arXiv:2602.03951v1 Announce Type: new Abstract: Robust generalization under distribution shift remains difficult to monitor and optimize in the absence of target-domain labels, as models with similar in-distribution accuracy can exhibit markedly different out-of-distribution (OOD) performance. While prior work has focused on training-time regularization and low-order representation statistics, little is known about whether the geometric structure of learned embeddings provides reliable post-hoc signals of robustness. We propose a geometry-based diagnostic framework that constructs class-conditional mutual k-nearest-neighbor graphs from in-distribution […]

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In this tutorial, we build a complete, production-style pipeline for detecting and redacting personally identifiable information using the OpenAI Privacy Filter. We begin by setting up the environment and loading a token classification model that identifies multiple categories of sensitive data, including names, emails, phone numbers, addresses, and secrets. We then design helper functions to normalize labels, extract structured spans, and transform raw model outputs into usable formats. From there, we implement a configurable redaction system that allows […]

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arXiv:2603.22325v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orthogonal mechanisms: RNNs compress the entire past into a fixed-size state, whereas self-attention’s state stores every past time step growing its state (the KV cache) linearly with the sequence length. This results in orthogonal strengths and weaknesses. Self-attention layers excel at retrieving information in the context but have large […]

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arXiv:2601.12291v1 Announce Type: new Abstract: Scalable and maintainable map representations are fundamental to enabling large-scale visual navigation and facilitating the deployment of robots in real-world environments. While collaborative localization across multi-session mapping enhances efficiency, traditional structure-based methods struggle with high maintenance costs and fail in feature-less environments or under significant viewpoint changes typical of crowd-sourced data. To address this, we propose OPENNAVMAP, a lightweight, structure-free topometric system leveraging 3D geometric foundation models for on-demand reconstruction. Our method unifies […]

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arXiv:2603.20242v1 Announce Type: new Abstract: Many speech enhancement (SE) methods rely on continuous representations. Recently, discrete audio tokens have been explored to enable autoregressive generation for SE. However, it remains unclear whether discretization itself consistently improves SE performance. In this paper, we introduce LL-SDR, a token-based speech enhancement framework that explicitly leverages discretization to better separate speech and noise. Our first contribution is a Variance-Ordered Residual Vector Quantizer (VO-RVQ), designed to disentangle speech and noise distributions during tokenization. […]

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arXiv:2309.08945v2 Announce Type: replace-cross Abstract: In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given input instance such that the classifier’s predicted label is changed in a desired way. Examples of these “inverse classification” problems are counterfactual explanations, adversarial examples and model inversion. All of them are fundamentally optimization problems over the input instance vector involving a […]

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arXiv:2509.25802v2 Announce Type: replace Abstract: We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wasserstein space. This approach overcomes key limitations of classical vector-based GSP, including the assumption of synchronous observations over vertices, the inability to capture uncertainty, and the requirement for strict correspondence in graph filtering. By representing signals as distributions, GDSs naturally encode uncertainty and stochasticity, while strictly generalizing traditional graph […]

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arXiv:2602.18549v1 Announce Type: new Abstract: The sudden influx of “TikTok refugees” into the Chinese platform RedNote in early 2025 created an unprecedented, large-scale online cross-cultural communication event between the West and East. Although prior HCI research has studied user behavior in social media, most work remains confined to monolingual or single-cultural contexts, leaving cross-linguistic and cultural dynamics underexplored. To address this gap, we focused on a particularly challenging cross-cultural encoding-decoding task that remains stubbornly beyond the reach of […]

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Hey everyone, I’m curious to hear if its possible to break into and do RL research/personal projects in robotics or related areas on a Mac M1 device? Aside from typical gym projects and stuff I suppose. I know there is the genesis engine so would that be the only option or are there other possibilities? Appreciate your thoughts. submitted by /u/Sad-Throat-2384 [link] [comments]

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