mathiasandClaude Sonnet 4.6 bde651b0df feat(backbone): replace TS-JEPA+SIGReg with HEPA causal JEPA ...

HEPA (Petersen et al., arXiv:2605.11130, ICML 2026 Spotlight):
- CausalEncoder: non-overlapping patches + per-patch LayerNorm +
  causal Transformer (generate_square_subsequent_mask) → all tokens (B, N, D)
- HorizonPredictor: MLP(cat(h_t, Δt)) → predicted future embedding;
  Δt sampled uniformly from [1, min(DELTA_T_MAX, N-1-c)] per epoch
- vicreg_loss: (1-α)·L1(norm(ĥ), norm(h*)) + α·(L_var + L_cov);
  joint training — no stop-gradient on target encoder
- Probe: last-token embedding [:, -1, :], fit on 2019-2021, eval on OOS

Results (true OOS 2022-2023):
  val_vol_r2: -0.45 (TS-JEPA+SIGReg) → +0.243/+0.276 (HEPA)
  effective_rank: 58.9/64 → 122.3/128 (near-full-rank, no collapse)
  Phase-0 gate on val_vol_r2: PASS ✓

Tests: 6/6 green (causal masking verified with non-uniform perturbation;
per-patch LayerNorm is mean-invariant so constant shifts are absorbed)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-06-25 08:05:33 +02:00

..

test_hepa.py

feat(backbone): replace TS-JEPA+SIGReg with HEPA causal JEPA

2026-06-25 08:05:33 +02:00