OracleTSC introduces a reward hurdle and uncertainty regularization to stabilize LLM-based reinforcement learning for traffic signal control, delivering 75% lower travel time and 67% lower queue length on benchmarks plus cross-intersection generalization.
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LaMSUM is a novel multi-level LLM framework with voting methods for extractive summarization of large incident report collections that outperforms prior extractive methods.
Peerispect extracts claims from peer reviews, retrieves evidence from the manuscript, and verifies them via NLI in a modular pipeline with a visual interface.
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OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
OracleTSC introduces a reward hurdle and uncertainty regularization to stabilize LLM-based reinforcement learning for traffic signal control, delivering 75% lower travel time and 67% lower queue length on benchmarks plus cross-intersection generalization.
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LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reports
LaMSUM is a novel multi-level LLM framework with voting methods for extractive summarization of large incident report collections that outperforms prior extractive methods.
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Peerispect extracts claims from peer reviews, retrieves evidence from the manuscript, and verifies them via NLI in a modular pipeline with a visual interface.