Multiagent Systems · Cross-listed from cs.LG
CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection
Lin Wu, Shu Yao, Yuheng Wang et al. (+11 more)
Training multi-
agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful attempts without useful correction. We introduce CoEvoke, a cross-domain framework that evokes these interactions during trajectory collection. Controlled perturbations create recoverable difficulties, while restricted reference-based guidance directs investigation. The resulting
dataset contains 4,567 reconstructable trajectories over 4,130 software-engineering and
reasoning tasks, linking public interactions to evidence, decisions, and task-native outcomes. Separate experiments over 2,287 same-task pairs show feedback increases of 47.72-100 percentage points across seven perturbation groups; guidance has smaller, model-dependent effects. With identical training tasks, decision-record counts, and updates, perturbation-enriched judge training improves ACPBench-Hard success from 35.38% to 42.12% (+6.73 points). SWE verifier training gains 7.60-9.40 points over interface-matched untrained references. Workflow and post-training evaluations preserve task-solving
topology with unperturbed inputs and no private-reference guidance. These results support designing collection conditions to obtain corrective supervision that improves collaborative decisions with frozen solving
agents.
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All authors Lin Wu, Shu Yao, Yuheng Wang, Jingru Fan, Huatao Li, Yihan Li, Liu Yixuan, Cheng Liu, Jian Chang, Zeyu Zhao, Aohua Zhang, Huiying Zhao, Shuyuan Zhu, Chen Qian
Multiagent Systems · Cross-listed from cs.CR
DIBench: Benchmarking Decision Integrity of GUI-based Mobile Agents Under Deceptive Injections
Li Hu, Kanghua Mo, Yingbin Jin et al. (+2 more)
As GUI-based mobile
agents rapidly progress, rigorous safety evaluation of their autonomous decision-making in realistic app interfaces becomes increasingly critical. Existing
benchmarks mainly focus on execution-level anomalies using task success or hijack rates, but fail to capture the in-task goal deviation risk in multi-candidate selection tasks, where the decision may be steered toward an attacker-specified target, even in violation of instruction-implied constraints (e.g., cheapest/highest-rated), without any overt execution anomalies. We present DIBench, a decision integrity
benchmark for measuring this risk in mobile
agents. DIBench covers 7 commercial and 3 simulated apps with 5 task types. Under a threat model restricted to non-privileged UI content, we construct 8 deceptive injection probe instantiations that can steer critical selections without overt anomalies. The
benchmark includes 1,000 clean and 36,672 injected instances, with a unified protocol and integrity metrics for comparison. Experiments spanning 4
agent frameworks and 7 base models show that completion-based evaluation can overestimate
agent trustworthiness and miss decision-integrity risks: deceptive injections steer selections and shift early action policies, inflating completion rates and creating a misleading illusion of safety. Common defenses, including detection, image preprocessing, and
prompt reminders, yield inconsistent integrity gains. Overall, DIBench provides a unified, reproducible
benchmark to quantify the risk of in-task goal deviation in mobile
agents and enable comparable evaluations of safety defenses.
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All authors Li Hu, Kanghua Mo, Yingbin Jin, Qingqing Ye, Haibo Hu
Multiagent Systems · Cross-listed from cs.LG
Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?
Yi Xie, Zhanke Zhou, Yi Fan et al. (+3 more)
Multi-
agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor's next decision, when raw context is preferable, and when a stronger sender helps. Our framework, receiver-relative bounded coordination, expresses message utility as receiver gain minus protocol tax. Compression beats raw context when tax savings exceed losses from omitted information and decoder mismatch. Even Bayes-sufficient compression can fail when a bounded executor cannot use its surface form. A three-stage decomposition separates externalization, absorption, and action closure, explaining how errors remain after the correct content reaches the receiver. Under a single-crossing condition, sender upgrades help above a receiver-burden threshold. Across six
benchmarks, the same Qwen protocol raises ContextBench joint accuracy from $0.633$ to $0.775$ but lowers ToolSandbox from $0.889$ to $0.653$. Fixed-message replay reveals closure failures despite correct artifact recovery. These results guide an inference-time selector that improves the accuracy-cost frontier on the evaluated communication regimes.
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All authors Yi Xie, Zhanke Zhou, Yi Fan, Yong Ge, Bo Han, Bo Liu
Multiagent Systems
CollabFlow: Recursive Self-Improvement of Agent Collaboration
Xiao Huang, Mingda Zhang, Junming Zhang et al. (+5 more)
Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-
agent systems,
Agents refine one another within a task, and outcomes improve how they collaborate across tasks. However, existing multi-
agent collaboration leaves this loop open: collaboration is pre-defined at the operator level,
topology-only learning keeps verbatim exchange that propagates errors, and reward maximization on a system's own outcomes concentrates on a few teams. To address these challenges, we propose CollabFlow, an RSI system of Learned
Agent Collaboration: a trainable Collab-Director constructs teams of complete
Agents, a frozen executor runs them, and each round's outcomes retrain the director. Within each round, the edges of a collaboration graph carry protocols of Evidence-Conditioned Communication: a receiver adopts a differing answer only when the sender's evidence is stronger by a margin, so the director learns who communicates and how. Across rounds, we further propose Collaborative Trajectory Balance (CTB), a flow-based objective that credits each team once across its construction orders and targets a reward-proportional distribution over teams, so several good teams stay in play. We also bound how far this self-generated target moves between rounds, which shrinks as records accumulate. On twelve
datasets, CollabFlow outperforms all baselines and keeps improving across rounds. Code is available at https://anonymous.4open.science/r/CollabFlow-631E.
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All authors Xiao Huang, Mingda Zhang, Junming Zhang, Qiang Huang, Hanwen Zhang, Yue Dai, Zijia Wang, Xiaoying Tang
Multiagent Systems · Cross-listed from cs.CL
Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation
Haibo Jin, Xinjie Li, Najmeh Sadoughi et al. (+4 more)
Long-form subtitle translation requires
reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent terminology and style. Existing single-LLM methods are largely sentence-level, and multi-
agent systems often use static workflows that do not adapt to scene complexity or production context. We propose SMART, a Self-evolving Multi-
Agent system for long-foRm subtitle Translation. During test-time training, SMART builds persistent series-level
memory and translates a subset of sentences through a dynamic router and Mixture-of-
Agents layer with tools for terminology verification, subtitle constraint validation, and contextual
retrieval. A judge-refiner loop scores candidates and uses textual critiques to update
agent prompts and routing policies without retraining the underlying LLMs. During test-time inference, the evolved configuration translates the remaining series. We also introduce Subtitle Arena, covering 14 genres, 2–198 episodes per series, production years 1959–2023, and 15 target locales, together with SubMQM, a subtitle-adapted MQM framework with seven dimensions and 19 error categories. SMART achieves the best overall MQM score in all 15 Subtitle Arena directions, reducing average penalty by 6.9% over the strongest competing
agent system. On a public
benchmark, MuSC, SMART obtains the best model result across all 4
language pairs. SMART also achieves the best result in human evaluation with an overall score of 4.50/5.
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All authors Haibo Jin, Xinjie Li, Najmeh Sadoughi, Yang Liu, Yibo Wang, Zhu Liu, Yuzong Liu
Multiagent Systems
Recursive Organization Improvement: A Modeling Specification for Human–Agent Organizations
Zilong Wang
Stronger AI
agents do not automatically produce better organizations: teams must also learn which work arrangements to retain and when to reconsider them. We propose a modeling specification for recursive organization improvement and evaluate it through an executable checker, a public-record mapping, and controlled simulation. The specification connects actor-visible histories, organizational
memory, decision rights, and evidence-carrying change contracts. The mechanism study crosses six decision rules, three
memory conditions, and three task environments under fixed resource ceilings. In a stationary environment, cumulative evidence raises balanced evaluation's normalized net value per task from 0.45224 to 0.48007. Repeated reassessment's disadvantage relative to this comparator falls from 0.01702 with reset evidence to 0.00007 with cumulative evidence. A reversal of the best workflow reveals the opposite cost: indefinite retention delays adaptation, while a finite window restores eventual performance at a transition cost. In exploratory controls, matching trial acquisition and label reuse reduces the apparent reassessment gain from 0.00607 to 0.00191. Program replacement adds no stable benefit across the tested reversal times. The study identifies evidence acquisition, reuse, and timely updating as mechanisms that must be separated from evaluator replacement when assessing organizational improvement.
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Multiagent Systems
From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs
Kunal Jha, Max Kleiman-Weiner and Natasha Jaques
Large
language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM
agents are increasingly orchestrated to work together on complex problems. However, self-improvement methods typically optimize one system at a time, and multi-
agent frameworks often have every model work toward a shared goal. We ask a different question. When each
agent pursues its own reward, can self-improving LLMs learn from one another well enough to improve the whole population? We call this capability recursive social improvement. We study populations that revise skill files and choose whether, when, and whom to copy from. Independent search, learning from peers, and acting all share one
token budget. In controlled environments, established social-learning
algorithms benefit from peers, but three LLMs do not. They earn less reward per
token than solo learners, and explore too narrowly or run out of
tokens before acting. We then let the models write and revise their own skills. Observing peers changes how they improve, helping one model find useful skills sooner and another spend less on private search. Neither, however, outperforms independent learners at the same cost. Skills are copied, revised, and passed on, so one discovery can seed further search. Yet these exchanges concentrate the population around fewer independent discoveries. Together, these results show that LLMs can make learning more efficient by copying from peers, but not yet more effective.
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Multiagent Systems
PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems
Matthew D. Laws and Cristina Nita-Rotaru
Existing architectures for LLM-based multi-
agent systems (MAS) cannot reliably and efficiently solve multi-step tasks at scale: they struggle to support large numbers of
agents and concurrent tasks, tolerate failures, govern
agent interactions, and accommodate the diverse planning and execution patterns different tasks require. We present PANDA, a decentralized architecture that connects a large collective of heterogeneous, independently administered
agents, letting them discover each other's capabilities and self-organize into small specialized teams per task. PANDA scales by decoupling collective communication from team communication, allowing
agents to participate in multiple teams simultaneously, load-balancing tasks across the collective, and scheduling concurrent work within each
agent. PANDA further separates the underlying architecture from the orchestration strategy, supporting three planning and execution patterns (
star, chain, and mesh) that can be selected according to the structure and requirements of each task. PANDA detects infrastructure and orchestration failures and recovers affected tasks by dynamically replanning around failed components. Finally, to provide governance without a centralized service that would limit scalability, PANDA uses a web-of-trust model to constrain
agent interactions to established trust relationships. We evaluate PANDA on the HotPotQA
benchmark, demonstrating that it scales to thousands of
agents, assembles teams in milliseconds, matches state-of-the-art accuracy at up to 8x the efficiency, and sustains 100% task completion under faults where existing systems fail.
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Multiagent Systems
Absorbing State Phase Transitions in Multi-Agent Search
Wenwen Zheng, Yuzhe Yang, Helen Qu et al. (+2 more)
Nontrivial dynamics can emerge in large
language model (LLM)-based multi-
agent systems, and preliminary evidence exists that formalisms from statistical mechanics can be effective at modeling and predicting such behaviors. In parallel, designing multi-
agent communication
topology for optimal task-solving is an active research question. In this paper, we focus on predicting the success of multi-
agent search tasks using the formalism of absorbing state phase transitions. We first taxonomize search tasks into four types, informed by classical results in combinatorial search. We then theoretically derive a critical communication degree $d_c$, the minimum number of
agents each
agent can communicate with, above which incorrect hypotheses do not proliferate uncontrollably and the search enters the solved state. Finally, we evaluate frontier LLM-based multi-
agent systems on real-world search and discovery tasks, software configuration debugging and physical mechanism discovery, and find that agreement with theory is mixed. LLM
agents may not communicate with their neighbors and can develop strategies that are individually beneficial but limits the benefits of collaboration.
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All authors Wenwen Zheng, Yuzhe Yang, Helen Qu, Xin Eric Wang, Haewon Jeong
Multiagent Systems · Cross-listed from physics.soc-ph
Multi-agent discussion gains less when dissent is withheld
Chand Sahil Mansuri, Xin Wang, Mengying Li et al. (+4 more)
Multi-
agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on whether discussion improves accuracy or leads to an incorrect consensus. Here, we introduce a parsimonious model that explains when discussion improves accuracy and when it ends in an incorrect consensus, built from four behaviors repeatedly observed in LLM
agents: (1) withholding dissent, (2) internalizing a stated answer, (3) reconsidering after seeing dissent, and (4) correcting toward the correct answer. The model shows that discussion can overturn an incorrect initial majority only when the withholding rate $c$ is below a critical rate $c^* = \gamma/(\gamma + a)$, set by the net correction rate $\gamma$ and the internalization rate $a$. We estimate these rates from conversation logs with a
Bayesian method and place LLM teams relative to $c^*$. As the model predicts, the gain from discussion shrinks as withholding rises, across LLMs and on a hidden profile
benchmark, HiddenBench, and MedEInst. Instructing
agents not to withhold dissent increases this gain. Turning
reasoning off also increases the gain, because
reasoning raises the internalization rate $a$ and keeps
agents from reconsidering a minority answer. These findings reconcile the conflicting reports and identify when discussion outperforms majority voting.
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All authors Chand Sahil Mansuri, Xin Wang, Mengying Li, Bryan Acton, Rory Eckardt, Dhaval Patel, Sadamori Kojaku
Multiagent Systems · Cross-listed from cs.CL
Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
Liangyu Teng, Hengsong Liu, Juncen Guo et al. (+4 more)
The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heterogeneous teaming is often observed to be effective in practice, existing approaches lack complementarity metrics that are computable, interpretable, and optimizable, leaving team composition to rely on heuristics. We propose a heterogeneity-driven team selection framework that performs offline profiling to characterize individual capability along with two complementary signals: one captures decorrelation in error patterns to reduce co-failures, while the other measures divergence in predictive behavior to capture strategy diversity. We formulate team selection as a standardized quality–complementarity combinatorial objective and apply an efficient greedy search to select a small team from a candidate pool. Experiments across multiple
benchmarks demonstrate that our framework consistently outperforms quality-only baselines under controlled candidate pools and team sizes, establishing reusable selection principles for multi-LLM systems.
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All authors Liangyu Teng, Hengsong Liu, Juncen Guo, Jingyu Zhang, Yang Liu, Jing Liu, Liang Song
Multiagent Systems · Cross-listed from cs.CR
VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
Yurong Hao, Wen Zhou, Guowei Guan et al. (+3 more)
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous
agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it simultaneously introduces a systemic vulnerability:
adversarial evidence injected into a single
agent can be rationalised into a legitimate preference narrative, written back into
memory, and propagated to other
agents through interaction contexts. We term the local rationalisation process reflection laundering, and its system-wide escalation through collaborative reflection collaborative-reflection hijacking. Existing attacks on recommender systems, whether based on interaction-level data poisoning or text-level
adversarial perturbations, assume static pipelines and thus cannot exploit this recurrent, multi-
agent amplification pathway. To bridge this gap, we first conduct a controlled vulnerability analysis that establishes two exploitable properties underlying collaborative-reflection hijacking: reflective persistence and cross-
agent propagation. Then building on these findings, we propose VirusCascade, the first black-box targeted promotion attack that jointly shapes semantic and structural attack surfaces: the former ensures the target item is naturally rationalised as satisfying broad user preferences, the latter positions it for system-wide propagation. Extensive experiments on four real-world
datasets across diverse LLM-ARS architectures demonstrate that VirusCascade consistently achieves state-of-the-art targeted exposure under evaluated stealth constraints, reaching a mean E@20 of 0.384 and surpassing the strongest baseline by an absolute margin of +0.185.
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All authors Yurong Hao, Wen Zhou, Guowei Guan, Tiantong Wu, Fuyao Zhang, Wei Yang Bryan Lim
Multiagent Systems · Cross-listed from cs.LG
Multi-Agent Flow Matching with Decoupled Generative Guidance
Ruoyu Lin, Magnus Egerstedt and Fabio Pasqualetti
Generative modeling is widely used for producing diverse objects from complex,
multimodal distributions. However, its expressivity does not, in general, come with formal guarantees that the generated objects satisfy hard constraints or requirements. In multi-
agent generation, this problem becomes more challenging because a hard requirement can depend on multiple
agents, while each
agent may need to determine its own guidance input without relying on the simultaneously computed guidance inputs of other
agents. To this end, we introduce DeGG-Flow, a general framework for multi-
agent flow matching with decoupled generative guidance. By representing the generative process as a control-affine dynamical system, we develop guidance conditions for two classes of coupled requirements: shared requirements whose satisfaction depends on multiple
agents together, and private requirements associated with each individual
agent dependent on its neighbors. For both classes, we establish feasibility conditions and finite-horizon convergence guarantees. We further derive a Wasserstein bound that characterizes the distributional deviation induced by the guidance. We demonstrate DeGG-Flow on multi-
robot collaboration for crossing a spatial gap by reconfiguring the environment, and on multi-object scene generation with affordance requirements. Across both applications, DeGG-Flow directly generates objects that satisfy all corresponding hard requirements, including at team sizes unseen during training.
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Multiagent Systems
IMPACT: Modeling Socially Interdependent Movement in a Generative Multi-Agent Simulation of a Pompeian Household
Tianqi Liu, Nayoung Kim, Julia Sebastien et al. (+1 more)
Simulations of archaeological sites can make interpretations of past cultural practices observable and examinable. Generative multi-
agent simulations offer a bottom-up approach to modeling how people collectively moved through and used historical spaces. However, current
agents designed to simulate everyday life often plan and act independently, limiting their ability to capture how movement depends on others' actions. We introduce IMPACT (Interdependent Movement Planning through Inter-
Agent Constraints and Triggers), an architecture that uses culturally specific roles and obligations to define dependencies among
agents' activities and guide coordination. IMPACT connects socially gated milestone planning, wait-or-
prompt resolution, structured directive issuance, and directive integration. These mechanisms determine whether and when activities can begin or change as social conditions evolve, producing socially constrained and prompted movement as their primary observable outcome. We instantiate IMPACT in a five-hour simulation of a Pompeian dinner involving ten
agents across interdependent roles. Analysis of five simulation runs shows how social roles, responsibilities, and status relations shape household activities and spatial practices, as reflected in patterns of co-location, asymmetric waiting, co-movement, and social directives. In a controlled ablation evaluation, thirty-seven participants rated the complete architecture's behavior as more socially coherent and believable than that of two reduced architectures. Interviews with six archaeology experts highlighted historically plausible movement patterns and the simulation's potential to support archaeological interpretation, while identifying areas requiring stronger historical grounding for future work.
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All authors Tianqi Liu, Nayoung Kim, Julia Sebastien, Andrea Stevenson Won
Multiagent Systems · Cross-listed from cs.AI
Rational Clarification by Assistive Agents via Value-of-Information Reasoning
T. Duy Nguyen-Hien, Yee Whye Teh, Wee Sun Lee et al. (+1 more)
Users of
language-based assistive
agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request — risking misalignment with the user — or ask a clarifying question. Which option is the most safe and helpful? A common approach is to ask questions that minimize uncertainty about the user's intent until a threshold is reached. However, this neglects the impact of uncertainty reduction on downstream performance, the costs of asking versus acting immediately, and the possibility that users may provide corrections without being asked. To navigate these trade-offs, we introduce Rational Enquiry via Value-of-Information
Reasoning (REVOIR). REVOIR makes clarification decisions via inference-time
reasoning about the value-of-information of a question, which captures the expected improvement in task reward due to the answer received. In two assistive tasks — ambiguous question answering (CondAmbigQA) and preference-aligned household task planning (ADAPT) — we show that REVOIR achieves greater success with fewer questions than approaches based on prompting, chain-of-thought, fine-tuning, or information gain, improving preference satisfaction on ADAPT by 13-15% over a fine-tuned clarification policy while requiring no training and asking five times fewer questions. Furthermore, when the assistant can receive cheap user corrections after acting, REVOIR naturally infers that asking questions is not always efficient, demonstrating the adaptivity of our approach. In contrast, we find that vanilla
reasoning agents fail to adaptively clarify user requests, and request fewer clarifications as
reasoning effort increases.
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All authors T. Duy Nguyen-Hien, Yee Whye Teh, Wee Sun Lee, Tan Zhi-Xuan
Multiagent Systems
RAVEN: Receiver-Conditioned Action-Value Encoding for Finite-Alphabet Multi-Agent Communication
Shuwei Sun, Chenxi Wang, Jian Huang et al. (+2 more)
A message drawn from a small alphabet helps a teammate only if it keeps the distinctions that change that teammate's next decision. We show that scoring messages by action values averaged over the receiver's situation can erase exactly these distinctions, and we propose RAVEN (Receiver-conditioned Action-Value ENcoding), which trains a four-symbol, one-step-delayed channel to preserve each receiver's centered action-value profile within the receiver's own context. The sender never needs to know that context: the receiver decodes every symbol with its private information. We give two
estimators of this target. With a teacher, offline RAVEN selects the codebook that exactly minimizes an empirical conditional distortion and distills it into a frozen sender; we bound the resulting codebook-selection error and one-step decision loss. Without a teacher, online RAVEN aligns, inside a QMIX learner, the deployed symbol pathway with a training-only continuous reference that shares its routing. Against five recent communication methods on eight navigation settings, offline RAVEN attains the highest return in seven, and removing receiver conditioning forfeits 83% of its communication gain. Online RAVEN raises predator-prey capture success from 53.2% to 96.0% over the same QMIX backbone without communication, and on SMAC and MPE it attains the best mean normalized score of 14 methods, including methods that exchange kilobit messages. Every RAVEN message costs 2 bits, 12-1,024x fewer than those of NDQ, CACOM and ExpoComm on navigation.
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All authors Shuwei Sun, Chenxi Wang, Jian Huang, Weiyun Ru, Hui Cao
Multiagent Systems
PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
Zhanhua Pan, Xin Qin, Xiao Liu et al. (+3 more)
Power
markets are a natural testbed for multi-
agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids. A
market-clearing mechanism then determines dispatch and prices subject to power grid constraints and
market settlement rules. However, existing MARL environments typically focus on a single
market setting, implement simplified clearing mechanisms, or rely on CPU-based optimization solvers that slow large-scale training and limit the systematic study of bidding strategies and
market behavior. We introduce PowerMarketJax, a
benchmark suite for MARL across five power
markets: day-ahead wholesale, real-time balancing, ancillary services, peer-to-peer double auctions, and local flexibility. Each environment implements its own clearing, pricing, and settlement rules while providing a common framework for learning and evaluation. We find that learned bidding behavior depends strongly on the
market design: independent learners can miss better strategies when gains require many
agents to change together, when more profitable strategies lie beyond a region of lower profit, or when profits disappear as more
agents adopt the same strategy. PowerMarketJax implements both
market simulation and policy training in JAX, allowing the entire pipeline to run on the GPU with 1,024 X 1,200 parallelisms across both environments and
market participants, achieving up to 33X speedup over CPU-based baselines. Our open-source
benchmark is available at: https://github.com/powermarketjax/PowerMarketJax.
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All authors Zhanhua Pan, Xin Qin, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
Multiagent Systems
FlowMAS: Learning Multi-Agent Workflow Topology via Information-guided Generative Flow Network
Haitao Wang, Chenjing Liang, Haipeng Zhang et al. (+6 more)
Automated multi-
agent systems offer clear advantages over manually designed ones in scalability and adaptability, but existing workflow
topology methods still face important limitations. Search-based methods are often computationally expensive, textual-gradient-based methods rely on coarse-grained feedback, and existing generation-based methods are not well suited to discrete workflow
topologies with complex dependencies. To address these limitations, we propose FlowMAS, a multi-
agent workflow
topology method based on Generative Flow Networks (GFlowNets). FlowMAS models workflow generation as reward-guided flow over the
topology space and introduces three components: a GFlowNet-based
topology generation backbone, a curiosity-driven module for structure-aware exploration, and an information-guided optimization module for evaluating intermediate
topologies. Concretely, the curiosity-driven module encourages exploration of structurally novel workflows, while the information-guided module measures both the information contribution and the communication efficiency of different operators to favor more informative and effective collaboration patterns. Experiments on six
benchmark datasets with three LLM backbones show that FlowMAS consistently outperforms multiple baselines.
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All authors Haitao Wang, Chenjing Liang, Haipeng Zhang, Jiawei Hu, Sicheng Wang, Songzhu Mei, Chenglu Wen, Siqi Shen, Cheng Wang
Multiagent Systems · Cross-listed from cs.RO
Adversarially Robust Geometric Safety Certificates for Nonholonomic Robots Against Maneuvering Obstacles
Chandan Kumar Sah, Bazeela Banday and Jishnu Keshavan
Safe navigation against obstacles that can actively maneuver within bounded capabilities remains challenging: robust control barrier function methods typically treat obstacle actions as generic disturbances, while differential-game approaches are computationally expensive for online navigation. We propose an adversarially robust
geometric certificate that accounts for the worst-case effect of admissible obstacle maneuvers directly in the safe-set geometry through a closed-form contraction of the certificate parameters. The construction exploits a structural property of line-of-sight (LoS) certificates: the
robot and obstacle actions enter the certificate through a common state-dependent
geometric gain. This gain cancels in the worst-case comparison, reducing the differential game to a direct comparison between obstacle maneuvering capability and the weaker of the
robot's longitudinal and steering authorities. Instantiated on the parabolic certificate, the construction yields Adversarially Robust Dynamic Parabolic Control Barrier Functions (AR-DPCBF), for which we establish sufficient conditions for forward invariance of the contracted safe set against all admissible obstacle maneuvers under kinematic bicycle dynamics with bounded inputs. When the obstacle capability is unknown, a sliding-window
estimator supplies a high-probability upper bound, allowing the guarantee to be retained with the corresponding coverage probability. We further formulate soft and buffered variants to recover feasibility in dense environments. Simulations across obstacle capabilities, densities, and capability mismatch show substantial reductions in barrier violations and collisions and demonstrate that pointwise robustification of the barrier derivative cannot substitute for contraction of its geometry.
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Multiagent Systems
LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent–Network Design Perspective
Chao Hu, Yuan Guo, Guanlin Wu et al. (+3 more)
As large
language models (LLMs) evolve from standalone models into collaborative
agents embedded in physical systems, their
reasoning and execution are increasingly distributed across wireless edge nodes. In this setting, wireless networks are experiencing a paradigm shift from only providing data connectivity to supporting the multi-
agent reasoning workflow itself. The task performance of such network-constrained LLM-based multi-
agent systems (MASs) is jointly affected by the multi-
agent reasoning dependencies as well as the underlying network connectivity and edge resources. This coupling gives rise to various technical challenges, including the metric misalignment and message redundancy, state inconsistency and
topology mismatch, as well as resource limitation and trust discontinuity. To address these challenges, this article develops a novel joint
agent–network design perspective that coordinates decisions on both sides of the system. Specifically, we present the joint design of
agent–interaction scheduling and resource allocation, the message selection-transmission co-design, as well as the joint
agent–network
topology design and workload–resource allocation. Furthermore, we consider the network-verified provenance that is linked with
agent-side information-flow control to constrain how received information affects subsequent operations. An illustrative vehicle-to-everything (V2X) case study shows that jointly adapting
agent-side interaction decisions and network operations improves task completion under communication and edge-resource constraints, outperforming the conventional
agent-only and wireless-only separate designs.
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All authors Chao Hu, Yuan Guo, Guanlin Wu, Yueling Che, Han Hu, Jie Xu
Multiagent Systems · Cross-listed from cs.AI
Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization
Guoqing Zhang, Rafik Hadfi and Takayuki Ito
Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization. While classical queueing theory, specifically open Baskett–Chandy–Muntz–Palacios (BCMP) networks, provides an interpretable mathematical
topology for healthcare operations, analytical models rely on stationary assumptions and fixed routing
matrices that degrade under state-dependent real-world dynamics. Conversely, centralized reinforcement learning approaches struggle to accommodate the decentralized structure of hospital governance, where individual clinical departments function with localized observations, heterogeneous resources, and divergent operational objectives. In this paper, we present a Multi-
Agent Systems (MAS) framework titled Physics-Informed Multi-
Agent Coordination, which embeds empirically calibrated BCMP queueing
topologies as physical priors within a decentralized multi-
agent reinforcement learning architecture. Formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) under coupled resource constraints, our method enables autonomous departmental
agents to cooperatively negotiate patient routing and dynamic service scaling. To mitigate environmental non-stationarity without inducing excessive communication overhead,
agents exchange localized action fingerprints along network edges and optimize a spatially decomposed reward structure. Empirical evaluations driven by real-world MIMIC-IV patient trajectories indicate that this cooperative multi-
agent approach substantially reduces cumulative system delay compared to static Markovian approximations, heuristic dispatching, and independent multi-
agent baselines, while maintaining clinical safety constraints.
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Multiagent Systems · Cross-listed from physics.soc-ph
An LLM-powered Agent Framework for Heterogeneous Evacuation Behavior Modeling under a Moving Threat in a Public Plaza
Jian Ma, Runxin Yu, Tianyu Tang et al. (+1 more)
Modeling heterogeneous evacuation behavior under a moving threat is difficult because human perception,
memory, and evidence evaluation are not well captured by fixed rules. We propose a novel LLM-powered
agent-based framework to represent these internal decision processes. Each pedestrian
agent perceives a private symbolic ASCII view, maintains a
Memory-based Knowledge Graph derived solely from individual observations, and makes decisions through persona-conditioned
prompts under a common sampling configuration. A compressed decision context with stateless
memory preserves trial-and-error experience across turns while excluding
reasoning traces, and a validation engine separates behavioral choice from physical feasibility by executing routes only over observed terrain. We evaluated eight personality compositions in eight paired randomized blocks within a simulated public plaza. Usable-exit knowledge was strongly associated with evacuation success: 89.5% of
agents possessing such knowledge evacuated, compared with 1.05% of those without it. Personality compositions also differed in their evaluation of remembered threat evidence: the proportion of danger assessments varied by 0.265, while high-urgency, low-directness decisions ranged from 11.41% to 34.33%. After direct threat sightings, responses converged, with 99.6% of assessments classifying the situation as dangerous. Movement was selected in 99.8% of decisions. Overall, evacuation outcomes were strongly associated with information access, while evacuation time was jointly associated with spatial geometry, information, and affect. The framework provides an auditable approach to generating endogenous behavioral heterogeneity through persona-conditioned LLM
agents in crowd-evacuation simulations.
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All authors Jian Ma, Runxin Yu, Tianyu Tang, Xiaolian Li
Multiagent Systems
GitHarness: Git Init Your Harness Working Memory for Perpetual User Requirements
Zhibang Yang, Xinke Jiang, Yuxuan Liu et al. (+9 more)
LLM-based
agents increasingly collaborate with users on long-horizon tasks, accumulating evidence, code, and drafts through extensive search,
reasoning, and execution. As users inspect these results, they may supply missing information requirement completion, introduce new requirements requirement elicitation, or revise existing ones requirement shift. These changes often affect only part of the accumulated work, yet
agents may carry forward obsolete information or turn local revisions into global rewrites. Existing approaches clarify current intent without determining how prior work should change, or reuse execution histories under a fixed objective. We address this gap by formulating dynamic-requirement collaboration as joint requirement tracking and local update. We introduce GitHarness, a pluggable Git-style framework that organizes requirement states and their corresponding harness work states into a branchable version history. A trainable Git
Agent resolves requirement changes and selects a semantically compatible historical state. A unified version interface then restores that state and creates a new branch, enabling the underlying harness to exclude obsolete information, inherit compatible work, and focus execution on affected parts. The Git
Agent is trained through interface-level black-box reinforcement learning, with downstream harnesses and task-execution models kept fixed. We also construct MTAgentBench, a verifier-preserving
benchmark covering mathematical
reasoning, text-to-SQL, agentic search, software engineering, and research synthesis. Experiments demonstrate strong task performance alongside effective requirement tracking, preservation of valid work, and efficient execution.
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All authors Zhibang Yang, Xinke Jiang, Yuxuan Liu, Mingyu Zhang, Zhixin Zhang, Zhengxing Song, Yue Fang, Guohong Qiu, Ruiqing Li, Xu Chu, Junfeng Zhao, Yasha Wang
Multiagent Systems
Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions
Ondřej Kubíček, Viliam Lisý and Tuomas Sandholm
Most successes of superhuman game-playing
algorithms are in games with discrete actions, yet in auctions, robotics, sports, or trading, actions are nearly continuous. Prior techniques either rely on expert-designed discretizations or are sample inefficient. We present a scalable policy-gradient
algorithm for large sequential games with continuous or mixed discrete and continuous actions. It combines
magnetic mirror descent with a mixture of Gaussians reparametrization, trained via self-play. We show that it approximates
equilibrium in games where gradient descent fails. In sequential games, it outperforms neural fictitious self-play and matches or outperforms the final strategies of policy space response oracles with 3.5–5.5$\times$ fewer samples. In heads-up no-limit Texas hold'em, it performs on par with Slumbot.
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Multiagent Systems · Cross-listed from cs.RO
Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning
Ruixiao Xu, Wong Lik Hang Kenny, Zhiqian Liu et al. (+12 more)
We study reinforcement learning (RL) methods for cooperative multi-
agent Vision-
Language-Action (VLA) models. This problem is challenging because VLAs are pretrained on large-scale single-
agent data and therefore lack the fine-grained coordination skills required for inter-
robot collaboration. Supervised fine-tuning (SFT) on multi-
robot demonstrations partially bridges this gap, but its performance is bounded by the demonstration data and cannot improve from its own experience. We present a three-stage reinforced fine-tuning (RFT) pipeline for multi-
agent VLAs. First, initialization-aware data collection sweeps over initial configurations and invokes human demonstrations only when the pretrained VLA repeatedly fails, yielding robustness to initialization shift with reduced human cost. Second, offline credit-filtered tuning assigns credit to individual
agents and fine-tunes on per-
agent trajectories with positive advantage rather than on entire joint rollouts. Third, we find existing online RL for VLAs are less effective for hard multi-
agent tasks, which we attribute to noisy co-exploration and unstable updates. We instead use online latent-space fine tuning, which freeze the VLA and perform RL in its latent noise space. We evaluate our multi-
agent VLA with both $\pi_0$ and $\pi_{0.5}$ backbones across 11 tasks in RoboTwin, RoboFactory and real-world manipulation with two Franka
robots. Our multi-
agent VLA improves the average success rate by $+23.1\%$, $+16.4\%$, and $+44\%$ on RoboTwin, RoboFactory, and real-world tasks, respectively. Code available at https://anonymous.4open.science/r/mavla_rft-2BC0/.
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All authors Ruixiao Xu, Wong Lik Hang Kenny, Zhiqian Liu, Jianing Guo, Hanxiao Li, Kejian Shi, Shuning Zhang, Pu Feng, Yongjia Ma, Yuqing Ma, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Simin Li
Multiagent Systems · Cross-listed from cs.AI
Human-AI Collaboration: From Paradoxes to Patterns
Michael Weiss
Evidence shows that humans and AI systems perform better together, by collaborating, than alone. This paper examines two key design dimensions of human-AI collaboration (autonomy and initiative) and explores the collaboration patterns that they generate. Documenting these patterns starts with identifying the underlying problems and solutions, followed by examining the internal tensions within the problems. The paper uses a paradox perspective to analyze those tensions. It describes a process for surfacing the tensions and mapping the underlying paradoxes. It also illustrates how the pattern descriptions can be derived from mapping these paradoxes. Finally, the paper documents four human-AI collaboration patterns: Instruction, Delegation, Assistance, and Co-creation.
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Multiagent Systems
Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps
Sheryl Paul, Kexin Wang, Ruolin Li et al. (+1 more)
Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, heterogeneous preferences, and imperfect information. We develop an
agent-based model of a macroscopic weaving-ramp framework in which individual vehicles adapt their lane choices using an evolutionary game-theoretic update rule and altruism-based objectives providing a microscopic interpretation of the original Wardrop model. We prove convergence of the decentralized dynamics to the unique
equilibrium predicted by the macroscopic theory. Beyond reproducing aggregate
equilibrium behavior, the framework enables the study of deployment-level questions that cannot be addressed by static analysis. Simulation results demonstrate close agreement with the macroscopic predictions while revealing how convergence rates, adaptation to changing traffic conditions, heterogeneous altruism levels among CAVs, and imperfect state information influence system performance and the distribution of altruistic burden across vehicles. These results provide a bridge between
equilibrium traffic theory and decentralized mixed-autonomy deployment.
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All authors Sheryl Paul, Kexin Wang, Ruolin Li, Jyotirmoy V. Deshmukh