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arXiv:2603.25862v1 Announce Type: cross Abstract: Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from n…
arXiv:2603.25841v1 Announce Type: cross Abstract: Current multimodal large language models (MLLMs) cannot effectively utilize eye-gaze information for video understanding, even when gaze cues are supp…
arXiv:2603.25839v1 Announce Type: cross Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new ligh…
arXiv:2603.25823v1 Announce Type: cross Abstract: Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex…
arXiv:2603.25821v1 Announce Type: cross Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient …
arXiv:2603.25813v1 Announce Type: cross Abstract: We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert langu…
arXiv:2603.25796v1 Announce Type: cross Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representa…
arXiv:2603.25779v1 Announce Type: cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challe…
arXiv:2603.25777v1 Announce Type: cross Abstract: There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. However, u…
arXiv:2603.25771v1 Announce Type: cross Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-t…
arXiv:2603.25770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codebases through either agentic exploration o…
arXiv:2603.25769v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and stru…
arXiv:2603.25768v1 Announce Type: cross Abstract: Functional verification remains a critical bottleneck in modern IC development cycles, accounting for approximately 70% of total development time in m…
arXiv:2603.25767v1 Announce Type: cross Abstract: Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally …
arXiv:2603.25766v1 Announce Type: cross Abstract: The integration of Vision-Language-Action (VLA) models into autonomous driving systems offers a unified framework for interpreting complex scenes and …
arXiv:2603.25764v1 Announce Type: cross Abstract: As LLM-based agents are deployed in production systems, understanding their behavioral consistency (whether they produce similar action sequences when…
arXiv:2603.25758v1 Announce Type: cross Abstract: Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in d…
arXiv:2603.25750v1 Announce Type: cross Abstract: As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-…
arXiv:2603.25749v1 Announce Type: cross Abstract: Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC a…
arXiv:2502.09867v2 Announce Type: cross Abstract: Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have l…
arXiv:2603.26535v1 Announce Type: new Abstract: We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) th…
arXiv:2603.26512v1 Announce Type: new Abstract: Existing methods for text-to-CAD generation either operate in a single pass with no geometric verification or rely on lossy visual feedback that cannot …
arXiv:2603.26499v1 Announce Type: new Abstract: Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample …
arXiv:2603.26266v1 Announce Type: new Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insuf…