receiver-awareness formalization

In standard signaling games, receivers struggle with compositional semantic interpretation: the loss of any signal component causes complete semantic breakdown. To address this, we propose two novel receiver architectures—the minimalist receiver, which learns atomic signal components in isolation, and the universal receiver, which integrates all components—within an evolutionary learning framework for signaling games. Our core contribution is the decoupling of signal component learning, enabling modular and composable semantic representations. Experimental results demonstrate significantly improved robustness: when partial information is missing, the semantics of remaining components are preserved and accurately decoded. This work achieves, for the first time in evolutionary signaling games, genuine compositional understanding—where meaning emerges systematically from structured combinations of learned primitives, rather than holistic, unstructured associations.

Classical Shannon information theory assumes semantic irrelevance, failing to account for logical knowledge held by communicating agents. This work addresses the problem of minimizing communication cost when a sender (Alice) and receiver (Bob) possess partial, possibly heterogeneous, logical knowledge, such that Bob must logically deduce Alice’s private proposition via first-order deduction. Method: We propose the first semantic-aware communication theory integrating logic-based reasoning with information theory. Crucially, we formally incorporate Bob’s first-order deductive capability into the communication model, establishing a semantics-sensitive transmission paradigm. Combining coding theory, game-theoretic analysis, and algorithm design, we derive tight upper and lower bounds on semantic communication complexity. Contribution/Results: We introduce the first knowledge-driven communication framework supporting logical inference; derive optimal code-length bounds for multiple semantic scenarios; design an asymptotically optimal algorithm; and empirically demonstrate significant gains in transmission efficiency over classical schemes.

This paper studies algorithmic persuasion under generative AI, where a sender aims to influence a receiver’s binary action via signaling, knowing only limited information about the receiver’s type distribution and needing to infer the type with minimal queries to a behavioral simulation oracle. Method: We integrate Bayesian persuasion with a queryable behavioral simulation oracle, proposing a polynomial-time joint optimization algorithm for optimal querying and signal design. Contribution/Results: We fully characterize the optimal signaling strategy for arbitrary type distributions; establish robustness under approximate oracles, general query structures, and cost-sensitive constraints; and empirically demonstrate significant improvements in both utility maximization and query efficiency.

This paper studies how a receiver (decision-maker) can commit ex ante to a robust decision rule to cope with ambiguity about both the sender’s (advisor’s) preferences and information structure within the Bayesian persuasion framework. Adopting robust optimization and minimax analysis, we develop a unified framework based on max-min expected utility and min-max regret. Our key contribution is the first proof that quota rules—i.e., decision rules enforcing pre-specified marginal action distribution constraints—are globally optimal in this setting. Such rules are agnostic to the sender’s specific signal structure, guarantee incentive compatibility universally, and achieve ex ante perfect incentive alignment—at the cost of sacrificing interim optimality. This result provides a tractable, interpretable theoretical foundation for robust mechanism design.

This paper studies the design of information verification mechanisms: how to probabilistically test agents’ reports to balance allocation efficiency and principal surplus. We propose a novel paradigm embedding statistical hypothesis testing into mechanism design, constructing a commitment mechanism with randomized verification—where each report type undergoes a binary test (pass/fail), and outcomes directly inform allocation and payment decisions. Innovatively, we reformulate the virtual value function and, under quasilinear preferences, derive the first closed-form solution for the optimal verification mechanism. Theoretically, we prove that higher verification accuracy strictly improves both allocation efficiency and the principal’s share of surplus, and that these two objectives are positively correlated. Our results establish a theoretically rigorous yet practically implementable foundation for credible information elicitation.

Protocol designers often face a high barrier to entry in using formal verification tools such as ProVerif and Tamarin due to the lack of systematic guidance on translating security properties into executable models. This work addresses this gap by conducting a systematic review of 53 studies published between 2022 and 2025, resulting in the first comprehensive taxonomy of security properties tailored to mainstream verification tools. The taxonomy integrates informal explanations, first-order logic definitions, and tool-specific modeling exemplars. By bridging the gap between theoretical formulations and practical modeling, this study significantly enhances the accuracy and efficiency of protocol modeling. An accompanying open-source repository of illustrative examples further lowers the practical barrier to adopting formal verification in real-world protocol design.

This work addresses the frequent disconnect between the mathematical certainty of numerical values in cryptographic protocols and their concrete representations, which undermines interoperability and formal verification. Drawing from representation theory, the paper introduces three classes of representations—algorithmically approximable, finitely precisely describable, and canonically normalizable—and proves that no universal computable canonicalizer can transform arbitrary approximate programs into a unique finite encoding. It extends the canonical encoding paradigm of the rational number system Σ_Q to practical cryptographic objects. By integrating computability theory with canonical serialization techniques, the approach is applied to symmetric and asymmetric encryption, hashing, and blockchain integrity protocols. Case studies such as Snaproot demonstrate that canonical representations are essential for achieving precise protocol specifications, ensuring interoperability, and enabling byte-level correctness arguments.

This work addresses the vulnerability of large language models to semantic deception in strategic interactions, emphasizing the urgent need to model receivers’ perceptual blind spots and develop robust defense mechanisms. The paper introduces the first formal framework of semantic signaling games, integrating prompt control, statistical detection, and perfect Bayesian Nash equilibrium analysis to shape awareness and regulate collective behavior, thereby steering systems toward benign equilibria. By employing Gaussian approximation and likelihood ratio–based decision rules, the approach effectively quantifies the impact of awareness levels, reveals the dynamics of cognitive evolution, and significantly reduces the success rate of phishing attacks, thereby enhancing the security of human–AI communication.

This work addresses the behavioral gap between formal verification and actual execution in traditional engineering approaches, which often neglect execution semantics. To bridge this semantic divide, the paper proposes a Modeling and Simulation-Based Engineering (MSBE) methodology that explicitly treats execution semantics as a first-class engineering entity. It defines executability as the admissible model space induced by the stabilization of execution conditions and unifies model behavior with physical execution through an iterative cycle of formal execution, experimental execution, verification, and activity-mediated validation. Integrating formal methods, simulation-based verification, activity theory, and constraint modeling, MSBE establishes a general-purpose engineering framework applicable to diverse cyber-physical systems (CPS). The approach demonstrates its generality and effectiveness across four CPS categories: human-centric, biophysical, technological, and digital twin systems.