diff --git a/evaluation/state_metrics/EXPERIMENT_SCENARIOS.md b/evaluation/state_metrics/EXPERIMENT_SCENARIOS.md
index 79df6ce..f96295a 100644
--- a/evaluation/state_metrics/EXPERIMENT_SCENARIOS.md
+++ b/evaluation/state_metrics/EXPERIMENT_SCENARIOS.md
@@ -1,156 +1,106 @@
 # State-metrics experiment scenarios — table + descriptions
 
 Standalone reference for the short-term-simulation state-metrics paper: the
-candidate experiment table and a plain-English description of every scenario.
-For design rationale, build plans, and implementation status see the companion
-`PAPER_SCENARIOS.md`.
+final experiment table and a plain-English description of every scenario, after
+David's review. For design rationale, build plans, and implementation status see
+the companion `PAPER_SCENARIOS.md`.
 
-**Goal:** show that the proposed per-instant state metric **replicates what the
-baselines detect and also covers cases they miss** (complementary, not
-adversarial).
+**Goal:** show that the proposed per-instant, per-case state metric **catches
+continuation errors the SOTA long-term metrics miss because those errors even
+out in the aggregate.**
 
 **Baselines referenced:** cycle-time MAE (`cycle_time`), N-gram distance
 (`ngd_n2`), relative event distribution (`red`), remaining-time distribution
-(`rtd`).
+(`rtd`) — the Arik et al. log-distance measures, designed for long-term fidelity.
 
-**Design principle:** hold every *aggregate / marginal* summary fixed (cycle
-time, resource utilization ρ, WIP/λ, overall ratios, activity counts) so the
-baselines are blind **by construction**, and change only *what is active*,
-*where in time*, or *which type is paired with which path* — which is exactly
-what the per-instant state metric measures.
+**Methodology:** apply the modification **in the BPS model** (not the log).
+Generate the ground truth with the *original* model; continue the ongoing cases
+with the *modified (wrong)* model via Prosimos-short-term; ask whether the metric
+detects the deviation. We **pair** the ongoing cases and evaluate each case's
+continuation with tuples `(case_id, …)`, so that when per-case errors even out
+the aggregation-only baselines are blind by construction.
 
 **Three "win" mechanisms:**
-1. **Composition** — *what* is active changes (activity mix / labels) while
-   aggregate timing is untouched.
-2. **Timeline localization** — *where / when* the error occurs, even when the
-   aggregate cycle-time distribution is unchanged.
-3. **Pairing** — case-type-aware: a shift visible only when comparing *paired*
-   sub-populations, invisible to any marginal distribution.
+1. **Composition** — *what* is active changes while aggregate timing is untouched.
+2. **Timeline localization** — *where / when* the error occurs.
+3. **Pairing** — a shift visible only when comparing each case's continuation to
+   its own ground-truth path.
 
 ## Table
 
-`Attr?`: **native** = real simulator case attribute · **labels** = post-hoc
-red/green tags · **no** = none. c-index: 1.0 = perfect monotone detection,
-0.5 = blind.
+c-index: 1.0 = perfect monotone detection, 0.5 = blind.
 
-| # | Scenario | Attr? | Invariants held | Mechanism | Winner / blind baselines | Family | Evidence |
-|---|----------|-------|-----------------|-----------|--------------------------|--------|----------|
-| 1 | **Single-activity rename** (pure composition) | no | cycle time, ρ, WIP, *timeline identical* | composition | **State** (`activity`, `activity_case`) wins; `cycle_time`/`ngd`/`red`/`rtd` blind | ✅ `relabel` | **Strong** — cycle_time exactly 0; activity c-index up to 1.0 |
-| 2 | **Parallel-branch automation** (automate non-critical path; critical path sets cycle time) | no | cycle time, ρ, WIP | composition / timeline | **State** wins; `cycle_time` blind | ✅ `parallel_auto` (sim-driven) | **Strong** — state (activity/case/cardinality/role) c-index 1.0; ngd/red/cycle_time/rtd all 0.5 |
-| 3 | **XOR redistribution**, equal-duration branches (shift gateway probs / mix ratio) | labels | mean cycle time, ρ, WIP | composition | mixed — **ngd competitive / wins** | ⚠️ `mix_ratio`, `gateway` | **Cautionary** — ngd ~0.97 most monotonic, state ~0.84 (state *loses*) |
-| 4 | **Front-load vs back-load** durations (+ swap resource capacity to keep ρ symmetric) | no | aggregate cycle time, ρ, WIP | **timeline / "where"** | **State (time-weighted)** wins; `cycle_time`, `ngd` blind | ✅ `front_back_load` (sim-driven) | **Strong** — state c-index 1.0; cycle_time/ngd/red/rtd all 0.5 (per-case total held exactly invariant) |
-| 5 | **Asymmetric case-type** (red/blue), distribution unchanged, pairing reveals it | **native** | cycle time **distribution**, ρ, WIP | **pairing** | **State** (`case_type`, `activity_type`) wins; **all** baselines ~0.5 (blind) | ✅ `case_route` (sim-driven) + `label_swap` (post-hoc) | **Strongest** — activity_type c-index ≈ 0.92–0.94; every baseline 0.5 |
-| 6 | **Temporal case-type drift** (case-type mix drifts along the arrival timeline) | **native** | cycle time, ρ, WIP, **global marginal** | **pairing + timeline** | **State** (`case_type` AND `activity_type`); all baselines blind | ✅ `case_type_drift` (sim-driven, synthetic controlled form) | **Strong** — `case_type`/`activity_type` c-index 1.0; every baseline AND type-agnostic state projection 0.5 |
-| 6r | *(open)* **Real-life** temporal case-type shift (same mechanism, real log) | **native** | — (observational) | **pairing** | **State** (`case_type`/`activity_type`); risk: Jaccard ~0.9 floor on real logs | ❌ not built | data-discovery task; `case_type_drift` is its controlled synthetic stand-in. Optional "icing" |
-| 7 | **Activity time-shift** (~30 min start/end shift) | no | composition, ρ, WIP | timeline | **`cycle_time` wins**; `ngd`/`red` blind — *state has no edge* | ✅ `calendar_shift` | **Cautionary** — cycle_time detects; dead-end for a state-advantage claim |
-| — | *(bonus)* **Per-case timeline jitter** | no | per-case duration, gaps, composition | timeline | **State** wins; `ctd`/`ngd`/`red` exactly 0 | ✅ `rephase` | **Strong** — state c-index 0.94–1.0 |
+| # | Scenario | Mechanism | Winner / blind baselines | Family | Status |
+|---|----------|-----------|--------------------------|--------|--------|
+| 2 | **Parallel-branch automation** (automate non-critical AND-branch) | composition / timeline | **State** (activity/case/cardinality/role c-index 1.0); cycle_time/ngd/red/rtd 0.5 | ✅ `parallel_auto` | validated — *"very good"* |
+| 3 | **Model-error routing** (invert `case_type` routing in the model) | **pairing** | **State** (`activity_case`, `activity_type`); cycle_time/ngd_n2 0.5, red 0.5 if branches balanced | 🔨 `route_error` *(new)* | to build |
+| 4 | **Compensating front/back XOR** (swap front-/back-loaded branches) | **timeline** | **State** (time-weighted); cycle_time/ngd **and red** 0.5 | 🔨 `front_back_load` *(redesign)* | to rebuild |
+| 1 | **Single-activity rename** (pure composition) | composition | **State** (activity/activity_case); cycle_time/ngd/red/rtd 0.5 | ✅ `relabel` | keep — needs justification |
+| 7 | **Per-case timeline jitter** (slide each case's whole timeline) | timeline | **State**; ctd/ngd/red exactly 0 | ✅ `rephase` | validated |
 
-**Status summary:**
-- ✅ **Ready state-wins:** #1, #2, #4, #5, #6, bonus
-- ⚠️ **Cautionary** (baseline wins / no edge — keep only for the "complementary" framing): #3, #7
-- ❌ **Not built** (optional): #6r (real-log)
+**Removed / de-featured:** old #3 XOR redistribution (`mix_ratio`/`gateway`, ngd
+wins — replaced by model-error routing); #5 asymmetric case-type as a log
+tag-swap (`label_swap` — folded into #3's model-error form); #6 temporal
+case-type drift (`case_type_drift` — Prosimos has no time-dependent case
+attribute); #7-old activity time-shift (`calendar_shift` — changes cycle time,
+no state edge). Code retained for provenance; not featured.
 
 ## Scenario descriptions
 
-### 1 · Single-activity rename — `relabel`
-- **Process:** any synthetic log.
-- **Change:** rename a fraction of one activity's occurrences to a new label
-  (e.g. "Review" → "Review_alt"); nothing else moves.
-- **Kept identical:** every timestamp, case, resource, duration — the *timing*
-  is byte-for-byte the same, only some event *names* change.
-- **Why baselines miss it:** cycle_time / red / rtd only look at timing → zero
-  difference.
-- **Why state catches it:** the set of *what's active* at each moment now holds
-  "Review_alt" instead of "Review" — a pure composition shift. *(Simplest
-  composition proof.)*
-
-### 2 · Parallel-branch automation — `parallel_auto`
-- **Process:** an AND-split runs two things at once — a long **Critical** task
-  (10 min) and a short **Non-critical** chain (3×2 min) on a *separate* team —
-  then they join. The critical task alone sets how long the case takes.
-- **Change:** "automate" the non-critical branch — shrink its durations toward
-  ~0 (a robot now does that paperwork instantly).
-- **Kept identical:** total cycle time (critical path unmoved), and the
-  non-critical activity still *appears* once per case (sequence/bigrams
-  unchanged).
-- **Why baselines miss it:** cycle_time unchanged; ngd sees the same activity
-  sequence.
-- **Why state catches it:** that non-critical task used to *occupy* the active
-  set for 6 min/case and now occupies ~0 → at any instant less is genuinely
-  happening in parallel. *(Classic BPR "automate the side-process" example.)*
-
-### 3 · XOR redistribution — `mix_ratio` / `gateway`  ⚠️
-- **Process:** a case takes one of two equally-long branches at a fork (or a
-  population mixes two case types).
-- **Change:** shift the proportions — e.g. 70% take branch A instead of 50%.
-- **Kept identical:** mean cycle time (branches equal-duration), ρ, WIP.
-- **Result (cautionary):** the baseline **`ngd` actually wins** (~0.97 vs state
-  ~0.84), because changing branch proportions changes the activity-sequence
-  (bigram) histogram — exactly what ngd detects. Keep only as an honest
-  "baselines also do well sometimes" point; **not** a state win.
-
-### 4 · Front-load vs back-load — `front_back_load`
-- **Process:** a simple 5-step sequence (Step 1→…→Step 5), all steps equal
-  length.
-- **Change:** redistribute the *time* across steps — front-loading makes early
-  steps long and late steps short (back-loading is the mirror) — **while
-  keeping each case's total duration exactly the same.**
-- **Kept identical:** total cycle time (held to the microsecond by
-  renormalization), ρ, the activity sequence.
-- **Why baselines miss it:** cycle_time and ngd see "same total, same steps."
-- **Why state catches it:** *where in time the work sits* changed — early there's
-  now more concurrent work, later less. The per-instant state view is the only
-  metric that localizes it. *(The "where is the error" / timeline story — most
-  novel.)*
-
-### 5 · Asymmetric case-type — `case_route` / `label_swap`  *(strongest)*
-- **Process:** cases carry a real red/blue attribute that **drives routing**
-  (red → branch A, blue → branch B), but both branches are equally long.
-- **Change:** swap the red/blue tags on a fraction of cases (so some "red" cases
-  now sit on the B-branch, etc.).
-- **Kept identical:** the red/blue *overall ratio* (50/50), all timing, activity
-  counts — every single-variable summary is unchanged.
-- **Why baselines (and even simple state views) miss it:** no marginal moved —
-  red still 50%, branch-A still 50%, durations identical. The change is *only in
-  the pairing*: which type is on which branch.
-- **Why state catches it:** the joint `(activity, case_type)` view sees
-  "branch-A is now run by blue cases" — the only metric comparing *paired*
-  sub-populations. *(All baselines 0.5; state ≈0.93. The unique contribution.)*
-
-### 6 · Temporal case-type drift — `case_type_drift`  *(the data-attribute one)*
-- **Process:** same red/blue attribute-routed process as #5.
-- **Change:** instead of random swaps, make the red/blue mix **drift over time**
-  (early arrivals skew blue, later ones skew red) — **while keeping the overall
-  50/50 ratio exactly fixed.**
-- **Kept identical:** global red/blue ratio, all timing, activity counts.
-- **Why baselines miss it:** overall composition is still 50/50 and the event
-  stream is untouched.
-- **Why state catches it — and how it differs from #5:** because the mix varies
-  *along the timeline*, at any window more of one color is active than baseline →
-  **both** the simple `case_type` view *and* the paired `activity_type` view fire
-  (in #5 only `activity_type` fired). That extra `case_type` signal is the
-  fingerprint of *temporal* drift.
-
-### 6r · Real-life temporal case-type shift  *(open / optional)*
-- Same mechanism as #6, but on a **real** log whose case-type mix genuinely
-  drifts over months. Observational (no controlled invariant), gated on finding
-  such a log and on Jaccard not hitting its ~0.9 real-log floor. `case_type_drift`
-  is its controlled synthetic stand-in; left as optional data-discovery.
-
-### 7 · Activity time-shift — `calendar_shift`  ⚠️
-- **Process:** a business-hours process.
-- **Change:** shift one team's working hours by ~30 min–N hours.
-- **Result (cautionary):** this is *not* cycle-time-neutral — shifting hours
-  desynchronizes hand-offs and **changes cycle time**, so `cycle_time` detects it
-  and **state has no special edge**. Kept only to illustrate a baseline's
-  strength.
-
-### — · Per-case timeline jitter *(bonus)* — `rephase`
-- **Process:** any synthetic.
+### 2 · Parallel-branch automation — `parallel_auto`  *(David: "very good")*
+- **Process:** an AND-split runs a long **Critical** task (10 min) and a short
+  **Non-critical** chain (3×2 min) on a *separate* team, then they join. The
+  critical task alone sets the cycle time.
+- **Change (model):** "automate" the non-critical branch — shrink its durations
+  toward ~0.
+- **Kept identical:** total cycle time (critical path unmoved); the non-critical
+  activity still appears once per case (sequence/bigrams unchanged).
+- **Why baselines miss it:** cycle_time unchanged; ngd sees the same sequence.
+- **Why state catches it:** that task used to occupy the active set for 6 min/case
+  and now occupies ~0 → at any instant less is genuinely happening in parallel.
+
+### 3 · Model-error routing — `route_error`  *(replaces XOR redistribution)*
+- **Process:** cases carry a real `case_type` ∈ {red, blue} attribute driving a
+  chain of 2–3 XOR splits separated by common activities. Branches are
+  **length-asymmetric** (one path longer) but branch totals are balanced, so a
+  50/50 population is cycle-time-neutral.
+- **Change (model):** **invert** the routing rules (red ↔ blue) for a graded
+  fraction of gateways; continue the ongoing cases with this wrong model.
+- **Kept identical:** red/blue ratio (50/50), each gateway's path marginal
+  (50/50), activity counts, aggregate cycle time.
+- **Why baselines miss it:** no marginal moved; common separators keep the 2-gram
+  histogram unchanged (`ngd_n2` blind, and it is a long-term dependency ngd can't
+  see); 50/50 compensation keeps cycle_time flat.
+- **Why state catches it:** pairing by `case_id` sees each ongoing case leave its
+  ground-truth path — plain `(case_id, activity)` (`activity_case`) fires;
+  `(activity, case_type)` (`activity_type`) fires too. *(David: `(case_id,
+  activity)` alone suffices; modifying the model, not the log, is the clean
+  framing.)*
+
+### 4 · Compensating front/back XOR — `front_back_load`  *(redesign)*
+- **Process:** an XOR with **two branches of equal total duration** — one
+  front-loaded, one back-loaded. 50/50 population.
+- **Change (model):** **swap** the two loadings. Each case's total duration is
+  unchanged.
+- **Kept identical:** per-case total → cycle time; the activity sequence; **and
+  the aggregate relative-event-distribution** (the branches compensate).
+- **Why baselines miss it — including RED:** same total/steps for cycle_time and
+  ngd; and because front/back compensate in aggregate, **`red` is blind too**
+  (the key upgrade over the old single-chain version, which RED would detect).
+- **Why state catches it:** *where in time the work sits* changed per case — the
+  per-instant active-set view localises it. The "where is the error" story.
+
+### 1 · Single-activity rename — `relabel`  *(keep — needs justification)*
+- **Change:** rename a fraction of one activity's occurrences; every timestamp,
+  case, resource, duration byte-identical.
+- **Why state catches it:** the set of *what's active* holds the new label — a
+  pure composition shift. *(David: just a composition change; include only with a
+  realistic justification. Warm-up, not headline.)*
+
+### 7 · Per-case timeline jitter — `rephase`
 - **Change:** slide each case's *entire* timeline earlier/later by a small random
-  offset — the case is internally unchanged (same durations, gaps, order).
-- **Kept identical (exactly zero by construction):** per-case cycle time, the
-  activity sequence, relative event positions → ctd / ngd / red are *exactly* 0.
-- **Why state catches it:** even with each case internally untouched, *across*
-  cases the overlap pattern changes — at each instant a different set of cases is
-  active. Only the per-instant state view sees it. (state c-index 0.94–1.0)
+  offset — internally unchanged (same durations, gaps, order).
+- **Kept identical (exactly 0 by construction):** per-case cycle time, activity
+  sequence, relative event positions → ctd / ngd / red exactly 0.
+- **Why state catches it:** across cases the overlap pattern changes — at each
+  instant a different set of cases is active. state c-index 0.94–1.0.
diff --git a/evaluation/state_metrics/PAPER_SCENARIOS.md b/evaluation/state_metrics/PAPER_SCENARIOS.md
index 670cccc..c051152 100644
--- a/evaluation/state_metrics/PAPER_SCENARIOS.md
+++ b/evaluation/state_metrics/PAPER_SCENARIOS.md
@@ -1,371 +1,201 @@
 # Paper experiment scenarios — where state metrics win
 
-Candidate experiments for the short-term-simulation state-metrics paper, drawn
-from the project meeting notes and cross-referenced with what is currently
-implemented in this repo. The goal is to show, on controlled synthetic data,
-that the proposed state-based metric **replicates what baselines detect and also
-covers cases they miss** (complementary, not adversarial).
+Final experiment set for the short-term-simulation state-metrics paper, after
+David's review of the scenario list. The goal is to show, on controlled
+synthetic data, that the proposed state-based metric **catches per-case
+continuation errors that the SOTA (long-term) metrics miss because those errors
+*even out* in the aggregate**.
 
 Baselines referenced: cycle-time MAE (`cycle_time`), N-gram distance (`ngd_n2`),
-relative event distribution (`red`), remaining-time distribution (`rtd`).
-
-## Design principle (from the meetings)
-
-> Hold **cycle time**, **resource utilization (ρ)**, and **WIP/λ** constant, so
-> the baselines see "no difference" while the state metric does.
-
-Every scenario below is constructed to keep one or more of these invariants
-fixed, isolating the effect that only the state metric is meant to catch.
+relative event distribution (`red`), remaining-time distribution (`rtd`) — the
+measures from the Arik et al. log-distance paper, designed for **long-term**
+simulation where aggregate fidelity is what matters.
+
+## Methodology (David's core instruction)
+
+> **Apply the modification in the BPS model, not the log.** Generate the ground
+> truth with the *original* model; then modify the model so that
+> Prosimos-short-term continues the ongoing cases with the *wrong* one.
+> Evaluation question: **can the metric detect the deviation from the original
+> model?**
+
+Story for the paper: *we have an ongoing process; we want to simulate its
+continuation, but the BPS model has an error (the modification); is the metric
+able to recognise it?* This is cleaner and more realistic than mutating the
+event log post-hoc.
+
+**Why our metric wins — the hypothesis.** We *pair* the ongoing cases and
+evaluate the continuation of each case with tuples `(case_id, …)`. When
+per-case errors **even out** — a *desirable* property for long-term simulation —
+the SOTA metrics, which only look at aggregations, are blind by construction.
+The per-instant, per-case state view is not. Every scenario below is built to
+create exactly that situation: hold every aggregate/marginal summary fixed
+(cycle time, utilisation ρ, WIP/λ, overall ratios, activity counts) and change
+only *what is active*, *where in time*, or *which case is on which path*.
 
 ## The three "win" mechanisms
 
-The metric's advantage reduces to one of three mechanisms; a good paper set
-covers all three with non-redundant examples:
-
-1. **Composition** — *what* is active changes (activity mix / labels) while
-   aggregate timing is untouched.
+1. **Composition** — *what* is active changes while aggregate timing is
+   untouched.
 2. **Timeline localization** — *where / when* the error occurs, even when the
    aggregate cycle-time distribution is unchanged.
-3. **Pairing** — case-type-aware: a shift visible only when comparing *paired*
-   sub-populations, invisible to any marginal distribution.
-
-## Candidate experiments
-
-`Attr?` = does the scenario use case attributes? **native** = real simulator
-case attribute; **labels** = artificial post-hoc red/green tags; **no** = none.
+3. **Pairing** — a shift visible only when comparing each case's continuation to
+   its own ground-truth path, invisible to any marginal distribution.
 
-| # | Scenario | Attr? | Invariants held | Mechanism | Expected winner / blind baselines | Implemented? | Evidence so far |
-|---|----------|-------|-----------------|-----------|-----------------------------------|--------------|-----------------|
-| 1 | **Single-activity rename** (pure composition) | no | cycle time, ρ, WIP, *timeline identical* | composition | **State** (`activity`, `activity_case`) wins; `cycle_time`/`ngd`/`red`/`rtd` blind | ✅ `relabel` | **Strong** — cycle_time exactly 0; activity c-index up to 1.0 |
-| 2 | **Parallel-branch automation** (automate non-critical path; critical path sets cycle time) | no | cycle time, ρ, WIP | composition / timeline | **State** wins; `cycle_time` blind | ✅ `parallel_auto` (sim-driven) | **Strong** — state (activity/case/cardinality/role) c-index 1.0; ngd/red/cycle_time/rtd all 0.5 |
-| 3 | **XOR redistribution**, equal-duration branches (shift gateway probs / mix ratio) | labels | mean cycle time, ρ, WIP | composition | mixed — **ngd competitive / wins** | ⚠️ `mix_ratio`, `gateway` | **Cautionary** — ngd ~0.97 most monotonic, state ~0.84 (state *loses*) |
-| 4 | **Front-load vs back-load** durations (+ swap resource capacity to keep ρ symmetric) | no | aggregate cycle time, ρ, WIP | **timeline / "where"** | **State (time-weighted)** wins; `cycle_time`, `ngd` blind | ✅ `front_back_load` (sim-driven) | **Strong** — state c-index 1.0; cycle_time/ngd/red/rtd all 0.5 (per-case total held exactly invariant) |
-| 5 | **Asymmetric case-type** (red/blue), distribution unchanged, pairing reveals it | **native** | cycle time **distribution**, ρ, WIP | **pairing** | **State** (`case_type`, `activity_type`) wins; **all** baselines ~0.5 (blind) | ✅ `case_route` (sim-driven) + `label_swap` (post-hoc) | **Strongest** — activity_type c-index ≈ 0.92–0.94; every baseline 0.5 |
-| 6 | **Temporal case-type drift** (case-type mix drifts along the arrival timeline) | **native** | cycle time, ρ, WIP, **global marginal** | **pairing + timeline** | **State** (`case_type` AND `activity_type`); all baselines blind | ✅ `case_type_drift` (sim-driven, synthetic controlled form) | **Strong** — `case_type`/`activity_type` c-index 1.0; every baseline AND type-agnostic state projection 0.5 |
-| 6r | *(open)* **Real-life** temporal case-type shift (same mechanism, real log) | **native** | — (observational) | **pairing** | **State** (`case_type`/`activity_type`); risk: Jaccard ~0.9 floor on real logs | ❌ not built | data-discovery task; `case_type_drift` is its controlled synthetic stand-in. Optional "icing" |
-| 7 | **Activity time-shift** (~30 min start/end shift) | no | composition, ρ, WIP | timeline | **`cycle_time` wins**; `ngd`/`red` blind — *state has no edge* | ✅ `calendar_shift` | **Cautionary** — cycle_time detects; dead-end for a state-advantage claim |
-| — | *(bonus)* **Per-case timeline jitter** | no | per-case duration, gaps, composition | timeline | **State** wins; `ctd`/`ngd`/`red` exactly 0 | ✅ `rephase` | **Strong** — state c-index 0.94–1.0 |
+## Final scenario set
 
-## Scenarios explained (plain English)
+| # | Scenario | Family | Mechanism | Detected by state | Blind baselines | Status |
+|---|----------|--------|-----------|-------------------|-----------------|--------|
+| 2 | **Parallel-branch automation** | `parallel_auto` | composition / timeline | activity, case, cardinality, role (c-index 1.0) | cycle_time, ngd, red, rtd | ✅ built & validated — *"very good"* |
+| 3 | **Model-error routing** (was "XOR redistribution") | `route_error` *(new)* | pairing | activity_case, activity_type | cycle_time, ngd_n2, (red if branches balanced) | 🔨 **to build** — replaces old #3 |
+| 4 | **Compensating front/back XOR** | `front_back_load` *(redesign)* | **timeline** | time-weighted state | cycle_time, ngd, **and red** | 🔨 **to rebuild** — beats RED |
+| 1 | **Single-activity rename** | `relabel` | composition | activity, activity_case | cycle_time, ngd, red, rtd | ✅ built — *keep, needs justification* |
+| 7 | **Per-case timeline jitter** | `rephase` | timeline | all projections | ctd, ngd, red (exactly 0) | ✅ built |
 
-Each scenario is built the same way: hold every *aggregate / marginal* summary
-fixed (cycle time, utilization, overall ratios, activity counts) so the baselines
-are blind **by construction**, and change only *what is active*, *where in time*,
-or *which type is paired with which path* — which is exactly what the per-instant
-state metric measures.
+**Removed / de-featured** (see bottom of file): old **#3 XOR redistribution**
+(`mix_ratio`/`gateway`) — ngd catches it, so replaced by model-error routing;
+**#6 temporal case-type drift** — Prosimos has no time-dependent case attribute
+and it adds little for the ongoing-case scope; **#5 asymmetric case-type as a
+log tag-swap** — folded into #3's model-error framing (invert routing in the
+model instead of swapping tags in the log).
 
-### 1 · Single-activity rename — `relabel`
-- **Process:** any synthetic log.
-- **Change:** rename a fraction of one activity's occurrences to a new label
-  (e.g. "Review" → "Review_alt"); nothing else moves.
-- **Kept identical:** every timestamp, case, resource, duration — the *timing*
-  is byte-for-byte the same, only some event *names* change.
-- **Why baselines miss it:** cycle_time / red / rtd only look at timing → zero
-  difference.
-- **Why state catches it:** the set of *what's active* at each moment now holds
-  "Review_alt" instead of "Review" — a pure composition shift. *(Simplest
-  composition proof.)*
+## Scenarios explained
 
-### 2 · Parallel-branch automation — `parallel_auto`
+### 2 · Parallel-branch automation — `parallel_auto`  *(David: "very good")*
 - **Process:** an AND-split runs two things at once — a long **Critical** task
   (10 min) and a short **Non-critical** chain (3×2 min) on a *separate* team —
   then they join. The critical task alone sets how long the case takes.
-- **Change:** "automate" the non-critical branch — shrink its durations toward
-  ~0 (a robot now does that paperwork instantly).
-- **Kept identical:** total cycle time (critical path unmoved), and the
-  non-critical activity still *appears* once per case (sequence/bigrams
-  unchanged).
-- **Why baselines miss it:** cycle_time unchanged; ngd sees the same activity
-  sequence.
+- **Change (in the model):** "automate" the non-critical branch — shrink its
+  durations toward ~0 (a robot now does that paperwork instantly).
+- **Kept identical:** total cycle time (critical path unmoved); the non-critical
+  activity still *appears* once per case (sequence/bigrams unchanged).
+- **Why baselines miss it:** cycle_time unchanged; ngd sees the same sequence.
 - **Why state catches it:** that non-critical task used to *occupy* the active
   set for 6 min/case and now occupies ~0 → at any instant less is genuinely
-  happening in parallel. *(Classic BPR "automate the side-process" example.)*
-
-### 3 · XOR redistribution — `mix_ratio` / `gateway`  ⚠️
-- **Process:** a case takes one of two equally-long branches at a fork (or a
-  population mixes two case types).
-- **Change:** shift the proportions — e.g. 70% take branch A instead of 50%.
-- **Kept identical:** mean cycle time (branches equal-duration), ρ, WIP.
-- **Result (cautionary):** the baseline **`ngd` actually wins** (~0.97 vs state
-  ~0.84), because changing branch proportions changes the activity-sequence
-  (bigram) histogram — exactly what ngd detects. Keep only as an honest
-  "baselines also do well sometimes" point; **not** a state win.
-
-### 4 · Front-load vs back-load — `front_back_load`
-- **Process:** a simple 5-step sequence (Step 1→…→Step 5), all steps equal
-  length.
-- **Change:** redistribute the *time* across steps — front-loading makes early
-  steps long and late steps short (back-loading is the mirror) — **while
-  keeping each case's total duration exactly the same.**
-- **Kept identical:** total cycle time (held to the microsecond by
-  renormalization), ρ, the activity sequence.
-- **Why baselines miss it:** cycle_time and ngd see "same total, same steps."
-- **Why state catches it:** *where in time the work sits* changed — early there's
-  now more concurrent work, later less. The per-instant state view is the only
-  metric that localizes it. *(The "where is the error" / timeline story — most
-  novel.)*
-
-### 5 · Asymmetric case-type — `case_route` / `label_swap`  *(strongest)*
-- **Process:** cases carry a real red/blue attribute that **drives routing**
-  (red → branch A, blue → branch B), but both branches are equally long.
-- **Change:** swap the red/blue tags on a fraction of cases (so some "red" cases
-  now sit on the B-branch, etc.).
-- **Kept identical:** the red/blue *overall ratio* (50/50), all timing, activity
-  counts — every single-variable summary is unchanged.
-- **Why baselines (and even simple state views) miss it:** no marginal moved —
-  red still 50%, branch-A still 50%, durations identical. The change is *only in
-  the pairing*: which type is on which branch.
-- **Why state catches it:** the joint `(activity, case_type)` view sees
-  "branch-A is now run by blue cases" — the only metric comparing *paired*
-  sub-populations. *(All baselines 0.5; state ≈0.93. The unique contribution.)*
-
-### 6 · Temporal case-type drift — `case_type_drift`  *(the data-attribute one)*
-- **Process:** same red/blue attribute-routed process as #5.
-- **Change:** instead of random swaps, make the red/blue mix **drift over time**
-  (early arrivals skew blue, later ones skew red) — **while keeping the overall
-  50/50 ratio exactly fixed.**
-- **Kept identical:** global red/blue ratio, all timing, activity counts.
-- **Why baselines miss it:** overall composition is still 50/50 and the event
-  stream is untouched.
-- **Why state catches it — and how it differs from #5:** because the mix varies
-  *along the timeline*, at any window more of one color is active than baseline →
-  **both** the simple `case_type` view *and* the paired `activity_type` view fire
-  (in #5 only `activity_type` fired). That extra `case_type` signal is the
-  fingerprint of *temporal* drift.
-
-### 6r · Real-life temporal case-type shift  *(open / optional)*
-- Same mechanism as #6, but on a **real** log whose case-type mix genuinely
-  drifts over months. Observational (no controlled invariant), gated on finding
-  such a log and on Jaccard not hitting its ~0.9 real-log floor. `case_type_drift`
-  is its controlled synthetic stand-in; left as optional data-discovery.
-
-### 7 · Activity time-shift — `calendar_shift`  ⚠️
-- **Process:** a business-hours process.
-- **Change:** shift one team's working hours by ~30 min–N hours.
-- **Result (cautionary):** this is *not* cycle-time-neutral — shifting hours
-  desynchronizes hand-offs and **changes cycle time**, so `cycle_time` detects it
-  and **state has no special edge**. Kept only to illustrate a baseline's
-  strength.
-
-### — · Per-case timeline jitter *(bonus)* — `rephase`
-- **Process:** any synthetic.
+  happening in parallel.
+- **Status:** built (`tools/generate_parallel_auto.py`,
+  `build_branch_automation_params`); state c-index 1.0, baselines 0.5.
+
+### 3 · Model-error routing — `route_error` *(new — replaces XOR redistribution)*
+The meeting example. Cases carry a real `case_type` ∈ {red, blue} attribute that
+**drives routing** through a chain of 2–3 XOR splits separated by common
+activities. The **branches are length-asymmetric** (one path several activities
+/ longer, the other 1–2 / shorter) so a wrong routing has a large per-case
+impact — but the population is 50/50, so the *aggregate* cycle time compensates.
+- **Ground truth:** the *correct* model (red → its branch, blue → its branch).
+- **Change (in the model):** **invert** the routing rules (red → the other
+  branch, blue → the other branch) for a graded fraction of the gateways, and
+  run Prosimos-short-term to continue the ongoing cases with this wrong model.
+  `level=0` is the correct model.
+- **Kept identical:** red/blue overall ratio (50/50), each gateway's path
+  marginal (50/50), activity counts, and — because branch totals are balanced —
+  aggregate cycle time.
+- **Why baselines miss it:** no marginal moved; the common separators keep the
+  2-gram histogram unchanged (`ngd_n2` blind); it is a long-term dependency the
+  n-gram cannot see; 50/50 compensation keeps `cycle_time` flat.
+- **Why state catches it:** pairing by `case_id` sees that each ongoing case is
+  no longer following its ground-truth path — the plain `(case_id, activity)`
+  (`activity_case`) projection already fires; `(activity, case_type)`
+  (`activity_type`) fires too and shows the attribute pickup.
+- **Note (David):** `(case_id, activity)` alone suffices — we do *not* need the
+  `case_type` tuple to detect it, because the case demonstrably leaves its
+  ground-truth path. Modifying the **model** (not the log) is the clean framing.
+
+### 4 · Compensating front/back XOR — `front_back_load` *(redesign)*
+- **Process:** an XOR with **two branches of equal total duration** — one
+  **front-loaded** (early steps long, late short) and one **back-loaded** (the
+  mirror). 50/50 population.
+- **Change (in the model):** **swap** the two loadings (the front-loaded branch
+  becomes back-loaded and vice-versa). Each case's total duration is unchanged.
+- **Kept identical:** per-case total duration → cycle time; the activity
+  sequence; **and the aggregate relative-event-distribution** — because the two
+  branches compensate each other across the population.
+- **Why baselines miss it — including RED:** cycle_time and ngd see "same total,
+  same steps"; and because the front/back branches compensate in aggregate,
+  **`red` is blind too**. (This is the key upgrade over the old single-chain
+  front/back-load, which RED *would* detect.)
+- **Why state catches it:** *where in time the work sits* changed per case — the
+  per-instant active-set view localises it. The "where is the error" story.
+
+### 1 · Single-activity rename — `relabel`  *(David: keep, needs justification)*
+- **Change:** rename a fraction of one activity's occurrences to a new label;
+  nothing else moves — every timestamp, case, resource, duration byte-identical.
+- **Why state catches it:** the set of *what's active* holds the new label; a
+  pure composition shift. Simplest composition proof.
+- **David's note:** just a composition change — include only if we can justify a
+  realistic "process change without affecting timing." Keep as a warm-up, not a
+  headline.
+
+### 7 · Per-case timeline jitter — `rephase`
 - **Change:** slide each case's *entire* timeline earlier/later by a small random
   offset — the case is internally unchanged (same durations, gaps, order).
-- **Kept identical (exactly zero by construction):** per-case cycle time, the
-  activity sequence, relative event positions → ctd / ngd / red are *exactly* 0.
-- **Why state catches it:** even with each case internally untouched, *across*
-  cases the overlap pattern changes — at each instant a different set of cases is
-  active. Only the per-instant state view sees it. (state c-index 0.94–1.0)
-
-## Attribute-related subset
-
-- **#5 Asymmetric case-type** — the core, using **native** simulator case
-  attributes. Pairing pillar; strongest evidence.
-- **#6 Temporal case-type drift** (`case_type_drift`) — **built**. A controlled
-  synthetic drift of the native `case_type` over the arrival timeline (global
-  marginal held fixed) on a genuinely attribute-routed short-term sim. Detected
-  by **both** `case_type` and `activity_type` (c-index 1.0) — the extra
-  `case_type` hit over `label_swap` is the temporal-drift signature. The
-  real-log form (#6r) remains optional data-discovery; this is its stand-in.
-- **#3 XOR redistribution (`mix_ratio`)** — only *attribute-adjacent*: it tags
-  cases red/green with **artificial post-hoc labels**, not native attributes.
-
-Everything else (#1, #2, #4, #7, bonus) is attribute-free.
-
-## Cautionary / negative cases (baselines win — keep for the "complementary" framing only)
-
-- **#3** — `ngd` is most monotonic; do not feature it as a state win.
-- **#7** — `cycle_time` detects the shift; state has no edge. Useful only to
-  show a baseline's strength, not ours.
-
-## Reading the table
-
-**Two cleanest pillars — already implemented and validated:**
-
-- **Composition pillar** → #1 `relabel` (baselines literally blind).
-- **Pairing pillar** → #5 `case_route` / `label_swap` (the headline: all
-  baselines at 0.5, state ~0.94). The unique contribution to protect.
-
-**Avoid leading with #3** — our own runs show `ngd` *beats* state there, so it is
-a weak/negative case to feature. Keep it only as an honest "baselines also do
-well here" point supporting the complementary framing.
-
-**Two highest-value gaps — now built and validated** — each cycle-time-neutral,
-demonstrating a *different* mechanism than the pillars:
-
-- **#4 front/back-loading** (`front_back_load`) — the candidate showcasing
-  **timeline localization** ("where the error is"). No baseline captures it
-  (all 0.5); state c-index 1.0. Per-case total duration held exactly invariant.
-- **#2 parallel-branch automation** (`parallel_auto`) — a structural composition
-  change baselines can't see; reviewer-friendly (maps to BPR literature, e.g.
-  the Ford A/P redesign cited in the notes). State c-index 1.0, baselines 0.5.
+- **Kept identical (exactly 0 by construction):** per-case cycle time, activity
+  sequence, relative event positions → ctd / ngd / red are exactly 0.
+- **Why state catches it:** across cases the overlap pattern changes — at each
+  instant a different set of cases is active. state c-index 0.94–1.0.
+
+## Removed / de-featured scenarios
+
+- **Old #3 · XOR redistribution** (`mix_ratio` / `gateway`) — shifting branch
+  proportions (50→70%) changes the activity-sequence histogram, so **`ngd`
+  detects it** (~0.97 vs state ~0.84). Replaced by the model-error routing
+  scenario above. Code retained only as an honest "baselines also do well
+  sometimes" data point; not featured.
+- **#5 · Asymmetric case-type as a post-hoc tag swap** (`label_swap` /
+  log-mutating `case_route`) — David: modify the **model** instead (invert the
+  routing). Folded into #3 (`route_error`). The old log-swap oracles stay in the
+  code as provenance but are not the featured form.
+- **#6 · Temporal case-type drift** (`case_type_drift`) — Prosimos has **no
+  time-dependent case attribute** (a probability is fixed for the whole run), so
+  the drift can only be faked as a post-hoc log transform, which contradicts the
+  model-error methodology; and for the ongoing-case scope it adds little over
+  #3. Removed from the featured set.
+- **#7 · Activity time-shift** (`calendar_shift`) — not cycle-time-neutral
+  (desynchronises hand-offs), so `cycle_time` detects it; no state advantage.
+  Already flagged cautionary; kept only to illustrate a baseline's strength.
 
 ## Suggested demonstration set
 
-A tight, non-redundant set covering all three mechanisms:
-
-- **#1** (composition) + **#4** (timeline) + **#5** (pairing) — the core.
-- **#2** as a second, *structural* (not just labeling) composition case.
-- **#3** only as an honest "baselines also do well here" data point.
+Tight, non-redundant, one per mechanism:
 
-## Implementation status note
-
-A constraint discovered while implementing #5: Prosimos can attribute-condition
-**routing** but **not durations**. So the realizable form of "asymmetric
-case-type" is duration-symmetric attribute-driven *routing* (`case_route`), not
-the red-slower / blue-faster duration design from the notes — but the scenario's
-*purpose* (pairing reveals what aggregates hide) is unchanged.
-
-See `evaluation/state_metrics/PERTURBATIONS.md` for the full perturbation
-catalogue and `tests/e2e/` for the end-to-end attribute/routing verification.
+- **#2** parallel-branch automation — structural **composition**.
+- **#3** model-error routing — **pairing** (the hypothesis, right on point).
+- **#4** compensating front/back XOR — **timeline localization** (beats RED).
+- **#1** / **#7** as supporting composition / timeline warm-ups.
 
 ---
 
-# Build plans — scenarios #2, #4, #6  ✅ DELIVERED
-
-> **Status (2026-06-28): all three are implemented, unit-tested, and validated
-> end-to-end with real Prosimos.** Smoke runs (2 replicates, p90_wip cutoff)
-> reproduce the intended result in every case — state metrics rank the
-> perturbation monotonically (c-index 1.0) while the baselines stay at 0.5:
->
-> | Scenario | Family | Dataset | State (c-index 1.0) | Baselines |
-> |---|---|---|---|---|
-> | #2 | `parallel_auto` | `synthetic_parallel_auto` | activity, case, cardinality, activity_role | ngd/red/cycle_time/rtd = 0.5 |
-> | #4 | `front_back_load` | `synthetic_linear_chain` | all projections | ngd/red/cycle_time/rtd = 0.5 |
-> | #6 | `case_type_drift` | `synthetic_case_route` | case_type, activity_type | ngd/red/cycle_time/rtd + type-agnostic state = 0.5 |
->
-> New assets: generators `tools/generate_parallel_auto.py` and
-> `tools/generate_linear_chain.py`; builders `build_branch_automation_params` /
-> `build_front_back_load_params` in `perturb.py`; the `drift_case_types`
-> transform + `_run_case_type_drift_levels` oracle in `pipeline.py`; dataset
-> specs + CLI choices (`--perturbation parallel_auto|front_back_load|case_type_drift`,
-> `--load-direction`). Unit tests in `tests/evaluation/state_metrics/`.
->
-> **JSD/Jaccard:** explicitly *not* pursued (per decision 2026-06-28 — we keep
-> Jaccard). The scenarios above are designed so the existing Jaccard +
-> cardinality distances already separate cleanly on synthetic data.
-
-The original build notes are kept below for design provenance.
-
-How the machinery works (so the plans below slot in):
-
-- **Simulator-driven param families** (`resources`, `duration`, `gateway`, …):
-  add a `build_<x>_params(base, *, level…, out)` to `perturb.py`, then a
-  dispatch branch in `pipeline._prepare_params_for_level` (the
-  `if cfg.perturbation == …` chain). The standard prefix → K short-term re-sims
-  → distances loop then runs automatically.
-- **Oracle families** (`relabel`, `rephase`, `case_route`, `mix_ratio`,
-  `label_swap`): a dedicated `_run_<x>_levels(...)` in `pipeline.py`, branched
-  early in `run_pipeline`, for post-hoc log transforms or custom sim loops.
-- Both need: a `--perturbation` choice in `run_pipeline.py`, any new `cfg`
-  fields on `PipelineConfig`, a `DatasetSpec` in `datasets.py`, and tests under
-  `tests/evaluation/state_metrics/`.
-
-Recommended order: **#2 → #4 → #6** (increasing effort and decreasing
-certainty). The planned **JSD distance** (see PERTURBATIONS / strategy notes)
-helps #4 and #6 by avoiding Jaccard's ~0.9 real-log floor; consider it a
-prerequisite for those two.
-
-## Plan — #2 Parallel-branch automation
-
-**Claim:** automating the *non-critical* branch of a parallel (AND) block leaves
-cycle time (set by the critical path) and ρ on the critical resource unchanged,
-but the non-critical activity stops occupying the active-instance set →
-**state wins, `cycle_time` and `ngd_n2` blind.**
-
-- **Dataset (new):** `tools/generate_parallel_auto.py` (mirror
-  `generate_case_route.py`) emitting `synthetic_parallel_auto.{bpmn,json}`:
-  `Start → AND-split → { Critical (mean D_c) | NonCritical chain (Σ means = D_nc, with D_nc < D_c) } → AND-join → End`.
-  Critical path strictly dominates so cycle time = D_c regardless of the
-  non-critical branch. Same resource pool size on both branches, sized so
-  removing non-critical work does not change critical-resource ρ.
-  - Alternative quick start: reuse `samples/dev-samples/synthetic_and_k5.bpmn`
-    (1 parallel gateway) if a clean critical/non-critical split can be
-    identified; a purpose-built generator is cleaner for the paper.
-- **Perturbation (new):** `build_branch_automation_params(base, *, automate_task_ids, level, out)`
-  scaling the non-critical task means by `(1 - level/100)` toward a small floor
-  (not exact 0 — keep the event in the trace so `ngd_n2` stays blind). `level=0`
-  no-op; `level=100` ≈ instantaneous.
-- **Integration:** param family `parallel_auto`; dispatch branch in
-  `_prepare_params_for_level`; `cfg.automate_task_ids`; choice + `DatasetSpec`.
-- **Invariants:** cycle time = D_c (constant while D_nc·(1-level/100) < D_c);
-  arrival/λ unchanged; ρ on the critical resource unchanged.
-- **Expected result / assertion:** `state` (`activity`, `activity_case`, and the
-  time-weighted summary) c-index high and monotonic in `level`; `cycle_time`
-  and `ngd_n2` ≈ 0.5. Ranking via existing `ranking.py`.
-- **Risks:** (a) Prosimos may drop zero-duration tasks from the log → keep a
-  duration floor so the label still appears (otherwise `ngd` would also move and
-  the "baseline blind" claim breaks). (b) If automation frees a resource, ρ can
-  shift — keep the non-critical pool separate / small so the critical ρ is
-  untouched. Verify ρ with the existing `_compute_utilization_rows`.
-- **Effort:** medium (one generator + one builder + dispatch + dataset + test).
-
-## Plan — #4 Front-load vs back-load durations
-
-**Claim:** redistributing duration mass along a sequential chain (early-heavy vs
-late-heavy) while holding the per-case total constant keeps aggregate cycle time
-and ρ fixed, but moves *where in time* work sits → **state (time-weighted)
-localizes it; `cycle_time` and `ngd_n2` blind.** (Honest caveat: `red`/`rtd`
-likely co-detect, since event positions move — the differentiator is the
-per-instant active-set view, not a marginal.)
-
-- **Dataset (new or reuse):** a linear chain of N comparable-duration activities
-  on one pool — generate `synthetic_linear_chain.{bpmn,json}` (N≈5) for a clean,
-  symmetric baseline; or reuse an existing sequential synthetic.
-- **Perturbation (new):** `build_front_back_load_params(base, *, chain_task_ids, shift, out)`
-  reweighting task means by position so `Σ means` per case is invariant. Signed
-  `level`: `+` front-loads (early tasks longer), `−` back-loads. E.g. mean_i ·=
-  `1 + (level/100)·w_i` with `w_i` antisymmetric about the chain midpoint and
-  Σ(w_i·mean_i)=0 (renormalize to hold the total exactly).
-- **Optional v2 (the notes' "swap resource capacity"):** also shift pool
-  capacity / calendars toward the heavy end so *instantaneous* ρ stays symmetric,
-  not just aggregate. Adds complexity; ship v1 (single pool, aggregate ρ held)
-  first.
-- **Integration:** param family `front_back_load`; signed levels; dispatch
-  branch; `cfg.chain_task_ids`; choice + `DatasetSpec` (signed default levels).
-- **Invariants:** per-case total duration (→ cycle time) constant; total work
-  (→ aggregate ρ) constant; λ unchanged.
-- **Expected result / assertion:** `state` time-weighted distance monotonic in
-  `|level|` and roughly symmetric for front vs back; `cycle_time`, `ngd_n2` ≈
-  0.5; report `red`/`rtd` as partial co-detectors (supports the "complementary"
-  framing rather than a clean shutout). Prefer **JSD/cardinality** over Jaccard
-  for the summary here.
-- **Risks:** deterministic durations may make the short-term re-sims too rigid;
-  keep distributional shapes. The active-overlap profile must actually change —
-  validate on a dry run before committing levels.
-- **Effort:** medium-high (builder reweighting + total-invariance math + dataset;
-  v2 capacity-swap is extra).
-
-## Plan — #6 Real-life temporal case-type shift (optional / "icing")
-
-**Claim:** on a real log whose case-type mix drifts over time, the
-`case_type`/`activity_type` projections track the composition shift while
-`cycle_time`/`ngd_n2` stay flat. Observational, not a controlled perturbation.
-
-- **Not a builder** — this is data discovery + a measurement script.
-- **Candidate logs/attributes:** BPIC-2017 (`ApplicationType`, `LoanGoal`),
-  BPIC-2012 (`AMOUNT_REQ` bucketed into red/blue), or `P2PFIN` (a categorical
-  case field). Need a *binary-izable* case attribute.
-- **Step 1 — drift profiling:** bin cases by arrival time into windows; compute
-  the case-type marginal per window; quantify drift (total-variation distance
-  between windows). Select a log/attribute where drift is real and material.
-- **Step 2 — experiment:** pick cutoffs in different drift regimes (or compare a
-  high-red vs high-blue window); run the existing short-term pipeline carrying
-  the real attribute as the `case_type` column (already supported by
-  `_load_prosimos_log` / `_write_prefix_csv`); measure projections vs baselines.
-- **Integration:** new `evaluation/state_metrics/realworld_case_type.py` +
-  `DatasetSpec` entries mapping the real attribute column → `case_type`.
-- **Risks:** (a) Jaccard ~0.9 floor on high-variability real logs → use the
-  planned JSD or `cardinality`. (b) A log with genuine temporal case-type drift
-  may not exist in our set — this is explicitly "icing on the cake," not
-  required for the core hypothesis. (c) No ground-truth control, so framing is
-  weaker than the synthetics.
-- **Effort:** high uncertainty (data-dependent); do last, only if #2/#4 land.
-
-## Cross-cutting prerequisite
-
-`distances.py` currently has only `jaccard_multiset` + `cardinality`. Adding a
-**JSD / distribution distance** (strategy note: prefer JSD over Jaccard; Maksym's
-Jaccard-sensitivity concern) should precede #4 and #6, since both risk the
-Jaccard real-log floor. Small, self-contained: `distances.py` + wire into
-`api.py` summary + `ranking.py` + a test.
+## Build plan — remaining work
+
+### P1 · `route_error` (scenario #3) — the headline, to build
+
+- **Dataset:** new `tools/generate_route_error.py` — initial `case_type`-driven
+  XOR chain (2–3 splits) separated by common activities; **length-asymmetric**
+  branches with **balanced totals**; distribution-based (not `fix`) durations.
+- **Perturbation:** `build_route_error_params(base, *, level, out)` in
+  `perturb.py` — inverts the branch rules (red↔blue) for a graded fraction of
+  gateways. `level=0` = correct model.
+- **Oracle:** `_run_route_error_levels` in `pipeline.py` — GT = short-term sim on
+  the correct model; sim = short-term sim on the inverted model from the **same
+  prefix/snapshot** (ongoing `case_type` restored via `src/attributes.py`, proven
+  by the case_route e2e). **Both sides real sims — no log mutation.**
+- **Expected:** `activity_case` + `activity_type` detect; `cycle_time` / `ngd_n2`
+  ≈ 0.5; `red` blind if branch totals balanced.
+- **Open question:** per-color 90/10 probabilistic routing (David's realism
+  enhancement) may exceed Prosimos `branch_rules` (deterministic conditions).
+  Ship deterministic red→A / blue→B first; verify 90/10 capability separately.
+
+### P2 · `front_back_load` (scenario #4) — redesign to compensating branches
+
+- Replace the single linear chain with an **XOR of a front-loaded and a
+  back-loaded branch of equal total**; the model error **swaps** the loadings.
+- Aggregate relative-event-distribution compensates → **RED blind**; per-instant
+  state localises it.
+
+### Not doing
+
+- **JSD distance** — decision stands: Jaccard + cardinality separate cleanly on
+  these synthetics.
+- **Real-life logs** — not required if the proofs + synthetics land.
+- **#6 temporal drift** — removed (Prosimos limitation; low value for ongoing
+  scope).
diff --git a/evaluation/state_metrics/datasets.py b/evaluation/state_metrics/datasets.py
index f701c34..56eb7d3 100644
--- a/evaluation/state_metrics/datasets.py
+++ b/evaluation/state_metrics/datasets.py
@@ -39,6 +39,20 @@ class DatasetSpec:
         remove_from_profile="Worker",
         default_levels=(0, 1, 2, 3),
     ),
+    # Model-error routing synthetic (scenario #3): a chain of case_type-driven
+    # XOR splits with LENGTH-ASYMMETRIC branches (long A-branch chain vs short
+    # single-activity B-branch), separated by common activities. The base model
+    # routes CORRECTLY (red->long, blue->short); the `route_error` perturbation
+    # inverts the routing on the first `level` gateways. Generated by
+    # tools/generate_route_error.py. `remove_from_profile` points at its single
+    # pool so the resources/duration families still work for sanity baselines.
+    "synthetic_route_error": DatasetSpec(
+        name="synthetic_route_error",
+        bpmn=REPO_ROOT / "samples" / "dev-samples" / "synthetic_route_error.bpmn",
+        params=REPO_ROOT / "samples" / "dev-samples" / "synthetic_route_error.json",
+        remove_from_profile="Worker",
+        default_levels=(0, 1, 2, 3),
+    ),
     # Parallel-automation synthetic (scenario #2): an AND block with a long
     # critical branch and a short non-critical chain on a SEPARATE pool.
     # Generated by tools/generate_parallel_auto.py. The non-critical tasks
diff --git a/evaluation/state_metrics/perturb.py b/evaluation/state_metrics/perturb.py
index 212bf84..1cd192d 100644
--- a/evaluation/state_metrics/perturb.py
+++ b/evaluation/state_metrics/perturb.py
@@ -642,6 +642,79 @@ def _is_split(entry: dict) -> bool:
     }
 
 
+def build_route_error_params(
+    base_json_path: str | Path,
+    *,
+    n_gateways_inverted: int,
+    out_json_path: str | Path,
+) -> dict:
+    """Invert the ``case_type`` routing on the first ``n_gateways_inverted``
+    XOR splits — the *model error* for scenario #3.
+
+    Designed for the *route-error* synthetic (see
+    ``tools/generate_route_error.py``), whose base params already route
+    CORRECTLY by case_type at every split gateway: the ``_a`` (long) branch is
+    bound to ``rule_red`` and the ``_b`` (short) branch to ``rule_blue`` via
+    ``condition_id``. This builder rewrites the first ``n_gateways_inverted``
+    split gateways (ordered by gateway id) to **swap** those bindings — the
+    ``_a`` branch now fires on ``rule_blue`` and the ``_b`` branch on
+    ``rule_red`` — so red cases take the short branch and blue cases the long
+    branch at those gateways.
+
+    Because the population is 50/50 red/blue, each gateway's path marginal stays
+    50/50, so cycle time, the 2-gram distribution and the aggregate
+    relative-event-distribution are all unchanged; only each case's *own* path
+    (paired by case_id) flips. ``n_gateways_inverted == 0`` is a no-op (copies
+    the base params verbatim).
+
+    Returns a manifest listing which gateways were inverted.
+    """
+    if n_gateways_inverted < 0:
+        raise ValueError("n_gateways_inverted must be >= 0")
+    params = _load_params(base_json_path)
+
+    # A "split" gateway here is any branching entry with exactly two paths whose
+    # ids end in _a / _b — the convention emitted by the route-error generator.
+    def _is_split(entry: dict) -> bool:
+        ids = [p["path_id"] for p in entry.get("probabilities", [])]
+        return len(ids) == 2 and any(i.endswith("_a") for i in ids) and any(
+            i.endswith("_b") for i in ids
+        )
+
+    splits = sorted(
+        (e for e in params.get("gateway_branching_probabilities", []) if _is_split(e)),
+        key=lambda e: e["gateway_id"],
+    )
+    if n_gateways_inverted > len(splits):
+        raise ValueError(
+            f"requested {n_gateways_inverted} inverted gateways but only "
+            f"{len(splits)} split gateways exist"
+        )
+
+    inverted: list[str] = []
+    for entry in splits[:n_gateways_inverted]:
+        for p in entry["probabilities"]:
+            # Swap the rule so the long (_a) branch fires on blue and the short
+            # (_b) branch on red — the inverse of the correct base routing.
+            if "condition_id" not in p:
+                raise ValueError(
+                    f"gateway {entry['gateway_id']!r} path {p['path_id']!r} has no "
+                    "condition_id; base params must route by case_type "
+                    "(generate_route_error bakes this in)"
+                )
+            p["condition_id"] = (
+                "rule_blue" if p["path_id"].endswith("_a") else "rule_red"
+            )
+        inverted.append(entry["gateway_id"])
+
+    _write_params(params, out_json_path)
+    return {
+        "n_gateways_inverted": n_gateways_inverted,
+        "inverted_gateways": inverted,
+        "n_split_gateways": len(splits),
+    }
+
+
 def build_branch_automation_params(
     base_json_path: str | Path,
     *,
diff --git a/evaluation/state_metrics/pipeline.py b/evaluation/state_metrics/pipeline.py
index 88ceec1..935dd7b 100644
--- a/evaluation/state_metrics/pipeline.py
+++ b/evaluation/state_metrics/pipeline.py
@@ -68,6 +68,7 @@
     build_gateway_biased_params,
     build_perturbed_params,
     build_role_swap_params,
+    build_route_error_params,
 )
 from src.process_state_prosimos_run import run_basic_simulation
 from src.runner import run_process_state_and_simulation
@@ -846,6 +847,19 @@ def _prepare_params_for_level(
             shift=signed,
             out_json_path=out_params,
         )
+    elif cfg.perturbation == "route_error":
+        if level < 0:
+            raise ValueError(
+                "route_error levels must be >= 0 (number of gateways inverted)"
+            )
+        # level = number of leading XOR splits whose case_type routing is
+        # inverted (red->short, blue->long) — the model error. The base params
+        # already route CORRECTLY, so level 0 is a verbatim copy.
+        manifest = build_route_error_params(
+            cfg.params_path,
+            n_gateways_inverted=level,
+            out_json_path=out_params,
+        )
     else:
         raise ValueError(f"unknown perturbation type {cfg.perturbation!r}")
     manifest = {"perturbation": cfg.perturbation, "level": level, **manifest}
diff --git a/evaluation/state_metrics/run_pipeline.py b/evaluation/state_metrics/run_pipeline.py
index 2aab0ee..d627a60 100644
--- a/evaluation/state_metrics/run_pipeline.py
+++ b/evaluation/state_metrics/run_pipeline.py
@@ -54,7 +54,7 @@ def main() -> None:
                             "arrival_burst", "relabel", "rephase",
                             "mix_ratio", "label_swap", "case_route",
                             "parallel_auto", "front_back_load",
-                            "case_type_drift"],
+                            "case_type_drift", "route_error"],
                    default="resources",
                    help="which perturbation family to apply")
     p.add_argument("--remove-from-profile", default=None,
@@ -164,6 +164,9 @@ def main() -> None:
     elif args.perturbation == "front_back_load":
         # Percentage of duration mass moved toward the chosen chain end.
         levels = spec.default_levels
+    elif args.perturbation == "route_error":
+        # Number of leading XOR splits whose case_type routing is inverted.
+        levels = spec.default_levels
     else:
         levels = spec.default_levels
 
diff --git a/samples/dev-samples/synthetic_route_error.bpmn b/samples/dev-samples/synthetic_route_error.bpmn
new file mode 100644
index 0000000..516ea2a
--- /dev/null
+++ b/samples/dev-samples/synthetic_route_error.bpmn
@@ -0,0 +1 @@
+<?xml version="1.0" encoding="UTF-8"?><bpmn:definitions xmlns:bpmn="http://www.omg.org/spec/BPMN/20100524/MODEL" id="Definitions_route_error" targetNamespace="http://bpmn.io/schema/bpmn"><bpmn:process id="Process_route_error" isExecutable="false"><bpmn:startEvent id="StartEvent_1" name="Start"><bpmn:outgoing>f_start</bpmn:outgoing></bpmn:startEvent><bpmn:task id="t_reg" name="Register"><bpmn:incoming>f_start</bpmn:incoming><bpmn:outgoing>f_reg_g1s</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g1s" name="XOR-split-1"><bpmn:incoming>f_reg_g1s</bpmn:incoming><bpmn:outgoing>f_g1s_a</bpmn:outgoing><bpmn:outgoing>f_g1s_b</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_a1_1" name="Assess_A1_1"><bpmn:incoming>f_g1s_a</bpmn:incoming><bpmn:outgoing>f_a1_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_a1_2" name="Assess_A1_2"><bpmn:incoming>f_a1_link1</bpmn:incoming><bpmn:outgoing>f_a1_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_a1_3" name="Assess_A1_3"><bpmn:incoming>f_a1_link2</bpmn:incoming><bpmn:outgoing>f_a1_g1j</bpmn:outgoing></bpmn:task><bpmn:task id="t_b1_1" name="Assess_B1_1"><bpmn:incoming>f_g1s_b</bpmn:incoming><bpmn:outgoing>f_b1_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_b1_2" name="Assess_B1_2"><bpmn:incoming>f_b1_link1</bpmn:incoming><bpmn:outgoing>f_b1_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_b1_3" name="Assess_B1_3"><bpmn:incoming>f_b1_link2</bpmn:incoming><bpmn:outgoing>f_b1_g1j</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g1j" name="XOR-join-1"><bpmn:incoming>f_a1_g1j</bpmn:incoming><bpmn:incoming>f_b1_g1j</bpmn:incoming><bpmn:outgoing>f_g1j_t_tri1</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_tri1" name="Triage_1"><bpmn:incoming>f_g1j_t_tri1</bpmn:incoming><bpmn:outgoing>f_t_tri1_g2s</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g2s" name="XOR-split-2"><bpmn:incoming>f_t_tri1_g2s</bpmn:incoming><bpmn:outgoing>f_g2s_a</bpmn:outgoing><bpmn:outgoing>f_g2s_b</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_a2_1" name="Assess_A2_1"><bpmn:incoming>f_g2s_a</bpmn:incoming><bpmn:outgoing>f_a2_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_a2_2" name="Assess_A2_2"><bpmn:incoming>f_a2_link1</bpmn:incoming><bpmn:outgoing>f_a2_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_a2_3" name="Assess_A2_3"><bpmn:incoming>f_a2_link2</bpmn:incoming><bpmn:outgoing>f_a2_g2j</bpmn:outgoing></bpmn:task><bpmn:task id="t_b2_1" name="Assess_B2_1"><bpmn:incoming>f_g2s_b</bpmn:incoming><bpmn:outgoing>f_b2_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_b2_2" name="Assess_B2_2"><bpmn:incoming>f_b2_link1</bpmn:incoming><bpmn:outgoing>f_b2_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_b2_3" name="Assess_B2_3"><bpmn:incoming>f_b2_link2</bpmn:incoming><bpmn:outgoing>f_b2_g2j</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g2j" name="XOR-join-2"><bpmn:incoming>f_a2_g2j</bpmn:incoming><bpmn:incoming>f_b2_g2j</bpmn:incoming><bpmn:outgoing>f_g2j_t_tri2</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_tri2" name="Triage_2"><bpmn:incoming>f_g2j_t_tri2</bpmn:incoming><bpmn:outgoing>f_t_tri2_g3s</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g3s" name="XOR-split-3"><bpmn:incoming>f_t_tri2_g3s</bpmn:incoming><bpmn:outgoing>f_g3s_a</bpmn:outgoing><bpmn:outgoing>f_g3s_b</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_a3_1" name="Assess_A3_1"><bpmn:incoming>f_g3s_a</bpmn:incoming><bpmn:outgoing>f_a3_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_a3_2" name="Assess_A3_2"><bpmn:incoming>f_a3_link1</bpmn:incoming><bpmn:outgoing>f_a3_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_a3_3" name="Assess_A3_3"><bpmn:incoming>f_a3_link2</bpmn:incoming><bpmn:outgoing>f_a3_g3j</bpmn:outgoing></bpmn:task><bpmn:task id="t_b3_1" name="Assess_B3_1"><bpmn:incoming>f_g3s_b</bpmn:incoming><bpmn:outgoing>f_b3_link1</bpmn:outgoing></bpmn:task><bpmn:task id="t_b3_2" name="Assess_B3_2"><bpmn:incoming>f_b3_link1</bpmn:incoming><bpmn:outgoing>f_b3_link2</bpmn:outgoing></bpmn:task><bpmn:task id="t_b3_3" name="Assess_B3_3"><bpmn:incoming>f_b3_link2</bpmn:incoming><bpmn:outgoing>f_b3_g3j</bpmn:outgoing></bpmn:task><bpmn:exclusiveGateway id="g3j" name="XOR-join-3"><bpmn:incoming>f_a3_g3j</bpmn:incoming><bpmn:incoming>f_b3_g3j</bpmn:incoming><bpmn:outgoing>f_g3j_t_dec</bpmn:outgoing></bpmn:exclusiveGateway><bpmn:task id="t_dec" name="Decide"><bpmn:incoming>f_g3j_t_dec</bpmn:incoming><bpmn:outgoing>f_dec_end</bpmn:outgoing></bpmn:task><bpmn:endEvent id="EndEvent_1" name="End"><bpmn:incoming>f_dec_end</bpmn:incoming></bpmn:endEvent><bpmn:sequenceFlow id="f_start" sourceRef="StartEvent_1" targetRef="t_reg" /><bpmn:sequenceFlow id="f_reg_g1s" sourceRef="t_reg" targetRef="g1s" /><bpmn:sequenceFlow id="f_g1s_a" sourceRef="g1s" targetRef="t_a1_1" /><bpmn:sequenceFlow id="f_a1_link1" sourceRef="t_a1_1" targetRef="t_a1_2" /><bpmn:sequenceFlow id="f_a1_link2" sourceRef="t_a1_2" targetRef="t_a1_3" /><bpmn:sequenceFlow id="f_a1_g1j" sourceRef="t_a1_3" targetRef="g1j" /><bpmn:sequenceFlow id="f_g1s_b" sourceRef="g1s" targetRef="t_b1_1" /><bpmn:sequenceFlow id="f_b1_link1" sourceRef="t_b1_1" targetRef="t_b1_2" /><bpmn:sequenceFlow id="f_b1_link2" sourceRef="t_b1_2" targetRef="t_b1_3" /><bpmn:sequenceFlow id="f_b1_g1j" sourceRef="t_b1_3" targetRef="g1j" /><bpmn:sequenceFlow id="f_g1j_t_tri1" sourceRef="g1j" targetRef="t_tri1" /><bpmn:sequenceFlow id="f_t_tri1_g2s" sourceRef="t_tri1" targetRef="g2s" /><bpmn:sequenceFlow id="f_g2s_a" sourceRef="g2s" targetRef="t_a2_1" /><bpmn:sequenceFlow id="f_a2_link1" sourceRef="t_a2_1" targetRef="t_a2_2" /><bpmn:sequenceFlow id="f_a2_link2" sourceRef="t_a2_2" targetRef="t_a2_3" /><bpmn:sequenceFlow id="f_a2_g2j" sourceRef="t_a2_3" targetRef="g2j" /><bpmn:sequenceFlow id="f_g2s_b" sourceRef="g2s" targetRef="t_b2_1" /><bpmn:sequenceFlow id="f_b2_link1" sourceRef="t_b2_1" targetRef="t_b2_2" /><bpmn:sequenceFlow id="f_b2_link2" sourceRef="t_b2_2" targetRef="t_b2_3" /><bpmn:sequenceFlow id="f_b2_g2j" sourceRef="t_b2_3" targetRef="g2j" /><bpmn:sequenceFlow id="f_g2j_t_tri2" sourceRef="g2j" targetRef="t_tri2" /><bpmn:sequenceFlow id="f_t_tri2_g3s" sourceRef="t_tri2" targetRef="g3s" /><bpmn:sequenceFlow id="f_g3s_a" sourceRef="g3s" targetRef="t_a3_1" /><bpmn:sequenceFlow id="f_a3_link1" sourceRef="t_a3_1" targetRef="t_a3_2" /><bpmn:sequenceFlow id="f_a3_link2" sourceRef="t_a3_2" targetRef="t_a3_3" /><bpmn:sequenceFlow id="f_a3_g3j" sourceRef="t_a3_3" targetRef="g3j" /><bpmn:sequenceFlow id="f_g3s_b" sourceRef="g3s" targetRef="t_b3_1" /><bpmn:sequenceFlow id="f_b3_link1" sourceRef="t_b3_1" targetRef="t_b3_2" /><bpmn:sequenceFlow id="f_b3_link2" sourceRef="t_b3_2" targetRef="t_b3_3" /><bpmn:sequenceFlow id="f_b3_g3j" sourceRef="t_b3_3" targetRef="g3j" /><bpmn:sequenceFlow id="f_g3j_t_dec" sourceRef="g3j" targetRef="t_dec" /><bpmn:sequenceFlow id="f_dec_end" sourceRef="t_dec" targetRef="EndEvent_1" /></bpmn:process></bpmn:definitions>
\ No newline at end of file
diff --git a/samples/dev-samples/synthetic_route_error.json b/samples/dev-samples/synthetic_route_error.json
new file mode 100644
index 0000000..cc2d2c3
--- /dev/null
+++ b/samples/dev-samples/synthetic_route_error.json
@@ -0,0 +1,27599 @@
+{
+  "task_resource_distribution": [
+    {
+      "task_id": "t_reg",
+      "resources": [
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_1"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_2"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_3"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_4"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_5"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_6"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_7"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_8"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_9"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_10"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_11"
+        },
+        {
+          "distribution_name": "norm",
+          "distribution_params": [
+            {
+              "value": 180.0
+            },
+            {
+              "value": 12.600000000000001
+            },
+            {
+              "value": 154.8
+            },
+            {
+              "value": 217.8
+            }
+          ],
+          "resource_id": "Worker_12"
+        },
+        {
+          "distribution_name": "norm",
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+        {
+          "id": "Worker_51",
+          "name": "Worker_51",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_52",
+          "name": "Worker_52",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_53",
+          "name": "Worker_53",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_54",
+          "name": "Worker_54",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_55",
+          "name": "Worker_55",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_56",
+          "name": "Worker_56",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_57",
+          "name": "Worker_57",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_58",
+          "name": "Worker_58",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_59",
+          "name": "Worker_59",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_60",
+          "name": "Worker_60",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_61",
+          "name": "Worker_61",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_62",
+          "name": "Worker_62",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_63",
+          "name": "Worker_63",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        },
+        {
+          "id": "Worker_64",
+          "name": "Worker_64",
+          "cost_per_hour": 0,
+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_reg",
+            "t_a1_1",
+            "t_a1_2",
+            "t_a1_3",
+            "t_b1_1",
+            "t_b1_2",
+            "t_b1_3",
+            "t_tri1",
+            "t_a2_1",
+            "t_a2_2",
+            "t_a2_3",
+            "t_b2_1",
+            "t_b2_2",
+            "t_b2_3",
+            "t_tri2",
+            "t_a3_1",
+            "t_a3_2",
+            "t_a3_3",
+            "t_b3_1",
+            "t_b3_2",
+            "t_b3_3",
+            "t_dec"
+          ]
+        }
+      ]
+    }
+  ],
+  "event_distribution": [],
+  "batch_processing": [],
+  "case_attributes": [
+    {
+      "name": "case_type",
+      "type": "discrete",
+      "values": [
+        {
+          "key": "red",
+          "value": 0.5
+        },
+        {
+          "key": "blue",
+          "value": 0.5
+        }
+      ]
+    }
+  ],
+  "branch_rules": [
+    {
+      "id": "rule_red",
+      "rules": [
+        [
+          {
+            "attribute": "case_type",
+            "comparison": "=",
+            "value": "red"
+          }
+        ]
+      ]
+    },
+    {
+      "id": "rule_blue",
+      "rules": [
+        [
+          {
+            "attribute": "case_type",
+            "comparison": "=",
+            "value": "blue"
+          }
+        ]
+      ]
+    }
+  ],
+  "prioritisation_rules": []
+}
\ No newline at end of file
diff --git a/tests/evaluation/state_metrics/test_perturb.py b/tests/evaluation/state_metrics/test_perturb.py
index ff9f3f3..7a4a3c5 100644
--- a/tests/evaluation/state_metrics/test_perturb.py
+++ b/tests/evaluation/state_metrics/test_perturb.py
@@ -12,6 +12,7 @@
     build_gateway_biased_params,
     build_perturbed_params,
     build_role_swap_params,
+    build_route_error_params,
 )
 
 
@@ -19,6 +20,7 @@
 XOR_JSON = _DEV / "synthetic_xor_loop.json"
 LOAN_JSON = Path(__file__).resolve().parents[3] / "samples" / "icpm-2025" / "synthetic" / "Loan-stable.json"
 CASE_ROUTE_JSON = _DEV / "synthetic_case_route.json"
+ROUTE_ERROR_JSON = _DEV / "synthetic_route_error.json"
 PARALLEL_AUTO_JSON = _DEV / "synthetic_parallel_auto.json"
 LINEAR_CHAIN_JSON = _DEV / "synthetic_linear_chain.json"
 
@@ -70,6 +72,52 @@ def test_case_route_rules_first_k_splits(tmp_path):
                for p in e["probabilities"] if len(e["probabilities"]) == 1)
 
 
+@pytest.mark.skipif(not ROUTE_ERROR_JSON.exists(), reason="synthetic_route_error.json missing")
+def test_route_error_zero_is_noop(tmp_path):
+    out = tmp_path / "p.json"
+    base = json.loads(ROUTE_ERROR_JSON.read_text())
+    manifest = build_route_error_params(ROUTE_ERROR_JSON, n_gateways_inverted=0, out_json_path=out)
+    assert manifest["n_gateways_inverted"] == 0
+    assert manifest["inverted_gateways"] == []
+    assert json.loads(out.read_text()) == base   # verbatim copy
+
+
+@pytest.mark.skipif(not ROUTE_ERROR_JSON.exists(), reason="synthetic_route_error.json missing")
+def test_route_error_inverts_first_k_splits(tmp_path):
+    out = tmp_path / "p.json"
+    manifest = build_route_error_params(ROUTE_ERROR_JSON, n_gateways_inverted=2, out_json_path=out)
+    assert manifest["inverted_gateways"] == ["g1s", "g2s"]   # ordered, first two
+    assert manifest["n_split_gateways"] == 3
+
+    data = json.loads(out.read_text())
+    by_id = {e["gateway_id"]: e for e in data["gateway_branching_probabilities"]}
+    # Inverted gateways: _a (long) now fires on blue, _b (short) on red.
+    for gid in ("g1s", "g2s"):
+        for p in by_id[gid]["probabilities"]:
+            assert p["condition_id"] == ("rule_blue" if p["path_id"].endswith("_a") else "rule_red")
+    # Untouched split keeps the CORRECT base routing (_a -> red, _b -> blue).
+    for p in by_id["g3s"]["probabilities"]:
+        assert p["condition_id"] == ("rule_red" if p["path_id"].endswith("_a") else "rule_blue")
+
+
+@pytest.mark.skipif(not ROUTE_ERROR_JSON.exists(), reason="synthetic_route_error.json missing")
+def test_route_error_too_many_raises(tmp_path):
+    with pytest.raises(ValueError, match="only 3 split"):
+        build_route_error_params(ROUTE_ERROR_JSON, n_gateways_inverted=99, out_json_path=tmp_path / "p.json")
+
+
+def test_route_error_missing_condition_raises(tmp_path):
+    base = tmp_path / "base.json"
+    base.write_text(json.dumps({
+        "gateway_branching_probabilities": [
+            {"gateway_id": "g1s", "probabilities": [
+                {"path_id": "f_g1s_a", "value": "0.5"}, {"path_id": "f_g1s_b", "value": "0.5"}]},
+        ],
+    }))
+    with pytest.raises(ValueError, match="no condition_id"):
+        build_route_error_params(base, n_gateways_inverted=1, out_json_path=tmp_path / "p.json")
+
+
 @pytest.mark.skipif(not CASE_ROUTE_JSON.exists(), reason="synthetic_case_route.json missing")
 def test_case_route_too_many_raises(tmp_path):
     with pytest.raises(ValueError, match="only 3 split"):
diff --git a/tools/generate_route_error.py b/tools/generate_route_error.py
new file mode 100644
index 0000000..9d2d1ab
--- /dev/null
+++ b/tools/generate_route_error.py
@@ -0,0 +1,328 @@
+"""Generate the synthetic *route-error* dataset (BPMN + Prosimos params).
+
+Scenario #3 (the "model-error routing" experiment). A chain of ``n_gateways``
+exclusive split/join gateways, each separated by a common ("triage") activity so
+that no two branch activities are ever adjacent in the activity stream::
+
+    Start -> Register
+          -> [G1 split -> {A-branch (long) | B-branch (short)} -> G1 join] -> Triage_1
+          -> [G2 split -> {A-branch (long) | B-branch (short)} -> G2 join] -> Triage_2
+          -> ...
+          -> Decide -> End
+
+Unlike the ``case_route`` synthetic, each branch is a **multi-activity chain**
+(A-branch: ``a_len`` activities, B-branch: ``b_len`` activities), so a wrong
+routing sends a case down a whole wrong sub-path. The branches are
+**structurally distinct but timing-symmetric** — same per-activity duration on
+the same pool and, by default, the same length — so a case's total duration does
+not depend on which branch it takes. The *base* params emitted here route by a
+genuine ``case_type`` ∈ {red, blue} case attribute (50/50) via Prosimos
+``branch_rules``: **red -> A-branch, blue -> B-branch** at every gateway. That is
+the CORRECT model — the ground truth.
+
+The state-metrics ``route_error`` perturbation
+(``evaluation.state_metrics.perturb.build_route_error_params``) then *inverts*
+the routing on the first ``level`` gateways (red -> B, blue -> A) and lets
+Prosimos-short-term continue the ongoing cases with that WRONG model. Because the
+population stays 50/50 red/blue, at every gateway 50 % of cases still take the
+A-branch and 50 % the B-branch, so:
+
+* aggregate cycle time is unchanged (the long/short mix is symmetric),
+* the 2-gram distribution is unchanged (the common triage activity breaks the
+  bigram between a branch choice and the next), so ``ngd_n2`` is blind,
+* the aggregate relative-event-distribution is unchanged, so ``red`` is blind.
+
+Only pairing each ongoing case to its own ground-truth continuation reveals that
+a red case that *should* be on the long A-branch is now on the short B-branch:
+the ``activity_case`` (case_id, activity) projection detects it, as does the
+attribute-aware ``activity_type`` (activity, case_type) projection.
+
+Run::
+
+    python -m tools.generate_route_error          # default 3-gateway asset
+    python -m tools.generate_route_error --gateways 3 --a-len 4 --out-dir samples/dev-samples
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from xml.sax.saxutils import escape
+
+BPMN_NS = "http://www.omg.org/spec/BPMN/20100524/MODEL"
+
+
+def _task(tid: str, name: str, incoming: str, outgoing: str) -> str:
+    return (
+        f'<bpmn:task id="{tid}" name="{escape(name)}">'
+        f"<bpmn:incoming>{incoming}</bpmn:incoming>"
+        f"<bpmn:outgoing>{outgoing}</bpmn:outgoing>"
+        f"</bpmn:task>"
+    )
+
+
+def _xor(gid: str, name: str, incoming: list[str], outgoing: list[str]) -> str:
+    inc = "".join(f"<bpmn:incoming>{f}</bpmn:incoming>" for f in incoming)
+    out = "".join(f"<bpmn:outgoing>{f}</bpmn:outgoing>" for f in outgoing)
+    return f'<bpmn:exclusiveGateway id="{gid}" name="{escape(name)}">{inc}{out}</bpmn:exclusiveGateway>'
+
+
+def _flow(fid: str, src: str, tgt: str) -> str:
+    return f'<bpmn:sequenceFlow id="{fid}" sourceRef="{src}" targetRef="{tgt}" />'
+
+
+def _norm(mean: float) -> dict:
+    """A *narrow* normal duration distribution (Prosimos ``norm`` order:
+    ``[mean, stddev, min, max]``). Std = 7 % of the mean, clamped to a
+    non-negative floor — deliberately tight ("not very wide") so two same-model
+    sims diverge little: this keeps the per-instant state metric's level-0 noise
+    floor low, so the (structurally large) per-case path change from a routing
+    inversion stands out against it."""
+    std = 0.07 * mean
+    return {
+        "distribution_name": "norm",
+        "distribution_params": [
+            {"value": mean},
+            {"value": std},
+            {"value": max(1.0, mean - 2 * std)},
+            {"value": mean + 3 * std},
+        ],
+    }
+
+
+def build_model(
+    n_gateways: int,
+    *,
+    a_len: int = 3,
+    b_len: int | None = None,
+    workers: int = 64,
+    arrival_mean: float = 60.0,
+    branch_service: float = 300.0,
+    common_service: float = 180.0,
+) -> tuple[str, dict]:
+    """Return (bpmn_xml, prosimos_params) for an ``n_gateways``-gateway chain.
+
+    Each gateway has two structurally-distinct branches: an A-branch chain of
+    ``a_len`` activities and a B-branch chain of ``b_len`` activities (default
+    ``b_len == a_len``). Both use the SAME per-activity duration ``branch_service``
+    on the SAME pool, so with equal lengths a case's total duration is
+    branch-independent — inverting the routing is cycle-time-/rtd-/red-neutral by
+    construction and only the per-case PATH changes. ``workers`` / ``arrival_mean``
+    set the offered load; the defaults keep a dense Scope-A window at ~75 %
+    utilization.
+    """
+    if b_len is None:
+        b_len = a_len
+    if n_gateways < 1:
+        raise ValueError("n_gateways must be >= 1")
+    if a_len < 1 or b_len < 1:
+        raise ValueError("a_len and b_len must be >= 1")
+
+    nodes: list[str] = []
+    flows: list[str] = []
+    # Activity id -> mean service, and the gateway branching spec for the JSON.
+    task_means: dict[str, float] = {}
+    gateway_specs: list[dict] = []
+
+    nodes.append('<bpmn:startEvent id="StartEvent_1" name="Start"><bpmn:outgoing>f_start</bpmn:outgoing></bpmn:startEvent>')
+
+    nodes.append(_task("t_reg", "Register", "f_start", "f_reg_g1s"))
+    task_means["t_reg"] = common_service
+    flows.append(_flow("f_start", "StartEvent_1", "t_reg"))
+
+    prev_out = "f_reg_g1s"          # flow feeding the next split gateway
+    prev_src = "t_reg"
+    for k in range(1, n_gateways + 1):
+        g_split = f"g{k}s"
+        g_join = f"g{k}j"
+        f_in = prev_out
+        # Flow ids leaving the split keep the _a / _b suffix convention the
+        # route builders rely on to identify the A / B branch.
+        f_sa, f_sb = f"f_g{k}s_a", f"f_g{k}s_b"
+        is_last = k == n_gateways
+        common_id = "t_dec" if is_last else f"t_tri{k}"
+        common_name = "Decide" if is_last else f"Triage_{k}"
+        f_join_out = f"f_g{k}j_{common_id}"
+
+        flows.append(_flow(f_in, prev_src, g_split))
+        nodes.append(_xor(g_split, f"XOR-split-{k}", [f_in], [f_sa, f_sb]))
+
+        def _branch(side: str, length: int, split_flow: str) -> str:
+            """Emit a ``length``-activity chain for one branch; return the flow
+            id that feeds the join. ``side`` is 'a' or 'b' (distinct labels)."""
+            label = side.upper()
+            ids = [f"t_{side}{k}_{i}" for i in range(1, length + 1)]
+            f_join_in = f"f_{side}{k}_g{k}j"
+            in_flows = [split_flow] + [f"f_{side}{k}_link{i}" for i in range(1, length)]
+            out_flows = [f"f_{side}{k}_link{i}" for i in range(1, length)] + [f_join_in]
+            for i, tid in enumerate(ids):
+                nodes.append(_task(tid, f"Assess_{label}{k}_{i + 1}", in_flows[i], out_flows[i]))
+                src = g_split if i == 0 else ids[i - 1]
+                flows.append(_flow(in_flows[i], src, tid))
+                task_means[tid] = branch_service
+            flows.append(_flow(f_join_in, ids[-1], g_join))
+            return f_join_in
+
+        # Two structurally-distinct but TIMING-SYMMETRIC branches: same number
+        # of activities and same duration distribution, only different labels.
+        # A case's total duration is therefore branch-independent, so inverting
+        # the routing is cycle-time-/rtd-/red-neutral by construction — only the
+        # per-case PATH identity changes (which pairing by case_id detects).
+        f_aj = _branch("a", a_len, f_sa)
+        f_bj = _branch("b", b_len, f_sb)
+
+        nodes.append(_xor(g_join, f"XOR-join-{k}", [f_aj, f_bj], [f_join_out]))
+
+        # Common activity after the join.
+        next_out = "f_dec_end" if is_last else f"f_{common_id}_g{k + 1}s"
+        nodes.append(_task(common_id, common_name, f_join_out, next_out))
+        flows.append(_flow(f_join_out, g_join, common_id))
+        task_means[common_id] = common_service
+
+        gateway_specs.append({
+            "gateway_id": g_split,
+            "split": True,
+            "paths": [f_sa, f_sb],     # [A-branch (red), B-branch (blue)]
+        })
+        gateway_specs.append({
+            "gateway_id": g_join,
+            "split": False,
+            "paths": [f_join_out],
+        })
+
+        prev_out = next_out
+        prev_src = common_id
+
+    nodes.append('<bpmn:endEvent id="EndEvent_1" name="End"><bpmn:incoming>f_dec_end</bpmn:incoming></bpmn:endEvent>')
+    flows.append(_flow("f_dec_end", "t_dec", "EndEvent_1"))
+
+    process_body = "".join(nodes) + "".join(flows)
+    bpmn = (
+        '<?xml version="1.0" encoding="UTF-8"?>'
+        f'<bpmn:definitions xmlns:bpmn="{BPMN_NS}" id="Definitions_route_error" '
+        'targetNamespace="http://bpmn.io/schema/bpmn">'
+        '<bpmn:process id="Process_route_error" isExecutable="false">'
+        f"{process_body}"
+        "</bpmn:process></bpmn:definitions>"
+    )
+
+    params = _build_params(task_means, gateway_specs,
+                           workers=workers, arrival_mean=arrival_mean)
+    return bpmn, params
+
+
+def _build_params(
+    task_means: dict[str, float],
+    gateway_specs: list[dict],
+    *,
+    workers: int,
+    arrival_mean: float,
+) -> dict:
+    cal_id = "cal_247"
+    periods = [{"from": "MONDAY", "to": "SUNDAY",
+                "beginTime": "00:00:00", "endTime": "23:59:59.999"}]
+    resource_ids = [f"Worker_{i}" for i in range(1, workers + 1)]
+    task_ids = list(task_means.keys())
+
+    task_resource_distribution = [
+        {
+            "task_id": tid,
+            "resources": [
+                {**_norm(task_means[tid]), "resource_id": rid}
+                for rid in resource_ids
+            ],
+        }
+        for tid in task_ids
+    ]
+
+    resource_profiles = [{
+        "id": "Worker_profile",
+        "name": "Worker",
+        "resource_list": [
+            {"id": rid, "name": rid, "cost_per_hour": 0, "amount": 1,
+             "calendar": cal_id, "assignedTasks": list(task_ids)}
+            for rid in resource_ids
+        ],
+    }]
+
+    # CORRECT routing baked in: split gateways route by case_type via condition.
+    #   _a branch (long)  -> rule_red   (red cases)
+    #   _b branch (short) -> rule_blue  (blue cases)
+    gateway_branching_probabilities = []
+    for spec in gateway_specs:
+        if spec["split"]:
+            a, b = spec["paths"]
+            probs = [
+                {"path_id": a, "condition_id": "rule_red"},
+                {"path_id": b, "condition_id": "rule_blue"},
+            ]
+        else:
+            probs = [{"path_id": spec["paths"][0], "value": "1"}]
+        gateway_branching_probabilities.append(
+            {"gateway_id": spec["gateway_id"], "probabilities": probs}
+        )
+
+    return {
+        "task_resource_distribution": task_resource_distribution,
+        "resource_calendars": [{"id": cal_id, "name": "24/7", "time_periods": periods}],
+        "gateway_branching_probabilities": gateway_branching_probabilities,
+        "arrival_time_distribution": {
+            "distribution_name": "expon",
+            "distribution_params": [{"value": arrival_mean}, {"value": 0}, {"value": 3600}],
+        },
+        "arrival_time_calendar": periods,
+        "resource_profiles": resource_profiles,
+        "event_distribution": [],
+        "batch_processing": [],
+        "case_attributes": [{
+            "name": "case_type",
+            "type": "discrete",
+            "values": [{"key": "red", "value": 0.5}, {"key": "blue", "value": 0.5}],
+        }],
+        "branch_rules": [
+            {"id": "rule_red", "rules": [[{"attribute": "case_type", "comparison": "=", "value": "red"}]]},
+            {"id": "rule_blue", "rules": [[{"attribute": "case_type", "comparison": "=", "value": "blue"}]]},
+        ],
+        "prioritisation_rules": [],
+    }
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Generate the route-error synthetic dataset")
+    ap.add_argument("--gateways", type=int, default=3)
+    ap.add_argument("--a-len", type=int, default=3,
+                    help="number of activities in the A-branch")
+    ap.add_argument("--b-len", type=int, default=None,
+                    help="number of activities in the B-branch (default: = a-len, "
+                         "so the branches are timing-symmetric)")
+    ap.add_argument("--workers", type=int, default=64)
+    ap.add_argument("--arrival-mean", type=float, default=60.0)
+    ap.add_argument("--branch-service", type=float, default=300.0)
+    ap.add_argument("--common-service", type=float, default=180.0)
+    ap.add_argument("--out-dir", type=Path,
+                    default=Path(__file__).resolve().parents[1] / "samples" / "dev-samples")
+    ap.add_argument("--name", default="synthetic_route_error")
+    args = ap.parse_args()
+
+    bpmn, params = build_model(
+        args.gateways, a_len=args.a_len, b_len=args.b_len, workers=args.workers,
+        arrival_mean=args.arrival_mean, branch_service=args.branch_service,
+        common_service=args.common_service,
+    )
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    bpmn_path = args.out_dir / f"{args.name}.bpmn"
+    json_path = args.out_dir / f"{args.name}.json"
+    bpmn_path.write_text(bpmn, encoding="utf-8")
+    json_path.write_text(json.dumps(params, indent=2), encoding="utf-8")
+    tasks = [r["task_id"] for r in params["task_resource_distribution"]]
+    print(f"wrote {bpmn_path}")
+    print(f"wrote {json_path}")
+    print(f"  tasks={len(tasks)} gateways={args.gateways} "
+          f"a_len={args.a_len} b_len={args.b_len if args.b_len is not None else args.a_len}")
+    print(f"  A-branch tasks: {[t for t in tasks if t.startswith('t_a')]}")
+    print(f"  B-branch tasks: {[t for t in tasks if t.startswith('t_b')]}")
+
+
+if __name__ == "__main__":
+    main()