho... | [
"Chung-En Sun",
"Linbo Liu",
"Ge Yan",
"Zimo Wang",
"Tsui-Wei Weng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.09252 | https://arxiv.org/pdf/2605.09252v2 | 2605.09252 | null | 5 | 0 | true | https://github.com/Trustworthy-ML-Lab/when2tool | null | 0.65 |
1c6654509e569f2015d971b216cac222f9327a5b11447305594ab32a57a15e4b | [
"arxiv",
"semantic_scholar"
] | SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System | Large language model (LLM) agent systems are increasingly expected to improve after deployment, but existing work often decouples two adaptation targets: skill evolution and multi-agent system (MAS) restructuring. This separation can create organization bottlenecks, context pressure, and mis-specialization. We present ... | [
"Shuai Pan",
"Yixiang Liu",
"Jiaye Gao",
"Te Gao",
"Weiwen Liu",
"Jianghao Lin",
"Zhihui Fu",
"Jun Wang",
"Weinan Zhang",
"Yong Yu"
] | [
"cs.MA",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.09341 | https://arxiv.org/pdf/2605.09341v2 | 2605.09341 | null | 1 | 0 | false | null | null | 0.35 |
39902d887ac8f9b0ac3c3bcb84508fcb788f66b432ba59685c1b8bfda5361d8a | [
"arxiv",
"semantic_scholar"
] | TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems | Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically optimize agent capabilities or communication topologies. However, existing methods either learn a topology that remains fixed at inference time or adapt only the topolo... | [
"Chen Xu",
"Yicheng Hu",
"Ruizi Wang",
"Xinyu Lin",
"Wenjie Wang",
"Dongrui Liu",
"Fuli Feng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.09539 | https://arxiv.org/pdf/2605.09539v1 | 2605.09539 | null | 0 | 0 | true | https://github.com/chenxu2-gif/TacoMAS-MultiAgent | null | 0.65 |
0fb6e3494c8ffcdb0061b65bc1df6d173a398edd7d9d5d3f9b2216593f027eb4 | [
"arxiv",
"semantic_scholar"
] | AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents | Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks rather than detect the compromises that slip through. Second, they have only been evaluated in English, leaving users of low-resource languages such as Kurdish and Arabic... | [
"Yassin H. Rassul",
"Tarik A. Rashid"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.11026 | https://arxiv.org/pdf/2605.11026v1 | 2605.11026 | null | 0 | 0 | true | https://github.com/Yassin-H-Rassul/AgentShield | null | 0.65 |
6d27f0b76e7e77d7e1d16936a5fc5e00982fa4c6476bb536fe6315dac2f1e10a | [
"arxiv",
"semantic_scholar"
] | CalBench: Evaluating Coordination-Privacy Trade-offs in Multi-Agent LLMs | Personal AI assistants are beginning to act as delegates with access to calendars, inboxes, and user preferences. Calendar scheduling makes the trust problem concrete: an assistant must coordinate with other assistants while deciding what to reveal about the person it represents. We introduce CalBench, a controlled ben... | [
"Chelsea Zou",
"Yiheng Yao",
"Selena She",
"Noah Goodman",
"Robert D. Hawkins"
] | [
"cs.MA",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.09823 | https://arxiv.org/pdf/2605.09823v3 | 2605.09823 | null | 0 | 0 | false | null | null | 0.35 |
f67bc9d13d94478275248363cc2a72f91d451b8c323d773daf65df6a6f063b97 | [
"arxiv",
"semantic_scholar"
] | Learning the Preferences of a Learning Agent | For AI systems to be useful to humans, they must understand and act in accordance with our values and preferences. Since specifying preferences is a hard task, inverse reinforcement learning (IRL) aims to develop methods that allow for inferring preferences from observed behavior. However, IRL assumes the human to be a... | [
"Karim Abdel Sadek",
"Mark Bedaywi",
"Rhys Gould",
"Stuart Russell"
] | [
"cs.AI",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.09217 | https://arxiv.org/pdf/2605.09217v1 | 2605.09217 | null | 0 | 0 | false | null | null | 0.35 |
2d867ea02e2a5e5d89e973f704c7e3bd7e84622ed27449c19286ea074345b937 | [
"arxiv",
"semantic_scholar"
] | Robust Multi-Agent LLMs under Byzantine Faults | Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability. However, these same interactions can also become a source of vulnerability, as unreliable or Byzantine agents may sway neighboring agents toward incorrect conclusions and degrade overall system performanc... | [
"Haejoon Lee",
"Vincent-Daniel Yun",
"Hyeonho Oh",
"Dimitra Panagou",
"Sai Praneeth Karimireddy"
] | [
"cs.MA",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.09076 | https://arxiv.org/pdf/2605.09076v1 | 2605.09076 | null | 0 | 0 | false | null | null | 0.35 |
91734bcd64b2c71d71b636b424dd40b3357b8d94aa4855bf2ea7fd5167a9526f | [
"arxiv",
"semantic_scholar"
] | Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs | Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot routing: they select a model once and return its output directly. Such routing-only designs provide no mechanism to critique intermediate dra... | [
"Wenzhi Fang",
"Liangqi Yuan",
"Guangchen Lan",
"Dong-Jun Han",
"Christopher G. Brinton"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.08686 | https://arxiv.org/pdf/2605.08686v1 | 2605.08686 | null | 0 | 0 | false | null | null | 0.35 |
3f49fe069af76a509b67d5e3e2faeeeceae2da2e54f0873735552e56236cc0ee | [
"arxiv",
"semantic_scholar"
] | Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows | Large language model (LLM) agents are increasingly expected to operate in enterprise environments, where work is distributed across specialized roles, permission-controlled systems, and cross-departmental procedures. However, existing enterprise benchmarks largely evaluate single agents with broad tool access, while ex... | [
"Tao Yu",
"Hao Wang",
"Changyu Li",
"Shenghua Chai",
"Minghui Zhang",
"Zhongtian Luo",
"Yuxuan Zhou",
"Haopeng Jin",
"Zhaolu Kang",
"Jiabing Yang",
"YiFan Zhang",
"Xinming Wang",
"Hongzhu Yi",
"Zheqi He",
"Jing-Shu Zheng",
"Xi Yang",
"Yan Huang",
"Liang Wang"
] | [
"cs.MA",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.08761 | https://arxiv.org/pdf/2605.08761v1 | 2605.08761 | null | 0 | 0 | false | null | null | 0.35 |
088ea5b6b4d83fb5696366f33443d3a40347cc377eddd82e22f87f1e74998962 | [
"arxiv",
"semantic_scholar"
] | EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems | Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborative reasoning. However, most automated multi-agent system design methods still follow a one-shot paradigm: a workflow is optimized or selected before execution and then... | [
"Chengdong Xu",
"Kaiqiang Ke",
"Ziheng Liu",
"Jiaqi Wei",
"Zibo Shao",
"Weile Guo",
"Chao Yu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.08769 | https://arxiv.org/pdf/2605.08769v1 | 2605.08769 | null | 0 | 0 | false | null | null | 0.35 |
e001b789103572eaa0f0290e444163e6bd5218d0c3cedb556103089c05273d29 | [
"arxiv",
"semantic_scholar"
] | AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization | Multi-agent reasoning has shown promise for improving the problem-solving ability of large language models by allowing multiple agents to explore diverse reasoning paths. However, most existing multi-agent methods rely on inference-time debate or aggregation, which can be vulnerable to incorrect peer influence and bias... | [
"Hyunmin Hwang",
"Jaemin Kim",
"Choonghan Kim",
"Hangeol Chang",
"Jong Chul Ye"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.08704 | https://arxiv.org/pdf/2605.08704v1 | 2605.08704 | null | 0 | 0 | true | https://github.com/HYUNMIN-HWANG/AgentPSO/ | null | 0.65 |
858b20f7ca653ee08ed9fff712e2b3a636fd60be41f2bf0ad8ec274c3ed23958 | [
"arxiv",
"semantic_scholar"
] | Insider Attacks in Multi-Agent LLM Consensus Systems | Large language models (LLMs) are increasingly deployed in multi-agent systems where agents communicate in natural language to solve tasks jointly. A key capability in such systems is consensus formation, where agents iteratively exchange messages and update decisions to reach a shared outcome. However, most existing mu... | [
"Xiaolin Sun",
"Zixuan Liu",
"Yibin Hu",
"Zizhan Zheng"
] | [
"cs.MA",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08268 | https://arxiv.org/pdf/2605.08268v1 | 2605.08268 | null | 0 | 0 | false | null | null | 0.35 |
c27c914eeff902f22ef61407be43d6180b29dce257679415cee2d0ac2ae1c0f5 | [
"arxiv",
"semantic_scholar"
] | OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing | Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, this capability introduces a new safety attack surface: harmful outputs may arise from tool orchestration, where individually benign steps combine into unsafe results, makin... | [
"Jianming Chen",
"Yawen Wang",
"Junjie Wang",
"Zhe Liu",
"Qing Wang",
"Fanjiang Xu"
] | [
"cs.MA",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.07414 | https://arxiv.org/pdf/2605.07414v1 | 2605.07414 | null | 0 | 0 | false | null | ICML 2026 | 0.55 |
c9b59a8a5e974825379d58bb4cc14231554f7c8bbcd2c535e8e0ac0e59e59a4a | [
"arxiv",
"semantic_scholar"
] | Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling | Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategies fall into two paradigms based on planning horizon: (1) full-horizon (FH), which generates a complete plan before execution, and (2) singl... | [
"Naoki Otani",
"Nikita Bhutani",
"Hannah Kim",
"Dan Zhang",
"Estevam Hruschka"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08477 | https://arxiv.org/pdf/2605.08477v1 | 2605.08477 | 10.1145/3786335.3813129 | 0 | 0 | false | null | null | 0.35 |
f8e3d7957d1cc653d7faef7523626ce06ebd6f84b596294792501b978e5cc843 | [
"arxiv",
"semantic_scholar"
] | Switchcraft: AI Model Router for Agentic Tool Calling | Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets. Model routing can mitigate this, but existing routers are designed for chat completion rather than tool use. We present Switchcraft, the first (to the best of our knowled... | [
"Sharad Agarwal",
"Pooria Namyar",
"Alec Wolman",
"Rahul Ambavat",
"Ankur Gupta",
"Qizheng Zhang"
] | [
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.07112 | https://arxiv.org/pdf/2605.07112v1 | 2605.07112 | null | 0 | 0 | false | null | null | 0.35 |
aedab8da480a11ae5a9d5537d8e589fa4220ed038a555eda35a4797cfd185d80 | [
"arxiv",
"semantic_scholar"
] | When Child Inherits: Modeling and Exploiting Subagent Spawn in Multi-Agent Networks | Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems that can delegate work through tools and newly spawned subagents. While these capabilities improve automation and scalability, they also pose new security risks in multi-ag... | [
"Ziwen Cai",
"Yihe Zhang",
"Xiali Hei"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08460 | https://arxiv.org/pdf/2605.08460v1 | 2605.08460 | null | 0 | 0 | false | null | null | 0.35 |
1f9a6a078a41bec8f3ac17d52c99ef2577b0a6acfb8c72570dbba35cbcd7ffb4 | [
"arxiv",
"semantic_scholar"
] | MASPrism: Lightweight Failure Attribution for Multi-Agent Systems Using Prefill-Stage Signals | Failure attribution in LLM-based multi-agent systems aims to identify the steps that contribute to a failed execution. This task remains difficult because a single execution can contain many agent actions and tool calls, failure evidence can appear many steps after the original mistake, and existing methods often rely ... | [
"Yang Liu",
"Hongjiang Feng",
"Junsong Pu",
"Zhuangbin Chen"
] | [
"cs.SE"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.07509 | https://arxiv.org/pdf/2605.07509v2 | 2605.07509 | null | 1 | 0 | false | null | null | 0.35 |
af66b865ac4131498f97bf55624e1777aa5d1dd13c733f754243a94c5d1178a1 | [
"arxiv",
"semantic_scholar"
] | Designing Intelligent Enterprise Agents: A Capability-Aligned Multi-Agent Architecture | Enterprise interest in multi-agent systems has shifted from generic software agents to large-language-model (LLM) based intelligent agents that plan, use tools, maintain contextual memory, inspect intermediate results, collaborate with other agents, and sometimes act in systems of record. This paper revises the enterpr... | [
"John deVadoss"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.08258 | https://arxiv.org/pdf/2605.08258v1 | 2605.08258 | null | 0 | 0 | false | null | null | 0.35 |
736f53aa911ba078da69934e4d1441ed69763aac9b91488ced5774b4926827c4 | [
"arxiv",
"semantic_scholar"
] | Beyond the Black Box: Interpretability of Agentic AI Tool Use | AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control. Agents may skip required tool calls, invoke tools unnecessarily, or take actions whose consequence becomes visible only after execution. Existing obser... | [
"Hariom Tatsat",
"Ariye Shater"
] | [
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06890 | https://arxiv.org/pdf/2605.06890v3 | 2605.06890 | null | 0 | 0 | false | null | null | 0.35 |
3c530d338dc537427335918fb544198e21f2a7fa96fe6bea5216fd0a3564a2ed | [
"arxiv",
"semantic_scholar"
] | AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization | Precoding is a key technique for interference management and performance improvement in multi-antenna wireless systems. However, existing precoding methods are typically developed for specific system models, objectives, and constraint sets, which limits their adaptability to the heterogeneous and evolving scenarios exp... | [
"Zijiu Yang",
"Zixiang Zhang",
"Shunpu Tang",
"Qianqian Yang",
"Zhiguo Shi"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06443 | https://arxiv.org/pdf/2605.06443v1 | 2605.06443 | null | 0 | 0 | false | null | null | 0.35 |
f20beb4bb6552c43b6995905216b6aee7054d79a4ff11de1ebe39a9b4d8f79ec | [
"arxiv",
"semantic_scholar"
] | MANTRA: Synthesizing SMT-Validated Compliance Benchmarks for Tool-Using LLM Agents | Tool-using large language model (LLM) agents are increasingly deployed in settings where their reliable behavior is governed by strict procedural manuals. Ensuring that such agents comply with the rules from these manuals is challenging, as they are typically written for humans in natural language while agent behavior ... | [
"Ashwani Anand",
"Ivi Chatzi",
"Ritam Raha",
"Anne-Kathrin Schmuck"
] | [
"cs.CL",
"cs.LG",
"cs.LO"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06334 | https://arxiv.org/pdf/2605.06334v1 | 2605.06334 | null | 0 | 0 | false | null | null | 0.35 |
f1a866f378e5eb661586d3ddae2c156b7b17b83f107ecf4eef0ee149db16185f | [
"arxiv",
"semantic_scholar"
] | MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems | Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role-specific prompts. While the quality of these prompts is pivotal, jointly optimizing them across interacting agents remains a non-trivial challenge, prim... | [
"Zhexuan Wang",
"Xuebo Liu",
"Li Wang",
"Zifei Shan",
"Yutong Wang",
"Zhenxi Song",
"Min Zhang"
] | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06623 | https://arxiv.org/pdf/2605.06623v1 | 2605.06623 | null | 1 | 0 | true | https://github.com/wangzx1219/MASPO | null | 0.65 |
20ac76aeeb3dae4e3307d84a6128e547a81decd1a0684ffd54ef5d654b8d78d5 | [
"arxiv",
"semantic_scholar"
] | Towards Security-Auditable LLM Agents: A Unified Graph Representation | LLM-based agentic systems are rapidly evolving to perform complex autonomous tasks through dynamic tool invocation, stateful memory management, and multi-agent collaboration. However, this semantics-driven execution paradigm creates a severe semantic gap between low-level physical events and high-level execution intent... | [
"Chaofan Li",
"Lyuye Zhang",
"Jintao Zhai",
"Siyue Feng",
"Xichun Yang",
"Huahao Wang",
"Shihan Dou",
"Yu Ji",
"Yutao Hu",
"Yueming Wu",
"Yang Liu",
"Deqing Zou"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06812 | https://arxiv.org/pdf/2605.06812v1 | 2605.06812 | null | 0 | 0 | false | null | null | 0.35 |
a9f973d14351bc6d65e95c3e0767d59066d0f9168aba8b0daa950076a19cb7dd | [
"arxiv",
"semantic_scholar"
] | A Self-Healing Framework for Reliable LLM-Based Autonomous Agents | Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a significant challenge due to unpredictable failures such as hallucinations, execution errors, and inconsistent reasoning. This paper proposes a reliability-aware self-heali... | [
"Cheonsu Jeong",
"Younggun Shin"
] | [
"cs.SE",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.06737 | https://arxiv.org/pdf/2605.06737v1 | 2605.06737 | null | 1 | 0 | false | null | null | 0.35 |
3d54e3b6ed1ae3d00206a55ee7284e7c5d2647d52142f389377c3f67cb0abaac | [
"arxiv",
"semantic_scholar"
] | Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems | Optimizing the communication structure of large language model based multi-agent systems (LLM-MAS) has been shown to improve downstream performance and reduce token usage. Existing methods typically rely on randomly sampled training tasks. However, tasks may differ substantially in difficulty and domain, and thus they ... | [
"Huchen Yang",
"Xinghao Dong",
"Dan Negrut",
"Jin-Long Wu"
] | [
"cs.MA",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-07T00:00:00 | https://arxiv.org/abs/2605.05703 | https://arxiv.org/pdf/2605.05703v2 | 2605.05703 | null | 0 | 0 | false | null | null | 0.35 |
9a4cde77ec3dd676758cdd0d50b62bbb05d28df120647d91081b91c81773855a | [
"arxiv",
"semantic_scholar"
] | GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking | Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models (LLMs) struggle despite their impressive general capabilities. We present GEM (Graph-Enhanced Mixture-of-Experts), a novel framework that combines language models and g... | [
"Ziqi Zhu",
"Adithya Suresh",
"Tomal Deb",
"Iman Abbasnejad"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04449 | https://arxiv.org/pdf/2605.04449v1 | 2605.04449 | null | 0 | 0 | false | null | null | 0.35 |
4ebb27d505e6809ee41dd63af486c8ffe20378ea93840108d8b40380cc5b8272 | [
"arxiv",
"semantic_scholar"
] | Strat-Reasoner: Reinforcing Strategic Reasoning of LLMs in Multi-Agent Games | While Large Language Models (LLMs) excel in certain reasoning tasks, they struggle in multi-agent games where the final outcome depends on the joint strategies of all agents. In multi-agent games, the non-stationarity of other agents brings significant challenges on the evaluation of the reasoning process and the credi... | [
"Yidong He",
"Yutao Lai",
"Pengxu Yang",
"Jiarui Gan",
"Jiexin Wang",
"Yi Cai",
"Mengchen Zhao"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04906 | https://arxiv.org/pdf/2605.04906v2 | 2605.04906 | null | 0 | 0 | true | https://github.com/ydhe1012/Strat-Reasoner | null | 0.65 |
5e3eb0748d8569f7c85916537b112d358e08cf73a5c9775c1734ef8a61dca37b | [
"arxiv",
"semantic_scholar"
] | SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition | Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is a cornerstone of mobile health, smart environments, and human-computer interaction. However, current deep learning-based HAR models often struggle with heavy reliance on labeled data, position-specific ambiguity, and a lack of transparent... | [
"Naiyu Zheng",
"Tianlong Yu",
"Haochen Yin",
"Xiaoyi Fan",
"Xiping Hu",
"Zhimeng Yin"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04608 | https://arxiv.org/pdf/2605.04608v1 | 2605.04608 | null | 0 | 0 | false | null | null | 0.35 |
80e92f9ff480a913b8ca1e13d02e84747febb5f65c93c6f9eb67ecaa6924dd7f | [
"arxiv",
"semantic_scholar"
] | Tree-based Credit Assignment for Multi-Agent Memory System | Memory systems are widely adopted to enhance LLMs for long-horizon tasks, and are commonly organized as multi-agent pipelines with memory building, summarizing, and retrieval agents. To empower this system, existing RL-based methods either apply final downstream task rewards (e.g., QA accuracy) for all agents uniformly... | [
"Marina Mao",
"Alexandr Liu",
"Pengbo Li",
"Siheng Li",
"Bo Zhou",
"Xiang Wang"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04811 | https://arxiv.org/pdf/2605.04811v1 | 2605.04811 | null | 0 | 0 | false | null | null | 0.35 |
baf4e67c0a656e99c144e57cba9a1a14a1706d0322af1c617759f0bac1d12f2b | [
"arxiv",
"semantic_scholar"
] | AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use | Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A single unsafe action, including accidental deletion, credential exposure, or data exfiltration, can cause irreversible harm. Existing defenses are incomplete: post-hoc benc... | [
"Chenglin Yang"
] | [
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04785 | https://arxiv.org/pdf/2605.04785v1 | 2605.04785 | null | 2 | 0 | false | null | null | 0.35 |
a35135d96838c6029392c3141c801349a6a859a7396aeebdc6452c64bc4f30f0 | [
"arxiv",
"semantic_scholar"
] | When Context Hurts: The Crossover Effect of Knowledge Transfer on Multi-Agent Design Exploration | The prevailing assumption in agent orchestration is that more context is better. We test this on multi-agent software design across 10 tasks, 7 context-injection conditions, and over 2,700 runs, and find a crossover effect: the same artifact type improves design exploration on some tasks (up to 20$\times$ tradeoff cove... | [
"Saranyan Vigraham"
] | [
"cs.AI",
"cs.SE"
] | [
"Computer Science"
] | 2026-05-05T00:00:00 | https://arxiv.org/abs/2605.04361 | https://arxiv.org/pdf/2605.04361v1 | 2605.04361 | null | 0 | 0 | false | null | null | 0.35 |
099465807ae3b25a77d39fd504f693d15c499ab325d8072de4b76328e157a19d | [
"arxiv",
"semantic_scholar"
] | Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems | Multi-agent LLM systems fail in production at rates between 41% and 87%, mostly due to coordination defects rather than base-model capability. Existing responses split between cataloguing failure modes empirically and shipping declarative orchestration frameworks as engineering tools; neither delivers a principled mapp... | [
"Maksym Nechepurenko",
"Pavel Shuvalov"
] | [
"cs.MA",
"cs.LG",
"q-fin.TR"
] | [
"Computer Science",
"Economics"
] | 2026-05-05T00:00:00 | https://arxiv.org/abs/2605.03310 | https://arxiv.org/pdf/2605.03310v1 | 2605.03310 | null | 0 | 0 | false | null | null | 0.35 |
b92ccd965fdd943f633455368b8271e65d49230aa730315160994b7c5733fdae | [
"arxiv",
"semantic_scholar"
] | Governed Collaborative Memory as Artificial Selection in LLM-Based Multi-Agent Systems | Persistent memory is turning language-model-based agents from stateless participants in isolated interactions into state-bearing components of LLM-based multi-agent systems. As memory becomes durable, reloadable, and behavior-shaping across agents, sessions, or versions, a design question arises that is not captured by... | [
"Diego F. Cuadros",
"Abdoul-Aziz Maiga",
"Helen Meskhidze",
"Andre Curtis-Trudel"
] | [
"cs.MA"
] | [
"Computer Science"
] | 2026-05-05T00:00:00 | https://arxiv.org/abs/2605.04264 | https://arxiv.org/pdf/2605.04264v1 | 2605.04264 | null | 0 | 0 | false | null | null | 0.35 |
4d380f48858784ab31c76c494d0a51e682fc35a9057f9f41548e25aefd7ba605 | [
"arxiv",
"semantic_scholar"
] | From Intent to Execution: Composing Agentic Workflows with Agent Recommendation | Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However, the creation of such MAS currently involves manual composition of the plan, manual selection of appropriate agents, and manual creation of execution graphs. T... | [
"Kishan Athrey",
"Ramin Pishehvar",
"Brian Riordan",
"Mahesh Viswanathan"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-05T00:00:00 | https://arxiv.org/abs/2605.03986 | https://arxiv.org/pdf/2605.03986v1 | 2605.03986 | null | 0 | 0 | false | null | null | 0.35 |
99514b0799b7951081a25cf5b5fc1cbd70288ae0c125f59cf5b933ed4c43ae32 | [
"arxiv",
"semantic_scholar"
] | SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents | LLM agents increasingly rely on reusable skills (e.g., SKILL markdown files) to execute complex tasks, yet these artifacts lack portability: agent frameworks are highly sensitive to prompt formatting, leading to a large performance variation for the same skill. Nevertheless, most skills are authored once as format-agno... | [
"Yipeng Ouyang",
"Yi Xiao",
"Yuhao Gu",
"Xianwei Zhang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-05T00:00:00 | https://arxiv.org/abs/2605.03353 | https://arxiv.org/pdf/2605.03353v4 | 2605.03353 | null | 1 | 0 | true | https://github.com/Nexa-Language/Skill-Compiler/ | null | 0.65 |
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