Search: “agents”

arXiv:2608.13723v1 Announce Type: new Abstract: Object-goal navigation requires an agent to reason over object relationships and prioritize target-relevant objects for efficient decision making in uns

arXiv:2608.14160v1 Announce Type: new Abstract: Pixel-goal navigation specifies targets directly in the agent's camera view, but a target pixel provides neither metric depth nor traversability, making

arXiv:2608.13612v1 Announce Type: new Abstract: Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queri

arXiv:2608.13394v1 Announce Type: cross Abstract: 6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands o

arXiv:2608.13463v1 Announce Type: cross Abstract: Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty

arXiv:2608.13221v1 Announce Type: new Abstract: The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models

arXiv:2608.12133v1 Announce Type: new Abstract: Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images. Existing LLM and VLM sys

arXiv:2506.04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies

arXiv:2608.12062v1 Announce Type: cross Abstract: Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized

arXiv:2603.20667v2 Announce Type: replace-cross Abstract: Existing prompt-optimization techniques rely on local signals, causing poor generalization across tasks. In addition, they also rely on weak u

arXiv:2606.09498v2 Announce Type: replace Abstract: The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Be

arXiv:2608.11229v1 Announce Type: new Abstract: Comparative feedback, asking people which of two behaviors they prefer, has become a standard way to align robot and agent behavior with human intent wh

arXiv:2608.10601v1 Announce Type: cross Abstract: Two instruments of EU digital law place inference at their centre and mean different things by it. Article 3(1) of the AI Act uses the capability to i

arXiv:2608.10664v1 Announce Type: new Abstract: The Relativity of Causal Knowledge (RCK) explains how a network of agents with different structural causal models can exchange causal knowledge through

arXiv:2608.11175v1 Announce Type: new Abstract: For autonomous space exploration, robotic agents need to perform motion planning in which environmental interactions may be unknown. Learning these inte

arXiv:2608.10775v1 Announce Type: new Abstract: Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual cont

arXiv:2608.09480v1 Announce Type: new Abstract: A model alone does not determine how a programming agent acts. What the model sees, how actions enter the environment, how feedback returns, and how one

arXiv:2607.28069v2 Announce Type: replace Abstract: Long-context retrieval and agentic workloads repeatedly reuse the same documents under changing instructions, histories, and document orders. Prefix

arXiv:2608.09185v1 Announce Type: cross Abstract: Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving c

arXiv:2511.17162v2 Announce Type: replace Abstract: The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, it

arXiv:2608.06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents. Robot execution is inherently iterative: each action reshapes

arXiv:2607.16999v2 Announce Type: replace-cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing framew

arXiv:2608.07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one. We show

arXiv:2608.07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters,

arXiv:2608.07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps

arXiv:2608.04825v1 Announce Type: new Abstract: UAV see-and-reach navigation requires an aerial agent to approach a language-specified target visible in its initial view and stop reliably near it. Exi

arXiv:2608.03468v1 Announce Type: new Abstract: Historical tool-use trajectories provide valuable experience for large language model (LLM) agents to plan and coordinate tool usage. Existing approache

arXiv:2608.00218v1 Announce Type: new Abstract: Agentic LLMs exhibit three consequential tool-use failures: invalid arguments (validity), unnecessary calls (over-calling), and omitted calls when tools

arXiv:2608.02046v1 Announce Type: new Abstract: LLM companions are deployed at scale in personally consequential settings, yet poorly evaluated. Existing benchmarks use hand-authored scenarios and pro

arXiv:2608.01565v1 Announce Type: new Abstract: Answering complex questions over large document collections requires assembling complementary evidence across sections and documents. GraphRAG offers st

arXiv:2608.00721v1 Announce Type: new Abstract: Drones in human environments often lack spatially grounded in- terfaces for situated communication. We present OmniAI, an em- bodied aerial agent that s

arXiv:2608.01652v1 Announce Type: new Abstract: LLM-based multi-agent coordination faces a fundamental trade-off between efficiency and adaptivity in dynamic environments. Existing approaches typicall

arXiv:2608.02583v1 Announce Type: cross Abstract: Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retri

arXiv:2603.03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rig

arXiv:2607.28638v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed

arXiv:2503.15225v3 Announce Type: replace-cross Abstract: The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities---such as rehabilitation therapy, spor

arXiv:2607.27536v1 Announce Type: cross Abstract: Learning a strategic task changes more than what is directly taught: fine-tuning on one game can either enhance or degrade an agent's ability to reaso

arXiv:2607.28497v1 Announce Type: new Abstract: Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, suc

arXiv:2607.27132v1 Announce Type: new Abstract: An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smalles

arXiv:2607.26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of k arms and observes their d-dimensional rewar

arXiv:2607.25554v1 Announce Type: new Abstract: Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictiv

arXiv:2607.26005v1 Announce Type: cross Abstract: Self-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observat

arXiv:2509.10847v3 Announce Type: replace-cross Abstract: As artificial intelligence (AI) companions become capable of human-like communication, including telling jokes, understanding how people cogni

arXiv:2607.24692v1 Announce Type: cross Abstract: Inference systems increasingly combine a fast path that returns predictions within the application's latency deadline together with a higher-accuracy

arXiv:2607.24667v1 Announce Type: new Abstract: A language model with a bounded working memory must repeatedly decide which stored items to keep. Every deployed method decides the moment an item arriv

arXiv:2607.23925v1 Announce Type: new Abstract: Gradient descent scales well to large models, but becomes unstable over long time horizons. Gradient-free optimizers can scale to arbitrary timespans, b