人工智能
83 篇 · 资讯流Gym-Anything for Robotics: Scaling physical simulations with digital agents
Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real w
原文 ↗Grounding LLM Agents in Business Processes: Process-Aware Knowledge Graphs for Reliable Multi-Step Service Invocation
Large language model (LLM) agents that call enterprise APIs must select the correct endpoints, follow process order, use
原文 ↗TAPESTRY: A Single Backbone for Video Generation and Robot Control
Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control
原文 ↗Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training,
原文 ↗Context Is Not Free: Carbon-Aware LLM Routing for Multi-Turn LLM Web Services across Geo-Distributed Data Centers
The sustainability of the Web has drawn growing attention, as large language model (LLM)-based web services leave a subs
原文 ↗LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference
The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental parad
原文 ↗A Deterministic Security Runtime for LLM Agents on MPI and Batch-Scheduled HPC Systems: Consensus-Governed, Hardware-Rooted Supervision of Every Rank
Large language model agents launch simulations on high-performance computing (HPC) systems, where one sbatch or mpirun c
原文 ↗Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-us
原文 ↗Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answeri
原文 ↗Automated Materials Data and Knowledge Extraction using Machine Learning and Large Language Models
Over the past decade, substantial efforts have been devoted to developing machine learning (ML) methods for extracting m
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