arXiv:2601.03392v1 Announce Type: new Abstract: Feedforward artificial neural networks (ANNs) trained on static images remain the dominant models of the the primate ventral visual stream, yet they are intrinsically limited to static computations. The primate world is dynamic, and the macaque ventral visual pathways, specifically the inferior temporal (IT) cortex not only supports object recognition but also encodes object motion velocity during naturalistic video viewing. Does IT’s temporal responses reflect nothing more than time-unfolded feedforward transformations, framewise features […]

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:::info Authors: Mathilde Caron, Facebook AI Research, Inria Hugo Touvron, Facebook AI Research, Sorbonne University Ishan Misra, Facebook AI Research Herve Jegou, Facebook AI Research Julien Mairal, Inria Piotr Bojanowski, Facebook AI Research Armand Joulin, Facebook AI Research ::: Abstract In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [19] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we […]

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In this tutorial, we explore kvcached, a dynamic KV-cache implementation on top of vLLM, to understand how dynamic KV-cache allocation transforms GPU memory usage for large language models. We begin by setting up the environment and deploying lightweight Qwen2.5 models through an OpenAI-compatible API, ensuring a realistic inference workflow. We then design controlled experiments where we simulate bursty workloads to observe how memory behaves under both elastic and static allocation strategies. Through systematic measurement and visualization, we directly […]

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Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a model of a physical system. This thesis proposes a new method of transfer learning for Recurrent Neural Networks (RNNs) based on time-warping. We prove that for a class of linear, first-order differential equations known as time lag models, an LSTM can approximate these systems with any desired accuracy, […]

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arXiv:2602.12318v1 Announce Type: new Abstract: We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow these character specifications, they can occasionally violate them in large-scale deployments. In this work, we aim to identify types of queries that are likely to produce such character violations at deployment, using much less than deployment-level compute. To do this, we introduce abstractive red-teaming, where we search […]

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Despite significant advances in Large Reasoning Models (LRMs) driven by reinforcement learning with verifiable rewards (RLVR), this paradigm is fundamentally limited in specialized or novel domains where such supervision is prohibitively expensive or unavailable, posing a key challenge for test-time adaptation. While existing test-time methods offer a potential solution, they are constrained by learning from static query sets, risking overfitting to textual patterns. To address this gap, we introduce Test-Time Variational Synthesis (TTVS), a novel framework that enables […]

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SQL is still central to modern systems, but much of the work around it is repetitive. Developers spend hours fixing typos, reformatting queries, and rewriting patterns they already know by heart. To be precise, nearly 40% of SQL time goes into these routine tasks every week. That’s a big reason AI tools are spreading so quickly. Today, about 84% of developers already use, or plan to use, AI to handle this kind of work. Instead of wrestling with boilerplate, this helps engineers focus on design, […]

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