Multi-Agent LLM Systems

LLM: Large Language Model

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Overview

Multi-agent large language model systems study how multiple language-model agents can divide work, share information, and combine decisions on tasks such as reasoning, coding, research, and planning. Common designs use specialized roles, debate or review loops, retrieval-augmented generation, shared memory, and orchestrators that route tasks or choose communication links; some systems also learn prompts, workflows, or policies through reinforcement learning. Researchers evaluate these approaches on benchmarks for math, question answering, software, and tool use, as well as in longer-running simulations and domain applications. A central open tension is that collaboration can improve accuracy and verification, but may also add latency and token costs or spread errors, bias, and unsafe instructions, so gains depend on the task, architecture, and quality of coordination.

Latest papers

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1,050 papers142 papers in the last four weeks, up 35% on the four weeks before. 0.8% of all new papers.

Jan 14, 2026

MACRO-LLM: LLM-Empowered Multi-Agent Collaborative Reasoning under Spatiotemporal Partial Observability

Large Language Model (LLM) agents deployed in complex real-world scenarios increasingly operate as spatially distributed entities. However, this physical dispersion constrains agents to limited local perception and finite temporal horizons. We characterize this bottleneck as spatiotemporal partial observability, where spatial and temporal limitations are fundamentally coupled: resolving spatial conflicts requires temporal reasoning about neighbors' future actions, while temporal planning requires spatial context beyond local perception. To bridge this gap, we introduce MACRO-LLM, LLM-empowered multi-agent collaborative reasoning under spatiotemporal partial observability. The architecture interleaves spatial and temporal reasoning within each decision cycle via three interdependent modules: (1) the CoProposer mitigates temporal uncertainty by verifying candidate actions via predictive rollouts; (2) the Negotiator overcomes spatial myopia by resolving conflicts through mean-field statistical aggregation, grounded in the CoProposer's rollout rewards; and (3) the Introspector closes the reasoning loop by analyzing environmental drift and attributing performance changes to refine strategies. Extensive evaluations on two complex long-horizon tasks, cooperative platoon planning and pandemic control, demonstrate that our framework enables robust coordination under spatiotemporal partial observability.

Handi Chen, Running Zhao, Xiuzhe Wu +2

The University of Hong Kong

Multi-Agent LLM SystemsMulti-Agent CoordinationMulti-Agent ReasoningLong-Horizon Agent Tasks
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Jan 9, 2026

StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management

Multi-agent systems based on large language models, particularly centralized architectures, have recently shown strong potential for complex and knowledge-intensive tasks. However, central agents often suffer from unstable long-horizon collaboration due to the lack of memory management, leading to context bloat, error accumulation, and poor cross-task generalization. To address both task-level memory inefficiency and the inability to reuse coordination experience, we propose StackPlanner, a hierarchical multi-agent framework with explicit memory control. StackPlanner addresses these challenges by decoupling high-level coordination from subtask execution with active task-level memory control, and by learning to retrieve and exploit reusable coordination experience via structured experience memory and reinforcement learning. Experiments on multiple deep-search and agent system benchmarks demonstrate the effectiveness of our approach in enabling reliable long-horizon multi-agent collaboration.

Ruizhe Zhang, Xinke Jiang, Zhibang Yang +12

School of Computer Science, Peking University, Beijing, China · 2National Engineering Research Center for Software Engineering, Peking University, Beijing, China · 3Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China +5

Multi-Agent LLM SystemsMulti-Agent CollaborationLong-Horizon LLM AgentsMemory-Augmented Language Model Agents
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Dec 9, 2025

An Agentic AI System for Multi-Framework Communication Coding

Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and reliability, especially when applied across various communication frameworks and clinical domains. In this study, we developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC), built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning, an Update Agent for maintaining up-to-date retrieval databases, a set of Annotation Agents that applies codebook-guided retrieval-augmented generation (RAG) with dynamic few-shot prompting, and a Verification Agent that provides consistency checks and feedback. To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders. We developed and evaluated MOSAIC using 26 gold standard annotated transcripts for training and 50 transcripts for testing, spanning rheumatology and OB/GYN domains. On the test set, MOSAIC achieved an overall F1 score of 0.928. Performance was highest in the Rheumatology subset (F1 = 0.962) and strongest for Patient Behavior (e.g., patients asking questions, expressing preferences, or showing assertiveness). Ablations revealed that MOSAIC outperforms baseline benchmarking.

Bohao Yang, Rui Yang, Joshua M. Biro +14

Department of Biostatistics and Bioinformatics, Duke School of Medicine, Durham, NC, USA · Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore · Medstar Health National Center for Human Factors in Healthcare, Washington, DC, USA +5

HealthcareClinical Information ExtractionMulti-Agent LLM SystemsAgentic RAGLLM Agent Orchestration
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Nov 28, 2025

MAS-Shield: A Defense Framework for Secure and Efficient LLM MAS

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the network. Existing defenses face a fundamental dilemma: lightweight single-auditor methods are prone to single points of failure, while robust committee-based approaches incur prohibitive computational costs in multi-turn interactions. To address this challenge, we propose MAS-Shield, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline. Rather than applying uniform scrutiny, MAS-Shield dynamically allocates defense resources through a three-stage protocol: (1) Critical Agent Selection strategically targets high-influence nodes to narrow the defense surface; (2) Light Auditing employs lightweight sentry models to rapidly filter the majority of benign cases; and (3) Global Consensus Auditing escalates only suspicious or ambiguous signals to a heavyweight committee for definitive arbitration. This hierarchical design effectively optimizes the security-efficiency trade-off. Experiments demonstrate that MAS-Shield achieves a 92.5% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70% compared to existing methods.

Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +8

Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240, China

Multi-Agent LLM SystemsLLM AuditingLLM Agent SecurityMulti-Agent System Security
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Nov 22, 2025

MASTEST: A LLM-Based Multi-Agent System For Testing RESTful APIs

Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system called MASTEST that combines LLM-based intelligent agents and programmed agents to automate REST API testing. They form a complete tool chain covering the whole workflow of REST API test with API specification in the OpenAPI Swagger format as the input. It also incorporates human testers in the process to review and correct LLM generated test artefacts to control the quality of testing activities. MASTEST is evaluated on two LLMs, GPT-4o and DeepSeek V3.1 Reasoner with five public APIs. Its performances on various testing activities are measured by a wide range of metrics, including adequacy and coverage metrics, the syntax and data type correctness of generated test scripts, the usability of LLM generated test cases and scripts, as well as the bug detection ability. Experiment results demonstrated that both DeepSeek and GPT-4o achieved a high overall performance but had strengths and weaknesses on different testing activities. MASTEST generated test cases achieved 94% and 98% unit test coverage and 79% and 78% system test coverage for GPT-4o and DeepSeek respectively in comparison with human designed test cases. The generated test scripts maintained 100% syntax correctness and only required minimal manual edits for semantic correctness. The generated test scripts contain assertions on the expected status code as well as contents in the response messages. They are highly capable of detecting bugs in the REST APIs. Experiment data shows that the bug detection rates are between 2.13 to 4.50 per operation. These findings indicate that MASTEST is highly efficient and effective.

Xiaoke Han, Hong Zhu

School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

Multi-Agent LLM SystemsAutomated Software TestingAutomated Test Generation
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Nov 19, 2025

Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response

Large language models (LLMs) promise to accelerate incident response in production systems, yet single-agent approaches generate vague, unusable recommendations. We present MyAntFarm.ai, a reproducible containerized framework demonstrating that multi-agent orchestration fundamentally transforms LLM-based incident response quality. Through 348 controlled trials comparing single-agent copilot versus multi-agent systems on identical incident scenarios, we find that multi-agent orchestration achieves 100% actionable recommendation rate versus 1.7% for single-agent approaches, an 80 times improvement in action specificity and 140 times improvement in solution correctness. Critically, multi-agent systems exhibit zero quality variance across all trials, enabling production SLA commitments impossible with inconsistent single-agent outputs. Both architectures achieve similar comprehension latency (approx.40s), establishing that the architectural value lies in deterministic quality, not speed. We introduce Decision Quality (DQ), a novel metric capturing validity, specificity, and correctness properties essential for operational deployment that existing LLM metrics do not address. These findings reframe multi-agent orchestration from a performance optimization to a production-readiness requirement for LLM-based incident response. All code, Docker configurations, and trial data are publicly available for reproduction.

Philip Drammeh

Independent Researcher

Multi-Agent LLM SystemsLLM Agent OrchestrationLLM Agent EvaluationCybersecurityLLM Agent Reliability
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Nov 14, 2025

Multi-agent Undercover Gaming: Hallucination Removal via Counterfactual Test for Multimodal Reasoning

Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a promising solution by promoting consensus among multiple agents to enhance reliability, it relies on the unrealistic assumption that all debaters are rational and reflective, which is a condition that may not hold when agents themselves are prone to hallucinations. To address this gap, we introduce the Multi-agent Undercover Gaming (MUG) protocol, inspired by social deduction games like "Who is Undercover?". MUG reframes MAD as a process of detecting "undercover" agents (those suffering from hallucinations) by employing multimodal counterfactual tests. Specifically, we modify reference images to introduce counterfactual evidence and observe whether agents can accurately identify these changes, providing ground-truth for identifying hallucinating agents and enabling robust, crowd-powered multimodal reasoning. MUG advances MAD protocols along three key dimensions: (1) enabling factual verification beyond statistical consensus through counterfactual testing; (2) introducing cross-evidence reasoning via dynamically modified evidence sources instead of relying on static inputs; and (3) fostering active reasoning, where agents engage in probing discussions rather than passively answering questions. Collectively, these innovations offer a more reliable and effective framework for multimodal reasoning in LLMs. The source code can be accessed at https://github.com/YongLD/MUG.git.

Dayong Liang, Xiao-Yong Wei, Linhui Xiao +1

School of Future Technology, South China University of Technology, Guangzhou, China · Department of networked intelligence, Peng Cheng Laboratory, Shenzhen, China · College of Computer Science, Sichuan University, Chengdu, China +1

Multi-Agent LLM SystemsMultimodal ReasoningMultimodal HallucinationHallucination Detection in VLMs
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Nov 11, 2025

Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents

Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation with agent- and message-level learning. We propose a theoretical framework that unifies cooperative game-theoretic attribution with process reward modeling to transform system evaluation to agent credit to response-level signals. Unlike prior approaches that rely only on attribution (Shapley) or step-level labels (PRM), our method produces local, signed, and credit-conserving signals. In success cases, Shapley-based credit assignment fairly allocates outcomes across agents and is refined into per-message rewards that promote cooperation while discouraging redundancy or sabotage; in failure cases, first-error localization yields repair-aware preferences that penalize harmful steps while rewarding corrective attempts. The resulting signals are bounded, cooperative, and directly compatible with reinforcement- or preference-based post-training, providing a unified and auditable pathway from global evaluation to local supervision in LLM multi-agent training. Our contribution is conceptual: we present a theoretical foundation and training signals, leaving empirical validation for future work.

Chih-Hsuan, Yang, Tanwi Mallick +5

Bella · Argonne National Laboratory · & University of Chicago

Multi-Agent LLM SystemsCooperative Game TheoryProcess Reward ModelsCredit Assignment in RLLLM Agent Training
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Oct 29, 2025

Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry

While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by language descriptions), their abilities to collaborate with each other to achieve a joint goal are not well explored. To address this limitation, this paper studies LLM agents in task collaboration, particularly under the condition of information asymmetry, where agents have disparities in their knowledge and skills and need to work together to complete a shared task. We extend Einstein Puzzles, a classical symbolic puzzle, to a table-top game. In this game, two LLM agents must reason, communicate, and act to satisfy spatial and relational constraints required to solve the puzzle. We apply a fine-tuning-plus-verifier framework in which LLM agents are equipped with various communication strategies and verification signals from the environment. Empirical results highlight the critical importance of aligned communication, especially when agents possess both information-seeking and -providing capabilities. Interestingly, agents without communication can still achieve high task performance; however, further analysis reveals a lack of true rule understanding and lower trust from human evaluators. Instead, by integrating an environment-based verifier, we enhance agents' ability to comprehend task rules and complete tasks, promoting both safer and more interpretable collaboration in AI systems. https://github.com/Roihn/EinsteinPuzzles

Run Peng, Ziqiao Ma, Amy Pang +5

University of Michigan · UNC, Chapel Hill · Georgia Tech +2

Multi-Agent LLM SystemsLLM Agent VerificationMulti-Agent SystemsLLM Agent TrainingMulti-Agent Communication
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Oct 28, 2025

MASPRM: Multi-Agent System Process Reward Model

Inference-time search over multi-agent systems (MAS) wastes compute when it cannot identify which agent's intermediate message advanced progress. We present the Multi-Agent System Process Reward Model (MASPRM), which scores routed transcripts (ordered sequences of messages between agents) and acts as an inference controller for step-level beam search (SBS) and Monte Carlo Tree Search (MCTS). MASPRM is trained from multi-agent MCTS rollouts labeled only with terminal outcome rewards, without human step-level annotations. We evaluate on GSM8K, MATH, MMLU, and LogiQA. Under matched scorer size and comparable MCTS budget, MASPRM exceeds a size-matched ORM by +2.0+2.0 to +3.0+3.0 points at 1.5B and +4.1+4.1 to +14.5+14.5 at 7B across all four benchmarks, with additional scorer-scaling gains over policy likelihood at 7B (avg +13.4+13.4 under MCTS). MASPRM also improves ranking quality, reducing Hit@1 to Hit@5 gaps by up to 10.310.3 points, with the largest gains under stepwise search that uses intermediate decisions. Code: https://github.com/milad1378yz/MASPRM

Milad Yazdani, Mahdi Mostajabdaveh, Zirui Zhou +1

Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T1Z4, Canada · Huawei Technologies Canada, Burnaby, BC V5C6S7, Canada

Multi-Agent LLM SystemsInference-Time SearchProcess Reward ModelsMulti-Agent Reasoning
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Oct 2, 2025

Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy

Inertial fusion energy promises nearly unlimited, clean power if it can be achieved. However, the design and engineering of fusion systems requires controlling and manipulating matter at extreme energies and timescales; the shock physics and radiation transport governing the physical behavior under these conditions are complex requiring the development, calibration, and use of predictive multiphysics codes to navigate the highly nonlinear and multi-faceted design landscape. We hypothesize that artificial intelligence reasoning models can be combined with physics codes and emulators to autonomously design fusion fuel capsules. In this article, we construct a multi-agent system where natural language is utilized to explore the complex physics regimes around fusion energy. The agentic system is capable of executing a high-order multiphysics inertial fusion computational code. We demonstrate the capacity of the multi-agent design assistant to translate natural-language design goals into simulation execution, physics-emulator construction, analysis, and iterative refinement of capsule-geometry parameters, ultimately achieving simulated ignition within the computational design workflow.

Meir H. Shachar, Dane M. Sterbentz, Harshitha Menon +10

Lawrence Livermore National Laboratory Livermore, CA, USA · Lawrence Livermore National Laboratory Livermore, CA 94550, USA · Sandia National Laboratories Albuquerque, NM, USA +3

Multiphysics SimulationMulti-Agent LLM SystemsAI Agents for Scientific DiscoveryPhysics-Based Simulation
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Sep 30, 2025

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.

Mingjin Li, Yu Liu, Huayi Liu +4

Baidu Inc., Beijing, China

Multi-Agent LLM SystemsSynthetic Data GenerationLLM Agent TrainingText Generation
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Sep 26, 2025

Generalized Multi-agent Social Simulation Framework

Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design. To address these issues, we designed and developed a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability. We inherited the framework to realize common derived classes. Additionally, a memory summarization mechanism is proposed to filter and distill relevant information from raw memory data, prioritizing contextually salient events and interactions. By selecting and combining some necessary derived classes, we customized a specific simulated environment. Utilizing this simulated environment, we successfully simulated human interactions on social media, replicating real-world online social behaviors. The source code for the project will be released and evolve.

Gang Li, Jie Lin, Yining Tang +6

Zhejiang University, China

Multi-Agent LLM SystemsLarge Language Model-Based Social SimulationComputational Social ScienceHuman Behavior SimulationMemory-Augmented Language Model Agents
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Aug 25, 2025

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

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, and multi-agent methods. Following our taxonomy, we review key application scenarios across scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks. It also compares the distinct features and evaluation strategies of each category. Through our taxonomy and comparisons, our survey explores the designs and strengths of LLM-based agentic frameworks in different scenarios, reviewing the fast-paced development of complex agentic systems in the real world.

Bingxi Zhao, Lin Geng Foo, Ping Hu +3

Multi-Agent LLM SystemsLLM Agents
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Aug 12, 2025

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

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 operations, leading to cognitive-load interference and unstable coordination. We present MSARL, a Multi-Small-Agent Reinforcement Learning framework that explicitly decouples reasoning from tool use. In MSARL, a Reasoning Agent decomposes problems and plans tool invocations, while multiple Tool Agents specialize in specific external tools, each trained via a combination of imitation learning and reinforcement learning with role-specific rewards. On mathematical problem solving with code execution, MSARL significantly improves reasoning stability and final-answer accuracy over single-agent baselines. Moreover, the architecture generalizes to diverse tool-use tasks, demonstrating that cognitive-role decoupling with small agents is a scalable blueprint for multi-agent AI design.

Dayu Wang, Yutong Liu, Jiaye Yang +3

Baidu Inc. · Peking University

Multi-Agent LLM SystemsMulti-Agent SystemsMulti-Agent ReasoningRL for Tool Use
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Aug 6, 2025

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However, scaling such systems can substantially increase inference cost, especially when multiple agents repeatedly process overlapping or role-irrelevant context. Many existing multi-agent frameworks focus on orchestration and role specialization, but still rely on full-context sharing or static context routing, leading to redundant token consumption, increased runtime, and inefficient memory utilization. We present Role-Conditioned Context Routing (RCR), an execution-time layer that allocates role-relevant shared-memory items to agents under token budgets. Given a structured shared memory and a set of role-specialized agents, RCR selectively routes task-relevant memory items to each agent according to its role, optional task-stage metadata, and token budget. RCR integrates role-conditioned relevance scoring, token-budgeted memory selection, and iterative memory updates to reduce redundant context exposure during multi-agent execution. Experiments on multi-hop QA, embodied reasoning (ALFWorld), and web-based decision-making (WebShop) show that RCR reduces token usage by 35% on average and runtime by 38% on average relative to Full-Context, while maintaining or improving task performance.

Jun Liu, Zhenglun Kong, Changdi Yang +12

Carnegie Mellon University · Northeastern University · Harvard University +5

LLM Context ManagementMulti-Agent LLM SystemsMulti-Agent SystemsShared-Memory Multi-Agent Systems
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Aug 1, 2025

ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism

In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information. We propose ContestTrade, a multi-agent trading system with an internal competitive mechanism inspired by institutional investment workflows. The system consists of two specialized teams: (1) a Data Team that processes and condenses massive market data into diversified textual factors optimized for constrained LLM context windows, and (2) a Research Team that produces parallelized multipath trading decisions via tool-augmented deep research. The core design is a "Quantify-Predict-Allocate" contest mechanism within each team: agent outputs are scored only after market outcomes become observable, future utility is predicted from historical scores, and resources are allocated to agents with positive predicted utility. In a post-2024 A-share backtest, ContestTrade achieves higher backtested return and risk-adjusted performance than the evaluated baselines. We further describe the temporal protocol, implementation choices, and limitations to clarify the scope of these results.

Rui Sun, Li Zhao, Zuoyou Jiang +5

1Stepfun · 2FinStep

Multi-Agent LLM SystemsLLM Agent OrchestrationMulti-Agent SystemsAlgorithmic Trading
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Jul 30, 2025

MASCA: LLM based-Multi Agents System for Credit Assessment

Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes. The framework employs a layered architecture where specialized LLM-based agents collaboratively tackle sub-tasks. Additionally, we integrate contrastive learning for risk and reward assessment to optimize decision-making. We further present a signaling game theory perspective on hierarchical multi-agent systems, offering theoretical insights into their structure and interactions. Our paper also includes a detailed bias analysis in credit assessment, addressing fairness concerns. Experimental results demonstrate that MASCA outperforms baseline approaches, highlighting the effectiveness of hierarchical LLM-based multi-agent systems in financial applications, particularly in credit scoring.

Gautam Jajoo, Atharva Pandey, Pranjal A Chitale +1

Kairosity · Microsoft Research · Independent Author

Financial ServicesMulti-Agent LLM SystemsCredit Scoring
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Jul 10, 2025

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their effectiveness remains constrained when reasoning extends beyond the knowledge acquired during pretraining. Conventional approaches, such as fine-tuning or retrieval-augmented generation, face limitations in either imposing high computational overhead or failing to fully exploit structured scientific data. To overcome these challenges, we propose DrugMCTS, a novel framework that synergistically integrates RAG, multi-agent collaboration, and Monte Carlo Tree Search for drug repositioning. The framework employs five specialized agents tasked with retrieving and analyzing molecular and protein information, thereby enabling structured and iterative reasoning. Extensive experiments on the DrugBank and KIBA datasets demonstrate that DrugMCTS achieves substantially higher recall and robustness compared to both general-purpose LLMs and deep learning baselines. Our results highlight the importance of structured reasoning, agent-based collaboration, and feedback-driven search mechanisms in advancing LLM applications for drug repositioning.

Zerui Yang, Yuwei Wan, Siyu Yan +3

City University of Hong Kong, Hong Kong, PR China · City University of Hong Kong Shenzhen Research Institute, Shenzhen, PR China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou PR China +1

Drug DiscoveryAgentic RAGMulti-Agent LLM Systems
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Jun 14, 2025

SheetMind: Actions Set Accuracy, Agents Set the Failure Mode

Spreadsheet agents are converging on elaborate multi-agent designs, yet it is unclear how much of their performance comes from the agents rather than from the action interface they share. We answer this with SheetMind, a Manager-Action-Reflection framework, in a controlled study over all 221 tasks of the SheetCopilot Benchmark: five architectural variants, four backbones, exact McNemar tests on paired outcomes, and a checker reproducing the official chart and pivot comparisons. Replacing the high-level action API with primitive cell operations costs 47.1 points (p < 0.0001) and leaves the agent below a do-nothing baseline, whereas both extra agents together are worth 3.2 points: the Reflection Agent adds +4.5 (p = 0.013), the Manager +1.4 (p = 0.68). Decomposition instead changes how the system fails, cutting silently wrong outputs from 33% to 25% of tasks (p = 0.010). Capability saturates: GPT-5 and the five-times-cheaper GPT-5-mini are not significantly different (61.1% vs. 58.4%, p = 0.15), while GPT-3.5 loses 16.3 points and fails differently. A reflector must judge the step it just took, not the subtask. SheetMind reaches 61.1% Pass@1 with GPT-5 on the full SCB-221, against a do-nothing baseline of 9.0%. Accuracy comes from the operations an agent can name; the agents decide how it fails.

Lyuhao Chen, Xi Cheng, Yanming Kang +11

Carnegie Mellon University · Cornell University · University of California, Berkeley +2

Multi-Agent LLM SystemsLLM Agent EvaluationSpreadsheet AutomationLLM Agent Reliability
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May 28, 2025

Seven Security Challenges in Cross-domain Multi-agent LLM Systems

Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or violate policy, producing risks driven by emergent multi-agent dynamics rather than classical software bugs. This position paper maps the security agenda for cross-domain multi-agent LLM systems. We introduce seven categories of novel security challenges, for each of which we also present plausible attacks, security evaluation metrics, and future research guidelines.

Ronny Ko, Jiseong Jeong, Shuyuan Zheng +4

Osaka University · Seoul National University · Yonsei University

Multi-Agent LLM SystemsLLM Agent SecurityMulti-Agent System Security
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May 20, 2025

Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning

Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmented reasoning for complex multi-hop questions. To address these challenges, this paper propose SPLIT-RAG, a multi-agent RAG framework that addresses these limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval. The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG. The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types, while lightweight LLM agents are assigned to partitioned subgraphs, and only relevant partitions are activated during retrieval, thus reduce search space while enhancing efficiency. Finally, a hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications. Extensive experimental validation demonstrates considerable improvements compared to existing approaches.

Ruiyi Yang, Hao Xue, Imran Razzak +3

University of New South Wales Sydney, NSW, Australia · Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi, UAE · Griffith University Queensland, Australia +1

Multi-Hop QAMulti-Agent LLM SystemsGraph Retrieval-Augmented GenerationGraph-Based Retrieval
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May 19, 2025

Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems

Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts based on their immediate task performance, overlooking the intrinsic qualities that determine their reliability. This outcome-centric view not only limits interpretability but also fails to account for the inherent stochasticity of large language models (LLMs). In this work, we bring attention to prompt stability-the consistency of model responses across repeated executions-as a key factor for building robust and effective prompt generation systems. To quantify this, we propose semantic stability as a criterion for assessing the response consistency of prompts. Based on the proposed metric, we developed the first stability-aware general-purpose prompt generation system that leverages stability feedback to iteratively enhance both prompt quality and system-level performance. Furthermore, we establish a logical chain between prompt stability and task success by analyzing the structural dependencies within our system, proving stability as a necessary condition for effective system-level execution. Empirical results across general and domain-specific tasks demonstrate that our stability-aware framework improves both accuracy and output consistency. By shifting the focus from one-off results to persistent reliability, our work offers a new perspective on prompt design and contributes practical tools for building more trustworthy general-purpose systems. The UI and source code for our system are publicly available at: https://xucheng63.github.io/MyDataPilot/.

Ke Chen, Xucheng Yu, Yufei Zhou +1

School of Information Sciences, University of Illinois Urbana-Champaign, Urbana, USA · Department of Economics, Duke University, Durham, USA

LLM PromptingAutomatic Prompt OptimizationLanguage Model Generation EvaluationMulti-Agent LLM Systems
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Apr 21, 2025

MARFT: Multi-Agent Reinforcement Fine-Tuning

Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) offer a promising approach to collaborative tasks, but applying reinforcement learning to these systems remains challenging because classical multi-agent reinforcement learning assumptions do not fully capture their dependency structures, heterogeneous roles, and autoregressive outputs. This article provides a tutorial-style overview of the transition from reinforcement learning to reinforcement fine-tuning in single-agent and multi-agent settings, highlighting the differences between classical MARL and LLM-based multi-agent optimization. We introduce Multi-Agent Reinforcement Fine-Tuning (MARFT) and formalize LaMAS optimization as a Flexible Markov Game (Flex-MG). The MARFT framework combines predecessor-conditioned policy optimization, a centralized critic, parameter-efficient adapters, and token-level credit assignment. Our token-level analysis clarifies how within-action discounting affects returns and establishes consistency with action-level returns when the token discount is one. Experiments on fixed two- to four-agent mathematical and coding workflows compare MARFT with and without policy parameter sharing against IPPO. Both variants report higher mean evaluation scores in all 24 benchmark-team-size settings, while the variant without parameter sharing shows the clearest advantage in final training reward. Role-specification ablations examine how prescribed roles affect training. We conclude with application perspectives and open challenges, including dynamic workflows, sample efficiency, and system-level reliability. Code: https://github.com/jwliao-ai/MARFT.

Junwei Liao, Muning Wen, Jun Wang +1

Shanghai Jiao Tong University · Shanghai Innovation Institute · OPPO Research Institute

Multi-Agent LLM SystemsRL Fine-TuningMulti-Agent Reinforcement Learning
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Apr 13, 2025

AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations

Multi-agent large language model simulations have the potential to model complex human behaviors and interactions. If the mechanics are set up properly, unanticipated and valuable social dynamics can surface. However, it is challenging to consistently enforce simulation mechanics while still allowing for rich and emergent dynamics. We present AgentDynEx, an AI system that helps set up, track, and repair simulations. Specifically, AgentDynEx introduces milestones that act as checkpoints and failure conditions that act as guardrails to ensure dynamics are relevant and mechanics are respected as the simulation progresses. It also introduces a method called nudging, where the system dynamically reflects on simulation progress and gently intervenes if it begins to deviate from intended outcomes. A technical evaluation found that nudging enables simulations to progress further without reducing the presence notable dynamics compared to simulations without nudging. A case study with AgentDynEx documented instances where real users were able to simulate lived experiences. We discuss the importance of nudging as a technique for guiding agents towards desirable behaviors while preserving their freedom of choice.

Jenny Ma, Riya Sahni, Karthik Sreedhar +1

Columbia University New York City, USA

Multi-Agent LLM SystemsHuman Behavior SimulationAgent-Based ModelingEmergent Behavior in Multi-Agent Systems
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Feb 26, 2025

Accelerating scientific discovery with Co-Scientist

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system's design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery, and explaining mechanisms of anti-microbial resistance. Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists.

Juraj Gottweis, Wei-Hung Weng, Alexander Daryin +48

Multi-Agent LLM SystemsScientific Hypothesis GenerationLarge Language Model-Guided Scientific Discovery
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Feb 6, 2025

Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents

The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive frameworks for dynamically changing environments. This paper introduces RL-Focal, a two-stage RL agent framework that routes and ensembles LLMs. First, we develop the Decider RL-agent, which learns to dynamically select an ensemble of small size (mim_i) among NN LLMs (mi≪Nm_i \ll N) for incoming queries from a user-defined downstream task ii, by maximizing both error-diversity and reasoning-performance of the selected ensemble through iterative updates of task-adaptive rewards and policy. Second, to enable effective fusion of dynamically selected LLMs, we develop the stage-2 Fusion RL-agent, which learns to resolve reasoning conflicts from different LLMs and dynamically adapt to different ensemble teams composed by the Decider Agent for different downstream tasks. {\em Third}, we introduce the focal diversity metric to better model the error correlations among multiple LLMs further improving the generalization performance of the Decider Agent, which actively prunes the ensemble combinations. By focal diversity, we enhance performance across tasks by effectively promoting reward-aware and policy-adaptive ensemble selection and inference fusion. Extensive evaluations on five benchmarks show that RL-Focal achieves the performance improvement of 8.48% with an ensemble of small size compared to the best individual LLM in a pool and offers stronger robustness. Code is available here.

Selim Furkan Tekin, Gaowen Liu, Ramana Rao Kompella +1

Georgia Institute of Technology · Cisco Systems

Multi-Agent LLM SystemsLanguage Model EnsemblesRL for LLM Agents
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Aug 23, 2024

DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment

Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature. This evidence can be incomplete or conflicting. DrugAgent is a large language model (LLM)-based multi-agent system focused on DTI evidence integration that integrates outputs from machine learning, knowledge graph, and retrieval-augmented generation (RAG) agents. DrugAgent converts agent outputs into interpretable representations, then summarizes conflict across the evidence. We evaluated DrugAgent on kinase screening data of 900 pairs spanning 178 kinases and 42 inhibitors, and an androgen receptor antagonist screening benchmark. On the kinase dataset, LLM-as-a-Judge evaluation indicated outputs were faithful to input evidence in 98.8% of cases. Biological plausibility of returned summarization was high (scores 3-4 out of 5) across ground-truth classes: 79% of Weak activity labels cases (81% for Moderate/77% Strong); Strong cases received higher scores than Weak/Moderate. Label stability showed 98% agreement across runs. Results on the antagonist benchmark were consistent with the kinase dataset. Retrieved literature provided the greatest benefit when direct drug-target evidence was available, highlighting the importance of evidence availability for RAG-based integration. DrugAgent provides heterogeneous evidence-grounded DTI assessment, complementing standalone DTI prediction. We provide strategies to model agreement, conflict, and uncertainty in biomedical evidence integration. Code: https://github.com/sciluna/DrugAgent.

Yoshitaka Inoue, Tianci Song, Xinling Wang +3

Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA · Computational Biology Branch, National Library of Medicine, Bethesda, MD, USA · Khoury College of Computer Sciences, Northeastern University, Arlington, VA, USA +2

Multi-Agent LLM SystemsLLM Agent ReliabilityDrug Discovery
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Jul 15, 2024

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent.

Chong Zhang, Xinyi Liu, Zhongmou Zhang +10

University of Liverpool, UK · Peking University, China · Shanghai University of Finance and Economics, China +6

Multi-Agent LLM SystemsHuman Behavior SimulationAI Agent EvaluationAlgorithmic Trading
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Jun 20, 2024

Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.

Gordon Dai, Weijia Zhang, Jinhan Li +5

New York Univeristy, New York, New York, USA · University of illinois at Urbana-Champaign, Urnana, llinois, USA · New York University, New York, New York, USA +1

Large Language Model-Based Social SimulationMulti-Agent LLM SystemsEmergent Behavior in Multi-Agent SystemsHuman Behavior Simulation
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