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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
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:
import { sigregWithBreakdown } from './src/sigreg/index.js';
const { loss, perSliceStatistic, maxSliceStatistic, runTag } = sigregWithBreakdown(embeddings);
Default matches the LeJEPA reference implementation:
const LEJEPA_DEFAULT_CONFIG = {
numSlices: 1024,
univariateTest: { numPoints: 17, sigma: 1.0 },
};
Default-off. Opt-in via .claude/gsd-skill-creator.json:
{
"heuristics-free-skill-space": {
"sigreg": { "enabled": true }
}
}
Ported from https://github.com/rbalestr-lab/lejepa under the MIT license.
© Randall Balestriero and LeJEPA Contributors. See ../../license_notices.md.
src/skill-isotropy/ — Skill Space Isotropy Audit (Phase 728) — read-only audit use caseまだレビューはありません。使ってみた感想をお寄せください。
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日本語の概要は準備中です。原文の説明を表示しています。