arXiv:2609.24526v2 Announce Type: replace Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints...
arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and...
arXiv:2604. 26963v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators.
The paper investigates how the skill paradigm—using modular abilities within agentic frameworks—works with small to medium open‑source language models (270 M–80 B parameters) in industrial, resource‑constrained settings. A large‑scale empirical study on an insurance‑claim classification task and a set of Python programming problems shows that very small models often fail to retrieve or select correct skills, while larger 30 B–80 B models benefit significantly from skill augmentation. The authors also present a deployment‑oriented GPU profile, reporting VRAM time occupancy (GB·min) across models, to help characterize GPU‑memory costs for different model scales. "whyItMatters":"The study provides concrete evidence on how skill augmentation scales with model size, task complexity, and GPU budget, offering practical deployment insights for industrial applications using open‑source language models."
LoongSage is a lightweight, fully asynchronous reinforcement learning framework designed for large language models that perform agentic tasks. It integrates with Megatron for distributed training and SGLang for high‑throughput inference, centralizing orchestration in a single controller and using Ray actors for GPU components. The framework introduces AgentFlow for unified execution of white‑box and black‑box agents, sliding‑window strategies to limit trajectory staleness, TransferMesh for efficient weight synchronization, and a memory‑efficient Teacher Manager that supports multi‑teacher on‑policy distillation. Experiments across four training runs show steady reward improvement, controlled train‑inference gaps, and sustained hardware utilization by eliminating synchronous idle periods.
arXiv:2610.03797v2 Announce Type: replace-cross Abstract: World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-...
arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.
arXiv:2608. 03701v1 Announce Type: cross Abstract: World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve.
OpenGCUA is an open‑stack project that aims to make generalist computer‑use agents (GCUs) fully accessible to the research community. The first release focuses on code execution, allowing agents to use intermediate results and programming skills directly in computer interaction, and introduces harness‑to‑model decision transfer to shift observation and task‑state decisions from fixed rules to the model. On the OSWorld‑v2 benchmark, the full configuration boosts task scores for all eight evaluated models—including GPT‑6 Astra and Claude Opus 5—by an average of +19.6 partial‑score points and cuts steps per task by 53 % compared to batched GUI‑only execution, while also providing a real‑device harness and cross‑device SDK. whyItMatters":"By releasing the full stack, OpenGCUA enables systematic study and broader experimentation with generalist computer‑use agents, addressing the current limitation of closed implementations and demonstrating significant performance gains across diverse models."
arXiv:2607. 22997v1 Announce Type: cross Abstract: Physical AI -- the integration of large vision-language-action (VLA) models with embodied agents that act in the real world -- has emerged as the next major frontier for AI, echoed by industry leaders such as Jensen Huang (``the next big thing is Physical AI, AI with a body,'' GTC Paris, June 2025) and Dr.
arXiv:2512. 23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important.
arXiv:2608. 12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance.