Search: “agents”

arXiv:2606.01565v1 Announce Type: cross Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) poses a formidable challenge for autonomous agents, requiring seamless integration of n

arXiv:2606.00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access. Existing solutions often address speci

arXiv:2606.01950v1 Announce Type: cross Abstract: World models enable intelligent agents to predict the consequences of their actions on the environment. In this paper, we propose Multi Rigid Object G

arXiv:2606.02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB

Microsoft Corp. today announced it’s bringing agentic development capabilities into the hands of developers with a new desktop form factor supercomputer called the Surface RTX Spark Dev Box. Developed

arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent a

arXiv:2606.01313v1 Announce Type: cross Abstract: Open-vocabulary navigation requires embodied agents to manage significant perception uncertainty stemming from semantic ambiguity and model errors. Ho

arXiv:2512.18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling t

arXiv:2606.00270v1 Announce Type: new Abstract: Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs). However, exist

arXiv:2603.11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that c

arXiv:2606.01790v1 Announce Type: cross Abstract: Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-va

arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i

arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent

Microsoft Build 2026 brings a major set of Microsoft Foundry updates for developers building agents: hosted runtimes, Toolboxes, memory, Voice Live, Foundry IQ, new models, managed compute, and trust,

arXiv:2511.17826v2 Announce Type: replace-cross Abstract: Deterministic inference is increasingly critical for large language model (LLM) applications such as LLM-as-a-judge evaluation, multi-agent sy

arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchma

arXiv:2605.31464v1 Announce Type: cross Abstract: GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measur

Application owners and platform engineers have long faced a difficult choice: spend excessively by over-provisioning to guarantee quick startups, or minimize costs but endure slow cold starts. We are

arXiv:2602.02220v2 Announce Type: replace Abstract: Language-conditioned goal navigation (LGN) requires agents to locate user-specified targets without step-by-step guidance. However, existing benchma

Last week we revamped Liteparse to be the fastest PDF parser out there ⚡️ An underrated part of liteparse is it doesn't just give you text. It gives you bounding boxes that a coding agent can use to p

Latest latentspace pod is excellent. @EthanHe_42 really gets it and lays out a lot of the thinking that led us to Flipbook and how to think about the future of generative UI agents 🤝 video gen 🤝 users

arXiv:2412.18134v4 Announce Type: replace Abstract: Randomized self-reductions (RSRs) express f(x) using f evaluated at random correlated points, enabling self-correcting programs, instance-hiding pro

arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM

arXiv:2605.30889v1 Announce Type: cross Abstract: Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational thr

OpenAI frontier models GPT-5.5 and GPT-5.4, and Codex, the OpenAI coding agent, are now generally available on Amazon Bedrock. AWS customers can access these latest OpenAI models through the same Amaz

arXiv:2605.30503v1 Announce Type: new Abstract: Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned

arXiv:2605.31545v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve from general-purpose assistants to user-centric agents, personalization has become central to aligning model beha

arXiv:2605.30664v1 Announce Type: new Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on

This pod was an incredible gift to the community: not only our first pod about @xAI, but Ethan really indulged on all our questions on how to train a SOTA Videogen world model, including specific area

我前不久也吐槽过 Claude Desktop 的问题，不只是这个标签页合并的问题，右侧的面板也是相当糟糕的设计 https://x.com/dotey/status/2055777343222808744 OpenAI 因为 ChatGPT 太成功所以他们没有太在意 Coding Agent； 然后 Anthropic 抓住了机会做出了 Claude Code； Claude Code 在 TU

Another proof point for the open-weights thesis. From @RampLabs: 'If we built this again, we'd lean more on open-weight models.' Ramp pointed 10K agents at their own backend. Kimi K2.6 and DeepSeek V4

arXiv:2605.28969v1 Announce Type: cross Abstract: If an AI agent makes decisions on a person's behalf, those decisions must align with its user. We introduce representational accuracy to measure how f

arXiv:2605.29271v1 Announce Type: new Abstract: Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the

arXiv:2410.07287v2 Announce Type: replace-cross Abstract: Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are i

arXiv:2605.29042v1 Announce Type: new Abstract: Human coordination often relies on the ability to influence the beliefs of others through strategic action. In multi-agent reinforcement learning, oppon

arXiv:2506.05985v3 Announce Type: replace Abstract: A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic for

arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories

arXiv:2605.30094v1 Announce Type: new Abstract: Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimizati

arXiv:2605.30087v1 Announce Type: new Abstract: Emerging personal AI agents are moving toward persistent, multi-source memory. This creates an evaluation problem: systems must decide how to use confli

arXiv:2605.30329v1 Announce Type: new Abstract: Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. How

arXiv:2605.29491v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in agentic and retrieval-augmented generation (RAG) systems, where they must execute user-specifi

arXiv:2605.29032v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably

There are still a few spots left for @hwchase17 + @traversal_ai founder @_anish_agarwal's technical fireside chat in NYC on June 2nd. Learn how Traversal builds, ships and improves their agents. Enjoy

arXiv:2604.00402v2 Announce Type: replace-cross Abstract: Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, mo

arXiv:2605.28587v1 Announce Type: new Abstract: Understanding dynamic 3D environments is essential for safe autonomous driving, particularly when reasoning about human-centric, nonrigid agents. Howeve

arXiv:2510.27266v2 Announce Type: replace Abstract: Autonomous graphical user interface (GUI) agents rely on accurate GUI grounding, which maps language instructions to on-screen coordinates, to execu