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arXiv:2604.02558v1 Announce Type: new Abstract: We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that …
arXiv:2604.03016v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual …
arXiv:2604.02585v1 Announce Type: new Abstract: LLMs are increasingly used for high-stakes decision-making, yet their sensitivity to spurious contextual information can introduce harmful biases. This is …
arXiv:2604.02601v1 Announce Type: new Abstract: Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed …
arXiv:2604.03141v1 Announce Type: new Abstract: Evaluating the factuality of long-form output generated by large language models (LLMs) remains challenging, particularly when responses are open-ended and …
arXiv:2604.02525v1 Announce Type: new Abstract: Low-precision training (LPT) commonly employs Hadamard transforms to suppress outliers and mitigate quantization error in large language models (LLMs). However, …
arXiv:2604.02556v1 Announce Type: new Abstract: Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. …
arXiv:2604.03136v1 Announce Type: new Abstract: As AI-generated fiction becomes increasingly prevalent, questions of authorship and originality are becoming central to how written work is evaluated. …
arXiv:2604.02733v1 Announce Type: new Abstract: Reasoning benchmarks typically evaluate whether a model derives the correct answer from a fixed premise set, but they under-measure a …
arXiv:2604.02371v1 Announce Type: cross Abstract: Visual long-document understanding is critical for enterprise, legal, and scientific applications, yet the best performing open recipes have not explored …
arXiv:2604.02362v1 Announce Type: cross Abstract: Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference …
arXiv:2302.08150v2 Announce Type: cross Abstract: We use both Bayesian and neural models to dissect a data set of Chinese learners' pre- and post-interventional responses to …