Papers by Weiyang Huang
SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking (2026.acl-long)
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| Challenge: | Large Reasoning Models (LRMs) produce excessively long Chains of Thought (COT) Existing solutions that improve token efficiency but sacrifice fine-grained control can disrupt the logical integrity of the reasoning process. |
| Approach: | They propose a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. |
| Outcome: | Experiments show that SAT reduces reasoning tokens by 40% while maintaining or improving accuracy. |