Search: “agents”

arXiv:2606.30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emoti

arXiv:2606.30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS). However, writing corr

arXiv:2606.28758v1 Announce Type: cross Abstract: Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relyi

arXiv:2603.09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they

arXiv:2606.27577v1 Announce Type: cross Abstract: This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajec

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory

arXiv:2603.09241v2 Announce Type: replace Abstract: Visual navigation requires agents to reach goals in complex environments through perception and planning. World models address this task by simulati

arXiv:2606.27406v1 Announce Type: cross Abstract: Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supp

arXiv:2606.28217v1 Announce Type: cross Abstract: We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and partic

arXiv:2606.27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward. Many planning environments, however, are not vectors or images; they are g

> Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. We do this for you in Deep Agents - see

Sakana Fugu Technical Report Instead of training one larger model, Sakana AI trains an orchestrator that reads each query and dynamically routes or composes GPT-5.5, Gemini-3.1-Pro, Claude Opus 4.8 an

arXiv:2606.27180v1 Announce Type: cross Abstract: Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly a

Dynamic workflows (generating harnesses on the fly) are a new form of test-time compute. But LLMs aren't great at building them. I often have to steer agents to generate complex patterns. Curious how

arXiv:2606.27068v1 Announce Type: cross Abstract: Open-source game theory studies agents whose behavior may depend on one another's decision procedures, but most existing models use discrete or symbol

arXiv:2606.27123v1 Announce Type: cross Abstract: Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllab

arXiv:2606.26523v1 Announce Type: new Abstract: We develop a framework for interpreting AI systems as agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanis

arXiv:2601.04390v2 Announce Type: replace Abstract: High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create. Such figures must

The big lesson from training @cursor_ai Composer 2: models exploit flaws in their training environment before learning what you actually want. Real RL for coding agents means production-faithful envir

Today on TITV: -Trump asks OpenAI to stagger release of new model | @leomschwartz & @amir, The Information -Google pressures publishers on AI licensing | @anngehan -Inside an AI power user’s agent wor

arXiv:2606.25527v1 Announce Type: new Abstract: Online reinforcement learning (RL) agents increasingly depend on knowledge acquired offline to achieve practical efficiency. Originally studied in offli

Incident response company BreachRx Inc. today launched the Rex Platform, an agentic artificial intelligence incident command center built for a future in which AI-accelerated attacks set off several b

arXiv:2604.25496v2 Announce Type: replace Abstract: Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction.

arXiv:2606.25497v1 Announce Type: new Abstract: Object-Goal Navigation (ObjNav) requires embodied agents to autonomously locate specified targets using only egocentric visual observations. Existing mo

arXiv:2606.25535v1 Announce Type: new Abstract: Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks

arXiv:2606.25880v1 Announce Type: new Abstract: Embodied Visual Tracking (EVT) requires an agent to continuously follow a specified target while actively moving through dynamic environments. However,

arXiv:2606.26027v1 Announce Type: new Abstract: Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancin

arXiv:2606.25964v1 Announce Type: cross Abstract: Small (sim2B) GUI-grounding agents are attractive for on-device deployment, accessibility tooling, and low-cost iteration, but at this scale they face

Decisions about how to use AI in your organization are increasingly organizational design and strategy decisions, not IT choices: How do you integrate agents into your firm? What intelligence will you

arXiv:2606.23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (extit{triggering}) under tight constraint

arXiv:2601.18747v2 Announce Type: replace-cross Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often comp

The conversations at last week’s AWS Summit NYC 2026 showed that AI evolution is entering a new phase. From physical robots tackling labor shortages to agentic systems reshaping enterprise operations,

arXiv:2606.21775v1 Announce Type: new Abstract: Recently, world models have emerged as a promising paradigm for building intelligent agents by learning predictive models that estimate future environme

arXiv:2606.23521v1 Announce Type: cross Abstract: Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losin

arXiv:2606.21496v1 Announce Type: cross Abstract: Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-spe

arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte

arXiv:2602.06776v2 Announce Type: replace-cross Abstract: We study the transit stop placement (TrSP) problem in general metric spaces, where agents travel between source-destination pairs and may eith

arXiv:2606.22424v1 Announce Type: new Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions in unseen scenes. While Large

arXiv:2606.21097v1 Announce Type: cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

arXiv:2606.21136v1 Announce Type: new Abstract: Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional tempor

arXiv:2606.20999v1 Announce Type: cross Abstract: Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models l

arXiv:2606.23296v1 Announce Type: new Abstract: Developing generalist embodied agents requires interactive environments providing visually realistic feedback and accurate action-conditioned dynamics.

arXiv:2506.02849v3 Announce Type: replace-cross Abstract: In this letter we study 1v1 quadrotor pursuit-evasion, where a pursuer and an evader are trained via reinforcement learning (RL) by competing

Live Stream: Welcome to open source AI Lots of new folk are starting out on their journey with open models. Come join our livestream with all your questions about local models, open coding agents, and

arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost

In this post, you will learn patterns for implementing production-ready multi-tenant systems using Amazon Bedrock AgentCore. You will see these patterns demonstrated through healthcare AI agents that