Search: “agents”

arXiv:2606.29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and

Agentic ecosystem security startup Vorlon Inc. today launched Guardian, a real-time enforcement gateway that aims to block risky actions by artificial intelligence agents before a transaction complete

FalkorDB and ActiveGraph enable querying, sharing, and scaling of agent graph projections beyond in-memory limitations, allowing developers to persist and manage complex agent architectures in a graph

Artificial intelligence cloud operator CoreWeave Inc. today launched ARIA, an AI research agent built into the Weights & Biases platform. The agent reads experiment data and surfaces insights research

arXiv:2606.27929v1 Announce Type: new Abstract: Embodied AI is increasingly becoming agentic, shifting robots from perception--control pipelines towards closed-loop systems that can retrieve context,

langchain is one of the few teams that actually explains the engineering behind agent concepts instead of just naming them. loop engineering, managed agents, memory systems. every time a new concept d

arXiv:2606.25836v1 Announce Type: new Abstract: To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many employer

arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and

arXiv:2511.21886v2 Announce Type: replace Abstract: Multi-Agent Path Finding (MAPF) studies how to coordinate multiple agents to reach their goals without collisions and underpins a range of large-sca

arXiv:2606.20621v1 Announce Type: cross Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduc

Hermes Agent v0.17.0, released by Nous Research, is a major update referred to as 'The Reach Release' that introduces new capabilities and improvements to the Hermes Agent framework. The changelog det

arXiv:2606.10106v1 Announce Type: cross Abstract: The term agent harness now circulates widely in software engineering with generative artificial intelligence. It names the layer that wraps a language

arXiv:2606.09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles. They plan across steps, call tools, request human input, coordinate with o

arXiv:2606.09507v1 Announce Type: new Abstract: Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single obser

arXiv:2606.08500v1 Announce Type: cross Abstract: Software engineering agents (SWE agents) increasingly work through tool-mediated trajectories in real repositories, yet their behavior remains difficu

arXiv:2606.08790v1 Announce Type: new Abstract: Autonomous agents negotiate, purchase, deploy code, and move funds, but no neutral mechanism determines whether they met their delegated obligation, who

arXiv:2606.08030v1 Announce Type: cross Abstract: Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response

arXiv:2606.06525v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains. Beyond reactive text genera

arXiv:2606.07513v1 Announce Type: new Abstract: Humans learn from social life. Simulating this process with LLM-powered agents represents a promising research direction, raising a natural question: wh

arXiv:2602.00471v2 Announce Type: replace Abstract: While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a c

arXiv:2606.07436v1 Announce Type: new Abstract: This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools an

imo there’s a pretty solid default recipe that everyone should use to optimize a system of Agent = Model + Harness you should “train” both 1. Build v1 agent using a sensible base harness and some task

Thousand Token Wood is a multi-agent economy simulation built on a 3 billion parameter language model, demonstrating how small models can power complex interactive systems with multiple agents. The pr

arXiv:2606.04315v1 Announce Type: new Abstract: LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tun

Devin Desktop now supports Agent Control Protocol (ACP), allowing users to integrate their preferred AI agents into the platform. This feature enables users to bring custom or third-party agents into

arXiv:2606.03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another. Protocols such as the Model Context Protocol (MCP) and the Agent2Agent p

arXiv:2606.01414v1 Announce Type: new Abstract: Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet

arXiv:2606.00832v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmark

arXiv:2606.00962v1 Announce Type: cross Abstract: Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-age

arXiv:2605.31361v1 Announce Type: cross Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly

arXiv:2605.31365v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on han

agent builder! Start creating agents using everyday language with LangSmith Fleet. Learn how to build no-code agents for real work. Take our free LangChain Academy course today: https://academy.langch

arXiv:2512.03109v2 Announce Type: replace-cross Abstract: Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success

arXiv:2605.30136v1 Announce Type: new Abstract: LLM-based multi-agent systems have demonstrated remarkable performance on complex tasks through collaborative reasoning. However, these systems tend to

arXiv:2605.29512v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended intera

arXiv:2605.27744v1 Announce Type: new Abstract: Multi-agent LLM systems have become the dominant production workload, but the serving stack was not built for them. The agent framework above knows agen

arXiv:2605.27396v1 Announce Type: cross Abstract: Autonomous AI agents now plan, decide, and act on behalf of users across healthcare, financial services, and workplace contexts, often without step-by

arXiv:2605.27954v1 Announce Type: new Abstract: Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external env

arXiv:2605.28104v1 Announce Type: new Abstract: Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making

arXiv:2605.27209v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning,

arXiv:2602.03238v2 Announce Type: replace Abstract: With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents present

arXiv:2602.22769v3 Announce Type: replace Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical

arXiv:2605.25929v1 Announce Type: cross Abstract: The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collabo

arXiv:2605.04906v2 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in certain reasoning tasks, they struggle in multi-agent games where the final outcome depends on the joint

arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fai

arXiv:2605.22786v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing