sigreg

# SIGReg — Sketched Isotropic Gaussian Regularization Port of SIGReg from Balestriero & LeCun (2025, *LeJEPA*) arXiv:2511.08544v3. The primitive computes a scalar loss measuring how far an embedding distribution is from the standard-normal target, using Cramér-Wold slicing plus the Epps-Pulley empirical characteristic function test. Linear O(N·M·K) time, naturally differentiable, multi-GPU friendly (all_reduce over ECF averages). ## Public API ```typescript import { sigreg } from './src/sigreg/index.js'; const loss = sigreg(embeddings); // embeddings: number[num_samples][num_dims] // scalar loss; use as L_total = L_pred + λ · loss ``` For telemetry: ```typescript import { sigregWithBreakdown } from './src/sigreg/index.js'; const { loss, perSliceStatistic, maxSliceStatistic, runTag } = sigregWithBreakdown(embeddings); ``` ## Configuration Default matches the LeJEPA reference implementation: ```typescript const LEJEPA_DEFAULT_CONFIG = { numSlices: 1024, univariateTest: { numPoints: 17, sigma: 1.0 }, }; ``` ## Feature flag Default-off. Opt-in via `.claude/gsd-skill-creator.json`: ```json { "heuristics-free-skill-space": { "sigreg": { "enabled": true } } } ``` ## Attribution Ported from https://github.com/rbalestr-lab/lejepa under the MIT license. © Randall Balestriero and LeJEPA Contributors. See `../../license_notices.md`. ## Related modules - `src/skill-isotropy/` — Skill Space Isotropy Audit (Phase 728) — read-only audit use case