Abstract:\FANOS{} is a PyTorch optimizer that augments RMS-preconditioned momentum with a scalar feedback controller over update energy. The public reference implementation stores momentum in parameter-update units, applies a non-negative thermostat damping coefficient, supports diagonal, factored, and raw-gradient preconditioning, and exposes diagnostics intended for stability audits. This study gives a complete mathematical specification of the released optimizer, including the exact parameter-unit update, the study-equation physical update mode, bounded log-ratio thermostat control, adaptive preconditioner softening, warmup guardrails, and the experimental \Fast{} profile. We report the v0.2 evidence: five-seed reduced-sample MNIST, Fashion-MNIST, and CIFAR-10 experiments show mean top-1 gains of 0.889, 2.197, and 2.666 percentage points over AdamW for \Fast{}, but with 49.8\%, 61.6\%, and 56.8\% higher wall-clock time. Preliminary scientific, PINN, and EEG smoke tests are mixed and are treated as hypothesis-generating only. The evidence supports \FANOS{} as an alpha-stage research optimizer with a reproducible lightweight-vision signal and an explicit runtime bottleneck.
| Comments: | 17 pages, 3 figures, 5 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2601.00889 [cs.LG] |
| (or arXiv:2601.00889v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.00889 arXiv-issued DOI via DataCite |
From: Nalin Dhiman [view email]
[v1]
Wed, 31 Dec 2025 11:49:49 UTC (74 KB)
[v2]
Fri, 8 May 2026 06:12:01 UTC (33 KB)
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