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cs.AI2026
Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation
Yu Hou, Nathaniel Kang, Pengkai Wang +1
Large language models (LLMs) can infer user preferences from interaction histories and reviews, yet the rationales they generate may not reflect the information actually used for r…
cs.LG2026
Learning to Perturb Hidden Representations for Generalizable Deep Learning
Hua Li
Deep neural networks process data through a cascade of representations: input features, hidden activations, logits, and loss. While perturbations at the input, logit, and label lev…
cs.LG2026
Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training
Hua Li
Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradients to parameter updates). W…