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arXiv:2603.20662v1 Announce Type: new Abstract: Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how …
arXiv:2603.21013v1 Announce Type: new Abstract: Despite recent advances in integrating Large Language Models (LLMs) into social robotics, two weaknesses persist. First, existing implementations on platforms …
NeurIPS 2026 Evaluations & Datasets Track Call for Papers The NeurIPS Evaluations & Datasets (E&D) Track invites submissions that advance the science and practice of …
March 23 2026 Introducing the Evaluations & Datasets Track at NeurIPS 2026 Communication Chairs 2026 2026 Conference We are excited to announce that the Datasets …
arXiv:2603.21029v1 Announce Type: new Abstract: Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural …
arXiv:2603.20209v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) combine the linguistic strengths of LLMs with the ability to process multimodal data, enbaling them …
arXiv:2603.20724v1 Announce Type: new Abstract: Multi-RF Fusion achieves a test ROC-AUC of 0.8476 +/- 0.0002 on ogbg-molhiv (10 seeds), placing #1 on the OGB leaderboard …
arXiv:2603.20986v1 Announce Type: new Abstract: Multiphysics simulation frameworks such as MOOSE provide rigorous engines for phase-field materials modeling, yet adoption is constrained by the expertise …
arXiv:2603.20270v1 Announce Type: new Abstract: Generating executable simulations from natural language specifications remains a challenging problem due to the limited reasoning capacity of large language …
arXiv:2603.20435v1 Announce Type: new Abstract: Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute …
arXiv:2603.20925v1 Announce Type: new Abstract: As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs, …
arXiv:2603.20285v1 Announce Type: new Abstract: Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited …
