Search: “agents”

arXiv:2606.25119v1 Announce Type: new Abstract: With the growing deployment of surveillance systems in factories, offices, and homes, integrating them with robots offers a promising direction for coll

arXiv:2606.22485v2 Announce Type: replace-cross Abstract: Decision-making in real-world settings rarely follows a fixed script. Instead, it unfolds as a dynamic reasoning process in which the appropri

arXiv:2606.23768v1 Announce Type: cross Abstract: We propose cryptographic certificates of validity for agentic AI systems. The core idea is to formally specify a correctness or policy condition as a

arXiv:2606.24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration. However,

arXiv:2606.24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operat

arXiv:2606.24598v1 Announce Type: cross Abstract: While expert-validated 'LLM + script' workflows deliver significant value, they remain static: they encode hard-won domain knowledge yet fail to adapt

arXiv:2606.21165v1 Announce Type: new Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context

arXiv:2606.20728v1 Announce Type: new Abstract: Vision foundation tools such as open-vocabulary detectors, segmentation models, and post-processing operators are powerful building blocks for computer

arXiv:2606.12386v1 Announce Type: cross Abstract: Advancing scientific understanding through mechanistic modeling requires posing the right experimental questions to yield maximally informative data.

arXiv:2606.11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native,

arXiv:2603.21350v2 Announce Type: replace Abstract: Epistemic reasoning requires agents to infer the state of the world from partial observations and information about other agents' knowledge. Prior w

arXiv:2606.10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervas

arXiv:2606.10921v1 Announce Type: new Abstract: Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers of

arXiv:2606.08369v1 Announce Type: cross Abstract: A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended envir

arXiv:2603.04177v2 Announce Type: replace-cross Abstract: LLM coding agents can generate working code, but their solutions often accumulate complexity, duplication, and architectural debt. Human devel

arXiv:2601.06077v2 Announce Type: replace-cross Abstract: This work aims to rigorously define the values of perception, prediction, communication, and common sense in decision making. The defined quan

arXiv:2606.07557v1 Announce Type: new Abstract: Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joi

arXiv:2606.07697v1 Announce Type: cross Abstract: As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a mai

arXiv:2606.08542v1 Announce Type: cross Abstract: Exploratory manipulation often turns an apparent failed attempt into the key evidence for what to do next. For example, a robot pulls a locked cabinet

arXiv:2606.07326v1 Announce Type: new Abstract: Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scena

arXiv:2502.16531v2 Announce Type: replace Abstract: We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem effici

arXiv:2602.04809v3 Announce Type: replace-cross Abstract: Recent years have seen an explosion of interest in autonomous cyber defence agents trained to defend computer networks using deep reinforcemen

arXiv:2509.15061v2 Announce Type: cross Abstract: The ultimate goal of embodied agents is to create collaborators that can interact with humans, not mere executors that passively follow instructions.

arXiv:2606.05793v1 Announce Type: new Abstract: While LLM-based agents excel at individual tasks, effective collaboration with realistic human partners remains challenging. Most of the existing conver

arXiv:2606.05896v1 Announce Type: new Abstract: Creating lifelike digital humans with genuine social intelligence requires unifying cognitive reasoning and multimodal generation within a coherent fram

arXiv:2606.05890v1 Announce Type: new Abstract: LLMs are increasingly deployed as Artificial Moral Advisors (AMA) in a variety of contexts: what kind of conversational patterns should they display? In

arXiv:2606.03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move. But for navigation, what matte

arXiv:2606.02915v1 Announce Type: new Abstract: Visual posters are a compact medium for communicating dense information, yet progress on automatic poster generation remains difficult to measure becaus

arXiv:2606.03175v1 Announce Type: new Abstract: Instance Goal Navigation (IGN) requires an embodied agent to find a specific object instance among distractors from an underspecified natural-language d

arXiv:2602.20213v2 Announce Type: replace-cross Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existi

arXiv:2606.03056v1 Announce Type: new Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend

arXiv:2603.19453v2 Announce Type: replace Abstract: We study LLM policy synthesis: using a language model to iteratively generate programmatic agent policies for multi-agent environments. Rather than

arXiv:2603.24511v2 Announce Type: replace-cross Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on whi

arXiv:2606.01435v1 Announce Type: new Abstract: LLM-based memory systems increasingly maintain facts that evolve over time, where a recurring failure is conflict resolution: when a fact has multiple c

arXiv:2606.01617v1 Announce Type: cross Abstract: Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are

arXiv:2606.00750v1 Announce Type: new Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing

arXiv:2602.03619v2 Announce Type: replace Abstract: Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward

arXiv:2606.01970v1 Announce Type: new Abstract: Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent de

arXiv:2603.22999v3 Announce Type: replace Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, exist

arXiv:2512.06182v2 Announce Type: replace Abstract: Autonomous driving in interactive traffic scenarios remains challenging because of the mutual influence among vehicles and the inherent uncertainty

arXiv:2605.22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personal

arXiv:2602.15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (V

arXiv:2606.02245v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricte

arXiv:2605.30976v1 Announce Type: cross Abstract: We study stochastic linear bandits under a natural combination of batching and communication constraints: the time horizon is partitioned into batches

arXiv:2605.31224v1 Announce Type: cross Abstract: The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial on

arXiv:2605.31330v1 Announce Type: cross Abstract: Institutional incentives are widely used to promote cooperation among autonomous, self-regarding agents, from human societies to multi-agent and AI sy