| name | sigreg |
| description | Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT). Default-off in v1.49.571. |
| version | 1.49.571 |
| tier | T1 |
| phase | 729 |
| source_paper | arXiv:2511.08544v3 |
| license_attribution | rbalestr-lab/lejepa (MIT) |
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
import { sigreg } from './src/sigreg/index.js';
const loss = sigreg(embeddings);
For telemetry:
import { sigregWithBreakdown } from './src/sigreg/index.js';
const { loss, perSliceStatistic, maxSliceStatistic, runTag } = sigregWithBreakdown(embeddings);
Configuration
Default matches the LeJEPA reference implementation:
const LEJEPA_DEFAULT_CONFIG = {
numSlices: 1024,
univariateTest: { numPoints: 17, sigma: 1.0 },
};
Feature flag
Default-off. Opt-in via .claude/gsd-skill-creator.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
