Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2606. 13757v1 Announce Type: cross Abstract: Large language model (LLM) reviewers are increasingly used in pull-request (PR) workflows, where their approvals help decide which code is merged into a repository.
arXiv:2606. 14357v1 Announce Type: cross Abstract: Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.
arXiv:2606. 14155v1 Announce Type: new Abstract: Context adaptation automates prompt engineering in LLM-based systems by iteratively revising tunable prompts from task feedback, without modifying model weights.
arXiv:2510. 05150v3 Announce Type: replace-cross Abstract: Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses.
arXiv:2606. 14200v1 Announce Type: new Abstract: Open platforms increasingly route tasks among heterogeneous LLM agents--differing in base model, scaffold, and tool stack--whose competence varies sharply by skill: an agent excellent at one skill may be useless at another.
arXiv:2606. 14589v1 Announce Type: cross Abstract: LLM agent systems increasingly run as long-lived autonomous runtimes: scheduling jobs, calling tools, maintaining memory, and pushing results to humans.
arXiv:2606. 14029v1 Announce Type: new Abstract: Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints.
arXiv:2606. 12817v2 Announce Type: replace Abstract: Understanding the digital world on mobile devices is shifting from static UI perception to dynamic action comprehension.
arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.
arXiv:2602. 00845v3 Announce Type: replace Abstract: Agentic reasoning enables large reasoning models (LRMs) to dynamically acquire external knowledge, but yet optimizing the retrieval process remains challenging due to the lack of dense, principled reward signals.
arXiv:2606. 14515v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns.
arXiv:2606. 14219v1 Announce Type: cross Abstract: Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity.
arXiv:2606. 13994v1 Announce Type: cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.
arXiv:2602. 00593v4 Announce Type: replace-cross Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy overlooked by existing benchmarks that evaluate these abilities in isolation.
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
arXiv:2606. 14211v1 Announce Type: new Abstract: LLMs are increasingly deployed as agents that interact with external environments and observe feedback such as execution results, error messages, and tool outputs.
arXiv:2606. 13949v1 Announce Type: new Abstract: Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments.
arXiv:2606. 13884v1 Announce Type: new Abstract: Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors.
arXiv:2606. 14517v1 Announce Type: cross Abstract: LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents.
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .