docs: archive submission plan with SSOT-aligned fixes

Move to docs/archive/ since it is a tactical checklist, not a planning
SSOT. Fix training-optimism, align fallback language to SSOT section 10,
split training milestones, and soften reward branch claim.

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

docs/SUBMISSION_PLAN_14H.md

@@ -1,111 +0,0 @@
-# Submission Plan — 14 Hours Remaining
-
-Date: 2026-03-07
-
-## Current state
-
-Done:
-
-- environment contract locked and stable
-- official constellaration verifier wired (low-fi run, high-fi submit)
-- 3 frozen reset seeds validated by measured sweep
-- reward V0 tested across 12 of 13 branches (replay playtest report)
-- random and heuristic baselines committed
-- PPO smoke passed on Northflank H100 (plumbing gate)
-- replay playtest script committed with full trace output
-
-Not done:
-
-- [ ] trained policy with demo-quality trajectories
-- [ ] HF Space deployment
-- [ ] Colab notebook
-- [ ] 1-minute demo video
-- [ ] README polish
-- [ ] paired high-fidelity fixture checks
-- [ ] submit-side manual playtest with successful high-fi outcome
-
-## Key finding: cross-fidelity gap
-
-The replay playtest (episode 5) confirmed that the canonical low-fi repair
-path from seed 0 crashes at high-fidelity evaluation. The state
-`(ar=3.6, elong=1.35, rt=1.6, tri=0.60)` is low-fi feasible but high-fi
-VMEC failure.
-
-Decision: **do not attempt to fix this in the remaining time**. Reasons:
-
-1. finding a high-fi-safe path requires sweep work with no time guarantee
-2. the plan doc already frames the trained policy as supporting evidence, not the product
-3. the gap itself is a strong honest finding for the submission narrative
-
-## Time allocation
-
-| Task | Hours | Notes |
-|------|-------|-------|
-| Train low-fi PPO | 2-3 | PPO smoke already passed. 24 discrete actions, 6-step budget. Target: visible score improvement across seeds. Run on Northflank H100. |
-| HF Space deployment | 2-3 | Hard requirement. Deploy FastAPI server, prove one clean remote episode. Debug dependency issues. |
-| Colab notebook | 1-2 | Connect to HF Space, run trained policy, show trajectory. Minimal but working. |
-| Demo video | 1 | Script around: environment clarity, human playability, trained agent, reward story. |
-| README and repo polish | 1 | Last step. Only after artifacts exist. |
-| Buffer | 2-3 | Deployment issues, training bugs, unexpected blockers. |
-| **Total** | **~11-14** | |
-
-## Execution order
-
-1. **Training** — start first because it can run while doing other work.
-   Use the existing `training/ppo_smoke.py` as the base. Train on all 3 seeds.
-   Stop when a few trajectories show clear repair-arc behavior (cross
-   feasibility, improve score). Do not overfit to one seed.
-
-2. **HF Space** — deploy while training runs or immediately after. The server
-   is already in `server/app.py`. Need to verify dependencies resolve on HF
-   infra and that one reset-step-submit cycle completes cleanly.
-
-3. **Colab notebook** — wire to the live HF Space endpoint. Load the trained
-   checkpoint. Run a short episode. Add minimal narrative connecting the
-   environment design to the trajectory evidence.
-
-4. **Demo video** — 1 minute. Structure:
-   - the problem (stellarator design is hard)
-   - the environment (narrow, human-playable, real verifier)
-   - human playtest (replay output showing legible reward)
-   - trained agent (trajectory with visible improvement)
-   - honest findings (cross-fidelity gap as a real insight)
-
-5. **README polish** — update with links to HF Space, Colab, and video.
-   Keep claims conservative. Reference the evidence docs.
-
-## What to cut if time runs short
-
-Priority order (cut from the bottom):
-
-1. Colab polish — minimal working notebook is enough
-2. Training length — a few readable improving trajectories over a long run
-3. README depth — link to docs, keep top-level short
-4. Do NOT cut HF Space — hard requirement
-5. Do NOT cut demo video — primary judge-facing artifact
-
-## Submission narrative
-
-Frame the cross-fidelity gap as a strength, not a failure:
-
-> The environment is instrumented well enough to reveal that low-fidelity
-> feasibility does not guarantee high-fidelity success. This is a real
-> challenge in multi-fidelity scientific design and exactly the kind of
-> insight a well-built environment should surface.
-
-The story is:
-
-1. we built a clear, narrow environment for one constrained design task
-2. we tested it thoroughly (sweep, baselines, replay playtest, 12/13 reward branches)
-3. we trained a policy that learns the low-fi repair arc
-4. we discovered and documented an honest cross-fidelity gap
-5. the environment is the product; the policy is evidence that it works
-
-## Risk assessment
-
-| Risk | Likelihood | Mitigation |
-|------|-----------|------------|
-| Training does not converge | Low — smoke already passed | Show the smoke trajectories as evidence. Document what was tried. |
-| HF Space dependency issues | Medium | Start deployment early. Have a local-only fallback with screen recording. |
-| High-fi submit never works | Already confirmed | Frame as documented finding. Do not promise high-fi results. |
-| Run out of time | Medium | Follow cut order above. Prioritize video and HF Space over polish. |

docs/archive/SUBMISSION_PLAN_14H.md

@@ -0,0 +1,137 @@
+# Submission Plan — 14 Hours Remaining
+
+Date: 2026-03-07
+
+Status: late-stage submission checklist, not a planning SSOT. The planning
+SSOT is `FUSION_DESIGN_LAB_PLAN_V2.md`. This file captures time allocation
+and cut priorities for the final push.
+
+## Current state (aligned to SSOT)
+
+Done:
+
+- environment contract locked and stable
+- official constellaration verifier wired (low-fi run, high-fi submit)
+- 3 frozen reset seeds validated by measured sweep
+- reward V0 replay playtest committed (see `P1_REPLAY_PLAYTEST_REPORT.md` for branch-level detail)
+- random and heuristic baselines committed
+- paired high-fidelity fixture checks complete (all 3 fixtures)
+- one successful submit-side manual trace recorded (Episode C in playtest log)
+- PPO smoke completed as a plumbing/debugging gate (exposed repeated-action collapse, not training success)
+- replay playtest script committed with full trace output
+
+Not done (per SSOT execution order):
+
+- [ ] reset-seed pool decision from paired checks
+- [ ] heuristic baseline refresh on repaired-family evidence
+- [ ] trained policy with non-degenerate trajectories (current smoke collapses to a single repeated action)
+- [ ] if non-degenerate: push toward demo-quality trajectories showing feasibility crossing
+- [ ] HF Space deployment
+- [ ] Colab notebook
+- [ ] 1-minute demo video
+- [ ] README polish
+
+## Cross-fidelity status
+
+The cross-fidelity picture is nuanced, not a blanket failure:
+
+- `lowfi_feasible_local.json` shows a successful paired high-fi evaluation at
+  `(ar=3.6, elong=1.4, rt=1.6, tri=0.60)` — constraints satisfied, score=0.292
+- Episode C manual trace shows a successful high-fi submit from seed 0 with
+  score=0.296, constraints satisfied
+- the replay playtest episode 5 crashed at high-fi from
+  `(ar=3.6, elong=1.35, rt=1.6, tri=0.60)` — one elongation decrease away
+
+So: high-fi works for some feasible states but not all. The gap is
+**path-dependent**, not universal. States closer to the original params
+(elong=1.4) survive high-fi; states with decreased elongation (elong=1.35)
+may not. This is a real multi-fidelity challenge, not a total blocker.
+
+Decision: do not spend time trying to map the full high-fi-safe region.
+Use the known-good submit path for the demo. Document the path-dependent
+gap honestly.
+
+## Time allocation
+
+| Task | Hours | Notes |
+|------|-------|-------|
+| Heuristic refresh + reset-seed decision | 1 | Per SSOT, this is the next execution step. Quick because the sweep and fixture evidence already exist. |
+| Train low-fi PPO | 2-3 | Smoke passed as plumbing only. 25 discrete actions (24 run + restore_best), 6-step budget. The repeated-action collapse needs more timesteps or reward tuning. Convergence risk is real — the smoke exposed a failure mode, not success. |
+| HF Space deployment | 2-3 | Hard requirement. Deploy FastAPI server, prove one clean remote episode. |
+| Colab notebook | 1-2 | Connect to HF Space, run trained policy, show trajectory. Minimal but working. |
+| Demo video | 1 | Script around: environment clarity, human playability, trained agent, reward story. |
+| README and repo polish | 1 | Last step. Only after artifacts exist. |
+| Buffer | 2-3 | Deployment issues, training tuning, unexpected blockers. |
+| **Total** | **~11-14** | |
+
+## Execution order (aligned to SSOT)
+
+1. **Heuristic refresh + reset-seed decision** — per SSOT, this comes before
+   broader training. The measured sweep and paired fixture evidence already
+   exist. Decide whether any seed should move, refresh the heuristic to use
+   the repair path from the playtest log, and save one comparison trace.
+
+2. **Training** — start after the heuristic is refreshed so training runs
+   against a confirmed environment configuration. Use `training/ppo_smoke.py`
+   as the base but increase timesteps significantly. The smoke ran 64
+   timesteps and collapsed to a single repeated action — this is the expected
+   outcome of a plumbing gate, not evidence that training will converge
+   easily. First milestone: non-degenerate trajectories (varied actions,
+   not single-action collapse). Second milestone: feasibility crossing in
+   at least one evaluation episode. Do not assume demo-quality trajectories
+   are reachable without tuning. Can run on Northflank H100 in the background.
+   **Do not block HF Space, notebook, or video on training success.**
+
+3. **HF Space** — deploy while training runs. The server is in `server/app.py`.
+   Verify dependencies, prove one clean remote episode.
+
+4. **Colab notebook** — wire to the live HF Space endpoint. Load trained
+   checkpoint if available; otherwise show the heuristic or manual
+   trajectory as evidence.
+
+5. **Demo video** — 1 minute. Structure:
+   - the problem (stellarator design is hard)
+   - the environment (narrow, human-playable, real verifier)
+   - the evidence (successful submit trace, replay playtest coverage)
+   - trained agent if available (trajectory with visible improvement)
+   - honest findings (path-dependent cross-fidelity gap)
+
+6. **README polish** — update with links to HF Space, Colab, and video.
+   Keep claims conservative. Reference the evidence docs.
+
+## What to cut if time runs short
+
+Priority order (cut from the bottom):
+
+1. Colab polish — minimal working notebook is enough
+2. Training length — a few readable trajectories over a long run
+3. README depth — link to docs, keep top-level short
+4. Reset-seed decision — keep current seeds if evidence is ambiguous
+5. Do NOT cut HF Space — hard requirement
+6. Do NOT cut demo video — primary judge-facing artifact
+
+If training remains weak or degenerate:
+
+- still ship the trained-policy demonstration, even if it only shows collapse or weak behavior
+- supplement with the heuristic baseline or manual playtest Episode C as the primary evidence of environment usability
+- document the training limitations plainly in the video and README
+- per SSOT fallback rules (`FUSION_DESIGN_LAB_PLAN_V2.md` section 10): "keep claims conservative about policy quality" and "still ship a trained-policy demonstration and document its limitations plainly"
+- do NOT wait for strong PPO before shipping HF Space, notebook, and video
+
+## Submission narrative
+
+The story is:
+
+1. we built a clear, narrow environment for one constrained design task
+2. we tested it thoroughly (sweep, baselines, replay playtest with broad reward branch coverage)
+3. the environment has a known-good submit path (Episode C: successful high-fi, score=0.296)
+4. we discovered a path-dependent cross-fidelity gap (some low-fi feasible states crash at high-fi)
+5. the environment is the product; the policy is evidence that it works
+
+## Risk assessment
+
+| Risk | Likelihood | Mitigation |
+|------|-----------|------------|
+| Training does not converge | Medium — smoke exposed collapse, not success | Show the heuristic trajectory or manual playtest as fallback evidence. Document what was tried. Keep claims conservative per SSOT fallback rules. |
+| HF Space dependency issues | Medium | Start deployment early. Have a local-only fallback with screen recording. |
+| Run out of time | Medium | Follow cut order above. Prioritize video and HF Space over polish. |