From b11d9c6da6ce32471e1c25a6f1b5e7a0a568774d Mon Sep 17 00:00:00 2001 From: Yuren Hao Date: Sat, 4 Jul 2026 22:35:16 -0500 Subject: magic-s2000 study: reg_delay/noadaptc flags, 4-arm queue v2, redx trajectory audit MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - lt_ep_train: --reg_delay N (reg-free early phase: resreg/jr/floss/adaptc off for first N steps) + --noadaptc (kill hidden jacreg==0 damping feedback that would pollute single-reg ablation arms) - queue v2: 4 arms delay-first (abl_delay = reg-free 2k -> proven pair) - eig_traj/2/3: ARPACK audit of redx_traj — the run crossed the edge EARLY and oscillated (s1000 rotating-unstable, s1400 excursion mu=+2.1 self-recovered, s2000 the ONLY stable snapshot mu=-0.02, s2100/s2200 already back out) => s2000 is a post-excursion STABILITY-DIP capture, dip width <100 steps; learning survives mild instability (val fell through unstable stretches). lead_rho cold-40 under-reads clusters — NOT a classifier; ARPACK for audits. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn --- ep_run/eig_traj.py | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 ep_run/eig_traj.py (limited to 'ep_run/eig_traj.py') diff --git a/ep_run/eig_traj.py b/ep_run/eig_traj.py new file mode 100644 index 0000000..4cf14a3 --- /dev/null +++ b/ep_run/eig_traj.py @@ -0,0 +1,44 @@ +"""E1 of the magic-s2000 study: trajectory fingerprint over the redx every-100-step checkpoints. +redx recipe = frozen jr 0.1, NO resreg (predates it) — it rode free, made the golden s2000 (val 3.13), +and blew at step 3300 (CE 2.74 -> 41). Question: does rho(step) show a monotone approach to the edge, +with s2000 sitting in a stable-but-critical sweet window before the ~s3200 crossing? That would make +'edge operator' the mechanism of the magic warm start — and abl_delay the way to manufacture it. +Per ckpt: rho/Re_mu of the forward map at the DEEP state (400-step relax; z_T1=150 readings are +state-contaminated per eig_v2_depth), res at 150 (training protocol) and 400, val CE (nb=4). +""" +import torch +from pathlib import Path +import lt_ep_train as L +from eig_control import lead_rho + +T1, DEEP, EPS, B, C = 150, 400, 0.1, 6, 1.0 +STEPS = list(range(600, 3700, 200)) + + +def load(path): + torch.manual_seed(0) + blk = L.EQBlock(512, 16, 256, 256, c=C, attn_mode='thick'); blk.qknorm = True + ck = torch.load(path, map_location=L.dev) + with torch.no_grad(): + for p, w in zip(blk.allp, ck['allp']): + p.copy_(w.to(L.dev)) + return blk + + +print(f"{'ckpt':>6} {'rho@400':>9} {'Re_mu':>8} {'res@150':>9} {'res@400':>9} {'val':>8}", flush=True) +for s in STEPS: + p = Path(f'runs/redx_traj/s{s}.pt') + if not p.exists(): + print(f"s{s:<5} MISSING", flush=True); continue + blk = load(p) + torch.manual_seed(42) # SAME batch for every ckpt + idx, _ = L.get_batch('train', B, 256) + xin = blk.embed(idx).detach() + z150 = L.relax(blk, xin.clone(), xin, T1, EPS) + r150 = (L.relax(blk, z150, xin, 1, EPS) - z150).norm().item() + z400 = L.relax(blk, z150, xin, DEEP - T1, EPS) + r400 = (L.relax(blk, z400, xin, 1, EPS) - z400).norm().item() + _, rho, mu = lead_rho(blk, z400, EPS, C, {}, iters=40) + val = L.evaluate(blk, T1, EPS, nb=4) + print(f"s{s:<5} {rho:>9.5f} {mu:>+8.4f} {r150:>9.2e} {r400:>9.2e} {val:>8.4f}", flush=True) +print("EIG_TRAJ_DONE", flush=True) -- cgit v1.2.3