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arXiv:2605.06832v1 Announce Type: cross Abstract: Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of

arXiv:2605.06833v1 Announce Type: cross Abstract: Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider fals

arXiv:2605.06835v1 Announce Type: cross Abstract: Tabular data plays an important role in many fields and industries, including those with elevated privacy cons

arXiv:2605.06839v1 Announce Type: cross Abstract: Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimiza

arXiv:2605.06846v1 Announce Type: cross Abstract: Recent work identifies secret loyalties as a distinct threat from standard backdoors. A secret loyalty causes

arXiv:2605.06850v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilitie

arXiv:2605.06859v1 Announce Type: cross Abstract: Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, w

arXiv:2605.06875v1 Announce Type: cross Abstract: Advanced driver-assistance systems (ADAS) require neural compute engines that deliver low-latency inference un

arXiv:2605.06885v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressi

arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable

arXiv:2605.06903v1 Announce Type: cross Abstract: Large language models are now embedded in everyday writing workflows, making reliable AI-generated text detect

arXiv:2605.06908v1 Announce Type: cross Abstract: Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance.

arXiv:2605.06914v1 Announce Type: cross Abstract: Recent methods expose intra-request parallelism in LLM outputs, allowing independent branches to decode concur

arXiv:2605.06920v1 Announce Type: cross Abstract: We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-

arXiv:2605.06924v1 Announce Type: cross Abstract: Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from

arXiv:2605.06927v1 Announce Type: cross Abstract: Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints whi

arXiv:2605.06936v1 Announce Type: cross Abstract: LLM-based agents are increasingly applied to the "last mile" of Electronic Design Automation (EDA): repairing

arXiv:2605.06938v1 Announce Type: cross Abstract: Building on the abstract Generalized Singular Value Decomposition (GSVD) theory of Brown et al. [2025], we pro

arXiv:2605.06946v1 Announce Type: cross Abstract: Sequence models face a fundamental tradeoff between memory capacity and computational efficiency. Transformers

arXiv:2605.06955v1 Announce Type: cross Abstract: Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to reco

arXiv:2605.06963v1 Announce Type: cross Abstract: This demo paper describes the development of the AI Teaching \& Learning Assistant, a modular Moodle plugin th

arXiv:2605.06965v1 Announce Type: cross Abstract: As language-based AI systems become more anthropomorphic, the question of whether they can have subjective exp

arXiv:2605.06971v1 Announce Type: cross Abstract: Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning syste

arXiv:2605.06977v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large l