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diff --git a/ONBOARDING.md b/ONBOARDING.md index ce6e549..5cb8962 100644 --- a/ONBOARDING.md +++ b/ONBOARDING.md @@ -114,3 +114,15 @@ private repo. (The full `runs/` history is larger; `pull_assets.py` fetches the *Repo layout:* `ep_run/` = code (start at `lt_ep_train.py`) · `docs/` = the above · `scripts/` = standalone analysis/probe scripts · `assets/` = PDFs/figures · `refs/` = external paper texts · `archive/` = stale snapshots. + +## 2026-07-11 — THE CASCADE PIVOT (read this before the older sections) +The project's product architecture changed on 2026-07-09: we now train **standard multi-layer +transformers** (plain-forward inference, identical to any GPT) with **equilibrium-mode cascade-EP** +— layered energy E = Σ½‖z_l − f_l(z_{l−1})‖², free phase = one forward pass (exact), nudged phase = +3 forward-backward message-passing rounds to the nudged equilibrium, per-layer local θ-reads. No +backprop anywhere in training (formally audited; invariant test `ep_run/test_bp_free.py`). +**Sealed result: matched-tuning parity with backprop** (2.0500±0.015 vs 2.0530±0.004, n=3, C256 L6) +at ~3.6× GPU cost. Entry points: `ep_run/casc_eq_train.py` (trainer), `ep_run/cascade_probe.py` +(gradient gate), `docs/campaign/CASCADE_ABLATION_PLAN.md` (program), FINDINGS.md 2026-07-09→11. +The looped/weight-tied single block (everything below) remains the dynamics-physics testbed and the +subject of the aep-dynamics paper; the cascade line is the scaling/product path. |