arXiv:2605. 11484v2 Announce Type: replace Abstract: Task completion in digital and physical environments increasingly involves complex temporal interaction, where actions and observations unfold over different time scales rather than align with fixed observation--action steps.
arXiv:2606. 14778v1 Announce Type: cross Abstract: Long-term action anticipation (LTA) aims to predict an ordered sequence of future verb-noun actions from a partially observed video.
arXiv:2608. 05729v1 Announce Type: new Abstract: As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time.
The paper introduces OODA-Tool, a typed closed‑loop policy that separates state preservation from action generation to reduce state‑action competition in multi‑turn tool use. It follows Boyd’s Observe‑Orient‑Decide‑Act cycle, reconstructing task state, deciding on execution, forming admissible actions, and then realizing outputs. Experiments with Qwen3 models show OODA‑Tool consistently improves task success, especially for smaller models and tasks requiring accumulated information.
arXiv:2605.30152v2 Announce Type: replace-cross Abstract: Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipel...
arXiv:2610.07578v1 Announce Type: new Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents are often trained under concurrent participation, while in many tasks some agents act...
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.
arXiv:2606. 17511v1 Announce Type: cross Abstract: Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment.
ProAct is a dual‑system framework for real‑time embodied social interaction that separates a low‑latency Behavioral System, which streams multimodal interaction and generates continuous non‑verbal motion, from a slower Cognitive System that performs long‑horizon social reasoning and produces proactive intentions. The Cognitive System uses an efficient memory mechanism and a user‑motivation prediction module to decide when to intervene, while the Behavioral System translates these intentions into fluid motion via an intention‑conditioned streaming flow‑matching generator with a disentangled ControlNet branch. The framework is deployed on a physical humanoid robot and validated through real‑world user studies, motion‑generation benchmarks, and a new ProActBench benchmark for proactive trigger detection and restraint.
arXiv:2604. 07392v3 Announce Type: replace Abstract: Autonomous agents operating in dynamic environments increasingly demand decision-making systems that are both efficient and interpretable.
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
TimeInteract introduces a new regime called Time-Series Interaction, enabling models to continuously perceive incoming time-series data and user intent, decide when to respond, and keep processing new observations during response generation. The system employs a dual-view streaming encoder, a response control mechanism, and a decoupled inference pipeline to avoid blocking. Evaluated on the newly created StreamTSI-34K dataset, TimeInteract outperforms existing LLMs, VLMs, and TSLMs across four interaction levels, achieving significant gains in accuracy, response triggering, and inference speed.
arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.