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Quanyan Zhu

Publications and source records attributed to Quanyan Zhu.

At least 19 recordsLinked to original sources

An Inspection Game for Monitor Design Against GNSS Timing Attacks in Synchronous Islanding

Phasor measurement units (PMUs) take their time from GNSS, so a spoofer that slowly drags a receiver clock rotates the reported phase without touching the power waveform. The threat is acute in synchronous islanding, where a controller keeps an islanded generator in phase with the grid, using one PMU on each side of an open breaker, so that the two can be reconnected at any moment: a differential clock bias of about $0.56$\,ms can make the controller hold the generator $10^\circ$ out of phase while it reports perfect synchronism. We cast the defense as a zero-sum inspection game. The defender, as inspector, chooses its timing monitors and audits the clocks periodically; the attacker, as inspectee, drags the two clocks to an unsafe bias by the next audit; the payoff is the evidence, the Kullback--Leibler divergence between the probability laws of the defender's record under attack and under normal operation. Our central result is that the evidence equals the cost of the cheapest innocent explanation of the attacked record. This single identity predicts which monitors work, turns the attacker's best response into a strictly convex quadratic program, and turns the defender's design into a convex program with a saddle point. Pairing explanations, which bound the evidence from above, with test statistics, which bound it from below, certifies audit periods in which every successful attack leaves at least a threshold of evidence. On a 50\,Hz island, these evidence-based periods, at the matched-test threshold, are up to 8.4 times as long as those of a rule that checks each monitor separately, and the case study favors placing the better oscillator on the utility side of the breaker.

eess.SY↗

Certified Execution Harness for Agentic AI: Safe and Least-Intrusive Petri-Net Supervisory Control Between Reasoning and Action

This paper studies the problem of safe tool execution for large language model (LLM) agents under prompt injection. Using Petri nets, we formalize the execution harness between the LLM and its tools as a supervisory control problem, in which marks represent capabilities, approvals and budgets. Since a transition fires only if the marks it needs are present, an injected request whose preconditions are unmet or already spent is refused regardless of its wording. Treating tool outcomes as uncontrollable, we enforce safety by the conventional maximally permissive supervisor, and this safety transfers to the running system under four assumptions. Moreover, to capture an agent that may propose again after a refusal, we introduce the notion of a replanning closed loop, in which each retry costs an inference call. We show that, in this loop, the conventional supervisor can be arbitrarily more intrusive than a stricter safe one. Furthermore, for a given proposer model, the maximally permissive least-intrusive supervisor is characterized as a value-threshold refinement of the conventional one. Under precise conditions, this refinement orders two concurrent actions by at most one monitor place. On an autonomous release agent, we find by synthesis that safe deployment requires a step that the supervisor can refuse after a rollout starts.

cs.AI↗

Computational Reflective Equilibrium for Agentic AI: A Rawlsian Alignment Scaffold for Governance

The rapid deployment of agentic AI has introduced complex challenges for the ethics and governance of AI systems. Agents built on large language models act through tools on behalf of people, and each deployment case brings its own instructions, policies, evidence, and stakeholders, so alignment must be formed case by case. To this end, this work proposes agentic reflective equilibrium (ARE), a computational reflective equilibrium framework that serves as an ethical alignment scaffold in the agent harness. Drawing inspiration from the reflective equilibrium of John Rawls, the scaffold links principles from the system prompt and operator policy, considered judgments, tool evidence, and candidate actions through typed support and attack relations, with provenance encoding the instruction hierarchy. The agent acts on an equilibrium of this state and calls tools or asks questions only while doing so is worth its cost, unless a due-diligence mandate requires them. Governance enters through value priorities, source authority, prohibitions, human-in-the-loop triggers, and mandates, and certificates attribute each action to the sources whose commitments drove it. We show that equilibria are Nash equilibria of a mutual-adjustment game and establish monotonicity, lexical priority, stability certificates, and a quarantine theorem for prompt injection. Two case studies illustrate the framework: a computer-use assistant told to win a refund "whatever it takes" files an honest warranty claim, refuses a fake review, and ignores an injected request for personal data; a hiring agent showcases how authority, oversight triggers, audit mandates, and hard prohibitions shape discrimination, privacy violations, and escalation. The framework offers traceability, coherence, and adaptivity, facilitating safe, secure, and trustworthy agentic AI.

cs.AI↗

From Optimal Transport to Optimal Quantization: A Variational Study of the Witsenhausen Counterexample

Witsenhausen's counterexample is a canonical problem in decentralized control: a two-stage linear-quadratic-Gaussian team problem in which nonlinear policies strictly outperform every affine one. This paper develops a variational and computational framework for the scalar counterexample based on optimal transport. Building on the transport formulation of Wu and Verdú, we absorb the first controller into a transport map and recast the problem as $J^\star=\inf_Q\{k^2W_2^2(P,Q)+\mathrm{mmse}(Q)\}$, which trades a quadratic Wasserstein transport cost against a minimum mean-square estimation error. The optimal first controller is the monotone rearrangement that pushes the prior $P$ to a minimizer $Q^\star$. We establish the existence and absolute continuity of minimizers, derive an Euler--Lagrange optimality condition, and obtain a semi-closed-form Gaussian benchmark with an explicit second-variation stability threshold. We then show that restricting to finitely supported laws yields an MMSE-regularized optimal quantizer: its optimal values decrease to $J^\star$, its weak limit points are minimizers of the continuous problem, and its stationarity conditions pair centroid levels with power cells, recovering Lloyd--Max quantization as control becomes expensive. This structure leads to an estimation-aware Lloyd algorithm with signalling-based initialization. As $k\to\infty$, the optimal controllers converge to the classical affine pair; as $k\to0$, $J^\star(k)=σ^2k^2+o(k^2)$ and the optimal laws collapse onto $δ_0$, so signalling constellations are a moderate-$k$ phenomenon. Numerical experiments corroborate the theory, and the framework extends to the vector counterexample, where the finite-level program becomes an MMSE-regularized vector quantizer.

math.OC↗

Game Petri Nets: Compositional Modeling and Analysis of Large-Scale Strategic Systems

Large-scale strategic systems, such as maintenance workflows, cyber campaigns, and multi-agent AI pipelines, consist of many interactions whose outcomes determine which later interactions become possible, who takes part, and with what resources. We introduce game Petri nets, formalized as outcome-driven compositional game Petri nets (OCGPNs), to model and analyze such systems. Local games are attached to places of a colored Petri net, and a game-to-token interface converts each resolved game into typed tokens that enable, synchronize, or block subsequent games. For general, concurrent nets, we give an operational semantics and a structural theory based on invariants and Farkas certificates, which shows that guards can certify unreachability whereas incentive instruments can only change values. For sequential nets, the net induces a stochastic game on its game places. We prove that global equilibria are exactly collections of equilibria of local games augmented by continuation values, characterize through successor-level pivotality when downstream games change upstream behavior, and compute values place by place via a decomposition into strongly connected components, at a cost additive in local game sizes. A composition calculus based on place fusion glues certificates across subnets and summarizes downstream subnets by their entry values. Two running examples, a maintenance workflow and an intrusion campaign derived from multi-resolution cyber-defense games, illustrate the theory, including the composition of a newly discovered attack scenario with an existing campaign.

cs.GT↗

Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies. Existing attribution methods often rely on counterfactual valuation, such as removing agents or comparing score changes across altered agent subsets. In language-mediated workflows, these methods require repeated model calls, introduce high variance, and do not explicitly capture the intermediate semantic states through which agents produce, preserve, and transform task-relevant information. We propose Semantic Cooperative Games (SCG), a framework that represents a realized language flow as a semantic generation hypergraph and induces an agent-level semantic value function on this structure. We define the Semantic Shapley Value (SSV) to allocate contribution over semantic support logic, and introduce SLIC, a single-trajectory algorithm that constructs the semantic hypergraph, recovers minimal semantic supports, applies Boolean absorption, and computes SSV without rerunning agent subsets. We prove that SSV reduces to the classical Shapley value under standard set-based, fully observable, and no-order-dependence conditions. On a medical benchmark satisfying these conditions, SLIC reduces computation cost by 93.3% while remaining highly consistent with a Monte Carlo Shapley baseline. In more general multi-role workflows, SSV aligns with perturbation-induced score-drop profiles and exposes cases where semantic contribution and failure impact diverge. Overall, SLIC provides a fast, counterfactual-free, and interpretable attribution method for complex LLM-based multi-agent systems.

cs.AI↗

Detection-Control Games under Hidden Modes: Resilience-Induced Blindness Phenomenon

This paper studies resilient control for cyber-physical systems operating under hidden degraded or compromised modes. We formulate hidden-mode detection and belief-dependent control as a game between two decision makers with different objectives: the detector seeks informative belief updates, while the controller seeks regulation performance. This objective mismatch shows why the usual separation intuition between detector design and controller design may fail, leading to a performance-reversal phenomenon induced by the resilience of the controller. For a two-mode linear Gaussian system, we theoretically characterize this phenomenon by linking the resilience margin to the log-likelihood evidence. The analysis shows that a well-performing controller with a large resilience margin can suppress mode-dependent information and slow belief adaptation, which in turn degrades the control performance. The resilience-induced blindness phenomenon and its partial mitigation through increased detector sensitivity are illustrated in numerical simulations.

eess.SY↗

ADAPTD: Adaptive Detection and Proactive Threat Defense for Autonomous APT attacks

Advanced persistent threat (APT) actors increasingly employ sophisticated techniques to propagate laterally through segmented enterprise networks. Timely detection and defense depend on cross-subnetwork coordination, yet maintaining global situational awareness generates substantial communication overhead. To manage this tradeoff, flexible monitoring and adaptable containment are imperative. This paper presents ADAPTD, a communication- and computation-efficient, decision-theoretic framework integrating: (i) compact kill chains for identifying diverse attack vectors, (ii) an immediate blocking mechanism for timely containment, and (iii) a predictive eviction strategy to restore system security. Our experiments validate ADAPTD's effectiveness across diverse threat scenarios. First, our decentralized belief update scheme outperforms state-of-the-art diffusion HMM. Second, ADAPTD substantially reduces false evictions compared to transformer-based detection. Third, under noisy environments, adaptive blocking contains attackers while minimizing unnecessary disruption. Lastly, the ablation study confirms that combining two defensive actions significantly reduces the defender's total cost.

cs.CR↗

Process-Aware Cross-Layer Adaptation for O-RAN-Enabled Industrial Systems

Wireless networks increasingly support closed-loop industrial applications in which sensed data must be transmitted, processed, and converted into an action before physical process makes the result obsolete. Throughput, latency, and inference accuracy measured separately cannot determine whether such an application completed a useful task. We propose a Process-Aware Co-adaptation Engine framework that combines application outcomes, process state, radio telemetry, edge-compute state, and sensing configuration to select coordinated operating points across the complete loop. We evaluate the proposed approach in a factory-inspection case study that integrates a physics-based digital twin, a programmable 5G O-RAN network, and edge-based visual inference. The experiments show that the preferred resource allocation changes with production speed and that adapting individual system components independently can be inefficient. We further show that efficient configurations can be identified with relatively few full-system evaluations.

eess.SY↗

A Games-in-Games Paradigm for Strategic Hybrid Jump-Diffusions: Hamilton-Jacobi-Isaacs Hierarchy and Spectral Structure

This paper develops a hierarchical games-in-games control architecture for hybrid stochastic systems governed by regime-switching jump-diffusions. We model the interplay between continuous state dynamics and discrete mode transitions as a bilevel differential game: an inner layer solves a robust stochastic control problem within each regime, while a strategic outer layer modulates the transition intensities of the underlying Markov chain. A Dynkin-based analysis yields a system of coupled Hamilton-Jacobi-Isaacs (HJI) equations. We prove that for the class of Linear-Quadratic games and Exponential-Affine games, this hierarchy admits tractable semi-closed form solutions via coupled matrix differential equations. We prove that for the class of Linear-Quadratic games and Exponential-Affine games, this hierarchy admits tractable semi-closed form solutions via coupled matrix differential equations. The framework is demonstrated through a case study on adversarial market microstructure, showing how the outer layer's strategic switching pre-emptively adjusts inventory spreads against latent regime risks, which leads to a hyper-alert equilibrium.

eess.SY↗

Decentralized No-Regret Frequency-Time Scheduling for FMCW Radar Interference Avoidance

Automotive FMCW radars are indispensable to modern ADAS and autonomous-driving systems, but their increasing density has intensified the risk of mutual interference. Existing mitigation techniques, including reactive receiver-side suppression, proactive waveform design, and cooperative scheduling, often face limitations in scalability, reliance on side-channel communication, or degradation of range-Doppler resolution. Building on our earlier work on decentralized Frequency-Domain No-Regret hopping, this paper introduces a unified time-frequency game-theoretic framework that enables radars to adapt across both spectral and temporal resources. We formulate the interference-avoidance problem as a repeated anti-coordination game, in which each radar autonomously updates a mixed strategy over frequency subbands and chirp-level time offsets using regret-minimization dynamics. We show that the proposed Time-Frequency No-Regret Hopping algorithm achieves vanishing external and swap regret, and that the induced empirical play converges to an $\varepsilon$-coarse correlated equilibrium or a correlated equilibrium. Theoretical analysis provides regret bounds in the joint domain, revealing how temporal adaptation implicitly regularizes frequency selection and enhances robustness against asynchronous interference. Numerical experiments with multi-radar scenarios demonstrate substantial improvements in SINR, collision rate, and range-Doppler quality compared with time-frequency random hopping and centralized Nash-based benchmarks.

eess.SY↗

Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration

The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework. The proposed architecture consists of cloud, edge/fog, and physical IoT layers connected through autonomous AI agents that perceive, reason, coordinate, and actuate across distributed cyber-physical environments. The paper formalizes IoAT as a coupled workflow-control problem with nested strategic and tactical decision making using a hylomorphic dynamic programming framework that links agentic planning with physical execution. Smart-building orchestration is presented as a representative use case, and key research challenges related to safety, security, governance, resilience, and trustworthy deployment are discussed.

cs.AI↗

AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation

Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments. A deployment is represented by a risk state that captures autonomy level, operational authority, permission exposure, governance maturity, and dependency concentration. The framework maps the risk state to event probabilities, loss severities, governance costs, premiums, deductibles, coverage allocation, and policy covenants, and formulates an optimization problem for insurance contract design under participation, profitability, and incentive compatibility constraints. The paper establishes structural properties of insurability, including characterization of an insurability region, monotone deterioration of feasibility with increasing exposure, and governance certification thresholds. Insurance is further interpreted as both an operational cost and a regulatory mechanism for AI deployment. A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems.

cs.AI↗

Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games

This paper develops a continuum theory of exit-and-join coalition dynamics in nonatomic cooperative games. We extend the Aumann-Shapley value and the Aumann-Drèze value to coalition structures in which each coalition is treated as a restricted nonatomic game, yielding a marginal-contribution-based payoff density that governs incentives for agents to remain in, exit, or join coalitions. We derive deterministic mean-field dynamics from decentralized switching rules and show that payoff-difference switching recovers replicator dynamics as a special case. We characterize exit-and-join equilibrium by the absence of profitable positive-mass deviations and prove its equivalence with stationarity of the induced mass dynamics under incentive-compatible and strictly payoff-responsive switching rates. For mass-based cooperative games, we construct a Lyapunov function and establish global convergence under strict concavity. We further show that the equilibrium is equivalent to a Wardrop equilibrium of an induced nonatomic population game and admits a variational inequality formulation. The framework is extended to incorporate switching costs and endogenous coalition acceptance rules, leading to constrained equilibria characterized by quasi-variational inequalities. The proposed theory unifies cooperative value allocation, noncooperative coalition mobility, mean-field dynamics, evolutionary game theory, and population games within a common framework for analyzing coalition formation and adaptation in large-scale multi-agent systems.

cs.GT↗

Divergence-based Safety Measure for Large Language Models via Rational Inattention

This paper proposes a divergence-based safety measure for large language models (LLMs) under embedding-input attacks. The proposed measure quantifies the worst-case Kullback--Leibler divergence between the clean and attacked LLMs' output distributions, subject to a stealthiness constraint. This constraint is constructed by leveraging the equivalence between transformer attention used in LLMs and rational inattention modeling human decision-making. We analyze the proposed divergence-based safety measure by investigating perfectly undetectable attacks and deriving its upper bound through a Bregman-divergence argument. The proposed safety measure is applied to two pretrained causal language models, GPT-2 and GPT-Neo-125M, to show nontrivial output-distribution shifts, illustrating that the measure can distinguish model-level safety profiles.

eess.SY↗

LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics

Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of communication and deception. This paper develops a semantic signaling game in which a sender selects a semantic control, an LLM generates a stochastic message, and a receiver evaluates the message using an awareness-dependent scoring mechanism. Receiver awareness is modeled as a type that determines which linguistic features are perceived and used for inference, providing a formal model of systematic blindness. The framework connects prompt-based control, statistical detection, and game-theoretic equilibrium analysis. Gaussian approximations of aggregate message scores enable likelihood-ratio decision rules, while Perfect Bayesian Nash equilibria characterize strategic behavior. The paper further develops mechanism-design approaches that reshape receiver awareness, penalize deceptive semantic controls, and modify receiver populations to induce benign pooling equilibria. Numerical experiments validate the Gaussian approximation, quantify awareness-ordering effects, analyze mindset dynamics under adaptive adversaries, and demonstrate how awareness shaping and guardrail costs reduce successful phishing attacks. The proposed framework provides a principled foundation for analyzing strategic language-mediated interactions in agentic AI systems and offers new tools for the design of robust and secure human-AI communication.

cs.GT↗

Multiscale Exit-Join Dynamics: Tactical Consensus and Strategic Coalition Formation

This paper develops a multiscale model of coalition formation in which strategic exit-and-join decisions are coupled with tactical consensus dynamics inside coalitions. Coalition value is generated endogenously from within-coalition information aggregation, while Aumann-Dreze payoffs, switching frictions, and acceptance rules govern strategic reconfiguration. The framework introduces a fast-slow architecture in which transferable coalition value emerges from DeGroot-style consensus processes, while coalition structures evolve through incentive-driven exit-and-join dynamics. The analysis characterizes joint tactical-strategic equilibria, conditions for tactical and strategic unanimity, segregation, polarization, and cognitive barriers that sustain stable coalition structures. A fixed-point characterization and existence results are established for the coupled dynamics. Numerical experiments reveal an instability-consensus paradox: low or negative switching barriers may prevent strategic convergence while simultaneously promoting temporal mixing sufficient to achieve global tactical consensus. The results provide a unified perspective on coalition formation, consensus dynamics, information aggregation, and strategic stability in multi-agent systems.

cs.GT↗

Exit-and-Join Dynamics for Decentralized Coalition Formation

This paper studies coalition formation as a decentralized dynamical process driven by unilateral exit-and-join decisions. Agents evaluate local moves using the Aumann-Dreze value, so payoffs are computed within the agent's current coalition rather than through a globally negotiated coalition structure. The resulting model links cooperative payoff allocation with noncooperative best-response behavior: a terminal partition is precisely a coalition structure with no admissible, individually profitable exit-and-join deviation. We establish equilibrium characterizations, identify conditions under which the dynamics admit scalar Lyapunov or exact-potential representations, and analyze how switching and acceptance costs shape local stability. Numerical experiments test finite-time stabilization, cost sensitivity, and a special convex-game benchmark.

cs.AI↗