EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
InProceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (CVPR)
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Equinox uses a barrier-function-derived marginal cost to enable value-based adaptive scheduling and neighbor offloading in energy-constrained satellite constellations, yielding 20-31% throughput gains and higher battery reserves in simulation.
citing papers explorer
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EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents
EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
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Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence
Equinox uses a barrier-function-derived marginal cost to enable value-based adaptive scheduling and neighbor offloading in energy-constrained satellite constellations, yielding 20-31% throughput gains and higher battery reserves in simulation.