arXiv:2602.07064v1 Announce Type: new Abstract: Physical understanding remains brittle in omni-modal models because key physical attributes are visually ambiguous and sparsely represented in web-scale data. We present OmniFysics, a compact omni-modal model that unifies understanding across images, audio, video, and text, with integrated speech and image generation. To inject explicit physical knowledge, we build a physical data engine with two components. FysicsAny produces physics-grounded instruction–image supervision by mapping salient objects to verified physical attributes through hierarchical retrieval over […]

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Banking house JPMorgan Chase is asking its roughly 65,000 engineers and technologists to use AI tools as part of their regular workflow. Business Insider reported that managers are tracking how often staff use these tools. That use may also influence performance reviews. The report states employees are encouraged to use tools like ChatGPT and Claude Code when writing code, reviewing documents, or handling routine tasks. Internal systems then classify workers based on their level of use. Some are […]

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This study explores the extent to which deep learning models can predict groove and its related perceptual dimensions directly from audio signals. We critically examine the effectiveness of seven state-of-the-art deep learning models in predicting groove ratings and responses to groove-related queries through the extraction of audio embeddings. Additionally, we compare these predictions with traditional handcrafted audio features. To better understand the underlying mechanics, we extend this methodology to analyze predictions based on source-separated instruments, thereby isolating the […]

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arXiv:2602.06518v1 Announce Type: cross Abstract: We present new auditors to assess Differential Privacy (DP) of an algorithm based on output samples. Such empirical auditors are common to check for algorithmic correctness and implementation bugs. Most existing auditors are batch-based or targeted toward the traditional notion of $(varepsilon,delta)$-DP; typically both. In this work, we shift the focus to the highly expressive privacy concept of $f$-DP, in which the entire privacy behavior is captured by a single tradeoff curve. Our […]

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A question for people working in RL and image generative models (diffusion, flow based etc). There seems to be more emerging work in RL fine tuning techniques for these models. I’m interested to know – is it crazy to try to train these models from scratch with a reward signal only (i.e without any supervision data)? What techniques could be used to overcome issues with reward sparsity / cold start / training instability? submitted by /u/amds201 [link] [comments]

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Author(s): Rizwanhoda Originally published on Towards AI. Inside Vector Databases: Engineering High-Dimensional Search for Modern AI Systems The real bottleneck in modern AI systems is not the LLM. Photo by Huzeyfe Turan on UnsplashVector databases serve as specialized infrastructure for managing high-dimensional search within modern AI systems, helping to address challenges in quickly retrieving millions of embeddings with accuracy. The article explores their architecture, benefits over traditional databases, and various applications including semantic search, recommendation systems, and multimodal […]

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arXiv:2603.16883v1 Announce Type: new Abstract: Inertial measurement unit-based online handwriting recognition enables the recognition of input signals collected across different writing surfaces but remains challenged by uneven character distributions and inter-writer variability. In this work, we systematically investigate two strategies to address these issues: sub-word tokenization and concatenation-based data augmentation. Our experiments on the OnHW-Words500 dataset reveal a clear dichotomy between handling inter-writer and intra-writer variance. On the writer-independent split, structural abstraction via Bigram tokenization significantly improves performance […]

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Go 1.21 includes new features to improve compatibility. Before you stop reading, I know that sounds boring. But boring can be good. Back in the early days of Go 1, Go was exciting and full of surprises. Each week we cut a new snapshot release and everyone got to roll the dice to see what we’d changed and how their programs would break. We released Go 1 and its compatibility promise to remove the excitement, so that new […]

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arXiv:2601.12298v1 Announce Type: new Abstract: Edge deployment of low-batch large language models (LLMs) faces critical memory bandwidth bottlenecks when executing memory-intensive general matrix-vector multiplications (GEMV) operations. While digital processing-in-memory (PIM) architectures promise to accelerate GEMV operations, existing PIM-equipped edge devices still suffer from three key limitations: limited bandwidth improvement, component under-utilization in mixed workloads, and low compute capacity of computing units (CUs). In this paper, we propose CD-PIM to address these challenges through three key innovations. First, we […]

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