diff --git a/dashboard.html b/dashboard.html
index c1cc297..4d0dc40 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -236,6 +236,7 @@
 <p class="muted">One row per active project — the next thing, what it is holding, and when it last ruled.</p>
 <table class="map">
 <tr><th>Project</th><th>Next task</th><th>Awaiting</th><th>Running</th><th>Last ruling</th></tr>
+<tr><td><b>analytic_gaussian</b></td><td>Do graphical and EP recover closed-form means and errors</td><td>0</td><td>1</td><td>-</td></tr>
 <tr><td><b>euclid_dr1_prelim</b></td><td>Do ordered Nautilus reruns reproduce the euclid catalogue values?</td><td>0</td><td>1</td><td>2026-09-10</td></tr>
 <tr><td><b>ic50_workspace</b></td><td>Does EP match the graphical joint fit end to end</td><td>0</td><td>1</td><td>-</td></tr>
 <tr><td><b>slope_hierarchy_scale</b></td><td>Does hierarchical slope recovery hold at N=25 to 50</td><td>0</td><td>1</td><td>-</td></tr>
@@ -257,6 +258,14 @@ <h3>Nothing open</h3>
 </ul></details>
 <a id="projects"></a><h2>Projects <a class="mdsrc" href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects.yaml">markdown version</a></h2>
 <section class="project">
+<h3>analytic_gaussian</h3>
+<p class="paths"><b>Local</b> <span class="pathchip">/mnt/c/Users/Jammy/Science/analytic_gaussian</span></p>
+<p class="paths"><b>RAL</b> <span class="pathchip">/mnt/ral/jnightin/analytic_gaussian</span></p>
+<p class="muted">active · ral partition · tasks: submitted 1</p>
+<div class="task"><button class="copy" data-cmd="cd /mnt/c/Users/Jammy/Science/analytic_gaussian &amp;&amp; hpc/sync jobs" aria-label="Copy the Claude command">📋</button><p><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/tasks/analytic_gaussian/ensemble_parity.md">Do graphical and EP recover closed-form means and errors</a><span class="tags"><span class="pill y">submitted</span></span><span class="facets"> — runs 342413_[0-199] · wall 0:00 of 2:00 (0%)</span></p></div>
+<p class="muted"><a href="https://github.com/PyAutoLabs/PyAutoCortex/tree/main/tasks/analytic_gaussian/">tasks/analytic_gaussian/</a></p>
+</section>
+<section class="project">
 <h3>euclid_dr1_prelim</h3>
 <p class="paths"><b>Local</b> <span class="pathchip">/mnt/c/Users/Jammy/Science/euclid_dr1_prelim</span></p>
 <p class="paths"><b>RAL</b> <span class="pathchip">/mnt/ral/jnightin/euclid_dr1_prelim</span></p>
diff --git a/dashboard.md b/dashboard.md
index d69c533..a1dcd59 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -29,6 +29,7 @@ One row per active project — the next thing, what it is holding, and when it l
 
 | Project | Next task | Awaiting | Running | Last ruling |
 |---|---|---:|---:|---|
+| analytic_gaussian | Do graphical and EP recover closed-form means and errors | 0 | 1 | - |
 | euclid_dr1_prelim | Do ordered Nautilus reruns reproduce the euclid catalogue values? | 0 | 1 | 2026-09-10 |
 | ic50_workspace | Does EP match the graphical joint fit end to end | 0 | 1 | - |
 | slope_hierarchy_scale | Does hierarchical slope recovery hold at N=25 to 50 | 0 | 1 | - |
@@ -104,6 +105,24 @@ One row per active project — the next thing, what it is holding, and when it l
 
 [markdown version](https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects.yaml)
 
+### analytic_gaussian
+
+**Local** `/mnt/c/Users/Jammy/Science/analytic_gaussian`
+
+**RAL** `/mnt/ral/jnightin/analytic_gaussian`
+
+active · ral partition · tasks: submitted 1
+
+<details><summary>📋 <a href="tasks/analytic_gaussian/ensemble_parity.md">Do graphical and EP recover closed-form means and errors</a> — <b>submitted</b> — runs 342413_[0-199] · wall 0:00 of 2:00 (0%)</summary>
+
+```
+cd /mnt/c/Users/Jammy/Science/analytic_gaussian && hpc/sync jobs
+```
+
+</details>
+
+[tasks/analytic_gaussian/](https://github.com/PyAutoLabs/PyAutoCortex/tree/main/tasks/analytic_gaussian/)
+
 ### euclid_dr1_prelim
 
 **Local** `/mnt/c/Users/Jammy/Science/euclid_dr1_prelim`
diff --git a/projects.yaml b/projects.yaml
index 345a195..59a7c59 100644
--- a/projects.yaml
+++ b/projects.yaml
@@ -101,6 +101,20 @@ euclid_dr1_prelim:
   status: active
   note: "created by euclid-dr1-prep phase 4 (a Cortex task after migration); every path is the planned one; active 2026-09-07: phase 4 launched (jobs 342301_[0-9])"
 
+analytic_gaussian:
+  remote: none
+  local_path: /mnt/c/Users/Jammy/Science/analytic_gaussian
+  ral_root: /mnt/ral/jnightin/analytic_gaussian
+  mirror: none
+  sync_cli: hpc/sync
+  sync_verbs: [push, pull, logs, sync, status, submit, push-submit, jobs, sacct, cancel, tail, du, check]
+  ledger: wiki/project/state.md
+  assistant: autofit_assistant
+  witness_file: results/**/*.json
+  partition: ral
+  status: active
+  note: "born 2026-09-09 as the graphical-ep campaign's phase-1 keystone at ensemble scale; benchmark model with a closed-form posterior, borrowed from autofit_workspace_test scripts/graphical (PR #92 lineage); CPU-only, no dataset (every draw is simulated in memory from its seed); remote none until the human runs gh repo create PyAutoLabs/analytic_gaussian --private"
+
 # --------------------------------------------------------------------------- #
 # dormant — git repos only. The non-git trees (subhalo, euclid_group, z_*) are
 # storage, not projects, and aris_PJ011646 is an assistant clone; none get a
diff --git a/tasks/analytic_gaussian/ensemble_parity.md b/tasks/analytic_gaussian/ensemble_parity.md
new file mode 100644
index 0000000..ae6426b
--- /dev/null
+++ b/tasks/analytic_gaussian/ensemble_parity.md
@@ -0,0 +1,104 @@
+# analytic_gaussian — task 1: does autofit recover the closed form over an ensemble of draws
+
+Project: analytic_gaussian
+Summary: Do graphical and EP recover closed-form means and errors
+State: submitted
+Gates:
+Witness: over >= 200 independent dataset draws at N=5, the a/b error distributions and the empirical [q05, q95] coverage per column meet the pre-registered thresholds in the `## Witness` section below, committed as `results/ens_n5/{summary.json, parity_ensemble.md}`
+Budget: 2:00
+Runs: 342413
+Ruling:
+Review-minutes: 20
+Epic: graphical-ep
+Filed: 2026-09-09
+Migrated-from: PyAutoMind/complete/2026/09/analytic-gaussian-benchmark.md
+
+## Question
+
+The `graphical-ep` campaign's end goal 1 is *"an analytic Gaussian model that statistically
+demonstrates the graphical and EP source code is correct (means **and** errors against closed
+form)"*. Phase 1 built the model and answered it at **one seed**, as a CI gate in
+`autofit_workspace_test/scripts/graphical/`. That form did its job — it found six PyAutoFit
+defects, D1–D6, all now shipped (#1558, #1560, #1562, #1572, #1573, #1574, #1576, #1578, #1580)
+— but a single seed cannot demonstrate anything statistically: its 41-cell count moved 37/41 →
+38/41 between two runs of identical code, because a verdict on a tolerance edge is a coin flip.
+
+So: over many independent dataset draws of the same conjugate hierarchical Gaussian, does each
+column recover the closed-form posterior — mean *and* error — and how often?
+
+    mu ~ N(50, 10^2),   x_i | mu ~ N(mu, sigma^2),   y_ij | x_i ~ N(x_i, s_i^2)
+
+    leg A   sigma = 10 fixed                          the graph is exactly Gaussian:
+                                                      Laplace EP should be EXACT here
+    leg B   sigma ~ TruncatedGaussian(10, 5, 0, 100)  the phase-2 scatter-collapse configuration
+
+    columns   closed form | minimal EP | autofit graphical (joint DynestyStatic) | autofit EP
+
+Three specific things this answers that the campaign has been carrying as assumptions:
+
+- **Is leg A fixed?** The parked CI script failed leg A: `mu` never left its starting prior. The
+  first local run on the post-fix library returns it exact (a = b = 0.000). Over 200 seeds, is
+  that exactness the rule or the lucky draw?
+- **How bad is leg B, really?** The Laplace-on-scatter caveat (`autofit/graphical/README.md`
+  §3.5, PyAutoFit#1405) is documented but never quantified: the first local seed returns
+  sigma 9.37 ± 3.57 against a reference 6.57 ± 2.88 — outside the cell tolerance, inside the
+  closed-form [q05, q95], not collapsed. A rate with an interval turns that caveat into a
+  measurement, and gives the moment-matching cure
+  (`PyAutoMind draft/feature/autofit/ep_hierarchical_scatter_moment_matching.md`) a number to
+  beat.
+- **Is the collapse basin still reachable?** The scale-collapse rate (sigma < 0.1) over 200
+  independent draws is the campaign's first unbiased estimate of how often PyAutoFit#1405's
+  basin is entered.
+
+The **minimal EP** column is the analytic ceiling, not a competitor: EP with a Gaussian site on
+sigma is biased on this model by construction, so it — not zero — is what an autofit fix should
+be judged against.
+
+## Witness
+
+Pre-registered before wave 1 was submitted, and mirrored machine-readably as `WITNESS` in
+`scripts/aggregate_ensemble.py`. A missed criterion is a finding to rule on, never a number to
+relax so a wave can pass; changing one is its own task, with the reason in the ruling.
+
+| # | criterion | threshold |
+|---|---|---|
+| 1 | minimal EP, leg A | max a, b ≤ 1e-6 on every row of every seed — a deterministic identity |
+| 2 | minimal EP, leg B (the analytic ceiling) | ≥ 95% of seeds within (scatter a 0.20 / b 0.30, other rows a 0.05 / b 0.16); E[sigma] coverage ≥ 0.90 |
+| 3 | autofit EP, leg A | ≥ 95% of seeds within (a 0.01, b 0.02) on **every** row, and no seed worse than a = 0.10 |
+| 4 | autofit EP, leg B | ≥ 90% of seeds within (a 0.15, b 0.25); E[sigma] inside the closed-form [q05, q95] for ≥ 90%; scale-collapse rate (sigma < 0.1) = 0 |
+| 5 | autofit graphical | ≥ 90% of seeds within (a 0.10, b 0.15) on both legs; scatter coverage ≥ 0.85 |
+| 6 | EP flag health | no seed in which any factor ends the run with zero SUCCESS updates — the STALE state #1562/#1574/#1576/#1580 taught the library to report |
+
+Deliverable: `results/ens_n5/summary.json` (the witness file) and `parity_ensemble.md` committed
+in `analytic_gaussian`, carrying every criterion's measured number — including a Wilson interval
+on each coverage rate, so "3/3 covered" cannot be read as certainty.
+
+Criterion 4 is **expected to miss**, on the campaign's own evidence. That is the point: the miss
+becomes a measured rate rather than a documented caveat, and the ruling records it as the cure's
+baseline.
+
+## Where to look
+
+- `analytic_gaussian` (this project's row): `scripts/ensemble_seed.py` (one seed per array task),
+  `scripts/aggregate_ensemble.py` (the referee), `wiki/project/state.md`
+- `autofit_workspace_test` @ `165a19e` — `scripts/graphical/analytic_{reference,ep_minimal,autofit}.py`,
+  the modules this project borrows verbatim, and `analytic_gaussian.py`, the single-seed CI form
+  parked NEEDS_FIX in `config/build/no_run.yaml`
+- Mind `draft/research/graphical_ep/ep_campaign.md` — phase 1 and end goal 1
+- PyAutoFit#1405 — the collapse basin, still open pending the moment-matching cure
+- Mind `draft/bug/graphical_ep/analytic_gaussian_unseeded_graphical_column.md` — the CI script's
+  unseeded graphical column; this project seeds dynesty's `rstate` from the seed instead, and
+  records `graphical_seeded` in every sidecar
+- `hpc/batch_cpu/submit_ensemble_n25` — the N=25 rung, written but **not submitted**: its cost is
+  unmeasured (27 free parameters in the joint fit against 7 at N=5), and the local timing run was
+  still going when wave 1 went out. Submit it once one seed has given a per-seed wall time; it
+  runs the expensive graphical column on seeds 0-19 only
+
+## Runs
+
+- 342413_[0-199]: submitted — ral — submitted 2026-09-09 — wall 0:00 — the N=5 seed ensemble, 200 seeds, 50 concurrent, `hpc/batch_cpu/submit_ensemble_n5`, sample `ens_n5`. RAL PyAuto mirror verified at PyAutoFit `66f9f8d5d` before submission, which **contains** `08207bad0` (#1580) — unlike phase 3's EP arm, this wave carries the full D1–D6 wave plus the review bundle
+
+
+## Ruling
+
+(none)