An adaptive delta-prioritization algorithm using cosine distance and Hamming-drift thresholds improves embedding distortion by 4.8-7.2% and next-token perplexity by 2.1-6.3% over periodic keyframing at matched low bitrates for tokenized driving world models.
Turbocharging gaussian process inference with approximate sketch-and-project
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On-policy GKD trains 5x smaller student LLMs to nearly match large teacher performance in AV motion planning on nuScenes while beating a dense-feedback RL baseline.
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.
citing papers explorer
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Network-Efficient World Model Token Streaming
An adaptive delta-prioritization algorithm using cosine distance and Hamming-drift thresholds improves embedding distortion by 4.8-7.2% and next-token perplexity by 2.1-6.3% over periodic keyframing at matched low bitrates for tokenized driving world models.
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On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
On-policy GKD trains 5x smaller student LLMs to nearly match large teacher performance in AV motion planning on nuScenes while beating a dense-feedback RL baseline.
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Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.