Valori: A Deterministic Memory Substrate for AI Systems
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901398245867520 |
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| author | Gudur, Varshith |
| author_facet | Gudur, Varshith |
| contents | <p>Modern AI systems rely on vector embeddings stored and searched using floating-point arith-metic. 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 re-places 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 deter-ministic 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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18022709 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Valori: A Deterministic Memory Substrate for AI Systems Gudur, Varshith Deterministic AI AI Memory Vector Databases Fixed-Point Arithmetic Reproducible Computation Approximate Nearest Neighbor Search State Machines Auditability Systems for AI <p>Modern AI systems rely on vector embeddings stored and searched using floating-point arith-metic. 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 re-places 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 deter-ministic 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.</p> |
| title | Valori: A Deterministic Memory Substrate for AI Systems |
| topic | Deterministic AI AI Memory Vector Databases Fixed-Point Arithmetic Reproducible Computation Approximate Nearest Neighbor Search State Machines Auditability Systems for AI |
| url | https://doi.org/10.5281/zenodo.18022709 |