arXiv:2602.12385v1 Announce Type: new Abstract: High-performance autonomous mobile robots endure significant mechanical stress during in-the-wild operations, e.g., driving at high speeds or over rugged terrain. Although these platforms are engineered to withstand such conditions, mechanical degradation is inevitable. Structural damage manifests as consistent and notable changes in kinodynamic behavior compared to a healthy vehicle. Given the heterogeneous nature of structural failures, quantifying various damages to inform kinodynamics is challenging. We posit that natural language can describe and thus […]

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I have an ACL submission, which I suspect that there is a chance of desk reject. Tonight is ICML abstract deadline, can anyone give me some advice, if I should submit abstract for this paper as insurance or not? (May rename and paraphrase through abstract), does it violate ACL policy of dual submission? If until ICML deadline there is no desk reject notification, I will not submit to ICML submitted by /u/_karma_collector [link] [comments]

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The technical design decisions that turned a personal hobby project into 2026’s most-discussed AI agent An open-source AI agent hit 100,000 GitHub stars in under two months. That’s not hype, that’s a signal. OpenClaw (née Clawdbot, briefly Moltbot) isn’t just another chatbot wrapper. It’s a locally-deployed autonomous agent that lives in your messaging apps and executes tasks on your machine. And beneath the viral TikTok demos and space lobster memes lies an architecture worth studying. Let’s dissect it. The Problem OpenClaw […]

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arXiv:2512.23763v2 Announce Type: replace-cross Abstract: We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times, or measurement angles, within a single optimization loop. By optimizing measurement locations directly rather than weighting a dense grid of candidates, the proposed approach enforces sparsity by […]

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arXiv:2602.05869v1 Announce Type: new Abstract: We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order-$k$ low-rank tensor of dimension $n times cdots times n$ from a subset of its entries. Unlike the standard uniform entry model (i.e., i.i.d. samples from $[n]^k$), wedge sampling allocates observations to structured length-two patterns (wedges) in an associated bipartite sampling graph. By directly promoting these length-two connections, the sampling design strengthens the spectral signal […]

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Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel […]

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arXiv:2602.03514v2 Announce Type: replace-cross Abstract: Modern learning systems often interpolate training data while still generalizing well, yet it remains unclear when algorithmic stability explains this behavior. We model training as a function-space trajectory and measure sensitivity to single-sample perturbations along this trajectory. We propose a contractive propagation condition and a stability certificate obtained by unrolling the resulting recursion. A small certificate implies stability-based generalization, while we also prove that there exist interpolating regimes with small risk where such […]

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arXiv:2601.18838v1 Announce Type: new Abstract: Particle Mesh Ewald (PME) methods accelerated through Fast Fourier Transforms (FFTs) for their reciprocal part are widely used to solve N -body problems over periodic structures with Laplace-like kernels. The FFT dependence of classical PME may mitigate its performance on parallel distributed-memory architectures. We here introduce a new variant of the reciprocal part based on Sum of Kronecker Products (SKP) instead of FFT. Moreover, our implementation of this new method is not linearithmic […]

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arXiv:2602.17698v1 Announce Type: new Abstract: Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains challenging due to highly non-uniform weight sensitivity and the lack of principled precision allocation. Existing solutions use irregular fine-grained mixed-precision with high runtime overhead or rely on heuristics or highly constrained precision allocation strategies. In this work, we propose ScaleBITS, a mixed-precision quantization framework that enables automated, […]

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