Bitcoin is poised to break below $90,000. Analysts predict that a sharp plummet might follow and this has caused many to search for a better place to put their money. They seek the best crypto to buy that will grow quickly. Mutuum Finance (MUTM) has emerged as a top choice, where its presale has already raked in $19,730,000. More than 18,700 individuals possess MUTM tokens which currently cost $0.04 during Phase 7. However, this price won’t be around […]

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arXiv:2603.22368v1 Announce Type: new Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models (VLMs) perform well on many chart understanding tasks, their ability to detect misleading visualizations, especially when deception arises from subtle reasoning errors in captions, remains poorly understood. Here, we evaluate VLMs on misleading visualization-caption pairs grounded in a fine-grained taxonomy of reasoning errors (e.g., Cherry-picking, Causal inference) and visualization design errors (e.g., […]

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arXiv:2604.22386v1 Announce Type: new Abstract: One of the major limits of kernel ridge regression (KRR) is that storing and manipulating the kernel matrix K_n for n samples requires O(n^2) space, which rapidly becomes unfeasible for large n. Nystrom approximations reduce the space complexity to O(nm) by sampling m columns from K_n. Uniform sampling preserves KRR accuracy (up to epsilon) only when m is proportional to the maximum degree of freedom of K_n, which may require O(n) columns for […]

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My journey building operational intelligence — and why most AI initiatives quietly die I’ve built AI-enabled systems that scaled revenue, cut operational cost by multiples, and replaced chaos with clarity. I’ve also watched brilliant AI initiatives fail — not because the models were bad, but because the system was never designed to carry them. That contrast is why I write this. Most AI content today talks about tools, agents, and models. Very little talks about how businesses actually operate once AI is introduced. Even less is […]

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arXiv:2603.16911v1 Announce Type: new Abstract: Geospatial foundation models generate high-dimensional embeddings that achieve strong predictive performance, yet their internal organization remains obscure, limiting their scientific use. Recent interpretability studies relate Google AlphaEarth Foundations (GAEF) embeddings to continuous environmental variables, but it is still unclear whether the embedding space exhibits a functional or hierarchical organization, in which some dimensions act as specialized representations while others encode shared or broader geospatial structure. In this work, we propose a functional interpretability […]

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Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural […]

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arXiv:2604.05007v1 Announce Type: new Abstract: In Audio-Visual Navigation (AVN), agents must locate sound sources in unseen 3D environments using visual and auditory cues. However, existing methods often struggle with generalization in unseen scenarios, as they tend to overfit to semantic sound features and specific training environments. To address these challenges, we propose the textbf{Binaural Difference Attention with Action Transition Prediction (BDATP)} framework, which jointly optimizes perception and policy. Specifically, the textbf{Binaural Difference Attention (BDA)} module explicitly models interaural […]

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arXiv:2509.24789v3 Announce Type: replace-cross Abstract: The evaluation of time series forecasting models is hindered by a critical lack of high-quality benchmarks, leading to a potential illusion of progress. Existing datasets suffer from issues ranging from pre-training data contamination in the age of LLMs to the temporal and description leakage prevalent in early multimodal designs. To address this, we formalize the core principles of high-fidelity benchmarking, focusing on data sourcing integrity, leak-free and causally sound design, and structural clarity. […]

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