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cs.AI2026
BrailleBench: Investigating Multi-Criteria Braille Comprehension in Large Language Models
Jinghan Zhang, Fengran Mo, Zhiyu Chen +3
Although Large language models (LLMs) mediate access to knowledge and computational assistance, their capabilities should benefit vulnerable groups in the same way. However, it is…
cs.AI2026
StaRPO: Stability-Augmented Reinforcement Policy Optimization
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +5
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-ans…
cs.AI2025
Entropy-based Exploration Conduction for Multi-step Reasoning
Jinghan Zhang, Xiting Wang, Fengran Mo +3
Multi-step processes via large language models (LLMs) have proven effective for solving complex reasoning tasks. However, the depth of exploration of the reasoning procedure can si…