Search papers, labs, and topics across Lattice.
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FlightLLM reveals how combining LLMs with structured prompts and statistical classifiers can transform complex flight safety data into interpretable insights.
Dynamically adjusting visual importance at test time can significantly enhance action prediction accuracy in VLA models.
Identity leakage drops to just 0.74% while maintaining high recognition accuracy, thanks to an innovative decoy-oriented approach in face recognition.
DRQN-CMDP achieves a unique trifecta of 83% lower gas costs, a 7.5% miss rate, and moderate detection latency, outperforming all existing methods in off-chain data auditing.
A new non-traditional code design boosts the reliability of Content Addressable Memory, tackling a critical gap in memory technology.
