Reinforcement learning on MIR features combined with cargo-fuzz validation reduces false positives in Rust static memory safety analysis, raising precision from 25.6% to 59.0% and accuracy to 65.2%.
Sutton and Andrew G
4 Pith papers cite this work. Polarity classification is still indexing.
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Paper Espresso deploys LLMs to summarize and analyze trends across 13,300+ arXiv papers over 35 months, releasing metadata that shows non-saturating topic growth and higher engagement for novel topics.
ADS-POI decomposes user mobility sequences into multiple parallel evolving latent sub-states with context-conditioned aggregation to improve next POI recommendation accuracy.
A neuro-symbolic DRL approach transfers partial policies as logical rules to bias exploration and rescale Q-values, showing improved performance over reward machine baselines in gridworld environments.
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Sample-Efficient Neurosymbolic Deep Reinforcement Learning
A neuro-symbolic DRL approach transfers partial policies as logical rules to bias exploration and rescale Q-values, showing improved performance over reward machine baselines in gridworld environments.