arXiv:2602.11236v1 Announce Type: new Abstract: Building general-purpose embodied agents across diverse hardware remains a central challenge in robotics, often framed as the ”one-brain, many-forms” paradigm. Progress is hindered by fragmented data, inconsistent representations, and misaligned training objectives. We present ABot-M0, a framework that builds a systematic data curation pipeline while jointly optimizing model architecture and training strategies, enabling end-to-end transformation of heterogeneous raw data into unified, efficient representations. From six public datasets, we clean, standardize, and balance samples […]

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arXiv:2603.03785v1 Announce Type: new Abstract: Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is utilized only for retrospective fusion, and remains discarded in prospective trial design due to bias concerns. We argue this “tabula rasa” data acquisition strategy is fundamentally inefficient. In this work, we propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior. This […]

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arXiv:2601.17108v1 Announce Type: new Abstract: This paper proposes a Mamba-assisted neural network framework incorporating self-attention mechanism to achieve improved channel estimation with low complexity for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation […]

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arXiv:2602.08350v1 Announce Type: cross Abstract: We study the sample complexity of the best-case Empirical Risk Minimizer in the setting of stochastic convex optimization. We show that there exists an instance in which the sample size is linear in the dimension, learning is possible, but the Empirical Risk Minimizer is likely to be unique and to overfit. This resolves an open question by Feldman. We also extend this to approximate ERMs. Building on our construction we also show that […]

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arXiv:2107.08686v3 Announce Type: replace-cross Abstract: Stochastic optimization is a cornerstone of modern machine learning. This paper studies the generalization performance of two classical stochastic optimization algorithms: stochastic gradient descent (SGD) and Nesterov’s accelerated gradient (NAG). We establish new learning rates for both algorithms, with improved guarantees in some settings or comparable rates under weaker assumptions in others. We also provide numerical experiments to support the theory.

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Efficiently and accurately converting floating-point numbers to decimal strings is a critical challenge in numerical computation and data exchange. While existing algorithms like Ryu, Dragonbox, and Schubfach satisfy the Steele-White (SW) principle for accuracy, they often suffer from performance bottlenecks due to branch prediction failures and high-precision multiplication overhead. This paper presents a novel floating-point to string conversion algorithm called “xjb”, an optimized variant of the Schubfach algorithm designed to deliver superior performance for IEEE754 single-precision (binary32) and […]

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Federated learning (FL) under data heterogeneity remains challenging: existing methods either ignore client differences (FedAvg), require costly cluster discovery (IFCA), or maintain per-client models (Ditto). All degrade when data is sparse or heterogeneity is multi-dimensional. We propose conditioning a single global model on locally-computed PCA statistics of each client’s training data, requiring zero additional communication. Evaluating across 97~configurations spanning four heterogeneity types (label shift, covariate shift, concept shift, and combined heterogeneity), four datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100), and […]

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This study proposes a novel distributionally robust portfolio optimization framework based on Wasserstein generative modeling, aiming to address the challenges of distributional uncertainty, tail risk, and structural drift in financial markets. The model integrates Wasserstein distance-based robust optimization with generative adversarial learning to jointly enhance risk control and return stability. Specifically, a Wasserstein generative adversarial network is employed to reconstruct the latent distribution of asset returns, enabling the capture of non-Gaussian features and tail dependencies in complex market […]

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LLMs hallucinate not because they are uncertain — but because they are overconfident. We introduce the Entropy Corridor, a non-invasive inference-time method that constrains layer-wise activation entropy within a bidirectional range. Unlike prior detection-only approaches, our method corrects hallucination in real time by targeting the specific layers where overconfidence originates. On TruthfulQA, the corridor halves hallucination rates while preserving truthfulness — at under 2% latency overhead, with no retraining required. https://x.com/elfatone82/status/2041258848992768289?s=46 submitted by /u/Both_Report_5367 [link] [comments]

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