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B. Naskrȩcki

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Preprint Sep 2026

Efficient Record-and-Replay Arithmetic for Quantum Elliptic-Curve Point Addition

We study reversible secp256k1 point-addition circuits developed through ECDSA.Fail for Shor's elliptic-curve discrete-logarithm algorithm. Two complementary constructions improve record-and-replay GCD arithmetic: Jump-2 groups binary-GCD steps and compresses their decisions using base-5 encoding, while ping-pong uses f...

Jie-Yi Long, Theodore Pender, Zhao-Feng Huang et al. · 0 citations
Preprint Oct 2026

Proving at Scale for Universal Algebra

We introduce SemiBase, a project that computes and formally certifies finite identity bases for small semigroups. Deciding finite basability is undecidable for finite algebras and remains open for finite semigroups. The task requires a proof that a candidate basis is complete, or a proof that none exists, rather than a...

João Araújo, Jan Hůla, Mikoláš Janota et al. · 0 citations
Preprint Sep 2026

ECDSA.Fail: Open Autoresearch for Optimizing Elliptic-Curve Point Addition in Shor's Algorithm

We propose Open Autoresearch, a paradigm in which humans and AI agents publish evaluator-verified improvements to a public leaderboard. We instantiate it in ECDSA.Fail, optimizing reversible secp256k1 point-addition circuits, a bottleneck in Shor's algorithm for elliptic-curve cryptography. The benchmark minimizes the...

Jie-Yi Long, Theodore Pender, Zhao-Feng Huang et al. · 0 citations
Review Aug 2026

AI Grinding for Fun and Cryptanalysis

An autonomous cryptanalysis workflow in which agents generate, test, and refine hypotheses before human review is presented, in which reproducible candidates with exact witnesses, controls, code, and run records are returned.

Lukasz Olejnik, B. Naskrȩcki · 0 citations
#artificial intelligence Preprint Sep 2026

Mirror, Mirror on the Wall: Prompt Echoing in Small Instruct Language Models

Prompt echoing is a recognized failure mode of instruct language models, in which a model instead of generating a response, mirrors the provided prompt, even though it did not receive a specific instruction to do so. Is this phenomenon a sign of the model leaking the content of its training dataset, or is it rather cau...

Inez Okulska, B. Naskrȩcki, Jan Piotrowski et al. · 0 citations
Preprint Aug 2026

Key Recovery from Residue-Confined Errors in Pradhan CRT-RLWE

It is shown that the CRT-FHE scheme of Pradhan et al. is insecure for laws within its assumed error distribution range, and that the transformation from ordinary Ring-LWE to CRT-RLWE does not preserve the error distribution, so it does not establish that CRT-RLWE is at least as hard as Ring-LWE.

Lukasz Olejnik, B. Naskrȩcki · 0 citations

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