docs: sync status trackers to verifier state

README.md

@@ -22,6 +22,7 @@ Implementation status:
 - docs are aligned to fresh `P1` wiring in this repo
 - shared models, baselines, and server/client entry points now reflect the locked `P1` contract
 - the current environment uses `constellaration` for low-fidelity `run` steps and high-fidelity `submit` evaluation
+- the remaining runtime work is fixture coverage, manual playtesting, heuristic refresh, and deployment evidence
 
 ## Execution Status
 
@@ -35,6 +36,8 @@ Implementation status:
 - [x] Replace the synthetic evaluator with `constellaration`
 - [ ] Add tracked `P1` fixtures under `server/data/p1/`
 - [ ] Run manual playtesting and record the first reward pathology
+- [ ] Refresh the heuristic baseline for the real verifier path
+- [ ] Pass the Northflank smoke test on the H100 workspace
 - [ ] Deploy the real environment to HF Space
 
 ## Known Gaps
@@ -47,7 +50,7 @@ Implementation status:
 Current mode:
 
 - strategic task choice is already locked
-- the next work is implementation, smoke validation, and manual playtesting
+- the next work is fixtures, manual playtesting, heuristic refresh, smoke validation, and deployment
 - new planning text should only appear when a real blocker forces a decision change
 
 ## Planned Repository Layout
@@ -100,15 +103,15 @@ uv sync --extra notebooks
 
 ## Immediate Next Steps
 
-1. Set up the Northflank Jupyter Notebook with PyTorch and attach persistent storage.
-2. Pass a Northflank smoke test:
+1. Add tracked `P1` fixtures under `server/data/p1`.
+2. Run manual playtest episodes and record the first real reward pathology, if any.
+3. Refresh the heuristic baseline using manual playtest evidence, then save one comparison trace.
+4. Pass a Northflank smoke test:
    - import `constellaration`
    - run one rotating-ellipse generation plus one low-fidelity verifier call
    - write an artifact to persistent storage
-3. Add tracked `P1` fixtures under `server/data/p1`.
-4. Refresh the heuristic baseline using manual playtest evidence, then save one comparison trace.
-5. Add the Colab notebook under `training/notebooks`.
-6. Run manual playtest episodes before heavy training work.
+5. Deploy the environment to HF Space.
+6. Add the Colab notebook under `training/notebooks`.
 
 These are implementation steps, not another planning phase.
 

TODO.md

@@ -36,9 +36,9 @@ Priority source:
 
 ```mermaid
 flowchart TD
-    A["Northflank Smoke Test"] --> C["constellaration Physics Wiring"]
+    A["Northflank Smoke Test"] --> E["Fixture Checks"]
     B["P1 Contract Lock"] --> D["P1 Models + Environment"]
-    C --> D
+    C["constellaration Physics Wiring"] --> D
     D --> E["Fixture Checks"]
     E --> F["Manual Playtest"]
     F --> G["Reward V1"]
@@ -121,6 +121,13 @@ flowchart TD
   [AGENTS.md](AGENTS.md),
   [Plan V2](docs/FUSION_DESIGN_LAB_PLAN_V2.md)
 
+- [ ] Write down whether `Reward V0` survives unchanged
+  Goal:
+  if playtesting does not reveal a real pathology, record that outcome explicitly instead of forcing a `V1`
+  Related:
+  [README.md](README.md),
+  [Plan V2](docs/FUSION_DESIGN_LAB_PLAN_V2.md)
+
 - [x] Decide the non-submit terminal reward policy
   Goal:
   budget exhaustion now yields a smaller end-of-episode reward than `submit`, so non-submitting agents still get terminal feedback without outranking explicit submit behavior
@@ -184,3 +191,4 @@ flowchart TD
 - [ ] Do not add training-first complexity before manual playtesting
 - [ ] Do not describe low-fidelity `run` metrics as equivalent to high-fidelity `submit` results
 - [ ] Do not describe the current baseline reset state as already feasible
+- [ ] Do not force a `Reward V1` story if `Reward V0` survives manual playtesting

baselines/README.md

@@ -1,5 +1,14 @@
 Random and heuristic baselines will live here.
 
+## Status
+
+- [x] random baseline exists
+- [x] heuristic baseline exists
+- [x] baseline comparison script exists
+- [x] baseline comparison rerun completed on the real verifier path
+- [ ] heuristic refreshed after the real-verifier rerun
+- [ ] presentation-ready comparison trace exported
+
 The first baseline milestone is:
 
 - one random agent

demo/README.md

@@ -1,5 +1,12 @@
 Demo assets belong here.
 
+## Status
+
+- [ ] stable episode capture exported
+- [ ] reward-iteration story note exported
+- [ ] baseline comparison figure exported
+- [ ] final 1-minute script drafted
+
 Expected contents:
 
 - one stable episode capture

docs/FUSION_DELIVERABLES_MAP.md

@@ -6,6 +6,16 @@ Northflank is the recommended compute workspace behind those artifacts. HF Space
 
 Use this map to sequence execution, not to reopen already-locked task choices.
 
+## Current Branch Status
+
+- [x] `P1` contract is frozen in code
+- [x] official `constellaration` verifier loop is wired
+- [x] baseline comparison has been rerun on the real verifier path
+- [ ] tracked fixtures are checked in
+- [ ] manual playtest evidence exists
+- [ ] heuristic baseline has been refreshed for the real verifier path
+- [ ] HF Space deployment is live
+
 ## Deliverables Tree
 
 ```mermaid
@@ -90,14 +100,12 @@ flowchart LR
 
 ## Priority Order
 
-1. Bring up the Northflank H100 workspace with persistent storage.
-2. Pass the Northflank smoke test.
-3. Prove the fresh local `P1` verifier loop.
-4. Freeze the environment contract and mark the initial reward as `V0`.
-5. Run verifier/fixture checks and then manual-playtest the environment.
-6. Fix obvious reward/pathology issues.
-7. Make one stable OpenEnv `P1` task work remotely with clear, reproducible rules.
-8. Get random and heuristic baselines.
-9. Use the notebook to show traces and comparisons; include training only if it adds signal.
-10. Record the demo around environment clarity, verifier fidelity, reward shaping, and one stable trajectory.
-11. Polish the repo only after the artifacts are real.
+1. Add tracked fixtures and run fixture sanity checks.
+2. Manual-playtest the environment and record the first real pathology, if any.
+3. Refresh the heuristic baseline from that evidence.
+4. Bring up the Northflank H100 workspace with persistent storage.
+5. Pass the Northflank smoke test.
+6. Make one stable OpenEnv `P1` task work remotely with clear, reproducible rules.
+7. Use the notebook to show traces and comparisons; include training only if it adds signal.
+8. Record the demo around environment clarity, verifier fidelity, reward shaping, and one stable trajectory.
+9. Polish the repo only after the artifacts are real.

docs/FUSION_DESIGN_LAB_PLAN_V2.md

@@ -4,6 +4,19 @@
 **Track:** Statement 3.1 (World Modeling — Professional Tasks)
 **Status:** Judge-aligned plan with `P1` locked
 
+## 0. Current Branch Status
+
+- [x] `P1` task family is locked
+- [x] rotating-ellipse `P1` contract is implemented in code
+- [x] real `constellaration` verifier wiring is in place
+- [x] low-fidelity `run` plus high-fidelity `submit` split is documented
+- [x] post-terminal `step()` guard is in place
+- [x] baseline comparison has been rerun on the real verifier path
+- [ ] tracked `P1` fixtures are added
+- [ ] manual playtest evidence is recorded
+- [ ] heuristic baseline is refreshed for the real verifier path
+- [ ] HF Space deployment evidence is recorded
+
 ## 1. Submission Thesis
 
 We are not primarily submitting "a trained model for fusion."
@@ -154,7 +167,7 @@ Allowed reuse:
 
 Implementation handoff:
 
-- the remaining work is now wiring, smoke validation, manual playtesting, baselines, and deployment
+- the remaining work is now fixture coverage, manual playtesting, heuristic refresh, smoke validation, and deployment
 - do not treat supporting decision notes as a new planning backlog
 
 ## 8.1 Compute Surfaces
@@ -296,6 +309,10 @@ We should expect at least some of these:
 
 The reward is only acceptable after we test for those behaviors.
 
+Important execution rule:
+
+- if manual playtesting does not reveal a real pathology, keep `Reward V0` and document that outcome rather than forcing a `Reward V1`
+
 ## 12. Verifier and Reward Fixture Checks
 
 Before training, we should validate environment wiring with a few fixed fixtures.

docs/FUSION_NEXT_12_HOURS_CHECKLIST.md

@@ -13,7 +13,9 @@ Do not expand scope beyond one stable task. Training is supporting evidence, not
 - [x] baselines and API surface have been moved to the `P1` contract
 - [x] add a post-terminal guard in `step()`
 - [x] replace the synthetic evaluator with `constellaration`
+- [x] re-run baselines on the real verifier path
 - [ ] add tracked fixtures and manual playtest evidence
+- [ ] refresh the heuristic baseline after the real-verifier rerun
 
 ## Plan V2 Inheritance
 
@@ -103,6 +105,7 @@ Transition rule:
    - bad behavior
    - refinement to reward V1
    - improved behavior
+8. If no real pathology appears, record that `Reward V0` survived playtesting and move on.
 
 Exit condition: you can explain why the environment now rewards the intended behavior.
 

docs/PIVOT_P1_ROTATING_ELLIPSE.md

@@ -6,6 +6,15 @@
 
 Use this file as rationale for the pivot, not as a fresh planning queue. Once the pivot is accepted, implementation should follow the SSOT plan docs.
 
+## Current Branch Status
+
+- [x] pivot accepted
+- [x] rotating-ellipse `P1` contract is implemented
+- [x] `constellaration` verifier path is wired
+- [ ] tracked fixtures are added
+- [ ] manual playtest evidence is recorded
+- [ ] heuristic baseline is refreshed for the real verifier path
+
 ## Decision
 
 Pivot the OpenEnv environment to use the official ConStellaration P1 benchmark with real VMEC physics, scoped to the rotating-ellipse low-dimensional parameter space.
@@ -147,7 +156,6 @@ current_params: {aspect_ratio, elongation, rotational_transform}
 best_params: {aspect_ratio, elongation, rotational_transform}
 initial_score: float
 best_score: float
-current_feasibility: float
 best_feasibility: float
 history: list[str]
 ```
@@ -171,13 +179,17 @@ history: list[str]
 
 ### Phase 1: Physics Backend (~1 hour)
 
+Status: done.
+
 Rewrite `server/physics.py` to wrap:
 - `constellaration.initial_guess.generate_rotating_ellipse` for boundary generation
 - `constellaration.forward_model.forward_model` with low-fi settings for evaluation
-- `constellaration.problems.GeometricalProblem` for official P1 scoring on submit
+- `constellaration.problems.GeometricalProblem` for official P1 scoring on every evaluation
 
 ### Phase 2: Environment Contract (~1 hour)
 
+Status: done.
+
 Update `server/environment.py`:
 - New observation schema with P1 metrics
 - New action schema for rotating-ellipse perturbations
@@ -188,6 +200,8 @@ Update `fusion_lab/models.py` for new schemas.
 
 ### Phase 3: Manual Playtest (~30 min)
 
+Status: open.
+
 Validate hypothesis: "6 actions is enough."
 - Play 5-10 episodes manually
 - Log: can a human reach feasibility? Improve elongation?
@@ -196,12 +210,16 @@ Validate hypothesis: "6 actions is enough."
 
 ### Phase 4: Baselines (~30 min)
 
+Status: partial. Baselines exist, but the heuristic needs refresh on the real verifier path.
+
 - Random agent
 - Heuristic agent (greedy toward known-good parameter region)
 - Comparison table
 
 ### Phase 5: Deploy + Evidence (~2 hours)
 
+Status: open.
+
 - Update Dockerfile/deps for constellaration
 - `openenv validate` + `openenv push`
 - Colab notebook connecting to live environment

server/data/README.md

@@ -1,3 +1,7 @@
 Baseline VMEC inputs and related static assets belong here.
 
 Do not commit generated solver outputs or large transient artifacts.
+
+## Status
+
+- [ ] tracked `P1` fixture assets added under `server/data/p1/`

server/data/p1/README.md

@@ -10,4 +10,11 @@ Intended contents:
 
 These fixtures are for verifier and reward sanity checks.
 
+## Status
+
+- [ ] known-good or near-winning fixture added
+- [ ] near-boundary fixture added
+- [ ] clearly infeasible fixture added
+- [ ] fixture sanity note written
+
 Do not copy the old `ai-sci-feasible-designs` harness here. Reuse only the specific JSON artifacts needed for the fresh `P1` environment.

training/README.md

@@ -1,3 +1,9 @@
 Training and evaluation notebooks belong here.
 
 This repository treats notebooks as supporting evidence for the environment, not the primary product.
+
+## Status
+
+- [ ] Northflank notebook artifacts saved
+- [ ] Colab notebook saved
+- [ ] training evidence included only if it is persuasive

training/notebooks/README.md

@@ -12,6 +12,12 @@ Recommended split:
 - Northflank notebook: main compute workspace on the team H100
 - Colab notebook: thin public artifact required by the hackathon
 
+## Status
+
+- [ ] Northflank smoke notebook note saved
+- [ ] manual-playtest notebook or trace notebook saved
+- [ ] thin public Colab notebook saved
+
 Operational defaults:
 
 - use the same Python dependency set as the repo runtime