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新论文 · v1arXiv:2611.12314

A Modular Perspective for Modeling MARL Systems

Tiziano Maisonhaute, Jean-Christophe Soulié, Fabien Michel

原文摘要

The growing complexity of Multi-Agent Reinforcement Learning (MARL) has fragmented the literature, obscuring underlying architectural choices behind diverse implementations. This fragmentation makes it difficult to evaluate critical modeling dimensions in isolation, identify how components affect the resulting system dynamics, and compare, reuse, or combine contributions across the literature. This paper proposes a modular perspective for modeling, analyzing, and implementing MARL systems. We decompose them into eight functional modules: agents, environments, learning, reward modeling, training and execution modes, communication, modeling of other agents, and evaluation criteria. For each module, we clarify its role and review representative approaches. We instantiate this decomposition in MARLKIT, a framework whose modules are explicit and interchangeable. MARLKIT demonstrates how the proposed perspective supports transparent experimental assumptions and the controlled variation and combination of MARL mechanisms. Experiments illustrate its ability to prototype, compare, reuse and combine modules while making differences between configurations explicit. Overall, the paper provides a structured perspective on MARL modeling and implementation, offering a common language for making modeling choices explicit, supporting clearer descriptions of MARL approaches, and supporting more controlled experimentation through the systematic isolation and combination of MARL mechanisms.

cs.LG · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12270

Modelling socio-economic effects of the pandemic in a metropolitan area

Jan E. Snellman, Rafael A. Barrio, Cecilia I. Ventura, Tzipe Govezensky, Kimmo K. Kaski, Maarit J. Korpi-Lagg

原文摘要

One of the most notorious measures taken by authorities in every country trying to prevent the uncontrollable spread of the Covid 19 pandemic was to impose severe measures to diminish the mobility of people. In recent publications we proposed a mathematical model to address the consequences of these measures and to predict the infections under different scenarios, for various different countries. In this paper we extend the model, called the BTH--SEIRS-model, to deal with the reduction of mobility in an urban area. The chosen region was the densely-populated metropolitan area around Helsinki, the capital of Finland. This choice obeyed to the fact that there are precise data related to mobility from a mobile telephone company, and that this region was completely isolated from the rest of the country for a short period during the pandemic. We also used public transport data from railways and the accommodation occupation rates in the area of interest. We show that the BTH--SEIRS model is capable of simulating the real infection data as well as the mobility-related economic activity, exhibiting the main trends present in the three indicators used for comparison. Our calculations allow us to highlight the difference of the behaviour of the pandemic in a totally different spatial scale, and to discover the hidden factors that govern the behaviour of people who are forced to commute every day from home to work.

physics.soc-ph · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12269

Agent4Crypto: A Dual-Reflective and Market-Feedback-Aligned LLM-Based Multi-Agent System for Cryptocurrency Trading

Guangyan Gan, Jiahao Li, Jiashun Wang, Lin William Cong

原文摘要

Cryptocurrency markets provide a natural testbed for autonomous AI agents because they are digital-native, high-frequency, and machine-speed environments, yet existing methods often struggle with non-stationary market regimes, opaque decisions, and fixed aggregation rules. In this paper, we propose Agent4Crypto, a dual-reflective and market-feedback-aligned multi-agent system. The framework coordinates specialized agents for data collection, analysis, risk assessment, reflection, and trading, synthesizing heterogeneous signals from prices, indicators, news, and charts in an interpretable decision pipeline. Our central design is a trader-centric decision mechanism: three style-specific trader agents, Aggressive, Neutral, and Conservative, generate candidate actions and natural-language rationales from the same market context, while their influence is learned rather than manually fixed. A PPO-based gating policy dynamically allocates trader weights through a dual-reflective feedback process. One reflection stream evaluates post-trade decision quality from realized portfolio outcomes and distills reusable lessons into a Financial ReasoningBank for retrieval in subsequent trader prompts, while the other estimates trader-level counterfactual contribution, indicating which style has recently aligned better with market movements. The PPO state combines Counterfactual Contribution Memory with market features, previous trader weights, and current trader actions, so trader influence adapts across bull, sideways, and bear regimes. Experiments on BTC, ETH, and XRP show that Agent4Crypto improves total return and Sharpe ratio over traditional strategies, machine learning and reinforcement learning baselines, and existing LLM-based trading systems, demonstrating the value of interpretable trader competition and market-feedback-driven adaptation in volatile cryptocurrency markets.

cs.MA · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.09128

SocialGym: Evaluating LLM Social Intelligence through Verifiable Multi-Agent Games

Keyu He, Xuhui Zhou, Maarten Sap

原文摘要

Large Language Model (LLM) agents are increasingly deployed in multi-agent social settings where they must cooperate, negotiate, and adapt to other agents. Measuring these social skills is hard because most social interactions offer no objective ground truth. Evaluations often fall back on LLM judges, which are costly, subjective, and noisy. We introduce SocialGym, an environment of 22 multi-agent social games, from matrix games to hidden-role deduction, with verifiable outcomes determined by game rules. We benchmark ten LLMs across all games and roles using an Elo tournament. Our results show that no model is best in every game, and rankings shift across game families and sides. Secret keeping correlates more strongly with theory-of-mind and privacy benchmarks than with general knowledge benchmarks. Half of the models give away their side's secret in Chameleon and Spyfall, and the smallest models are not reliably better than chance at voting for hidden opponents in most hidden-role games. Finally, we examine whether models can improve their play by reflecting on self-play and writing strategy playbooks, finding mixed results across models and games. SocialGym provides verifiable measures for diagnosing LLM social capabilities across settings and roles.

cs.CL · cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.37022

Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization

Guoqing Zhang, Rafik Hadfi, Takayuki Ito

原文摘要

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 \emph{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.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.22212

A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification

Aleesha Zainab, Asifullah Khan, Muhammad Ahmed Khalid, Faheem Ullah Khan

原文摘要

Organizations in critical national infrastructure sectors must assess heterogeneous documents for sensitivity before routing or storage. Manual assessment is slow, inconsistent, and unscalable. Extending our prior leakage-controlled benchmark, BERT established the top single-encoder baseline (89.14% accuracy, 89.33% F1-score under 5-fold cross-validation on the Strategic 16K corpus). However, transformer baselines suffer from a structural limitation: fixed input length truncation discards evidence beyond the retained window-precisely where sensitive cables tend to be longest. We present Channel-Boosted MAS (CB-MAS) and instantiate it as IC-MAS (Iterative Consultation Multi-Agent System) to solve this without long-context computational costs. A Channel Critic Agent learns document-adaptive trust weights governing Gated Channel Boosting between two first-window encoders, while paired Consultation Agents iteratively exchange belief states to reconcile evidence from the beginning and end of long documents. IC-MAS holds computation constant regardless of document length by reconciling fixed windows in a compact representation space. Ablation studies show critic-controlled Channel Boosting provides the bulk of accuracy gains, while consultation recovers recall without precision collapse. Critic-Controlled Gated Channel Boosting with Max-Pool fusion and Blackboard Adaptive Consultation achieves 90.72% accuracy, 91.23% F1-score, 92.01% sensitive recall, and 90.46% sensitive precision, using about 54% less average computation than a fixed-round baseline. Gains over the single-encoder baseline are statistically significant (McNemar's test, p less than 0.000001; paired t-test). We include LIME/SHAP explainability, multi-agent evaluation, and an honest accounting of limitations.

cs.CL · cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.41132

When Disclosure Is Compulsory: Evaluating Ride-Hailing Rebalancing Policies with Self-Interested LLM Driver Agents

Xinyu Wang, Andres Fielbaum

原文摘要

The EU Platform Work Directive, to be transposed by December 2026, requires ride-hailing platforms to state in plain language the main parameters of their automated decisions and how a worker's behaviour affects them. Such rules are exactly what a large language model (LLM) can read, so drivers will be able to let one decide for them. We compare two main families of rebalancing policy once their rules are public: a reactive policy (R) that sends idle drivers to cover requests just lost, and an anticipatory policy (A) that plans moves on a demand forecast. Each driver is an LLM agent maximizing its own net income, given no rules (N) or the rules at the directive's level of detail (L). We test two LLMs, Claude Haiku 5.5 and Jev, which understand the rules equally well, in a three-zone model and in Manhattan. With the rules disclosed, drivers follow the platform more closely: in Manhattan, the share of relocation offers they decline falls from about four in five to one in four under both policies. Yet the effects differ by policy. For the platform, disclosure costs R 3.7% of its profit in Manhattan, as R now pays for moves drivers used to make for free, while A's profit is unchanged; the two policies, which tie on profit under full compliance, end 6.1% apart. For drivers, disclosure raises income in the small model under both policies and LLMs; for users, it raises the service rate there by 10 to 18 percentage points. In Manhattan, self-interested drivers also make A a win-win-win against full compliance, raising driver income, platform profit and service rate by 10 to 12% each. Rebalancing policies that perform equally well under full compliance can thus show opposite results under mandatory disclosure. The study offers a framework to evaluate such policies under the new requirement and is, to our knowledge, the first to do so with LLM agents.

cs.MA · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11867

Competing Influence in LLM Populations: Budget Timing and Baseline Drift

Mehwish Nasim, Nicolas Fay

原文摘要

When competing campaigns target a population of large language model (LLM) agents, observed opinion changes may reflect both campaign influence and changes that would occur without intervention. We study how the timing of campaign expenditure and the population's own evolution shape outcomes in a network of persona-conditioned LLM agents that exchange and revise opinions in natural language. A proponent and a challenger are tasked with generating opposing messages under equal delivery budgets, while a separate stance probe measures opinions without influencing the simulation. Across eight claims, we compare single-campaign and competitive conditions against matched no-campaign runs, and evaluate all nine combinations of early, sustained, and late spending on a reference topic. Baseline stance drift varies substantially across claims and, on some, accounts for a sizeable fraction of apparent campaign effects. After accounting for this drift, the direction of observed campaign-associated changes aligns more closely with the claim's epistemic standing than with the campaigner's assigned role. Uninfluenced populations also consistently lose stance diversity, demonstrating that directional opinion change and convergence are distinct phenomena. In networks with two opinion-segregated communities, campaigns primarily reduce differences between communities rather than diversity within them. LLM campaign agents do not always argue for their assigned positions, particularly when these conflict with established evidence. Finally, campaign outcomes depend on the opponent's expenditure schedule: unlike a matched numerical bounded-confidence population receiving interventions at the same times and locations, the LLM population exhibits a payoff structure with no pure-strategy equilibrium.

physics.soc-ph · cs.CL · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08950

AIM: Adaptive Interaction in Multi-Agent Debate for Multimodal LLM Inference

Wei Fan, JinYi Yoon, Bo Ji

原文摘要

Multi-Agent Debate (MAD) improves reasoning on multimodal tasks, but its accuracy and token efficiency depend on how agents interact during debate. A largely overlooked design choice is which interaction modalities agents use to exchange evidence. Existing methods rely on fixed text- or graph-based interaction, which fails to convey fine-grained multimodal evidence and thus degrades accuracy while inflating token cost due to verbose descriptions. To address this, we propose Adaptive Interaction in Multi-Agent Debate (AIM), an adaptive routing framework that dynamically selects the optimal modality combination for inter-agent interaction in multimodal MAD. Beyond text and graph, AIM introduces task-dependent modality interaction, where agents exchange targeted regions of interest from the task-specific multimodal input, such as image regions, audio segments, or spatio-temporal video clips. To determine the best combination for each instance, AIM first generates a structured baseline response and extracts interpretable routing features that capture which evidence the instance requires, how confident the baseline agent is, and how much new evidence each modality would add beyond the baseline response. A lightweight router then uses these routing features to select a modality combination that avoids both under-fusion (i.e., agents miss decisive evidence) and over-fusion (i.e., redundant modalities inflate token cost) risks. Across eight benchmarks spanning three multimodal task families, AIM achieves the highest accuracy. Combining modalities improves accuracy by up to 6.4% over the best single-modality baseline, and adapting the combination per instance adds up to 4.2% over the most accurate fixed combination of each benchmark with up to 53.4% fewer tokens.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11842

A Benchmark and Layered Evaluation Protocol for Evidence-Grounded Shared Belief Updating in Connected Robotics

Ranulfo Bezerra, Kimitoshi Yamazaki

原文摘要

Connected physical systems fuse observations from robots, IoT sensors, and edge-AI modules into a shared account of the world, yet most evaluations score only the final answer or action and cannot tell whether a method attributed the evidence, updated its belief, handled contradictions, and justified its decision. We argue that shared reasoning in Connected Robotics should be evaluated as an evidence-to-belief trajectory, and introduce the Connected Robotics Evidence Graph (CREG) Benchmark to make that trajectory measurable. CREG comprises 350 deterministically generated scenarios across seven regimes (justified revision, belief preservation, relation association, contradiction onset and resolution, stale/delayed evidence, and source reliability), each exposing a normative posterior reference at every evidence prefix, validated by an independent log-odds checker and screened by a blinded expert audit. Nine representation conditions separate relation content from serialization, ordering, and metadata. Evaluating four open-weight instruction models and six transparent fusion baselines yields three findings. First, entity grounding accounts for a substantial part of the weak-model deficit. Second, deleting semantic relations costs 15.3 points of strict belief-state accuracy on relation-dependent scenarios (Holm-corrected p<0.001), but an information-matched structured list outperforms the full graph and shuffling the graph does not hurt: relation content matters where required, while graph serialization confers no consistent advantage. Third, belief accuracy is strongly non-monotonic along the evidence sequence, with mid-trajectory failures that endpoint-only evaluation cannot detect. All scenarios, references, prompts, metrics, and analysis code are released for offline, hardware-free reproduction.

cs.RO · cs.AI · cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11720

ShadowBench-MCP: Measuring Shadow Invocations in MCP-Style Tool Routing

Eyhab Al-Masri, Wenjun Yang

原文摘要

LLM agents increasingly act as routers for tools, APIs, and software-supply-chain workflows, making an execution path - rather than generated text alone - a security-relevant model output. We study shadow invocation: a task-preserving request variant that causes an LLM router to select a riskier execution path, potentially skipping validation, provenance, staging, redaction, or audit controls even though the request does not ask for a malicious action. We introduce ShadowBench-MCP, a benchmark and metric suite that separates task preservation (TPSS), operational pressure (OPSS), and route-geometric drift (RGAS), and combines them in the pre-execution SODS score. In a focused policy-ablation study using a Groq-hosted qwen/qwen3-32b router, 120 routing decisions yield 96 original-versus-variant comparisons across six security-sensitive workflows. Efficiency-first routing produces a 29.2% shadow-invocation rate and 26.7% safety-tool skip rate. Policy-aware routing reduces these to 4.2% and 1.7%, respectively, corresponding to an 85.7% relative reduction in shadow invocations. Pre-execution signals distinguish shadow routes with ROC AUC up to 0.94. These results are early, single-router evidence of a policy-dependent routing-layer security effect, not a claim of universal vulnerability.

cs.CR · cs.AI · cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11621

ODSA: Orchestrated Discovery and Self-Aggregation

Macardle Meng, Haomin Zhang

原文摘要

Test-time scaling improves the accuracy of a language model's answers by spending more computation at inference time. Established methods do so either sequentially, revising a single solution over several passes, or in parallel, generating several independent solutions and selecting among them. We present ODSA (Orchestrated Discovery and Self-Aggregation), an LLM-directed scaling framework in which an orchestrator LLM is given existing test-time scaling methods as actions --- discovery of a new independent solution, comparison of existing solutions to build a better one, and acceptance of a proposed answer, which triggers its verification --- and directs stateless workers to carry them out. The orchestrator does not solve the problem itself: it allocates computation, feeds the results of earlier actions into later ones, and takes every action from a single context, so that each decision is informed by everything tried before. On Humanity's Last Exam, ODSA improves accuracy by 4.7 to 18.0 points across three models over a single direct call to the model with no test-time scaling. On LiveCodeBench, where a single direct call already solves most problems, it improves accuracy by 0.6 to 14.0 points.

cs.AI · cs.CL · cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.21828

Implicit Credit Learning for Sparse-Reward Multi-Agent Reinforcement Learning

The Viet Bui, Tien Mai, Hong Thanh Nguyen

原文摘要

When a cooperative team receives feedback only at the end of an episode, a single outcome must guide many agents' earlier actions. Learning a dense team reward helps, but its decomposition does not automatically give each agent a useful policy update. We introduce IMAP, an online method that fits a shared Q/V reward model by comparing completed episodes according to their observed terminal outcomes. Local Q/V residuals provide agent-specific rewards; the same normalized weights combine them for a centralized critic and scale local advantages for decentralized PPO actors. We characterize when these local updates follow the learned team objective. With full returns and frozen reward heads, a sharp bound relates their gradient error to variation in future Q/V compatibility; an exact identity identifies additional continuation terms for general GAE. In experiments with terminal-only feedback, IMAP's local-actor configuration attains the highest reported mean among the compared methods on all nine SMACv2 scenarios and four MAMuJoCo tasks, and on two of three SMACLite scenarios. Controlled relay interventions confirm the predicted changes in actor credit while holding the learned team reward fixed. The gradient result concerns the fitted objective; benchmark returns measure performance on the original task outcomes.

cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00627

Autonomous Analysis of Thermoelectric Materials Big Data by Multi-Agentic AI and Construction of a Knowledge Base and Specialized AI

Yusuke Hashimoto, Takaaki Tomai

原文摘要

Large databases of paper-derived experimental materials data hide essential information on materials. Their analysis is demanding, large-scale and complex, making manual knowledge extraction very difficult. To address this challenge, we developed Multi-Agentic AI (MAA) for automated, autonomous analysis of materials experimental big data and a specialized AI built on the obtained knowledge. MAA's nine role-specific AI agents, under predefined instructions and quality checks, autonomously perform data extraction, compositional feature generation, machine-learning modeling, feature-importance analysis and knowledge-base recording, iterating while inheriting previous knowledge. Delegating numerical processing to code makes large-scale data tractable; recording every choice and result enables later tracing to sources. In four thermoelectric-data experiments varying theme and cycle counts, the knowledge graph grew: knowledge clustered by theme and vocabulary was shared across themes. The specialized AI, searching and reading these records, answered materials questions citing sources. Retrospective verification also revealed errors, traced through the records to their sources.

cs.AI · cond-mat.mtrl-sci · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11266

ErosionBench: Probing Alignment Decay in Enterprise Multi-Agent Systems

Riya Bharathwaj, Akshara Prabhakar, Roshan Ram, Avni Kothari, Sarah Tan, Huan Wang, Silvio Savarese

原文摘要

As enterprises delegate consequential work to multi-agent LLM systems, benign-looking requests can escalate into unsafe actions as they propagate across sessions through poisoned shared state, long-context documents, and trusted peer messages: no single message is malicious, yet the assembled tool action is. Prior benchmarks isolate a single agent under prompt injection or score multi-agent attacks by their final output, leaving cross-session erosion of shared state out of scope. We introduce ErosionBench, an environment-grounded benchmark that executes such attacks and verifies alignment decay at the process level, scoring the realized tool call against authorized intent and provenance with deterministic contracts, not output text. Each scenario is authored as a deterministic state graph before any prose is rendered, so every state change traces to a known cause. It spans four enterprise domains and four decay mechanisms across 1,728 scenarios with matched benign controls, labeled against the OWASP Top-10 for LLM Applications, plus 198 image-based attacks, each paired with a text equivalent, so a defense must survive the same attack in either channel. Perimeter defenses suppress attacks only by over-refusing 54-92% of legitimate work, so a low attack-success rate can conceal severe over-refusal. As a reference defense, we propose a calibrated Meta-Auditor pairing synchronous state checks with asynchronous trajectory monitoring; on a family-disjoint 906-scenario held-out split it reaches 98.5% recall at a 2.9% false-positive rate. Since it monitors trajectory, not surface content, its detection is channel-invariant: the strongest auditor flags image-delivered attacks about as reliably as their text equivalents (recall gap $\leq$2 points per model). Across GPT, Gemini, and Claude auditors, calibration keeps false positives low while recall tracks model capability, strongest for Claude.

cs.CR · cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11234

LoMASA: A Local Multi-Agent Sales Assistant with Confidence-Gated Delegation for B2B Quoting

Yu Liu, Xingbang Du

原文摘要

Frontline sales staff spend much of their day re-coordinating quotes after customers change quantities, ask for discounts or new payment terms, or costs move. We present LoMASA (Local Multi-Agent Sales Assistant), a training-free multi-agent system that runs entirely on one desktop computer. A 35B mixture-of-experts model (System-1) makes each decision in a single forward pass and reads its confidence from the probabilities of the answer options. A 27B dense model (System-2) writes customer replies and escalation briefs and is consulted when System-1 is unsure. Code computes every price, margin and option, and hard pricing rules cannot be overridden. On a test split of 500 simulated episodes with 1512 decisions built from public supply-chain data, the salesperson has to decide only 19.5% of decisions: 38.0% are settled automatically, with a median latency of 2.67 seconds, and 42.5% are escalated to a manager by the hard rules. On the automatically settled decisions LoMASA is as accurate as a rule baseline (0.894 vs.\ 0.894) and makes fewer commitments whose margin later falls below the floor (2.61% vs 4.53%); the price is additional escalations to the manager. Option probabilities are well calibrated without any training, unlike confidence stated in words by the same model. We also report where the design does not help: the 27B model rarely changes a decision.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11212

Deterministic Narrative Gravity: Bridging Theory of Mind and Historical Simulation through ABM-LLM Architectures

Alicia Colmenero-Fernández, Lidia Ortega Alvarado

原文摘要

Historical social simulation faces a fundamental reproducibility challenge: the stochastic nature of large language models (LLMs) introduces a digital butterfly effect that prevents exact replication across multi-turn runs. Furthermore, the inability to control low-level server parameters in commercial LLM hosting environments severely limits reproducibility. To overcome this opacity, we present a deterministic multi-agent framework coupling LLMs to an Agent-Based Modelling (ABM) pipeline grounded in Theory of Mind (ToM) and Cognitive Archaeology, and validate exact artifact identity within local, pinned execution environments using SHA-256 hashing. Run locally at temperature zero with clean per-call model reloads, the system injects differentiated cognitive states per turn through dissonance-driven behavioural modes and gated internal voices. Evaluating seven model configurations spanning 4B to 31B parameters against an ablated baseline reveals clear operational boundaries. Small models ($\leq$8B) suffer from context capacity overload, producing repetitive, specular dialogue loops. However, at $\geq$12B parameters, the ABM scaffolding breaks the monotonous response patterns seen in ablation. Crucially, these scaffolded models exhibit spontaneous diegetic expansion: they coherently expand scenario lore through tactically motivated, anachronism-free details rather than escalating into aggression. By resisting the structural pull toward premature scene closure, scaffolded sessions sustain an open terminal Narrative Vector ($V_N = \mathrm{Open}$ in 12/21 overall; 9/9 in official $\geq$12B configurations) whereas unscaffolded ablation never does (0/21). This framework provides a transparent, auditable benchmark architecture for virtual archaeology, with the full pipeline released as open-source software to enable independent replication.

cs.MA · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11064

GARP: Real-Time Generative Agents on Local Hardware in a Game Engine

Piero Molino, Patrick John Chia, Federico Bianchi, Jonathan P. Chen, Paul Szerlip

原文摘要

Generative agents are game characters whose plans, conversations, and memories are written on the fly by a large language model (LLM). The original Generative Agents demonstration used cloud inference in a custom simulation. We present GARP, a platform for real-time generative agents inside Godot, and describe the Character Behavior System used in GARP 2. The complete loop runs on local hardware using a 3B-parameter open-weight model and no cloud service. Alongside the original planning, memory, and reflection, GARP gives each agent an emotion system driven by LLM appraisal, four graded personality axes drawn from the Big Five that set simulation parameters as well as prompt text, six needs whose urgency grows over time, and pairwise relationships updated after every conversation. The system narrows 50+ candidate actions to typically 3-8, constrains model responses with JSON schemas, and surfaces important memories in decision prompts. A layered optimization strategy (an adjustable game clock, decisions taken ahead of need, multi-tier caching, prompt compression, and fully asynchronous execution) keeps the game responsive while the model thinks. In a nine-scenario evaluation, 25 of 27 runs passed log-based checks. Both failures occurred in the birthday party scenario, where one guest omitted the event from their daily plan.

cs.AI · cs.HC · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.05516

Distributed Learning of $α$-Potential Functions in Strategic Multi-agent Systems

Yifei Chen, Chinmay Maheshwari

原文摘要

We study the problem of computing the tightest \(α\)-potential approximation of a general-sum game over continuous action spaces, within a prescribed class of potential functions and when each player has access only to its own utility function. The difficulty is twofold: the approximation error involves a worst-case search over an infinite set of unilateral deviations, and the required utility information is distributed across players. For a linear-in-parameters potential class, we use an exact finite-tuple reformulation that separates the problem into a global outer search over deviation tuples and distributed convex inner problems. We develop a primal--dual inner oracle tailored to this structure and establish a uniform one-sided accuracy guarantee. This oracle can be combined with global outer search to obtain an end-to-end guarantee on the outer optimization error. We also develop a projected zeroth-order outer method as a computationally lighter alternative for higher-dimensional problems. Numerical experiments illustrate the accuracy--computation tradeoff between the two outer-search methods and show that the proposed optimization framework can improve upon analytical \(α\)-potential constructions.

cs.MA · cs.GT · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11040

Advancements in Cloud-Native Systems: A Systematic Mapping Study of Reliability and Scalability across Cloud-Native, Edge-Native and AI-Enabled Systems

Shrinivass Arunachalam Balasubramanian

原文摘要

Cloud-native systems now extend to the network edge and host artificial-intelligence (AI) workloads, including AI agents, but evidence on their reliability and scalability is scattered and uneven. We report a PRISMA 2020 systematic mapping study with an evidence-graded synthesis. Five logged OpenAlex searches on 11 October 2026 covered cloud-native systems, edge AI, agentic AI for operations, reliability of agentic applications and physical AI; 189 records from an earlier exploratory search were added separately. After 301 duplicate removals and an automated peer-review venue rule, 4,611 records were screened against written criteria and a five-level relevance scale. The review includes 1,794 primary studies from 2017-2026 and 95 secondary studies, with every decision released in Dataset S1. Each primary study was assigned to a 13-category taxonomy and graded as simulation, prototype or testbed, production data, production deployment, or not determinable from the abstract. Of 1,003 studies with a determinable level, 66.2% used prototypes or testbeds, 18.0% simulations and 15.8% production data or deployments. A 93-study synthesis core, appraised from indexed abstracts, shows production deployments of cascading-failure prevention, trace- and graph-based root-cause analysis and predictive autoscaling, and production-trace evaluations of cold-start mitigation. Edge reliability and physical AI rely mainly on simulations and testbeds. AI-agent reliability uses tool-grounded diagnosis, explicit safety properties, trace-driven repair and workload-aware scheduling, while empirical studies show that agent frameworks remain fragile. Conclusions are limited to the documented corpus. The principal gaps are production evidence for edge-native systems, shared resilience benchmarks and dependable agentic operations.

cs.SE · cs.AI · cs.MA · cs.NI · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10984

Network Contention State Aware Dynamic Optimizer for Accelerator Collective Communication Libraries

Rajat Bisht, Tevfik Kosar, Anthony Skjellum, Gene Cooperman, MD S Q Zulkar Nine

原文摘要

Collective Communication Libraries (CCL) are critical for large-scale distributed AI/ML training, yet their configurations are determined by static heuristics that do not account for real-time network conditions. Our empirical study shows that these heuristics perform near-optimally in contention-free environments, but degrade substantially under background traffic. To address this, we present a cooperative multi-agent run-time optimizer for accelerator collective communication. Our approach employs a two-agent architecture: (i) detector- a Long Short Term Memory(LSTM) recurring neural network fed classifier head with a sequential probability ratio test that distinguishes sustained contention from transient spikes; and (ii) selector- a configuration selector built on a Dueling Double DQN (D3QN) with Conservative Q-Learning that adapts configuration parameters in response. We implemented this dynamic tuner over NCCL, a widely used CCL developed by NVIDIA. When evaluated on Chameleon Cloud's isolated bare-metal cluster, this dynamic tuning approach reduces AllReduce latency by 36% over NCCL and 32% over a fully converged AutoCCL. We further evaluated it across six real AI workloads spanning convolutional networks, transformers, and recommendation architectures. Our multi-agent tuner approach delivers up to 65% speedups on Delta cluster with NVIDIA A100 GPUs and Slingshot-11 fabric internode connectivity.

cs.DC · cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.00449

Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout

Amirhossein Dezhboro, Fateme Maleki, Arman Adibi, Erfan Amini, Jose E. Ramirez-Marquez

原文摘要

We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose Gradient Tracking with Probabilistic Edge Dropout (GT-PD), which combines a universal self-centred projection that clips incoming messages to a radius-$τ$ ball around the receiver with a fully decentralized probabilistic dropout rule driven by a threshold-gated, dual-metric trust score in the decision and tracking channels. These identity-free defences bound adversarial perturbations while preserving doubly stochastic mixing. The projection preserves the honest network average exactly in every realization, even when honest messages are clipped. Under complete Byzantine isolation ($p_b=0$), GT-PD converges linearly to a neighbourhood of order $ασ^2/(μn_{\mathcal H})$ plus higher-order network terms, matching centralized stochastic gradient descent to first order in the step size. We also give a radius computable from problem data that ensures honest messages remain unclipped with high probability. Under partial isolation ($p_b>0$), a leaky variant, GT-PD-L, controls accumulated tracking perturbations and converges linearly to a bounded neighbourhood governed by stochastic variance and the clipping-to-leak ratio, under explicit conditions on the step size, leak, and aggregate Byzantine mixing weight. With two-tier dropout and $p_h=1$, Byzantine isolation adds no variance to honest consensus dynamics. MNIST experiments under Sign Flip, ALIE, and Inner Product Manipulation attacks show that GT-PD-L outperforms coordinate-wise trimmed mean by up to 4.3 percentage points under stealth attacks.

cs.LG · cs.MA · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.26302

Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems

Jianing Zhu, Yeonju Ro, John Robertson, Kevin Wang, Junbo Li, Haris Vikalo, Aditya Akella, Zhangyang Wang

原文摘要

Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still mostly evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how does an agent's reliability change after deployment? Even when model weights are frozen, an agent's effective state keeps changing as it compresses interaction history, retrieves from a growing memory store, revises facts after updates, and undergoes routine maintenance. Reliability therefore becomes a lifespan property of the full agent harness, not only a snapshot property of the base model. We introduce AgingBench, a longitudinal reliability benchmark for agent lifespan engineering: measuring not only whether deployed agents degrade, but what form the degradation takes and where repair should target. AgingBench organizes agent aging into four mechanisms: compression aging, where writing and rewriting drops future-relevant details; interference aging, where accumulated similar memories crowd out the target fact; revision aging, where changed or derived state is not updated correctly; and maintenance aging, where lifecycle events such as model swapping trigger regressions. To diagnose these failures, AgingBench uses temporal dependency graphs and counterfactual probes that produce diagnostic profiles for the write, retrieval, and utilization stages of the memory pipeline. Across 6 scenarios, 10 memory systems and 6 models from two families, we find that agent aging is not one-dimensional over 400 runs spanning 50-200 sessions: a well-formed reply can hide a value the memory no longer supplies; derived state collapses as updates accumulate; and the same wrong answer can require different repairs depending on what the diagnostic profile points to.

cs.AI · cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10848

MultiTurnRoute: A Platform for Building and Evaluating Multi-Turn LLM Routers

Jiaqi Xue, Mengxin Zheng, Heng Huang, Qian Lou

原文摘要

In a multi-turn conversation, the choice of language model at one turn can affect which model should answer the next. The selected model's reply shapes the context for the next query, and its cached prompt prefix can reduce the cost of using that model again. We introduce MultiTurnRoute, a platform for building and evaluating multi-turn LLM routers. A shared interface gives built-in and user-defined routers the current query, dialogue history, and an estimate of each model's reusable prefix length. Our routing algorithm, Chain-of-Route (CoR), uses a learned gate over recent and recurrent dialogue history to predict response quality and estimates cost from each model's reusable prefix. MultiTurnRoute Arena ranks routers on recorded conversations and compares session quality and cost in interactive runs where a simulated user responds to each reply. On a benchmark drawn from WildChat and LMSYS-Chat-1M with per-turn quality annotations for five LLMs, CoR reduces the normalized cost required to match the strongest model's quality by up to 20.3% relative to the strongest routing baseline, while matching or improving peak response quality.

cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10841

A Gamified Framework for Conflict-Driven Design: Discovering Strategies for Collaborative Resilience with Multi-Agent Learning

Jin Gao

原文摘要

Conflicting stakeholder objectives make it difficult to coordinate investments in urban resilience. We model conflict-driven urban design as a non-zero-sum digital board game in which stakeholder agents allocate scarce resources and balance individual payoffs against shared urban development and resilience objectives. We use multi-agent reinforcement learning (MARL) to train policies and examine their behavior across three settings. On a 4 x 4 board, learned policies reach about 84% of the total return found by a simulated annealing benchmark. On an 8 x 8 board with spatial and resource constraints, trained agents favor projects that improve resilience in self-play. In a 12 x 12 simulation initialized from historical maps of Tatsumi, Tokyo, trained agents achieve a mean total score of 452.1, compared with 357.3 for a random-policy baseline across 10,000 simulation runs. Gains in adaptability, sustainability, flood resistance, and greenery come with reductions in vitality and density. A human-in-the-loop interface supports replay and lets participants take over stakeholder decisions, providing a testbed for exploring these trade-offs in participatory urban planning.

cs.AI · cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10819

Selective Cooperative Perception via Blind Spot Reasoning and Agentic Dialogue with Vision-Language Models

Chengyuan Ma, Shixiao Liang, Hangyu Li, Zheng Li, Hang Zhou, Keke Long, Bofeng Cao, Zhaohui Liang 等 9 位作者

原文摘要

Real-time cooperative perception (CP) in multi-agent traffic environments is highly resource-intensive, which posing scalability challenges for large-scale deployment. Efficiency can be significantly improved by selectively performing CP only in safety-critical scenarios and for key occluding objects. Vision-Language Models (VLMs) are well-suited for complex reasoning in dynamic and uncertain environments, making them a promising tool for this selective CP task. This study proposes a VLM-driven agentic framework for blind spot reasoning and dialogue-based CP. A mixed-model architecture is designed that integrates conventional object detection for stable perception, vision-language reasoning for identifying impactful occlusions, and a physics-based geometric module to align reasoning with real-world visibility constraints. The framework also incorporates a Chain-of-Thought (CoT) prompting mechanism to enhance interpretability and intent-aware decision-making. We collect real-world data from connected vehicles operating in urban traffic and compare the driving-critical blind spot identification and dialogue performance of four state-of-the-art VLMs. Experimental results demonstrate that our method achieves over 80% accuracy in driving-critical blind spot detection and over 90% success in dialogue-driven perception sharing. This work provides a scalable, interpretable, and bandwidth-efficient alternative to conventional CP pipelines, and offers new insights into agentic communication design for future connected traffic systems.

cs.RO · cs.AI · cs.CV · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22420

Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model

Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond

原文摘要

Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually drive the behaviours. In such a context, sensitivity analysis stumbles upon a structural feature of many complex systems: the parameters are not a flat vector but a decision tree in which many parameters appear only when a higher-level choice activates them. Classical global sensitivity analysis presupposes independent inputs over a fixed Cartesian product and is ill-posed on such a branched space and biased by construction. We instead treat the structure of the decisions as a first-class modelling object. Using advanced surrogate modelling techniques, we encode the crop-management itinerary of the high-resolution agro-ecological modelling platform MAELIA as a hierarchical design space featuring both categorical and numerical variables. We propose a novel dedicated method to analyse this kind of hierarchical space, a random-forest-supervised HSIC-ANOVA, which ranks main effects and interactions natively on the tree while placing rarely and always active decisions on an equal footing. We show how, when applied to simulation outputs such as nitrogen leaching, soil organic carbon variation, and yield predictions, it can deliver an interpretable, decision-oriented reading.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.22969

Multi-Agent Conditional Diffusion Planning for Decentralized Wireless Resource Allocation

Kechen Meng, Sinuo Zhang, Rongpeng Li, Yansha Deng, Chan Wang, Zhifeng Zhao

原文摘要

In wireless communication systems, efficient and adaptive resource allocation is essential for enhancing overall Quality of Service (QoS). Compared with conventional Model-Free Reinforcement Learning (MFRL), Model-Based RL (MBRL) first learns a generative world model for subsequent planning, enabling more stable training through the reuse of historical experience. However, deploying MBRL in large-scale wireless networks remains challenging due to high-dimensional stochastic dynamics, strong inter-agent cooperation, and stringent communication constraints. To overcome these challenges, we propose a Multi-Agent Conditional Diffusion Model Planner (MA-CDMP) for decentralized communication resource management. Following the Decentralized Training with Decentralized Execution (DTDE) paradigm, MA-CDMP models each communication node as an autonomous agent and employs Diffusion Models (DMs) to capture environment dynamics. An inverse dynamics model is further incorporated to guide action generation, improving sample efficiency and policy scalability. Moreover, to efficiently approximate large-scale agent interactions, we introduce a Mean Field (MF) mechanism to assist the classifier in conditional DMs. This mitigates inter-agent non-stationarity and enhances cooperation with minimal communication overhead. We further establish an upper bound on the distributional approximation error induced by MF-based diffusion generation, guaranteeing convergence stability and reliable modeling of multi-agent stochastic dynamics. Extensive experiments demonstrate that MA-CDMP consistently outperforms existing MARL baselines in terms of average reward and QoS metrics, showcasing its scalability and practicality for real-world wireless network optimization.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.06377

Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics

Philip Jordan, Maryam Kamgarpour

原文摘要

We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agents cannot fully observe the underlying state. Prior work in this setting relies on centralization or information sharing, and suffers from sample and computational complexity that scales exponentially in the number of players. We focus on a subclass of POMGs with independent state transitions, where agents remain coupled through their rewards, and assume that the underlying fully observed Markov game is a Markov potential game. For this class, we present an independent learning algorithm in which players, observing only their own actions and observations and without communication, jointly converge to an approximate Nash equilibrium. Due to partial observability, optimal policies may in general depend on the full action-observation history. Under a filter stability assumption, we show that policies based on finite history windows provide sufficient approximation guarantees. This enables us to approximate the POMG by a surrogate Markov game that is near-potential, leading to quasi-polynomial sample and computational complexity for independent Nash equilibrium learning in the underlying POMG.

cs.GT · cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10620

What Do Collusion Probes Read? Three Pitfalls in Evaluating Multi-Agent Collusion Monitors

Mainul Hasan Mohsin, Mohammad Shahidur Rahman

原文摘要

Activation probes look like near-perfect monitors for secret collusion among large language model (LLM) agents. We show that they mostly detect that an agent was told to collude, not that it decided to. Our benchmark (2,500 committee and 1,476 code-review episodes) adds agents who receive the collusion instruction and refuse it, separating instruction from decision. Published detectors barely tell colluders from these refusers, and our best probe separates them mostly by reading the instruction: without it, agent AUROC falls from 1.00 to 0.61. Two further pitfalls inflate reported results: scored after the vote, a rule that only reads the votes reaches AUROC 0.990 but finds nothing before it; and in 57% of attempts no colluder argues for the target in public. The decision is still there: a probe trained on agents' own choices reads it (AUROC 0.73 without the instruction), and probes find colluders whose goal was fine-tuned into the weights (AUROC 0.995; pre-registered). We turn the controls into a protocol for testing any collusion monitor, and adapt scan statistics into an online monitor for groups of any size.

cs.LG · cs.AI · cs.CL · cs.MA

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