Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2609.35150v1 Announce Type: cross Abstract: Successful intercultural communication requires more than grammatical competence. It demands sensitivity to culturally embedded social norms whose vi...
arXiv:2609.35916v1 Announce Type: cross Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions fac...
arXiv:2609.37143v1 Announce Type: cross Abstract: Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks wi...
MERID is a framework that uses recursive self‑improvement agents to autonomously develop multimodal pipelines for detecting major depressive disorder. It aligns multimodal records with depression targets, jointly modifies representations, fusion, and predictors, and guides revisions through evidence‑guided evolution to validate improvements before inheritance. Experiments on depression benchmarks show MERID outperforms existing multimodal and agent‑based baselines, especially highlighting the importance of acoustic and linguistic cues.
arXiv:2608.00677v2 Announce Type: replace Abstract: AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-mo...
The paper introduces a method for a Builder model to learn meta-skills—principles that guide when and how to provide support—to improve the execution environments of a Target model during test time. By training on feedback from the Target on a development set, the Builder constructs harnesses for new tasks using a frozen skill bank. Experiments on Harness-Bench and NewtonBench show that using the full meta-skill bank boosts macro-average performance by 8.95 percentage points compared to no-skill construction and by 12.02 points over directly delivering the same skill bank to the Target.
arXiv:2609.35823v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes....
arXiv:2609.37089v1 Announce Type: new Abstract: Real-world videos provide rich demonstrations of manipulation, but turning them into reusable robot skills requires visually aligned environments, exec...
arXiv:2609.37190v1 Announce Type: new Abstract: Multi-turn visual search agents answer questions about high-resolution images by iteratively deciding where to look. Reinforcement learning for these a...
arXiv:2609.37317v1 Announce Type: new Abstract: Omnimodal evaluation should go beyond independent text, image, and speech production: individually plausible outputs may not express a coherent shared...
arXiv:2609.37655v1 Announce Type: new Abstract: Advancing spatial intelligence in Multimodal Large Language Models (MLLMs) is bottlenecked by the scarcity of complex, scalable 3D question-answer (QA)...
arXiv:2609.37870v1 Announce Type: new Abstract: Raindrops adhered to camera lens or windshield are inevitable in rainy scenes and can become an issue for many computer vision systems such as autonomo...
arXiv:2609.37923v1 Announce Type: new Abstract: Agents can learn from past executions, but enabling different agents to reuse and build on one another's experience remains challenging. We introduce E...
arXiv:2609.38008v1 Announce Type: new Abstract: Computer use agents (CUAs) have demonstrated strong capabilities in completing digital tasks. However, existing CUAs either rely solely on graphical us...
arXiv:2609.38119v1 Announce Type: new Abstract: Long-form video understanding requires multimodal agents to iteratively gather evidence over many reasoning steps. However, most existing agentic metho...
arXiv:2609.37602v1 Announce Type: cross Abstract: Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where envi...
arXiv:2507.13628v3 Announce Type: replace Abstract: Separating moving and static objects from a moving camera viewpoint is essential for 3D reconstruction, autonomous navigation, and scene understand...
arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle t...
arXiv:2603.14733v2 Announce Type: replace Abstract: Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos...
arXiv:2605.19410v2 Announce Type: replace Abstract: Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-ho...