Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computation in game theory. Most existing online learning algorithms rely on emph{numeric} utility feedback from the environment, which may be unavailable in human-in-the-loop applications and/or may be restricted by privacy concerns. In this paper, we study an online learning model in which the learner only observes a emph{ranking} over a set of proposed actions at each […]

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How I designed a single Python backend that serves a real estate agency in Dubai, a dental practice in Brazil, and a food retailer — each with fully isolated AI behavior, context memory, and business logic — without any off-the-shelf automation tools. Most WhatsApp AI tutorials show you how to build a bot for one use case. You connect to the API, craft a system prompt, call OpenAI, send a reply. It works. But it doesn’t scale. When I started building AI automation […]

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arXiv:2602.10235v1 Announce Type: new Abstract: While sign language translation systems promise to enhance deaf people’s access to information and communication, they have been met with strong skepticism from deaf communities due to risks of misrepresenting and oversimplifying the richness of signed communication in technologies. This article provides empirical evidence of the complexity of translation work involved in deaf communication through interviews with 13 deaf Chinese content creators who actively produce and share sign language content on video sharing […]

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arXiv:2601.14261v1 Announce Type: new Abstract: The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings due to high computational demands, information loss, and a lack of holistic semantic understanding for design error identification. This paper proposes a novel three-stage framework for intelligent power grid drawing review, driven by pre-trained Multimodal Large Language Models (MLLMs) through advanced prompt engineering. Mimicking the human expert review process, the […]

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arXiv:2601.22327v1 Announce Type: new Abstract: Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model […]

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arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively richer informational settings. TemporalBench adopts a four-tier task taxonomy that examines historical structure interpretation, context-free forecasting, contextual temporal reasoning, and event-conditioned prediction across four real-world domains: retail, healthcare, energy, and physical systems. By controlling access to future targets […]

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In less than 3 pages, this tutorial covers signal processing and convolution quite thoroughly, even advanced concepts. I illustrate the techniques with the Riemann zeta function and the kernel method, along with short, home-made Python code that shows all the detailed steps, rather than based on Blackbox Python libraries. The document looks like a cheat sheet. It may be useful for scientists who need a refresher on the topic, or professionals in quantitative fields who don’t know much […]

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arXiv:2602.23629v2 Announce Type: replace Abstract: We propose a Multivariate Spatio-Temporal Neural Hawkes Process for modeling complex multivariate event data with spatio-temporal dynamics. The proposed model extends continuous-time neural Hawkes processes by integrating spatial information into latent state evolution through learned temporal and spatial decay dynamics, enabling flexible modeling of excitation and inhibition without predefined triggering kernels. By analyzing fitted intensity functions of deep learning-based temporal Hawkes process models, we identify a modeling gap in how fitted intensity behavior […]

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