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cs.LG2026
Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data
Kaihua Ding
Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet tools such as Microsoft Copilo…
cs.LG2026
Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study
Kaihua Ding
Prior classical-ML learning-curve work fits power laws to tree, linear, and kernel models on tabular data, but at small scale: typically one curve, one team, a handful of cells. We…
cs.AI2026
When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
Kaihua Ding
LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or "mixture-of-exper…