Search: “agents”

arXiv:2609.33117v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can now interpret a standard ten-second, twelve-lead electrocardiogram (ECG) with clinically grounded, reward-v

arXiv:2609.35338v1 Announce Type: new Abstract: Mechanistic discovery in neuronal microenvironments requires interventions and measurements that separate competing explanations of solute transport and

arXiv:2609.34415v1 Announce Type: new Abstract: The transition from Large Language Models (LLMs) to agents shifts safety stakes from toxic text to irreversible environmental harm. While current defens

arXiv:2609.33646v1 Announce Type: cross Abstract: Browser-use agents require seamless alignment between structured web metadata and visual information, while preserving relevant context across long in

arXiv:2609.32802v1 Announce Type: cross Abstract: One variable sets what an upstream fault costs a staged pipeline of language-model agents: re-derivability, how much of what a stage needs it can rebu

arXiv:2609.34567v1 Announce Type: new Abstract: This review examines the evolution of plant phenotyping from conventional manual trait measurement to high-throughput, robotic, and artificial intellige

arXiv:2609.35501v1 Announce Type: cross Abstract: Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hyp

arXiv:2609.33627v1 Announce Type: new Abstract: Despite recent progress in agentic multi-shot video generation, producing coherent and consistent long-form stories remains challenging. Existing agenti

arXiv:2609.31568v1 Announce Type: new Abstract: AI tutoring could markedly improve learning outcomes for students in developing regions such as Vietnam, yet the two obvious paths both fall short. Clou

arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic

arXiv:2609.31286v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) learns cooperative policies from fixed datasets without further environment interaction and a learned

arXiv:2609.31451v1 Announce Type: cross Abstract: The growing popularity of short videos has driven demand for one-click content creation. Visual templates turn uploaded images into personalized conte

arXiv:2609.30690v1 Announce Type: cross Abstract: Ensuring operational safety in threat-prone environments remains a critical challenge for multi-UAV networks serving as aerial base stations. This pap

arXiv:2609.28963v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) methods, such as GRPO and its variants, have become a leading paradigm for training reasoning and agentic large

arXiv:2609.29578v1 Announce Type: new Abstract: Long-horizon tool agents often make useful progress without reaching terminal success, motivating partial-credit evaluation. Yet evaluators may reward m

arXiv:2609.30249v1 Announce Type: cross Abstract: Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work int

arXiv:2609.27994v1 Announce Type: cross Abstract: Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remain

arXiv:2609.27349v1 Announce Type: new Abstract: Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy mult

arXiv:2609.28372v1 Announce Type: cross Abstract: Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using 'Tool-Lab,' an adaptation of

arXiv:2609.26835v1 Announce Type: cross Abstract: COBOL remains widely deployed, yet representative corpora reflecting real production code are rarely available, limiting rigorous benchmarking of mode

arXiv:2609.26146v1 Announce Type: new Abstract: Risk monitoring in multi-agent systems is commonly built on a per-state primitive that scores each state independently and selects the top K. Under crow

arXiv:2609.26793v1 Announce Type: new Abstract: Compositional 3D scene reconstruction has recently been explored from two directions: agentic reasoning that provides semantic understanding of spatial

arXiv:2609.22111v1 Announce Type: new Abstract: Large language model agents are increasingly capable of conducting research autonomously, producing research documents alongside the code and experiment

arXiv:2609.22235v1 Announce Type: new Abstract: While multi-agent systems based on large language models (LLMs) have shown promise in automating the progressive workflow of academic research, extendin

arXiv:2609.22130v1 Announce Type: new Abstract: Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and de

arXiv:2609.22678v1 Announce Type: cross Abstract: Density-Driven Optimal Control (D2OC) provides an effective framework for steering multi-agent systems toward prescribed spatial distributions. Howeve

arXiv:2609.22209v1 Announce Type: new Abstract: Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Desi

arXiv:2609.23875v1 Announce Type: new Abstract: The rapid growth of resident space objects is increasing the complexity of space situational awareness sensor tasking, challenging classical optimizatio

arXiv:2609.21167v1 Announce Type: new Abstract: Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order

arXiv:2609.20080v1 Announce Type: new Abstract: The growing complexity of multi-domain operational environments (land, aerospace, naval, cyber, and electromagnetic spectrum) has increased the volume a

arXiv:2609.19793v1 Announce Type: new Abstract: Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable inte

arXiv:2609.19789v1 Announce Type: new Abstract: Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial i

arXiv:2609.19636v1 Announce Type: new Abstract: Reinforcement learning now trains language-model agents that act over dozens of steps in live environments. The gains are large, and they are read as be

arXiv:2609.19124v1 Announce Type: new Abstract: Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formati

arXiv:2609.18805v1 Announce Type: cross Abstract: Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents ma

arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impra

arXiv:2609.16680v1 Announce Type: new Abstract: Manufacturing consumes one third of global energy and still has significant room for improvement in terms of energy efficiency. Optimal process control

arXiv:2609.16995v1 Announce Type: new Abstract: Autoresearch agents are reshaping the research ecosystem, but they can also let flawed claims enter the literature at scale. Human advisors catch such i

arXiv:2609.16065v1 Announce Type: cross Abstract: Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a c

arXiv:2609.13856v1 Announce Type: new Abstract: Enterprise customer support systems must answer customer questions correctly, retrieve the right policy information, use customer context, and pass diff

arXiv:2609.15983v1 Announce Type: new Abstract: Language models can produce plausible short proofs, but may still be unreliable on long-horizon research problems, where progress depends on a sequence

arXiv:2609.13348v1 Announce Type: cross Abstract: This paper presents ViFA-Council, a three-stage multi-agent framework that employs multiple large language models (LLMs) to tackle two culturally comp

arXiv:2505.15063v3 Announce Type: replace Abstract: The rapid adoption of Large Language Models (LLMs) has raised important concerns about the factual reliability of their outputs, particularly in low

arXiv:2609.10817v1 Announce Type: cross Abstract: How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate b

arXiv:2609.06543v1 Announce Type: new Abstract: Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows,

arXiv:2609.06887v1 Announce Type: cross Abstract: Automated experimentation is most effective when the observables, available actions, and objective are defined before the experiment starts, as is the

arXiv:2609.10433v1 Announce Type: cross Abstract: This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfi