Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2605.21139v4 Announce Type: replace-cross Abstract: Current end-to-end autonomous driving models are fundamentally constrained by the behavioral cloning ceiling of imitation learning. While rei...
arXiv:2611.05720v1 Announce Type: new Abstract: Search can help LLM agents correct mistakes, but their starting conditions may shape which evidence they find and use. Drawing on cognitive-bias paradi...
arXiv:2611.06444v1 Announce Type: new Abstract: Skills let language agents reuse experience across tasks as natural-language documents. Recent work evolves skills from interaction, but existing metho...
arXiv:2611.06545v1 Announce Type: new Abstract: Long-running LLM agents compact their context, replacing most history with a summary. We ask whether handoff summaries state permissions or requirement...
arXiv:2611.06876v1 Announce Type: new Abstract: LLM agents use context compaction to manage growing interaction histories, but summarizing long histories with large models can introduce substantial d...
arXiv:2611.05730v1 Announce Type: cross Abstract: Two foundational aspects of human cognition, abstractness and gender, are typically examined as separable. Here, we find they are systematically link...
arXiv:2604.08567v3 Announce Type: replace Abstract: Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing system...
arXiv:2605.09252v3 Announce Type: replace Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and l...
arXiv:2606.18902v3 Announce Type: replace Abstract: Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients d...
arXiv:2611.06083v1 Announce Type: new Abstract: Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model siz...
arXiv:2611.06302v1 Announce Type: new Abstract: Fine-grained action understanding requires vision-language models to ground what an agent does, how it acts, and why it acts in spatio-temporal evidenc...
arXiv:2611.06635v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV)-based collaborative perception enables multiple agents to share complementary perceptual information, overcoming limitati...
arXiv:2611.06701v1 Announce Type: new Abstract: Deep learning methods for pathology are constrained by the limited availability and high cost of large-scale pixel-level annotations. Synthetic data ca...
arXiv:2611.06453v1 Announce Type: cross Abstract: Closed-loop driving benchmarks in CARLA rank agents by a driving score that multiplies route completion by an infraction penalty. This score does not...
arXiv:2511.17481v2 Announce Type: replace Abstract: World models learn to predict the temporal evolution of visual observations given a control signal, potentially enabling agents to reason about env...
arXiv:2609.34378v2 Announce Type: replace Abstract: Humans naturally possess the ability to work persistently toward long-term goals. Given a challenging task, humans can continuously work for months...
The paper investigates the role of thought supervision in distilling large language model agents. By independently perturbing teacher thoughts and actions during distillation, the authors find that removing or altering thoughts has little impact on student performance across various benchmarks and model scales. However, thought supervision becomes important when the action does not reveal the information contained in the thought, suggesting that including thoughts in the training data may be unnecessary for many cases.
The paper introduces GraphRail, a verification framework for search-augmented reasoning systems that checks whether evidence covers the question, reasoning addresses the question, and reasoning is grounded in the evidence. GraphRail represents these components as structured claims and compares them without relying on a generative judge or gold answer. Experiments on three multi‑hop QA benchmarks and three reasoning frameworks show that GraphRail improves error recall over generative verifiers and remains stable under stochastic extraction, while also providing diagnoses that can guide online reasoning to detect and correct intermediate errors.
EmoReward is a self‑evolving reward calibration framework for emotional video generation using reinforcement learning. It distills reliability from reward traces during training to adaptively weight multiple reward primitives—such as visual quality, motion, and text alignment—based on emotional evidence. The method employs grounding cues, heterogeneous signal primitives, reflective reward gating, and a RewardMemory module to calibrate fusion weights across training runs, demonstrating improvements on HunyuanVideo 1.5 and transferability to SD 3.5 and FLUX.
The paper presents a VR exergame that gamifies High‑Intensity Interval Training (HIIT) using 11 predefined combo moves and a standard smartphone camera on a tripod for full‑body pose estimation. Real‑time biomechanical data are processed by a backend built on the Model Context Protocol and GPT‑4o to provide personalized feedback on form, efficiency, intensity, and injury risk. A within‑subject study with 20 participants showed higher enjoyment, immersion, and recommendation likelihood in VR sessions compared to non‑VR, while the MCP system’s feedback agreed strongly with expert evaluation.