The paper constructs hash-linked evidence graphs that bind hardware measurement quantities to their verification records, enabling offline auditing with probabilistic matrix checks and security measures against probe attacks on GPUs.
Impacts of floating-point non-associativity on reproducibility for hpc and deep learning applications.arXiv preprint
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
LLMs show implementation-induced randomness even at T=0 that can be characterized as an effective background temperature T_bg estimated via an ideal reference system.
citing papers explorer
-
Self-Verifying Measurement Records: Hash-Linked Evidence Graphs for Hardware Benchmarking
The paper constructs hash-linked evidence graphs that bind hardware measurement quantities to their verification records, enabling offline auditing with probabilistic matrix checks and security measures against probe attacks on GPUs.
-
Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon
Kernel Contracts is a specification language that formalizes correctness requirements for ML kernels to ensure consistent results across heterogeneous silicon platforms.
-
Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models
LLMs show implementation-induced randomness even at T=0 that can be characterized as an effective background temperature T_bg estimated via an ideal reference system.