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Learning interpretable multimodal representations inherently relies on uncovering the conditional dependencies between heterogeneous features. However, sparse graph estimation techniques, such as Graphical Lasso (GLasso), to visual-linguistic domains is severely bottlenecked by high-dimensional noise, modality misalignment, and the confounding of shared versus category-specific topologies. In this paper, we propose Cross-Modal Graphical Lasso (CM-GLasso) that overcomes these fundamental limitations. By coupling a novel text-visualization strategy with a unified vision-language encoder, we strictly align multimodal features into a shared latent space. […]

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Release: scan-for-secrets 0.2 CLI tool now streams results as they are found rather than waiting until the end, which is better for large directories. -d/–directory option can now be used multiple times to scan multiple directories. New -f/–file option for specifying one or more individual files to scan. New scan_directory_iter(), scan_file() and scan_file_iter() Python API functions. New -v/–verbose option which shows each directory that is being scanned.

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Release: scan-for-secrets 0.1 I like publishing transcripts of local Claude Code sessions using my claude-code-transcripts tool but I’m often paranoid that one of my API keys or similar secrets might inadvertently be revealed in the detailed log files. I built this new Python scanning tool to help reassure me. You can feed it secrets and have it scan for them in a specified directory: uvx scan-for-secrets $OPENAI_API_KEY -d logs-to-publish/ If you leave off the -d it defaults to […]

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In many modern applications, a carefully designed primary study provides individual-level data for interpretable modeling, while summary-level external information is available through black-box, efficient, and nonparametric machine-learning predictions. Although summary-level external information has been studied in the data integration literature, there is limited methodology for leveraging external nonparametric machine-learning predictions to improve statistical inference in the primary study. We propose a general empirical-likelihood framework that incorporates external predictions through moment constraints. An advantage of nonparametric machine-learning prediction is […]

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Model-free deep reinforcement learning (DRL) methods suffer from poor sample efficiency. To overcome this limitation, this work introduces an adaptive reduced-order-model (ROM)-based reinforcement learning framework for active flow control. In contrast to conventional actor–critic architectures, the proposed approach leverages a ROM to estimate the gradient information required for controller optimization. The design of the ROM structure incorporates physical insights. The ROM integrates a linear dynamical system and a neural ordinary differential equation (NODE) for estimating the nonlinearity in […]

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Release: research-llm-apis 2026-04-04 I’m working on a major change to my LLM Python library and CLI tool. LLM provides an abstraction layer over hundreds of different LLMs from dozens of different vendors thanks to its plugin system, and some of those vendors have grown new features over the past year which LLM’s abstraction layer can’t handle, such as server-side tool execution. To help design that new abstraction layer I had Claude Code read through the Python client libraries […]

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In this tutorial, we explore the full capabilities of Z.AI’s GLM-5 model and build a complete understanding of how to use it for real-world, agentic applications. We start from the fundamentals by setting up the environment using the Z.AI SDK and its OpenAI-compatible interface, and then progressively move on to advanced features such as streaming responses, thinking mode for deeper reasoning, and multi-turn conversations. As we continue, we integrate function calling, structured outputs, and eventually construct a fully […]

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Video editing has always had a dirty secret: removing an object from footage is easy; making the scene look like it was never there is brutally hard. Take out a person holding a guitar, and you’re left with a floating instrument that defies gravity. Hollywood VFX teams spend weeks fixing exactly this kind of problem. A team of researchers from Netflix and INSAIT, Sofia University ‘St. Kliment Ohridski,’ released VOID (Video Object and Interaction Deletion) model that can […]

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Multi-task representation learning (MTRL) is an approach that learns shared latent representations across related tasks, facilitating collaborative learning that improves the overall learning efficiency. This paper studies MTRL for multi-task reinforcement learning (RL), where multiple tasks have the same state-action space and transition probabilities, but different rewards. We consider T linear Markov Decision Processes (MDPs) where the reward functions and transition dynamics admit linear feature embeddings of dimension d. The relatedness among the tasks is captured by a […]

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