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cs.LG2025
Bridging Efficiency and Safety: Formal Verification of Neural Networks with Early Exits
Yizhak Yisrael Elboher, Avraham Raviv, Amihay Elboher +4
Ensuring the safety and efficiency of AI systems is a central goal of modern research. Formal verification provides guarantees of neural network robustness, while early exits impro…
cs.CL2025
PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Shangjian Yin, Shining Liang, Wenbiao Ding +4
High-quality instruction data is critical for LLM alignment, yet existing open-source datasets often lack efficiency, requiring hundreds of thousands of examples to approach propri…