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cs.CL2026
Caliper: Probing Lexical Anchors versus Causal Structure in LLMs
Zhenyu Yu, Shuigeng Zhou
Large language models reach 50 to 70% accuracy on causal reasoning benchmarks such as CLadder, but it is unclear whether this reflects structural reasoning or lexical pattern match…
cs.CL2026
PRIME: Policy-Reinforced Iterative Multi-agent Execution for Algorithmic Reasoning in Large Language Models
Jiawei Xu, Zhenyu Yu, Ziqian Bi +3
Large language models have demonstrated remarkable capabilities across diverse reasoning tasks, yet their performance on algorithmic reasoning remains limited. To handle this limit…
cs.CL2025
Towards Transparent AI: A Survey on Explainable Large Language Models
Avash Palikhe, Zhenyu Yu, Zichong Wang +1
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decisio…