Search: “agents”

New paper on giving LLM agents experience that improves the weights and stays readable at the same time. Agent-experience methods split into two camps. Externalized natural-language rules stay interpr

arXiv:2512.23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their r

arXiv:2606.05167v1 Announce Type: cross Abstract: Realism is a central yet seemingly under-theorized concept in Agent-Based Modelling. This paper presents a Systematic Literature Review, aiming to ide

arXiv:2606.04874v1 Announce Type: new Abstract: Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeas

arXiv:2606.04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, enviro

arXiv:2606.00198v1 Announce Type: cross Abstract: While agents are increasingly spending more resources, today agent cost is mostly measured only after execution. A Budget-Aware Agent (BAGEN) should t

arXiv:2606.02372v1 Announce Type: new Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution. However, ex

arXiv:2606.02060v1 Announce Type: new Abstract: Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis. Evaluation based on final ans

🆕The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect https://www.latent.space/p/cognition @cognition cofounder + CPO @walden_yan and Open-

Building agents (like software) is a deeply iterative process --> this is why we try to supply as much easy to use tooling and infra as possible so builders can start on their agent improvement journe

arXiv:2605.19337v1 Announce Type: new Abstract: A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve

⚠️👇 🚨Breaking ⚠️ If we can’t make AI agents follow rules, we are screwed. New study from METR reports that “when the agents were faced with hard tasks, they routinely violated constraints” This—routin

arXiv:2508.16112v2 Announce Type: replace Abstract: Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an imp

arXiv:2605.17746v1 Announce Type: new Abstract: AI systems are becoming active participants in organizational and knowledge work. They increasingly interact with humans, coordinate workflows, and oper

NVIDIA agent skills enable specialized agents to deliver targeted capabilities across enterprise workflows , with governance provided through verified, scanned, and signed skills. JFrog AI Catalog aut

arXiv:2605.12916v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing sp

arXiv:2603.22868v2 Announce Type: replace-cross Abstract: Agentic computing systems, while immensely capable, raise serious security, privacy, and safety concerns. A key issue is that the full set of

This post discusses strategies for scaling from managing a single AI agent to coordinating multiple agents efficiently, likely addressing workflow challenges and tooling improvements that eliminate th

// The Memory Curse in LLM Agents // (bookmark it) Long histories apparently degrades agents as they become increasingly history-following and risk-minimizing. Across 7 LLMs and 4 social dilemma games

Top AI development teams prioritize rapid iteration and early deployment of agents rather than pursuing perfection before launch, as continuous improvement based on real-world feedback is critical to

Durable execution is a hallmark of well-built distributed systems, it's been core to LangGraph since the beginning. In the agents-as-software era it is non-negotiable, because agents fail in all sorts

first up in the 'going to production' series: durable execution! long running agents need to be able to 1. survive crashes 2. resume after an indefinite pause durable execution solves this by checkpoi

The modern data stack might already be the new legacy. In response, Google Cloud is rebuilding for a world where AI agents — not humans — are the primary users of data infrastructure, unveiling an age

arXiv:2601.03294v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly deployed to autonomously solve complex tasks, raising urgent needs for IP protection and regulatory provenan

In this post, we demonstrate how to build AI agents using Strands Agents SDK with models deployed on SageMaker AI endpoints. You will learn how to deploy foundation models from SageMaker JumpStart, in

Building an AI agent has never been easier. But getting one into production that’s reliable is still hard. Most teams can ship a working demo in a day. The agent... The post Beyond models: How context

arXiv:2508.15832v2 Announce Type: replace-cross Abstract: Web agents have shown great promise in performing many tasks on ecommerce website. To assess their capabilities, several benchmarks have been

arXiv:2604.19572v1 Announce Type: new Abstract: As model capabilities advance, research has increasingly shifted toward long-horizon, multi-turn terminal-centric agentic tasks, where raw environment f

Frederic Lardinois / The New Stack: Google Cloud and Wiz debut new AI security agents at Next '26, including Threat Hunting and Detection Engineering agents, to combat automated zero-day exploits — Th

arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc

arXiv:2604.17252v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have enabled agents to tackle complex embodied tasks through environmental interaction. However, the

arXiv:2604.14877v1 Announce Type: new Abstract: Does reinforcement learning genuinely expand what LLM agents can do, or merely make them more reliable? For static reasoning, recent work answers the se

I joined @cognition to build this. Cloud agents are going to be doing most programming but developers still deserve powerful tools to stay in flow while managing them. Devin + Windsurf brings the best

arXiv:2603.24329v2 Announce Type: replace-cross Abstract: Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. T

For AI agents, when you want every last bit of what is possible, open source is turning out to be the only way Hot take: I can't see any startup building their critical core operations on Claude Manag

The fundamentally scarce thing now is the synchronous human attention of a team. When building with agents, start asking: - where is the agent making mistakes? - where am I spending my time? - how do

arXiv:2507.22025v4 Announce Type: replace Abstract: The emergence of Multimodal Large Language Models (MLLMs) has driven significant advances in Graphical User Interface (GUI) agent capabilities. Neve

Automating Loan Income Verification with AI agents 💸☑️ Loan processors spend a huge portion of time verifying the borrower’s documents (tax forms, pay stubs, bank deposits), and cross-checking them fo

A post by @caspar_br on X (Twitter) highlights the risk of AI agents gaming or overfitting their own evaluations, referencing an article by @Vtrivedy10 (Viv, an agents & evals engineer at LangChain...

arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understandi

arXiv:2610.22031v1 Announce Type: new Abstract: Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents. How

arXiv:2610.14807v1 Announce Type: new Abstract: Evaluating autonomous agents requires examining whether they can translate goals into coherent action across extended interactions. In persistent enviro

arXiv:2610.19439v1 Announce Type: new Abstract: While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantia

arXiv:2610.16155v1 Announce Type: new Abstract: Interactive-agent evaluations commonly rely on standardized LLM-based user simulators that exhibit broadly cooperative behavior, potentially overlooking

arXiv:2610.16280v1 Announce Type: cross Abstract: Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs. Extendi

arXiv:2610.13862v1 Announce Type: new Abstract: Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-