arXiv:2412.18134v4 Announce Type: replace Abstract: Randomized self-reductions (RSRs) express $f(x)$ using $f$ evaluated at random correlated points, enabling self-correcting programs, instance-hiding protocols, and applications in complexity theory and cryptography. Yet discovering RSRs has required manual expert derivation for over 40 years, limiting their practical use. We present Bitween for automated RSR learning. First, we formalize RSR learning with sample complexity analysis under correlated sampling. Second, we develop Vanilla Bitween, which integrates multiple backends (linear regres
The accelerating pace of AI development and the increasing complexity of AI systems necessitate automated methods for discovering fundamental algorithmic structures like Randomized Self-Reductions.
Automating the discovery of Randomized Self-Reductions (RSRs) promises to unlock new capabilities in cryptography, complexity theory, and self-correcting programs, which have been historically bottlenecked by manual derivation.
The ability to automatically learn RSRs will transition this field from artisanal, expert-driven derivation to scalable, AI-driven discovery, greatly expanding its practical applications.
- · AI researchers
- · Cryptography industry
- · Cybersecurity sector
- · Software developers
- · Manual algorithm designers
- · Legacy cryptographic methods
Automated discovery of advanced algorithms will accelerate innovation in security and computational efficiency.
New classes of self-correcting and instance-hiding programs could emerge, enhancing software reliability and privacy.
The democratization of complex algorithmic design might lead to a broader range of applications in AI and distributed systems previously deemed too difficult to achieve.
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