arXiv:2602.05812v1 Announce Type: cross Abstract: We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025), we establish confidence regions with theoretical coverage guarantees for deep-learning-based CT reconstructions. We consider a realistic forward model following the Beer-Lambert law, i.e., a log-linear forward model with Poisson noise, closely reflecting clinical and scientific imaging conditions. The framework is general and applies to both classical algorithms and deep […]

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Imagine telling Claude: “Reply ‘Running 10 minutes late’ to Sarah and attach that funny cat meme I just uploaded.” Seconds later — done. No switching apps. No copy-pasting. Everything stays local, private, and scripted through natural language. That’s exactly what whatsapp-mcp-go enables. This lightweight, pure-Go project bridges your personal WhatsApp account to Claude Desktop (or Cursor) via the Model Context Protocol (MCP). Claude can read chats, search contacts, send messages (text, images, voice notes), and download media — […]

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Key Highlights: OpenAI has been among the key players in the AI industry, or let’s just say everyone’s favorite, except for Elon Musk. Many tech giants have invested in the company, including Microsoft in its initial days. Apparently, the bet taken by Satya Nadella, for which Bill Gates was skeptical, has been turning out fruitful for the Redmond giant. OpenAI reportedly set to receive massive investments from NVIDIA, Netflix, and Amazon Now, according to The Information, NVIDIA, Microsoft, […]

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Hello everybody, I’m at my third (and last year) of my phd in computer vision, and I want to start preparing for technical interviews. What I want to do is work as a research scientist, preferably at companies like Meta. In terms of publications and research knowledge I think I have a quite decent profile with 4 papers at A* conferences. However I have heard that the coding interviews can be quite thought even for research scientist jobs. […]

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arXiv:2602.18511v1 Announce Type: new Abstract: Modern compilers optimize programs through a sequence of modular passes over intermediate representations (IR). While this pass-by-pass paradigm offers engineering benefits, it suffers from a pass coordination problem: locally beneficial transformations may block more profitable optimizations in later stages. This limitation stems from the lack of an explicit notion of optimization intent, defined as a holistic strategy for coordinating multiple transformations toward a global performance objective. Recent LLM-based approaches formulate IR optimization as […]

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arXiv:2602.15844v1 Announce Type: new Abstract: The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and semantics of these tables makes it challenging to build a unified method that can effectively leverage the information they contain. Meanwhile, Large language models (LLMs) are becoming an increasingly integral component of web infrastructure for tasks like semantic search. This raises a crucial […]

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Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the underlying causal structure of the data. Dependence on spurious connections makes it challenging for traditional Graph Neural Networks (GNNs) to generalize effectively across different graphs. Furthermore, traditional aggregation methods tend to amplify these spurious patterns, limiting model robustness under distribution shifts. To address these issues, we propose Causal Neighbourhood Learning with […]

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We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a posteriori inference in graphical models can be improved. Our results apply whenever the potentials over maximal cliques of the triangulated graph are factored over subcliques. This is common in many real applications, as we illustrate with several examples. The new algorithms are easily implemented, and experiments show substantial speed-ups over the classical Junction-Tree Algorithm. This enlarges the class of models for which exact inference […]

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arXiv:2602.15867v1 Announce Type: new Abstract: In this positioning paper, we evaluate the problem-solving and reasoning capabilities of contemporary Large Language Models (LLMs) through their performance in Zork, the seminal text-based adventure game first released in 1977. The game’s dialogue-based structure provides a controlled environment for assessing how LLM-based chatbots interpret natural language descriptions and generate appropriate action sequences to succeed in the game. We test the performance of leading proprietary models – ChatGPT, Claude, and Gemini – under […]

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