arXiv:2604.18543v4 Announce Type: replace
Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue…
arXiv:2601.00791v2 Announce Type: replace
Abstract: Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and…
arXiv:2602.11801v2 Announce Type: replace
Abstract: Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex…
arXiv:2606.12299v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often…
arXiv:2606.10632v1 Announce Type: cross
Abstract: Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in…
arXiv:2606.10738v1 Announce Type: cross
Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for…
arXiv:2605.27914v2 Announce Type: replace-cross
Abstract: Benchmarking is mature where answers are verifiable — math, code, reasoning — but the fastest-growing uses of LLMs are subjective and…
arXiv:2606.10698v1 Announce Type: cross
Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a…
arXiv:2606.10130v1 Announce Type: cross
Abstract: Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training…
arXiv:2606.10361v1 Announce Type: cross
Abstract: Convergence-rate analysis for classifiers is often conducted under either Tsybakov margin or Massart margin. The former is a relatively weak…