March 2026

Multimodal learning enables neural networks to integrate information from heterogeneous sources, but active learning in this setting faces distinct challenges. These include missing modalities, differences in modality difficulty, and varying interaction structures. These are issues absent in the unimodal case. While the behavior of active learning strategies in unimodal settings is well characterized, their behavior under such multimodal conditions remains poorly understood. We introduce a new framework for benchmarking multimodal active learning that isolates these pitfalls using synthetic […]

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Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. […]

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An agent that operates sequentially must incorporate new experience without forgetting old experience, under a fixed memory budget. We propose a framework in which memory is not a parameter vector but a stochastic process: a Bridge Diffusion on a replay interval $[0,1]$, whose terminal marginal encodes the present and whose intermediate marginals encode the past. New experience is incorporated via a three-step emph{Compress–Add–Smooth} (CAS) recursion. We test the framework on the class of models with marginal probability densities […]

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In this quiz, you’ll test your understanding of Test-Driven Development With pytest. By working through this quiz, you’ll revisit creating and executing Python unit tests with pytest, practicing test-driven development, finding bugs before users, and checking code coverage. Use this quiz to confirm what you learned and spot gaps to review. Return to the video course for hands-on examples and guidance. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick […]

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The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data-driven LPV modelling, existing approaches do not quantify the uncertainty of the obtained LPV models. Consequently, assessing model reliability for analysis and control or detecting operation outside the training regime requires extensive validation and user expertise. This paper proposes a Bayesian approach for the joint estimation of […]

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I have extensively searched on long video understanding datasets such as Video-MME, MLVU, VideoBench, LongVideoBench and etc. What I have seen there these datasets are focused on different categories such dramas, films, TV shows, documentaries where focus on tasks like ordering, counting, reasoning and etc. I feel that multi-step reasoning is less explored and then what i have did i designed the questions with no options just ground truth and asked the VLM to give me the answer […]

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