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.
Sutton and Andrew G
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
citing papers explorer
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Paper Espresso: From Paper Overload to Research Insight
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.
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ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation
ADS-POI decomposes user mobility sequences into multiple parallel evolving latent sub-states with context-conditioned aggregation to improve next POI recommendation accuracy.
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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.