RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
Space-llava: a vision-language model adapted to extraterrestrial applications
3 Pith papers cite this work. Polarity classification is still indexing.
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SpaceMind is a self-evolving modular VLM agent framework that achieves 90-100% navigation success in nominal conditions and recovers from failures via experience distillation, with zero-code transfer to physical robots for on-orbit tasks.
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.
citing papers explorer
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RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation
RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
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SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
SpaceMind is a self-evolving modular VLM agent framework that achieves 90-100% navigation success in nominal conditions and recovers from failures via experience distillation, with zero-code transfer to physical robots for on-orbit tasks.
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OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.