arXiv:2604.04974v1 Announce Type: new Abstract: Video is a scalable observation of physical dynamics: it captures how objects move, how contact unfolds, and how scenes evolve under interaction — all without requiring robot action labels. Yet translating this temporal structure into reliable robotic control remains an open challenge, because video lacks action supervision and differs from robot experience in embodiment, viewpoint, and physical constraints. This survey reviews methods that exploit non-action-annotated temporal video to learn control interfaces for robotic […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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arXiv:2506.13717v2 Announce Type: replace-cross Abstract: Contrastive self-supervised learning based on point-wise comparisons has been widely studied for vision tasks. In the visual cortex of the brain, neuronal responses to distinct stimulus classes are organized into geometric structures known as neural manifolds. Accurate classification of stimuli can be achieved by effectively separating these manifolds, akin to solving a packing problem. We introduce Contrastive Learning As Manifold Packing (CLAMP), a self-supervised framework that recasts representation learning as a manifold packing […]

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arXiv:2602.07632v2 Announce Type: replace Abstract: In this work, we investigate the large-scale mean-field variational inference (MFVI) problem from a mini-batch primal-dual perspective. By reformulating MFVI as a constrained finite-sum problem, we develop a novel primal-dual algorithm based on an augmented Lagrangian formulation, termed primal-dual variational inference (PD-VI). PD-VI jointly updates global and local variational parameters in the evidence lower bound in a scalable manner. To further account for heterogeneous loss geometry across different variational parameter blocks, we introduce […]

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arXiv:2601.12137v1 Announce Type: new Abstract: The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture-of-Experts (MoE) architectures as a promising path towards greater efficiency. However, MoE models are plagued by two fundamental challenges: 1) a load imbalance problem known as the“rich get richer” phenomenon, where a few experts are over-utilized, and 2) an expert homogeneity problem, where experts learn redundant representations, negating their purpose. Current solutions typically employ an auxiliary load-balancing loss that, […]

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arXiv:2602.05068v1 Announce Type: new Abstract: Neural networks achieve strong empirical performance, but robustness concerns still hinder deployment in safety-critical applications. Formal verification provides robustness guarantees, but current methods face a scalability-completeness trade-off. We propose a hybrid verifier in a branch-and-bound (BaB) framework that efficiently tightens both upper and lower bounds until an $epsilon-$global optimum is reached or early stop is triggered. The key is an exact nonlinear program with complementarity constraints (NLP-CC) for upper bounding that preserves the […]

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Two months ago, I was looking for a way to program an AI for a video game and I stumbled upon Behavior Trees. Today I’ll be talking about what they are, why they are used and how. Behavior Trees Originally, behavior trees were used in the game industry to model the behavior of NPC characters, it then started getting used in other domains, such as robotics.

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I spent years calling models via API : Bedrock, Sagemaker, Anthropic. This week was about understanding what’s actually happening inside the box, and why that matters for building systems around it. Coming into this project, I had built production AI systems on AWS Bedrock using flows, agents. I knew how to wire models into applications. What I didn’t know was what the models were actually doing with the text I gave them, why RAG works at all, or […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## How to Build an AI Medical Scribe With AssemblyAI By @assemblyai [ 6 Min read ] Building a medical scribe requires more than transcription accuracy. It’s about creating a system that fits into clinical workflows while respecting privacy. Read More. The Engineering Leader’s Playbook […]

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