The paper introduces Agentic Semantic Sensing (Agentic SemS), a closed‑loop framework for AI‑enabled radio access networks that dynamically adjusts sensing configurations within a communication‑feasible profile set. A profile‑conditioned causal Transformer updates semantic beliefs from streaming data, while a semantic utility network guides the selection of the next sensing profile and determines when to exit early, balancing task benefit against sensing cost. Experiments on Widar3.0 demonstrate that Agentic SemS reduces cumulative sensing cost by 25.33% compared to a full‑sequence baseline while maintaining 85.79% Macro‑F1, and that semantic early exit yields an additional 12.35% cost savings with minimal performance loss.
arXiv:2602. 14033v1 Announce Type: cross Abstract: Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking.
arXiv:2607. 26865v1 Announce Type: cross Abstract: LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems.
The paper introduces Imagine-then-Plan (ITP), a framework that lets agents learn by interacting with a learned world model to generate multi-step imagined trajectories. ITP features an adaptive lookahead mechanism that balances ultimate goals with task progress, producing richer signals about future outcomes. Experiments on various benchmarks show that ITP outperforms existing baselines, and analyses suggest the adaptive lookahead improves reasoning for complex tasks.
arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.
RS-Claw-Evolution is an environment-feedback-driven framework designed to enhance lightweight remote sensing agents for long-horizon tasks. It improves agents through three stages—interaction evolution, experience evolution, and decision evolution—using executable code, failure-aware trajectory generation, and reinforcement learning with multi-dimensional rewards. On Earth-Bench, a Qwen3-4B agent trained with this framework reaches 65.9% accuracy, surpassing larger baselines and approaching GPT-5 performance.
arXiv:2606. 12852v1 Announce Type: new Abstract: Rapid advances have been made in developing general-purpose embodied agent in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches.
arXiv:2609.39915v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents...
arXiv:2609.14138v1 Announce Type: cross Abstract: As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on pre...
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 optimizati...
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
arXiv:2609.00814v1 Announce Type: new Abstract: Remote sensing visual models have continuously advanced various interpretation tasks. However, the research process behind model improvement still heav...
arXiv:2607. 17317v1 Announce Type: cross Abstract: Autonomous systems rely on a perception module to navigate through dynamic environments.