Large Language Models

Another iPhone update has just reached its first developer beta. There was a chance it would include the first glimpse of the brand-new Siri, but so far there’s

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Lightview is an open-source UI toolkit designed to enable safe collaboration between large language models and developers. By introducing a sandboxed computatio

This article explains how agentic AI is transforming data engineering by shifting systems from batch-based analytics to real-time, context-driven architectures.

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phi-1.5 is a 1.3B-parameter Transformer trained mainly on synthetic, textbook-quality data. Despite its small size, it matches or beats much larger models on co

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arXiv:2603.25747v1 Announce Type: new Abstract: The rapid evolution of Large Multimodal Models (LMMs) has enabled agents to perform complex digital and physical

arXiv:2603.26005v1 Announce Type: new Abstract: The growing availability of building operational data motivates the use of reinforcement learning (RL), which ca

arXiv:2603.26266v1 Announce Type: new Abstract: Large vision-language models have endowed GUI agents with strong general capabilities for interface understandin

arXiv:2603.26499v1 Announce Type: new Abstract: Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous

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 re

arXiv:2603.26535v1 Announce Type: new Abstract: We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Grou

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

arXiv:2603.25764v1 Announce Type: cross Abstract: As LLM-based agents are deployed in production systems, understanding their behavioral consistency (whether th

arXiv:2603.25766v1 Announce Type: cross Abstract: The integration of Vision-Language-Action (VLA) models into autonomous driving systems offers a unified framew

arXiv:2603.25768v1 Announce Type: cross Abstract: Functional verification remains a critical bottleneck in modern IC development cycles, accounting for approxim

arXiv:2603.25769v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but

arXiv:2603.25770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codeba

arXiv:2603.25771v1 Announce Type: cross Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios

arXiv:2603.25796v1 Announce Type: cross Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear n

arXiv:2603.25813v1 Announce Type: cross Abstract: We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, trai

arXiv:2603.25823v1 Announce Type: cross Abstract: Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks t

arXiv:2603.25839v1 Announce Type: cross Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over comp

arXiv:2603.25841v1 Announce Type: cross Abstract: Current multimodal large language models (MLLMs) cannot effectively utilize eye-gaze information for video und

arXiv:2603.25861v1 Announce Type: cross Abstract: Activation-based probes have emerged as a promising approach for detecting deceptively aligned AI systems by i

arXiv:2603.25864v1 Announce Type: cross Abstract: Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software

arXiv:2603.25898v1 Announce Type: cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from on

arXiv:2603.25924v1 Announce Type: cross Abstract: Multimodal AI systems are evaluated by downstream task accuracy, but high accuracy does not mean the underlyin

arXiv:2603.25931v1 Announce Type: cross Abstract: Flow-matching video generators produce temporally coherent, high-fidelity outputs yet routinely violate elemen

arXiv:2603.25942v1 Announce Type: cross Abstract: Multi-modal Large Language Models (MLLMs) show promise in video understanding. However, their reasoning often

arXiv:2603.25960v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in medical settings, yet their sensitivity to prompt fo

arXiv:2603.25981v1 Announce Type: cross Abstract: Navigating to a visually specified goal given natural language instructions remains a fundamental challenge in

arXiv:2603.26008v1 Announce Type: cross Abstract: While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven pe

arXiv:2603.26045v1 Announce Type: cross Abstract: We present H-Node Adversarial Noise Cancellation (H-Node ANC), a mechanistic framework that identifies, exploi

arXiv:2603.26064v1 Announce Type: cross Abstract: Non-contact automatic deception detection remains challenging because visual and auditory deception cues often

arXiv:2603.26078v1 Announce Type: cross Abstract: Subject-driven text-to-image diffusion models have achieved remarkable success in preserving single identities