An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.
L2D2: Low latency distributed downlink for LEO satel- lites
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EarthSight reduces average compute time per image by 1.9x and 90th-percentile end-to-end latency from 51 to 21 minutes by distributing inference decisions between orbit and ground with shared backbones and early rejection filters.
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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SMT-Based Active Learning of Weighted Automata
An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.
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EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence
EarthSight reduces average compute time per image by 1.9x and 90th-percentile end-to-end latency from 51 to 21 minutes by distributing inference decisions between orbit and ground with shared backbones and early rejection filters.
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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.