Search: “agents”

arXiv:2602.16246v3 Announce Type: replace Abstract: Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Ben

The dual-threat landscape of enterprise AI security is coming into focus. The same autonomous agents transforming workforce productivity are also expanding the attack surface — and most organizations

Coming up at Interrupt: 🎙️ How @clay scales their GTM Engineering agents with Head of AI @JeffBarg. 🎙️ @ummadisetti and @kordelfrance on how @Toyota Motor North America equipped 56,000 employees with

Cursor highlights that companies like Decagon, Amplitude, BILT, and Snyk leverage development environments within Cursor to enable AI agents to complete tasks independently from start to finish. This

arXiv:2605.11051v1 Announce Type: cross Abstract: LLM-based Software Engineering agents face a critical bottleneck: context length limitations cause failures on complex, long-horizon tasks. One promis

arXiv:2605.11225v1 Announce Type: cross Abstract: Large language model (LLM)-based agents frequently generate seemingly coherent plans that fail upon execution due to infeasible actions, constraint vi

arXiv:2605.08426v1 Announce Type: cross Abstract: Ensuring that AI agents behave safely and beneficially when interacting with other parties has emerged as one of the central challenges of modern AI s

arXiv:2605.10832v1 Announce Type: new Abstract: Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visua

arXiv:2605.07926v1 Announce Type: new Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar wo

arXiv:2605.07110v1 Announce Type: new Abstract: Computer-use agents(CUAs)are moving frombounded benchmarks toward real software environments, wherethey operate browsers, desktops, mobile applications,

arXiv:2605.07161v1 Announce Type: new Abstract: AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE). Current SR

arXiv:2506.21582v5 Announce Type: replace-cross Abstract: Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrie

Harrison Chase suggests that AI agents designed to handle knowledge work tasks will probably require browser capabilities to effectively access and interact with web-based information and resources. T

My favourite new stack: Agents + MCP + Markdown + HTML “Files over apps” is a vibe! LLM Wikis + HTML Artifacts are insanely powerful. You should seriously consider this in your workflows. LLM Wikis ca

one way to view langsmith is as a platform for the whole org to collaborate on building agents helps speed up that feedback loop between different personas @hwchase17 the tooling side is maturing fast

arXiv:2512.04388v5 Announce Type: replace Abstract: Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In

arXiv:2605.04984v1 Announce Type: cross Abstract: Long-horizon LLM agents depend on intermediate information-gathering turns, yet training feedback is usually observed only at the final answer, becaus

Anthropic is building out their managed agents platform, adding Dreaming (memory) and Outcomes (rubrics). The idea I'm wrestling with: how differentiated are these platform features really? I initiall

arXiv:2511.20657v2 Announce Type: replace-cross Abstract: The development of agents with emotional intelligence is becoming increasingly vital due to their significant role in human-computer interacti

arXiv:2605.01783v1 Announce Type: new Abstract: Procedural Content Generation (PCG) enables game content to be created algorithmically without direct manual level-design effort, but it introduces a se

arXiv:2605.01329v1 Announce Type: new Abstract: In-group favoritism refers to the phenomena of favoring members of one's in-group over out-group members and is widely observed in numerous social coope

arXiv:2605.03596v1 Announce Type: cross Abstract: Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a

Meir Orbach / CTech: Cisco agrees to acquire Astrix Security, which helps companies monitor and control the permissions granted to AI agents, a source says for approximately $400M — The Israeli startu

arXiv:2605.00370v1 Announce Type: new Abstract: Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effe

Jerry Liu predicts that genuinely useful long-running AI agents will become a reality by 2026, viewing this development as simultaneously inevitable and remarkable. The post reflects on how advances i

Digital security company DigiCert Inc. today introduced a new AI Trust framework to help organizations secure AI systems and their outputs, along with new capabilities to help secure autonomous agents

Google Cloud is assembling an ambitious agentic enterprise stack it believes will close the gap between AI ambition and real business outcomes. But the transition from traditional, linear workflows to

arXiv:2604.26020v1 Announce Type: cross Abstract: Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs)

arXiv:2604.23990v1 Announce Type: new Abstract: This paper presents PSA-Eval, a failure-centered runtime evaluation framework for deployed trilingual public-space agents. The central claim is that, wh

arXiv:2604.23194v1 Announce Type: new Abstract: Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ pla

arXiv:2604.24348v1 Announce Type: new Abstract: The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS

arXiv:2603.18563v2 Announce Type: replace Abstract: As autonomous AI agents increasingly mediate online platform markets, a fundamental question emerges: do these markets generate stable strategic out

arXiv:2604.21896v1 Announce Type: new Abstract: This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxo

arXiv:2604.19789v1 Announce Type: new Abstract: We present an autonomous large language model (LLM) agent for end-to-end, data-driven materials theory development. The model can choose an equation for

benchmarking agents by their ability to play video games 🎮 We are excited to launch VideoGameBench on Antim Labs, created by @a1zhang, Thomas L. Griffiths (@cocosci_lab), @karthik_r_n, and @OfirPress

arXiv:2602.18571v2 Announce Type: replace-cross Abstract: While significant progress has been made in automating various aspects of software development through coding agents, there is still significa

arXiv:2604.19457v1 Announce Type: new Abstract: Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy mem

arXiv:2604.18847v1 Announce Type: new Abstract: As LM agents gain the ability to execute actions on real computer systems, we need ways to not only prevent harmful actions at scale but also effectivel

LiteParse: our open-source, layout-aware PDF parser for AI agents. The secret? Grid projection. Instead of heavy ML layout models or flat text extraction, it projects text onto a monospace grid so ali

arXiv:2504.15304v2 Announce Type: replace Abstract: Can AI agents deal with hard choices -- cases where options are incommensurable because multiple objectives are pursued simultaneously? Adopting a t

arXiv:2510.07043v2 Announce Type: replace Abstract: Human decision-making often involves constrained optimization. As LLM agents are deployed to assist with real-world tasks like travel planning, shop

arXiv:2504.13541v5 Announce Type: replace-cross Abstract: Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. R

arXiv:2604.15075v1 Announce Type: cross Abstract: Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as

arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these

Simon Willison discusses the perception that LLMs and coding agents are primarily useful for greenfield development (starting new projects from scratch) rather than for maintaining and modifying large

arXiv:2509.21823v2 Announce Type: replace Abstract: Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods strugg