Knowledge catalogue
arXiv:2605.08257v1 Announce Type: cross Abstract: Motivated by the challenge to improve the adversarial robustness, security, and trust of medical decision making intelligent agents, this study develo
arXiv:2605.06737v1 Announce Type: cross Abstract: Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a si
arXiv:2512.10371v2 Announce Type: replace Abstract: The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for th
# Agents as Products This post likely explores how AI agents can be effectively deployed as standalone products, discussing their practical applications and advantages over traditional software soluti
arXiv:2603.06859v2 Announce Type: replace-cross Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts t
arXiv:2603.24755v2 Announce Type: replace-cross Abstract: Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmar
The future of Math is mathematicians and AI agents working together. Very pleased to introduce @GoogleDeepMind's AI co-mathematician: a multi-agent system designed to actively collaborate with human e
New course: Build agents that respond to users with not only plaintext, but custom UIs like charts, forms, and whiteboards, generated on demand and displayed right in the chat. This short course is bu
arXiv:2605.04036v1 Announce Type: cross Abstract: Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominat
BabyAGI, an AI agent project, generated significant public interest and search attention according to Google Trends data, potentially catalyzing broader awareness of AI agents as a category. The obser
arXiv:2605.01489v1 Announce Type: cross Abstract: Frontier scientific reasoning is rapidly emerging as a key foundation for advancing AI agents in automated scientific discovery. Deep research agents
arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi
here's a deep dive on how middleware lets you customize your agent harness, excellent writeup by @Vtrivedy10 !! deepagents offers a powerful base harness that you can customize for your use case! crea
arXiv:2604.27872v1 Announce Type: new Abstract: Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumul
arXiv:2604.27264v1 Announce Type: cross Abstract: Autonomous agents can adapt their behaviour to changing environments, but remain bound to requirements, goals, and capabilities fixed at design time,
A Deep Dive into Deep Agents 🚀DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections
arXiv:2604.26752v1 Announce Type: new Abstract: We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environmen
arXiv:2604.26235v1 Announce Type: cross Abstract: We introduce LATTICE, a benchmark for evaluating the decision support utility of crypto agents in realistic user-facing scenarios. Prior crypto agent
resharing this note, find it helpful given all the great open evals work + teams building vertical agents Evals are a proxy for the behavior we want our agent to exhibit in production Model+Harness pu
arXiv:2604.24039v1 Announce Type: cross Abstract: Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this pape
arXiv:2603.03456v2 Announce Type: replace Abstract: Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate
The conclusions here feel wrong to me. The two lessons I see are: 1. Don't run agents anywhere they might be able to access production environment credentials - it's on you to know which credentials t
Next up: Hermes Agents on Tuesday in Lisbon 🇵🇹 as we work to make our office the go-to space for AI builders in Portugal, then we have another major one this week. we will this time be joined by the t
agents that are made to support specific teams end up helping a ton! great read on how we use deepagents every day to maintain our docs. Our docs are both a reference for our libraries but also a grea
Google Duplex, introduced in 2018, represented an early attempt at customer experience automation through AI-powered phone conversations, while Google Agents (expected circa 2026) represents a more ma
arXiv:2510.08240v2 Announce Type: replace Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing
arXiv:2603.14975v2 Announce Type: replace-cross Abstract: Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering
Artificial intelligence security platform company DeepKeep Ltd. today announced the launch of Vibe AI Red Teaming, a capability designed for human-steered, dynamic testing and attack simulation on AI
arXiv:2604.15384v1 Announce Type: cross Abstract: We introduce LinuxArena, a control setting in which agents operate directly on live, multi-service production environments. LinuxArena contains 20 env
I've commented that 'this is the year of subagents', but that is largely an optimization problem. the inverse problem - having agents that compose and boss agents that manage/query them - is a capabil
OpenAI's Agents SDK represents an evolution of their framework for building agentic AI applications, providing developers with tools to create, orchestrate, and deploy AI agents that can perform multi
arXiv:2601.11044v3 Announce Type: replace Abstract: Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. Howev
arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem
🚀 deepagents 0.5 release 👉 Async subagents - kick off background tasks on any Agent Protocol backed server while you continue to interact with the main agent. Start multiple background tasks in parall
arXiv:2604.11655v1 Announce Type: cross Abstract: The rapid adoption of Large Language Models (LLMs) in interactive systems has enabled the creation of dynamic, open-ended Role-Playing Agents (RPAs).
**cuba-memorys v0.7.0** is a persistent memory library for AI agents, shared in the r/ollama community, designed to give local LLM-powered agents the ability to retain information across sessions. It
the most important abstraction in AI agents isnt the model — its the harness it orchestrates tools, memory, prompts. this is where all the alpha is deepagents is our take: built-in tools, memory, smar
arXiv:2604.07833v1 Announce Type: new Abstract: Embodied agents are evolving from passive reasoning systems into active executors that interact with tools, robots, and physical environments. Once gran
@IeloEmanuele is on a roll! you can now use claude code's advisor strategy with @LangChain agents because langchain/deepagents are provider agnostic, you can use different providers for your advisor a
Claude Agent SDK tracing in LangSmith just got an upgrade. Now you can trace: → Subagents → Child runs inside MCP tools → Cost tracking + more Update to the latest Python SDK to try it out. Docs: http
we just added a langchain-task-steering middleware to our community registry, s/o @EHallvaxhiu for the contribution! keep your agents on track w/ ordered task pipelines. this is great for structured d
Curriculum Learning for harnesses - should we teach agents how we teach kids? start small and easy and progressively get harder for my research friends here's an under-explored area we're thinking abo
Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb
arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex
arXiv:2610.20845v1 Announce Type: new Abstract: Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-impr
arXiv:2610.18603v1 Announce Type: new Abstract: Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with the c
arXiv:2606.18947v2 Announce Type: replace Abstract: Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injec
arXiv:2610.18881v1 Announce Type: new Abstract: Voice agents built on large language models (LLMs) now handle customer calls, but in regulated applications completing the task is not enough: the agent
arXiv:2610.16125v1 Announce Type: new Abstract: An agent can retain successful trajectories without learning which part of an experience should repair a new discrepancy. We formulate passive imitation
arXiv:2610.19591v1 Announce Type: cross Abstract: The lasting gains in AI have come from methods that absorb more computation, and language models already scale computation within a call. Useful work,
arXiv:2610.17288v1 Announce Type: new Abstract: Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We in
arXiv:2610.20334v1 Announce Type: cross Abstract: A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path
arXiv:2609.31937v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents built on the ReAct pattern concentrate four responsibilities in one model: selecting a strategy, choosing ea
arXiv:2610.17039v1 Announce Type: cross Abstract: Tool-using LLM agents are increasingly used to automate workflows governed by procedural manuals, such as company rules for booking travel or procurin
arXiv:2610.17118v1 Announce Type: cross Abstract: Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. W
Grok 4.7 is now available on Amazon Bedrock. In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three reasoning tasks at four reasoning effort levels, measuring correctness and
arXiv:2610.12789v1 Announce Type: new Abstract: Self-evolving LLM agents typically improve how they act through accumulated experience, reflection, or skills. Yet even procedurally capable agents can
arXiv:2610.12549v1 Announce Type: cross Abstract: Embodied coding agents construct robot policies from natural-language instructions by generating executable programs and refining them from execution
arXiv:2610.13534v1 Announce Type: new Abstract: Long-horizon embodied agents must maintain task-relevant information, coordinate heterogeneous robots, and adapt their decisions as execution unfolds. E
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. It launches with two core designs: - Elastic Scheduler: reallocates GPUs between rollout & training as demand