Search: “agents”

arXiv:2510.16079v2 Announce Type: replace-cross Abstract: Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from th

arXiv:2605.06716v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. Whi

arXiv:2605.07594v1 Announce Type: new Abstract: Existing memory systems for embodied agents typically inject retrieved memory as static context at episode start, a paradigm we term Ahead-of-time Monol

arXiv:2605.06110v2 Announce Type: replace Abstract: Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or t

arXiv:2510.00568v3 Announce Type: replace Abstract: Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement l

arXiv:2605.07112v1 Announce Type: new Abstract: Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets. Mo

arXiv:2605.03117v1 Announce Type: cross Abstract: Repository-level fault localization (FL) and automated program repair (APR) require an agent to identify the relevant code units across files, follow

arXiv:2605.01214v1 Announce Type: new Abstract: This position paper argues that agentic AI systems should be designed and evaluated as marginal token allocation economies rather than as text generator

arXiv:2512.22608v3 Announce Type: replace Abstract: Due to the high value and high failure rates of startups, predicting their success is a critical challenge. Existing approaches typically model star

arXiv:2605.02939v1 Announce Type: new Abstract: Multimodal controversy detection (MCD) identifies controversial content in videos and their associated user comments, to support risk management for soc

arXiv:2601.21972v4 Announce Type: replace Abstract: Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL). However, most MARL fine-tuning approaches r

arXiv:2605.03228v1 Announce Type: cross Abstract: As large language model (LLM)-powered agents are increasingly deployed to perform complex, real-world tasks, they face a growing class of attacks that

arXiv:2502.08597v3 Announce Type: replace-cross Abstract: We analyze the performance of heterogeneous learning agents in asset markets with stochastic payoffs. Our main focus is on comparing Bayesian

arXiv:2605.00803v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering be

arXiv:2605.00420v1 Announce Type: cross Abstract: Evaluating the true forecasting ability of AI agents requires environments resistant to overfitting, free from centralized trust, and grounded in ince

arXiv:2604.27955v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces

arXiv:2604.27419v1 Announce Type: new Abstract: With the advancement of multimodal large language models (MLLMs) and coding agents, the website development has shifted from manual programming to agent

arXiv:2604.27151v1 Announce Type: new Abstract: Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfa

arXiv:2604.27691v1 Announce Type: new Abstract: Across millennia, complex societies have faced the same coordination problem of how to organize collective action among cognitively bounded and informat

arXiv:2604.26197v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.

arXiv:2601.15808v2 Announce Type: replace Abstract: Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing ef

arXiv:2604.26091v1 Announce Type: new Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX

arXiv:2604.24971v1 Announce Type: cross Abstract: We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocat

arXiv:2604.23278v1 Announce Type: new Abstract: The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailin

arXiv:2604.23124v1 Announce Type: cross Abstract: As software systems grow in complexity, they must satisfy an increasing number of competing quality attributes, making it essential to balance them in

arXiv:2604.24320v1 Announce Type: new Abstract: Large language model (LLM) agents that follow the sequential 'reason-then-act' paradigm have achieved superior performance in many complex tasks.However

arXiv:2604.23338v1 Announce Type: cross Abstract: Agentic AI systems face security challenges that stateless large language models do not. They plan across extended horizons, maintain persistent memor

arXiv:2604.22795v1 Announce Type: cross Abstract: This study presents a multi-agent reinforcement learning (MARL) framework for load-constrained wind farm flow control (WFFC). While wake steering can

arXiv:2604.22818v1 Announce Type: cross Abstract: This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can gene

arXiv:2604.22888v1 Announce Type: cross Abstract: Agent skills introduce a new and more severe form of indirect injection for LLM agents: unlike traditional indirect prompt injection, attackers can hi

arXiv:2603.25111v2 Announce Type: replace Abstract: Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm,

arXiv:2604.24594v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their

arXiv:2604.23646v1 Announce Type: new Abstract: Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally con

arXiv:2604.23090v1 Announce Type: new Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a centra

arXiv:2603.22823v3 Announce Type: replace Abstract: Context. The problem of comparative evaluation of communication protocols for task orchestration by large language model (LLM) agents is considered.

arXiv:2604.20134v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) increasingly encounter difficulties in correlating heterogeneous alerts, interpreting multi-stage attack progressio

arXiv:2604.19785v1 Announce Type: cross Abstract: Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messag

arXiv:2506.22598v3 Announce Type: replace Abstract: Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, t

arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio

arXiv:2604.18509v1 Announce Type: new Abstract: Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when

arXiv:2604.16968v1 Announce Type: new Abstract: Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self

arXiv:2604.17475v1 Announce Type: cross Abstract: Small Vision-Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typical

arXiv:2604.15664v1 Announce Type: new Abstract: The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to eval

arXiv:2510.27617v2 Announce Type: replace Abstract: Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show prom

arXiv:2604.13047v1 Announce Type: cross Abstract: In recent years, the spread of fake news has triggered a growing interest in Information Disorders (ID) on social media, a phenomenon that has become