Valori: A Deterministic Memory Substrate for AI Systems

Identical AI systems can produce different results on different hardware: Current AI memory systems using floating-point arithmetic create "silent data divergence," meaning the same model, code, and input can generate different outputs depending on whether it runs on x86 or ARM processors—a problem users and auditors would never detect.

This non-determinism breaks accountability in regulated industries: The inability to replay and verify AI decisions makes it impossible to create reliable audit trails, which is critical for sectors like healthcare, finance, and criminal justice where decisions must be explainable and reproducible for compliance and legal purposes.

Valori solves this through fixed-point arithmetic: By replacing floating-point operations with fixed-point math (Q16.16 format) and treating memory as a replayable state machine, Valori guarantees bit-identical results across all hardware platforms, enabling trustworthy verification and auditing.

Deterministic memory is foundational for AI safety: The paper argues that determinism at the memory level—ensuring consistent, reproducible behavior—should be considered a basic requirement for deploying AI systems responsibly, not an optional feature.

The solution is practical and open-source: The researchers provide a working implementation available to the public, suggesting that deterministic AI memory is achievable without requiring fundamental redesigns of existing systems.