Every few decades, computing changes its shape. Once again, we’ve arrived at that moment. But what form factor will it take? Which innovations will it require? In the first part of this series, I broke down the signals identifying AI-powered XR computing as the next major leap in tech. In Part II, I’ll look at why Big Tech can’t deliver this on its own — and why making this vision a reality requires rethinking the foundations of science, engineering, […]

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I was reading an article this morning that mentioned a styometric analysis of a controversial paragraph written by Roman historian Flavius Josephus. I’ve written several posts that could be called stylometry or adjacent, but I haven’t used that word. Here are some posts that touch on the statistical analysis of a text or of an author. Authorship in the Federalist Papers Using TF-IDF to pick out important words Natural language processing and unnatural text Estimating vocabulary size with […]

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arXiv:2601.08959v1 Announce Type: new Abstract: As zero-day Android malware attacks grow more sophisticated, recent research highlights the effectiveness of using image-based representations of malware bytecode to detect previously unseen threats. However, existing studies often overlook how image type and resolution affect detection and ignore valuable textual data in Android Application Packages (APKs), such as permissions and metadata, limiting their ability to fully capture malicious behavior. The integration of multimodality, which combines image and text data, has gained momentum […]

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arXiv:2601.15476v1 Announce Type: new Abstract: This paper examines how to make large language models reliable for high-stakes legal work by reducing hallucinations. It distinguishes three AI paradigms: (1) standalone generative models (“creative oracle”), (2) basic retrieval-augmented systems (“expert archivist”), and (3) an advanced, end-to-end optimized RAG system (“rigorous archivist”). The authors introduce two reliability metrics -False Citation Rate (FCR) and Fabricated Fact Rate (FFR)- and evaluate 2,700 judicial-style answers from 12 LLMs across 75 legal tasks using expert, […]

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Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others’ behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents’ actions without knowledge of their rewards. The agents independently pursue […]

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arXiv:2603.22319v1 Announce Type: new Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both sparse observations and the governing PDE typically requires per-instance test-time optimization, which becomes a major bottleneck in time-critical applications. To alleviate this, amortized reconstruction using generative models has recently […]

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