The paper proposes ZK-Flex, a flexible software-hardware co-designed framework that significantly accelerates Zero-Knowledge Proof (ZKP) generation by efficiently handling diverse polynomial and ellip…

The paper proposes ZK-Flex, a flexible software-hardware co-designed framework that significantly accelerates Zero-Knowledge Proof (ZKP) generation by efficiently handling diverse polynomial and ellip…

NANOZK introduces a novel, highly efficient zero-knowledge proof system that allows users to cryptographically verify that the output of a large language model (LLM) was generated by a specific, claim…

The paper introduces AlphaEvolve, an evolutionary search framework that automates the optimization of Fully Homomorphic Encryption (FHE) kernels on TPUs, achieving significant speedups over human-engi…

ZK-Value introduces a practical, scalable zero-knowledge system for calculating data valuations (Shapley values) in data marketplaces, significantly reducing proving time while maintaining high accura…

The paper proves that platform-deterministic inference is a necessary and sufficient condition for trustworthy AI, establishing that AI trust fundamentally relies on consistent arithmetic.

The paper introduces a four-stage structural dependency analysis hierarchy that enables scalable, sound first-order masking verification for large, production-level post-quantum cryptographic accelera…

The paper proposes Hermes Seal, a zk-SNARK framework that enables autonomous vehicles to generate cryptographic proofs of their internal computations and perceptions without revealing sensitive propri…

The paper proposes a federated formal verification architecture that treats verification as a polyglot proof system, successfully validating it on complex production subsystems like a Raft consensus m…

The paper proposes a co-design paradigm, 'Meeting in the Middle,' to make Fully Homomorphic Encryption (FHE) practical for AI inference by optimizing both the cryptographic schemes and the underlying…

The paper provides machine-checked proofs demonstrating that fresh per-stage arithmetic masking ensures pipeline-level security for Number Theoretic Transform (NTT) accelerators used in Post-Quantum C…

The paper proposes a method for bit-exact verification of AI inference outputs without sacrificing performance, demonstrating that deterministic, precise re-computation is possible even across differe…

The paper introduces a novel hardware aging attack that exploits the commutative properties of addition to induce unbalanced stress on AI accelerator transistors, significantly degrading model accurac…

The paper introduces a lightweight, sampling-based cryptographic protocol for verifiable AI inference that drastically reduces proving overhead from minutes to milliseconds by leveraging statistical p…

The paper proposes a novel triple-hoisted baby-step giant-step algorithm and a memory-optimized FPGA accelerator to significantly reduce the ciphertext rotations and off-chip memory access latency whe…

KVerus is a retrieval-augmented system that significantly improves the scalability and resilience of formal verification for Rust code by managing complex cross-module dependencies and adapting to cod…

Hawkeye is a system that allows perfect, precision-preserving reproduction of GPU-level matrix multiplication operations on a CPU, enabling efficient and trustworthy third-party auditing of machine le…

This paper provides an explicit cost analysis of Toom-4 multiplication specifically tailored for the incomplete Number Theoretic Transform (NTT) framework, offering a concrete cost model for hybrid la…

The paper develops a formal theory to analyze how throughput changes in AI-enhanced cybersecurity pipelines when stage capacities are perturbed by multipliers.

This paper presents a hardware-oriented description of GoldenFloat, a static-split floating-point family, and its concrete artefacts.