3 papers
cs.AI2025
Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization
Boyang Gu, Hongjian Zhou, Bradley Max Segal +6
Recent advances in large language models (LLMs) have shown strong reasoning capabilities through large-scale pretraining and post-training reinforcement learning, demonstrated by D…
cs.AI2025
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
Zifeng Ding, Shenyang Huang, Zeyu Cao +11
Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural appro…
cs.AI2025
ARIES: Autonomous Reasoning with LLMs on Interactive Thought Graph Environments
Pedro Gimenes, Zeyu Cao, Jeffrey Wong +1
Recent research has shown that LLM performance on reasoning tasks can be enhanced by scaling test-time compute. One promising approach, particularly with decomposable problems, inv…