Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2607.10664v1 Announce Type: new Abstract: In this paper, we provide a systematic investigation of SO(2) theory to machine learning interatomic potentials (MLIPs) and identify the limitations…
arXiv:2508.00110v2 Announce Type: replace Abstract: Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the…
arXiv:2607.10410v1 Announce Type: new Abstract: Reliable forecasting of several interrelated environmental variables - such as regional precipitation and temperature, or other correlated geophysical fields - across…
arXiv:2511.04275v2 Announce Type: replace Abstract: Conformal prediction has emerged as a powerful framework for constructing distribution-free prediction sets with guaranteed coverage assuming only the exchangeability assumption.…
arXiv:2510.14074v2 Announce Type: replace Abstract: We develop a framework for analyzing the learning dynamics of high-dimensional problems trained using one-pass stochastic gradient descent (SGD) with data…
arXiv:2404.03578v3 Announce Type: replace-cross Abstract: The sim-to-real gap, which represents the disparity between training and testing environments, poses a significant challenge in reinforcement learning (RL). A…
arXiv:2606.20859v2 Announce Type: replace Abstract: A fundamental assumption in statistics and machine learning is that ``the future looks like the past,'' formalized as exchangeability: the joint…
arXiv:2607.09747v1 Announce Type: new Abstract: When is a material system a candidate for life at all? We argue that this question is prior to behavior, functional…
arXiv:2607.10041v1 Announce Type: new Abstract: We study a symbolic search space for the Collatz conjecture based on finite exponent codes of the accelerated map. Each code…
arXiv:2607.11065v1 Announce Type: new Abstract: Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely…
arXiv:2607.11289v1 Announce Type: new Abstract: Backpropagation is the computational engine of deep learning, yet its mathematical structure is typically treated as a procedural traversal of computational…
arXiv:2607.09889v1 Announce Type: cross Abstract: Fixed-state sequence models compress an unbounded past into a bounded state, which caps their associative recall at roughly the state dimension;…
arXiv:2607.10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimensional representations have…
arXiv:2607.10021v1 Announce Type: cross Abstract: Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the…
arXiv:2607.11445v1 Announce Type: cross Abstract: A substantial number of patients experience diminished mobility due to disabilities, diseases, or accidents. Although modern prostheses, powered by deep neural…
arXiv:2607.10681v1 Announce Type: cross Abstract: In pre-LayerNorm looped transformers, LayerNorm inside the recurrent block acts as an implicit gain controller: by coupling the block's local Lipschitz…
arXiv:2607.11826v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in…
arXiv:2607.11821v1 Announce Type: cross Abstract: In multi-objective combinatorial optimization, unsupported non-dominated points typically outnumber supported points and are often significantly more challenging to compute. Recent studies…
arXiv:2511.02584v2 Announce Type: replace-cross Abstract: Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the…
arXiv:2512.23945v2 Announce Type: replace Abstract: Real-world constrained multi-objective optimization problems (CMOPs) commonly involve multiple constraints, and understanding and exploiting their coupling relationships is crucial for efficient…
arXiv:2606.13485v2 Announce Type: replace-cross Abstract: Safe rehabilitation is an interaction-dynamics problem: the controller must regulate a prescribed motion while absorbing involuntary spasm, voluntary effort, actuator compliance,…
arXiv:2512.09586v2 Announce Type: replace-cross Abstract: Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework…
arXiv:2607.04597v2 Announce Type: replace Abstract: In this paper, we study the universal approximation property of residual neural networks, and obtain some new results. For input and…
arXiv:2607.09697v1 Announce Type: new Abstract: Existing safety mechanisms for multimodal large language models (MLLMs) face a fundamental trade-off between safety and utility. Model fine-tuning achieves robust…