In February 2026, something extraordinary happened in China. An AI chatbot launched a Lunar New Year promotion offering free bubble tea. Within 9 hours, more than 10 million orders flooded in. The total transaction value reached approximately 250 million yuan, or $36 million. Stores ran out of ingredients. Delivery riders queued for hours. Servers buckled. Cafes temporarily shut down. This was not a cyberattack. It was an AI-driven stress test of the physical economy. And the physical economy […]

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arXiv:2601.20000v1 Announce Type: new Abstract: In this paper, we propose an adaptive high-order method for hyperbolic systems of conservation laws. The proposed method is based on a dual formulation approach: Two numerical solutions, corresponding to conservative and nonconservative formulations of the same system, are evolved simultaneously. Since nonconservative schemes are known to produce nonphysical weak solutions near discontinuities, we exploit the difference between these two solutions to construct a smoothness indicator (SI). In smooth regions, the difference between […]

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arXiv:2604.13125v1 Announce Type: new Abstract: We introduce behavioral fidelity — a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal, sequential, and structural behavioral patterns that distinguish real-world entity activity. Existing frameworks evaluate statistical fidelity (marginal distributions and correlations) and downstream utility (classifier AUROC on synthetic-trained models), but neither tests for the behavioral signals that operational detection and analysis systems actually rely on. We formalize a taxonomy of four behavioral fraud patterns […]

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arXiv:2603.12458v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve expert-level performance on standard medical benchmarks through single-hop factual recall, they severely struggle with the complex, multi-hop diagnostic reasoning required in real-world clinical settings. A primary obstacle is “shortcut learning”, where models exploit highly connected, generic hub nodes (e.g., “inflammation”) in knowledge graphs to bypass authentic micro-pathological cascades. To address this, we introduce ShatterMed-QA, a bilingual benchmark of 10,558 multi-hop clinical questions designed to rigorously evaluate deep […]

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arXiv:2601.02397v1 Announce Type: new Abstract: Dynamic nonzero sum games are widely used to model multi agent decision making in control, economics, and related fields. Classical methods for computing Nash equilibria, especially in linear quadratic settings, rely on strong structural assumptions and become impractical for nonlinear dynamics, many players, or long horizons, where multiple local equilibria may exist. We show through examples that such methods can fail to reach the true global Nash equilibrium even in relatively small games. […]

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arXiv:2603.15656v1 Announce Type: new Abstract: The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models to address this problem often necessitates arduous data cleaning and model retraining, resulting in huge computational and manual overhead. In this work, we leverage rank-one model editing to establish an attribution-guided model rectification framework that effectively locates and corrects model unreliable behaviors. We first distinguish our rectification setting […]

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In February 2026, I took part in a collective investigation based exclusively on open-source intelligence (OSINT) to contextualize vague references contained in public court records released by the United States Department of Justice (DOJ) in connection with the Epstein case. This release — one of the largest ever related to the convicted financier Jeffrey Epstein — made millions of pages publicly available starting in January 2026, under the so-called Epstein Files Transparency Act. What began as collaborative analysis […]

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arXiv:2604.15418v1 Announce Type: new Abstract: This paper presents a preliminary draft of a framework around the use of anthropomorphic deception, defined here as misleading users towards humanlike affordances in the design of autonomous systems. The goal is to promote reflection among HCI and HRI researchers, as well as industry practitioners, to think about levels of anthropomorphic design that are: a) functionally necessary, b) socially appropriate, and c) ethically permissible for their use case. By reviewing the relevant literature […]

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arXiv:2604.14332v1 Announce Type: new Abstract: Diffusion-model inference and overdamped Langevin dynamics are formally identical. A physical substrate that encodes the score function therefore equilibrates to the correct output by thermodynamics alone, requiring no digital arithmetic during inference and potentially achieving a $10{,}000times$ reduction in energy relative to a GPU. Two fundamental barriers have until now prevented this equivalence from being realized at production scale: non-local skip connections, which locally coupled analog substrates cannot represent, and input conditioning, in […]

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