Search: “agents”

arXiv:2609.06815v1 Announce Type: cross Abstract: An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to ca

arXiv:2609.01779v1 Announce Type: cross Abstract: Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inte

arXiv:2606.19319v2 Announce Type: replace-cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively

arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling

arXiv:2608.24099v1 Announce Type: new Abstract: GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack sy

arXiv:2608.24017v1 Announce Type: cross Abstract: The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent

arXiv:2608.21101v1 Announce Type: cross Abstract: As large language model (LLM) agents move from conversation to executing code, reading local files, and orchestrating external tools, a single agent h

arXiv:2608.20274v1 Announce Type: new Abstract: Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced

arXiv:2608.18066v1 Announce Type: new Abstract: Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have sho

arXiv:2608.10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical groundin

arXiv:2608.10716v1 Announce Type: cross Abstract: Speech-to-speech (S2S) voice agents are increasingly being incorporated into enterprise for customer care and as daily companions for consumers owing

arXiv:2607.18171v2 Announce Type: replace Abstract: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose effici

arXiv:2608.05886v1 Announce Type: cross Abstract: Modern LLM coding agents such as Claude Code and OpenHands share a common inefficiency: they spend much of their token budget finding the file to patc

arXiv:2608.01042v1 Announce Type: cross Abstract: Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could se

arXiv:2607.24625v1 Announce Type: cross Abstract: Autonomous LLM agents processing mixed-confidentiality data face severe security risks from prompt injection attacks and reasoning errors. While dynam

arXiv:2607.13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, acti

arXiv:2606.20950v2 Announce Type: replace Abstract: Executable evaluation -- checking the consequences of an agent's actions with a program rather than grading its prose -- has become a prominent way

arXiv:2606.28456v1 Announce Type: cross Abstract: LLMs agents are increasingly used in multi-agent settings, yet their behaviour in sustainability games remains largely unexplored. This work investiga

arXiv:2606.28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial

arXiv:2606.03895v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model ca

arXiv:2605.29174v1 Announce Type: new Abstract: DeFi investment agents, systems that use AI for autonomous on-chain trading, have attained over USD 3 billion in combined token valuations since late 20

arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m

arXiv:2605.08693v1 Announce Type: new Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selec

arXiv:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs

arXiv:2604.22820v1 Announce Type: cross Abstract: Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks for recovery and exploration, but added multi-agent workflow flexibil

arXiv:2503.16416v2 Announce Type: replace Abstract: LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environ

arXiv:2604.19264v1 Announce Type: new Abstract: Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is obser

arXiv:2604.13346v1 Announce Type: new Abstract: Language-model agent systems commonly rely on reactive prompting, in which a single instruction guides the model through an open-ended sequence of reaso

arXiv:2604.10182v1 Announce Type: new Abstract: Current evaluations of autonomous coding agents assume an unrealistic, infinite-resource environment. However, real-world software engineering is a reso

arXiv:2604.11088v1 Announce Type: new Abstract: Developers increasingly guide AI coding agents through natural language instruction files (e.g., CLAUDE.md, .cursorrules), yet no controlled study has m

arXiv:2604.10923v1 Announce Type: cross Abstract: While large language model--powered agents can self-evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert age

arXiv:2610.12932v1 Announce Type: new Abstract: Search agents extend large reasoning models with multi-round access to external knowledge, yet most existing systems largely conflate retrieving evidenc

arXiv:2610.08452v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring a

arXiv:2610.01348v1 Announce Type: new Abstract: When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate

arXiv:2609.38270v1 Announce Type: cross Abstract: Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents

arXiv:2609.39325v1 Announce Type: new Abstract: The ability of Large Language Model (LLM) agents to complete daily and professional work is receiving increasing attention. Training such agents require

arXiv:2609.35886v1 Announce Type: cross Abstract: AI agents now hold spend authority and settle payments without per-action human confirmation. The resulting loss is often not a security failure: a co

arXiv:2609.35875v1 Announce Type: new Abstract: Multi-agent debate (MAD) reportedly improves reasoning and factuality over single-model inference, but prior work treats agents as symmetric peers, leav

arXiv:2609.30971v1 Announce Type: new Abstract: Embodied agents offer a promising route to automating scientific experimentation, yet their progress is constrained by the lack of reliable and systemat

arXiv:2609.29892v1 Announce Type: new Abstract: The rapid progression of large language models is extending AI from passive content generation into the active workflows of engineering and scientific d

arXiv:2609.29875v1 Announce Type: new Abstract: Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions h

arXiv:2609.18357v1 Announce Type: new Abstract: Large language model (LLM) pricing agents may respond to how market data is presented, even when its numerical values remain unchanged. We introduce mar

arXiv:2609.15293v1 Announce Type: new Abstract: When Emergence World placed frontier LLM agents in an unsupervised multi-agent simulation, the results were alarming: agents committed crimes, starved,

arXiv:2608.29800v1 Announce Type: new Abstract: Tool-using agents are being proposed for medical imaging, and their behaviour when a tool returns a false finding is largely unmeasured. We audit whethe

arXiv:2602.09159v2 Announce Type: replace Abstract: Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based ass

arXiv:2608.22192v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in persistent social environments, where generated claims can be posted, replied to, remembered, and reused.