Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
Spine: Online semantic planning for missions with incomplete natural language specifications in unstructured environments
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
FESTS uses Spatial Regular Expressions compiled from queries to generate 27k training tuples that raise a 3B-parameter LLM's frame-level F1 on spatio-temporal video reasoning from 48.5% to 87.5%, matching GPT-4.1 while staying far smaller.
citing papers explorer
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Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Co-GLANCE distills vision-language models into an end-to-end onboard model for occlusion segmentation and robot allocation, using conformal prediction plus selective abstention to trigger active perception and achieve 25-36% higher accuracy with 350x lower latency than cloud baselines.
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Spatio-Temporal Grounding of Large Language Models from Perception Streams
FESTS uses Spatial Regular Expressions compiled from queries to generate 27k training tuples that raise a 3B-parameter LLM's frame-level F1 on spatio-temporal video reasoning from 48.5% to 87.5%, matching GPT-4.1 while staying far smaller.