Knowledge catalogue
arXiv:2606.20661v1 Announce Type: cross Abstract: The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize ex
arXiv:2606.20717v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inheren
arXiv:2511.07332v2 Announce Type: replace-cross Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen element
arXiv:2602.22638v2 Announce Type: replace Abstract: Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through na
arXiv:2506.09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable,
arXiv:2606.10522v1 Announce Type: new Abstract: Graphical User Interfaces (GUIs) serve as the dominant medium for human-computer interaction, yet building GUI agents that generalize across the vast di
arXiv:2606.10304v1 Announce Type: new Abstract: When LLM agents are coerced into covertly encoding sensitive data (Base64, ROT13, acrostic, synonym chains, and beyond), the resulting outputs evade out
arXiv:2606.09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically pre
arXiv:2606.09421v2 Announce Type: replace Abstract: Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, valida
arXiv:2606.07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS
arXiv:2606.08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action t
arXiv:2606.09764v1 Announce Type: new Abstract: A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device,
Tiger Data today introduced a managed PostgreSQL database service designed specifically for AI agents, saying conventional database architectures are poorly suited to a future in which software is inc
Zscaler Inc. today unveiled a set of products designed to secure autonomous artificial intelligence agents, with the cybersecurity company claiming it has built the industry’s first complete zero-trus
arXiv:2505.14289v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental
arXiv:2510.01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user i
arXiv:2606.06960v1 Announce Type: new Abstract: Experience-based self-evolution is crucial for LLM agents, but existing benchmarks often assume explicit goals, stable task patterns, and clear feedback
we just shipped support for rubrics in deepagents ✅ give your agent a clear definition of what 'done' looks like, and force it to run in a loop until said goal is complete this is similar to /goal in
arXiv:2606.05622v1 Announce Type: new Abstract: Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are pro
arXiv:2606.05513v1 Announce Type: cross Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming proces
arXiv:2606.05828v1 Announce Type: cross Abstract: As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerg
arXiv:2606.04701v1 Announce Type: cross Abstract: GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications viol
arXiv:2606.04391v1 Announce Type: new Abstract: Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online sk
arXiv:2606.04296v1 Announce Type: new Abstract: As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agen
arXiv:2606.04037v1 Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability bench
arXiv:2606.03463v1 Announce Type: new Abstract: Conversational AI agents require memory systems that are both scalable and semantically coherent across long interaction horizons. Existing approaches r
arXiv:2512.03627v2 Announce Type: replace Abstract: Despite rapid progress in large-scale language and vision models, AI agents still suffer from a fundamental limitation: they cannot remember. Withou
arXiv:2606.03692v1 Announce Type: new Abstract: Recent AI agents can flexibly invoke skills to solve complex tasks, but their long-term improvement is fundamentally constrained by a lack of systematic
We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier
arXiv:2512.00986v3 Announce Type: replace Abstract: A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, exi
arXiv:2606.00547v1 Announce Type: new Abstract: Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretati
Barry Schwartz / Search Engine Land: Microsoft releases Web IQ, a search service for AI agents that is powered by Bing, currently used by Copilot, ChatGPT, and other platforms — Table of Contents — Mi
arXiv:2606.00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To addre
arXiv:2606.02388v1 Announce Type: cross Abstract: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little superv
This likely describes a framework or system for deploying AI agents that autonomously improve their performance over time through continuous learning and adaptation in enterprise environments. The sel
arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe
arXiv:2606.00384v1 Announce Type: new Abstract: Fitting quantitative models to data is a central step in scientific workflows, yet it remains one of the least automated. Recent agent-based systems lev
arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet wo
arXiv:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, a
arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr
arXiv:2605.29668v1 Announce Type: new Abstract: LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the
arXiv:2605.29559v1 Announce Type: new Abstract: Mastering terminal environments requires language agents capable of multi-step planning, feedback-grounded execution, and dynamic state adaptation. Howe
arXiv:2602.01869v3 Announce Type: replace Abstract: LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. Thi
arXiv:2605.30256v1 Announce Type: cross Abstract: Natural human conversation is full-duplex and audio-visual: people simultaneously speak and listen while continuously interpreting and producing nonve
Enterprise leaders are implementing AI agents across their organizations through strategies that address deployment, governance, and integration challenges. The article from Databricks likely covers b
arXiv:2605.28114v1 Announce Type: new Abstract: As autonomous AI agents are deployed in persistent, interacting networks -- coordinating tasks, routing resources, and accumulating reputational histori
arXiv:2605.28046v1 Announce Type: new Abstract: Existing agent memory systems universally follow what we term a Memory-as-Tool paradigm where a single query triggers one-shot retrieval of flat passage
arXiv:2406.03474v2 Announce Type: replace Abstract: Language-guided autonomous driving requires bridging a large abstraction gap between high-level natural-language instructions and low-level vehicle
arXiv:2605.27240v1 Announce Type: new Abstract: Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to us
arXiv:2602.22190v2 Announce Type: replace-cross Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks. This gap stems from two limitations: a
Conductor migrated its parallel coding agents infrastructure from local laptops to the cloud using Vercel Sandbox, enabling improved scalability and distributed execution of AI-driven code generation
arXiv:2605.26177v1 Announce Type: cross Abstract: Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on e
In this post, we demonstrate the capabilities of AgentWatch through practical implementation. You will see how the solution performs infrastructure checks every 15 minutes, summarizing CloudWatch metr
arXiv:2605.24183v1 Announce Type: cross Abstract: We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through latent world recovery. AvalancheBench improves on existing benc
In this post you'll learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture th
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its exte
arXiv:2605.25632v1 Announce Type: new Abstract: Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuari
arXiv:2602.02474v2 Announce Type: replace-cross Abstract: Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixe
arXiv:2605.23949v1 Announce Type: cross Abstract: As Large Language Models (LLMs) evolve into interactive agents, understanding their behavioral alignment within human social dynamics becomes essentia
arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely