Papers by Ruijie Hou

2 papers
Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)

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Challenge: Existing research to improve CoT efficiency falls into three categories, each with distinct limitations.
Approach: They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination.
Outcome: Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy.
LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)

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Challenge: a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally.
Approach: They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation.
Outcome: The proposed framework restores model performance prior to contamination on potentially leaked datasets.

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