Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past information. Most methods address this by summing competing loss terms, creating gradient conflicts that are managed with complex and often inefficient strategies such as external memory replay or parameter regularization. We propose a reformulation of the continual learning objective using Douglas-Rachford Splitting (DRS). This reframes the learning process not as a direct trade-off, but as a negotiation between […]

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arXiv:2603.25791v1 Announce Type: new Abstract: Existing hand-object interactions (HOI) methods are largely limited to rigid objects, while 4D reconstruction methods of articulated objects generally require pre-scanning the object or even multi-view videos. It remains an unexplored but significant challenge to reconstruct 4D human-articulated-object interactions from a single monocular RGB video. Fortunately, recent advancements in foundation models present a new opportunity to address this highly ill-posed problem. To this end, we introduce ArtHOI, an optimization-based framework that integrates and […]

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arXiv:2601.02609v1 Announce Type: cross Abstract: Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB–14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states–exceeding even A100-40GB capacity. We present Chronicals, an open-source training framework achieving 3.51x speedup over Unsloth through four synergistic optimizations: (1) fused Triton kernels eliminating 75% of memory traffic via RMSNorm (7x), SwiGLU (5x), and QK-RoPE (2.3x) fusion; (2) Cut Cross-Entropy reducing logit memory from 5GB to 135MB through […]

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arXiv:2405.06727v2 Announce Type: replace Abstract: In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation error can be bounded from above by a quantity proportional to the uniform norm of the target function and inversely proportional to the product of network width and depth. We inherit this approximation error bound from Fourier features residual networks, a type of neural network that […]

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arXiv:2602.18545v1 Announce Type: new Abstract: Property-based testing (PBT) is a popular technique for establishing confidence in software, where users write properties — i.e., executable specifications — that can be checked many times in a loop by a testing framework. In modern PBT frameworks, properties are usually written in shallowly embedded domain-specific languages, and their definition is tightly coupled to the way they are tested. Such frameworks often provide convenient configuration options to customize aspects of the testing process, […]

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arXiv:2603.16938v1 Announce Type: new Abstract: Contemporary AI governance frameworks rely heavily on post hoc oversight, policy guidance, and behavioral alignment techniques, yet these mechanisms become fragile as systems gain autonomy, speed, and operational opacity. This paper presents Aegis, a runtime governance architecture for autonomous AI systems that treats policy and legal constraints as execution conditions rather than advisory principles. Aegis binds each governed agent to a cryptographically sealed Immutable Ethics Policy Layer (IEPL) at system genesis and enforces […]

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Hi everyone! I hope you’re having a great sunday. Me and my collegue decided this week to rewrite an api made with Laravel Lumen using ASP.NET Core. While doing so, I was wondering wheter some kind of tool existed to help generate the documentation of the API (since we all know writing a documentation is the least fun thing in programming) and I came across Swagger.

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arXiv:2512.17632v2 Announce Type: replace Abstract: High-dimensional covariance estimation is notoriously sensitive to outliers. While statistically optimal estimators exist for general heavy-tailed distributions, they often rely on computationally expensive techniques like semidefinite programming or iterative M-estimation ($O(d^3)$). In this work, we target the specific regime of textbf{Sub-Weibull distributions} (characterized by stretched exponential tails $exp(-t^alpha)$). We investigate a computationally efficient alternative: the textbf{Cross-Fitted Norm-Truncated Estimator}. Unlike element-wise truncation, our approach preserves the spectral geometry while requiring $O(Nd^2)$ operations, which represents […]

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