Apple ML Research unveils fast interactive proofs for large data

Apple Machine Learning Research has published a paper detailing "Doubly Sub-linear Interactive Proofs of Proximity" (dsIPPs). These proofs allow for ultra-fast generation by reading only a small portion of a large input, with even faster approximate verification. The research constructs such a proof system for properties decidable by constant-width read-once oblivious branching programs and explores applications for proving approximate assertions about an input's Hamming weight and graph bipartiteness. AI

IMPACT Introduces novel proof systems that could enhance the efficiency of verifying properties in large datasets, potentially impacting AI model training and validation.

RANK_REASON Academic paper published by a major tech company's research division. [lever_c_demoted from research: ic=1 ai=0.7]

Read on Apple Machine Learning Research →

• Apple Machine Learning Research
• Conference on Neural Information Processing Systems
• Doubly Sub-linear Interactive Proofs of Proximity
• Guy N. Rothblum
• International Conference on Learning Representations
• Lean 4 Programming Language
• MATH-AI 2025
• Noga Amir
• Oded Goldreich
• Weizmann Institute of Science

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →