The paper systematically explores a vast design space of cryptographic Boolean networks by formalizing six structural constraints, finding that optimal designs result from sparse, mutually compatible…

The paper investigates the ability of evolutionary computation to discover monotone Boolean functions with high nonlinearity, demonstrating that genetic programming is a highly effective encoding for…

The paper analyzes the algorithmic complexity of finding collisions in single-layer binary neural networks, establishing that the collision resistance depends critically on the activation function's t…

The paper introduces FHE-DiCSNN, a novel framework that uses the TFHE scheme to enable secure and efficient computation on Spiking Neural Networks (SNNs), achieving high accuracy and fast inference ti…

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

This paper quantifies the polymorphic capacity of a commercial LLM, demonstrating that it can cheaply generate large populations of structurally diverse, yet behaviorally equivalent, offensive code pa…

The paper establishes a universal, machine-checked 1-Bit Barrier for the internal wire map of masked Barrett reduction, providing a strong side-channel leakage bound for post-quantum cryptography.

HighTide is an evolving, AI-assisted, open-source benchmark suite for VLSI design, providing a comprehensive and scalable platform for hardware development.

The paper proposes a constant-time implementation methodology for activation functions on microcontrollers to prevent timing side-channel attacks during embedded neural-network inference.

The paper proposes SAGE, a novelty-aware gate that efficiently controls memory updates in agentic LLMs by classifying new facts as clearly novel, clearly redundant, or uncertain, thereby significantly…

The paper empirically and theoretically demonstrates that incorporating Lamarckian and Baldwinian mechanisms into evolutionary algorithms significantly outperforms standard Darwinian evolution, especi…

This paper introduces a novel algorithm, CiSC, to efficiently and optimally synthesize circuit implementations of linear codes for hardware security, significantly outperforming existing state-of-the-…

The paper proposes BaSE, a multi-armed bandit approach, to optimally allocate a fixed budget of LLM calls across parallel evolutionary search trajectories, significantly improving mean fitness and rel…

The paper demonstrates that quadratic integrate-and-fire (QIF) neurons are superior to leaky integrate-and-fire (LIF) neurons for gradient descent training in spiking neural networks because their con…

This paper proposes and validates a novel hardware architecture, ITP-STDP, to significantly reduce the energy consumption and hardware overhead associated with training Spiking Neural Networks (SNNs).

The paper analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

This paper presents a unified framework for end-to-end co-design of neural network processors.

The paper proposes a novel set of combined cellular automaton (CA)-based pseudo-random number generators (PRNGs) that overcome the weak equidistribution issues of existing CA-based PRNGs, achieving ma…

The paper demonstrates that the location and nature of state encoding in sequence models are not fixed architectural traits but are highly dependent on the specific task, showing that the encoding pro…

FuzzPilot is a controller for AFL++ that validates candidate mutation recipes by running short micro-campaigns, demonstrating a mechanism to manage fuzzing plateaus, though initial results on a satura…