Noise2Noise (N2N) trains denoisers on pairs of independently corrupted observations, eliminating clean references. We stress-test two natural conjectures about why the L1 loss outperforms L2 here. First, the hypothesis that the L1 loss confers robustness via parameter sparsity confuses the loss with Lasso regularizatio...
Ding-Yan Shang, Zhen-Yu Xu, You Wang et al.· 0 citations
CriticalSCM-Bench v1 is presented, a controlled synthetic benchmark with causal ground truth, paired factual/counterfactual rollouts, and an explicit net-value objective that identifies regimes in which adaptive ranking adds value and those in which simpler structural policies remain preferable.
Shi Zhuo Huang, Jiani He, Ding-Yan Shang et al.· 4 citations
Context masking is established as necessary for attributing early answer availability to an explanation rather than its hidden input when early-prefix evidence disappears after masking.
Bo-Nan Shen, Ding-Yan Shang, You Wang et al.· arXiv.org· 0 citations
The results support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations and support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations.
Bo-Nan Shen, Ding-Yan Shang, You Wang et al.· 0 citations
Naming the benchmark, metric, target behavior, and model panel is the minimum a safety claim needs, and both instruments score harmful compliance, so this is evidence of convergent validity rather than general safety.
You Wang, Xiao Han, Ding-Yan Shang et al.· arXiv.org· 1 citation
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