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cs.AI2026
Dual-Dimensional Consistency: Balancing Budget and Quality in Adaptive Inference-Time Scaling
Rongman Xu, Yifei Li, Tianzhe Zhao +3
Large Language Models (LLMs) have demonstrated remarkable abilities in reasoning. However, maximizing their potential through inference-time scaling faces challenges in trade-off b…
cs.AI2026
LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
Yanrui Wu, Lingling Zhang, Xinyu Zhang +5
Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world…
cs.AI2025
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
Xinyu Zhang, Yuxuan Dong, Yanrui Wu +6
Large language models demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based re…