Valori: A Deterministic Memory Substrate for AI Systems
Modern AI systems rely on vector embeddings stored and searched using floating-point arithmetic. While effective for approximate similarity search, this design introduces fundamental non-determinism: identical models, inputs, and code can produce different memory states and retrieval results across hardware architectures (e.g., x86 vs. ARM). This prevents replayability and safe deployment, leading to silent data divergence that prevents post-hoc verification and compromises audit trails in regulated sectors. We present Valori, a deterministic AI memory substrate that replaces floating-point memory operations with fixed-point arithmetic (Q16.16) and models memory as a replayable state machine. Valori guarantees bit-identical memory states, snapshots, and search results across platforms. We demonstrate that non-determinism arises before indexing or retrieval and show how Valori enforces determinism at the memory boundary. Our results suggest that deterministic memory is a necessary primitive for trustworthy AI systems. The reference implementation is open-source and available at https://github.com/varshith-Git/Valori-Kernel (archived at https://zenodo.org/records/18022660).
69533db489916ff627aa3ec8
Varshith Gudur
claimed_verified
2025-12-31T20:57:18.264Z
66cb582d0aaaa6753b10d6ab
https://cdn-avatars.huggingface.co/v1/production/uploads/66cb582d0aaaa6753b10d6ab/YEmHDjkPVQJ6wFKQCJwCm.jpeg
Varshith
varam17
user
varam17
Valori: A Deterministic Memory Substrate for AI Systems
Modern AI systems rely on vector embeddings stored and searched using floating-point arithmetic. While effective for approximate similarity search, this design introduces fundamental non-determinism: identical models, inputs, and code can produce different memory states and retrieval results across hardware architectures (e.g., x86 vs. ARM). This prevents replayability and safe deployment, leading to silent data divergence that prevents post-hoc verification and compromises audit trails in regulated sectors. We present Valori, a deterministic AI memory substrate that replaces floating-point memory operations with fixed-point arithmetic (Q16.16) and models memory as a replayable state machine. Valori guarantees bit-identical memory states, snapshots, and search results across platforms. We demonstrate that non-determinism arises before indexing or retrieval and show how Valori enforces determinism at the memory boundary. Our results suggest that deterministic memory is a necessary primitive for trustworthy AI systems. The reference implementation is open-source and available at https://github.com/varshith-Git/Valori-Kernel (archived at https://zenodo.org/records/18022660).
Valori introduces a deterministic AI memory system using fixed-point arithmetic to ensure bit-identical results across platforms, addressing non-determinism in vector embeddings and similarity search for trustworthy AI.
vector embeddings floating-point arithmetic fixed-point arithmetic (Q16.16) approximate similarity search memory state machine replayable state machine bit-identical memory states deterministic memory non-determinism silent data divergence snapshot