Search: “agents”

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

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:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime o

arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning,

arXiv:2605.26195v1 Announce Type: cross Abstract: LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to ada

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

arXiv:2605.20456v1 Announce Type: cross Abstract: Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capa

arXiv:2605.22564v1 Announce Type: new Abstract: Today, tool-calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and assoc

arXiv:2601.22292v2 Announce Type: replace-cross Abstract: Multi-agent social dilemmas, such as the tragedy of the commons, capture settings where individual incentives conflict with collective well-be

arXiv:2605.18991v1 Announce Type: cross Abstract: We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted comp

arXiv:2512.18552v2 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivit

arXiv:2604.01658v2 Announce Type: replace Abstract: Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge

arXiv:2605.17373v1 Announce Type: cross Abstract: AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies ra

arXiv:2605.16303v1 Announce Type: cross Abstract: Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demog

arXiv:2605.17076v1 Announce Type: cross Abstract: Concurrent LLM agents sharing mutable natural-language state produce Structural Race Conditions (SRCs): write-write and cross-shard stale-read conflic

arXiv:2605.16565v1 Announce Type: new Abstract: Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is

arXiv:2605.15301v1 Announce Type: new Abstract: Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks att

arXiv:2605.14537v1 Announce Type: new Abstract: We introduce extsc{Cattle Trade, a multi-agent benchmark for evaluating large language models (LLMs) as agents in strategic reasoning under imperfect in

arXiv:2605.14212v1 Announce Type: new Abstract: Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automati

arXiv:2605.13821v1 Announce Type: new Abstract: Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, e

arXiv:2605.12388v1 Announce Type: cross Abstract: Effective multi-agent cooperation requires agents to adopt diverse behaviors as task conditions evolve-and to do so at the right moment. Yet, current

arXiv:2605.09283v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) and the software agents built on them (AI agents) marks a turning point in the transition from a human-cen

arXiv:2603.29632v2 Announce Type: replace-cross Abstract: As AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cogni

arXiv:2605.10516v1 Announce Type: new Abstract: This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under sem

arXiv:2605.08613v1 Announce Type: new Abstract: The evolution of 6G networking toward agentic AI networking (AgentNet) systems requires a shift from traditional data pipelines to task-aware, agentic A

arXiv:2602.06733v2 Announce Type: replace-cross Abstract: Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their

arXiv:2605.09721v1 Announce Type: cross Abstract: Tool-enabled AI agents are increasingly deployed in cloud-hosted environments and offered as services, where they perform side-effecting operations th

arXiv:2605.10913v1 Announce Type: new Abstract: We introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechaniz

arXiv:2605.09650v1 Announce Type: new Abstract: Modern agents built on frontier language models often cannot adapt their weights. What, then, remains trainable? We argue it is the agent's workspace, t

arXiv:2509.03736v2 Announce Type: replace Abstract: The impressive capabilities of Large Language Models (LLMs) raise the possibility that synthetic agents can serve as substitutes for real participan

arXiv:2605.06623v1 Announce Type: cross Abstract: Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orch

arXiv:2508.10880v3 Announce Type: replace-cross Abstract: The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage other

arXiv:2605.05558v2 Announce Type: replace Abstract: A natural intuition about the economics of AI agents is that, because agents can be replicated at very low marginal cost, agent labor may be supplie

arXiv:2605.03195v1 Announce Type: new Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibiliti