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Causal discovery from observational data remains a fundamental challenge in machine learning and statistics, particularly when variables represent inherently positive quantities such as gene expression levels, asset prices, company revenues, or population counts, which often follow multiplicative rather than additive dynamics. We propose the Hybrid Moment-Ratio Scoring (H-MRS) algorithm, a novel method for learning directed acyclic graphs (DAGs) from positive-valued data by combining moment-based scoring with log-scale regression. The key idea is that for positive-valued variables, the moment […]

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arXiv:2604.07551v1 Announce Type: new Abstract: The Model Context Protocol (MCP) enables large language models (LLMs) to dynamically discover and invoke third-party tools, significantly expanding agent capabilities while introducing a distinct security landscape. Unlike prompt-only interactions, MCP exposes pre-execution artifacts, shared context, multi-turn workflows, and third-party supply chains to adversarial influence across independently operated components. While recent work has identified MCP-specific attacks and evaluated defenses, existing studies are largely attack-centric or benchmark-driven, providing limited guidance on where mitigation responsibility […]

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arXiv:2604.07549v1 Announce Type: new Abstract: Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialogue corpora are largely dyadic or lack the multi-party workflow and annotations needed for this setting. We introduce an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. The pipeline yields EMSDialog, a dataset of 4,414 synthetic multi-speaker EMS conversations […]

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arXiv:2604.07548v1 Announce Type: new Abstract: Artificial Intelligence (AI) chatbots are increasingly used for emotional, creative, and social support, leading to sustained and routine user interaction with these systems. As these applications evolve through frequent version updates, changes in functionality or behavior may influence how users evaluate them. However, work on how publicly expressed user feedback varies across app versions in real-world deployment contexts is limited. This study analyzes 210,840 Google Play reviews of the chatbot application Character AI, […]

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arXiv:2604.07546v1 Announce Type: new Abstract: This paper examines how the rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets. It argues that existing copyright frameworks are ill-equipped to govern AI agent-mediated interactions that occur at scale, speed, and with limited human oversight. The paper introduces the concept of agentic copyright, a model in which AI agents act on behalf of creators and users to negotiate access, attribution, and compensation for copyrighted […]

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arXiv:2604.07544v1 Announce Type: new Abstract: Fictitious play (FP) is a history-based strategy to choose actions in normal-form games, where players best-respond to the empirical frequency of their opponents’ past actions. While it is well-established that FP converges to the set of Nash equilibria (NE) in zero-sum games, the question of whether it converges to a single equilibrium point, especially when multiple equilibria exist, has remained an open challenge. In this paper, we establish that FP does not necessarily […]

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arXiv:2604.07539v1 Announce Type: new Abstract: We present a constructive proof that a single C program, the emph{Vulnerability Factory}, admits a countably infinite set of distinct, independently CVE-assignable software vulnerabilities. We formalise the argument using elementary set theory, verify it against MITRE’s CVE Numbering Authority counting rules, sketch a model-checking analysis that corroborates unbounded vulnerability generation, and provide a Turing-machine characterisation that situates the result within classical computability theory. We then contextualise this result within the long-running debate on […]

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arXiv:2604.07536v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on external tools to perform time-sensitive tasks and real-world actions. While tool integration expands LLM capabilities, it also introduces a new prompt-injection attack surface: tool poisoning attacks (TPAs). Attackers manipulate tool descriptions by embedding malicious instructions (explicit TPAs) or misleading claims (implicit TPAs) to influence model behavior and tool selection. Existing defenses mainly detect anomalous instructions and remain ineffective against implicit TPAs. In this paper, we present […]

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arXiv:2604.07535v1 Announce Type: new Abstract: Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when the human user is prompted by an AI agent, e.g., for advice or guidance. In this paper, we show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user’s self-confidence […]

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arXiv:2604.07534v1 Announce Type: new Abstract: This paper provides approximation orders for a class of nonlinear interpolation procedures for univariate data sampled over $sigma$ quasi-uniform grids. The considered interpolation is built using both essentially nonoscillatory (ENO) and subcell resolution (SR) reconstruction techniques. The main target of these nonlinear techniques is to reduce the approximation error for functions with isolated corner singularities and in turn this fact makes them useful for applications to other fields, such as shock capturing computations […]

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