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+# 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`.
+
+**Goal:** show that the proposed per-instant state metric **replicates what the
+baselines detect and also covers cases they miss** (complementary, not
+adversarial).
+
+**Baselines referenced:** cycle-time MAE (`cycle_time`), N-gram distance
+(`ngd_n2`), relative event distribution (`red`), remaining-time distribution
+(`rtd`).
+
+**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.
+
+**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.
+
+## 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.
+
+| # | 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 |
+
+**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)
+
+## 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.
+- **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)
diff --git a/evaluation/state_metrics/PAPER_SCENARIOS.md b/evaluation/state_metrics/PAPER_SCENARIOS.md
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+# 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).
+
+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.
+
+## 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.
+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.
+
+| # | 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 |
+
+## Scenarios explained (plain English)
+
+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.
+
+### 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.
+- **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.
+
+## 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.
+
+## 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.
+
+---
+
+# 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.
diff --git a/evaluation/state_metrics/datasets.py b/evaluation/state_metrics/datasets.py
index 5b0053a..f701c34 100644
--- a/evaluation/state_metrics/datasets.py
+++ b/evaluation/state_metrics/datasets.py
@@ -20,6 +20,10 @@ class DatasetSpec:
     # Ladder used when --perturbation duration is selected. Values are
     # integer percentages, mapped by the pipeline to scale = 1 + level/100.
     default_duration_levels: tuple[int, ...] = (0, 25, 50, 100, 200, 300)
+    # parallel_auto: the non-critical AND-branch tasks to automate.
+    automate_task_ids: tuple[str, ...] = ()
+    # front_back_load: the ordered sequential-chain tasks to reweight.
+    chain_task_ids: tuple[str, ...] = ()
 
 
 DATASETS: dict[str, DatasetSpec] = {
@@ -35,6 +39,29 @@ class DatasetSpec:
         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
+    # (t_nc1..t_nc3) are the automation targets for the `parallel_auto` family.
+    "synthetic_parallel_auto": DatasetSpec(
+        name="synthetic_parallel_auto",
+        bpmn=REPO_ROOT / "samples" / "dev-samples" / "synthetic_parallel_auto.bpmn",
+        params=REPO_ROOT / "samples" / "dev-samples" / "synthetic_parallel_auto.json",
+        remove_from_profile="NonCritical",
+        default_levels=(0, 25, 50, 75, 100),
+        automate_task_ids=("t_nc1", "t_nc2", "t_nc3"),
+    ),
+    # Linear-chain synthetic (scenario #4): a duration-symmetric sequence of
+    # five steps on one pool. Generated by tools/generate_linear_chain.py. The
+    # `front_back_load` family reweights t_step1..t_step5 total-invariantly.
+    "synthetic_linear_chain": DatasetSpec(
+        name="synthetic_linear_chain",
+        bpmn=REPO_ROOT / "samples" / "dev-samples" / "synthetic_linear_chain.bpmn",
+        params=REPO_ROOT / "samples" / "dev-samples" / "synthetic_linear_chain.json",
+        remove_from_profile="Worker",
+        default_levels=(0, 20, 40, 60, 80),
+        chain_task_ids=("t_step1", "t_step2", "t_step3", "t_step4", "t_step5"),
+    ),
     "synthetic_xor_loop": DatasetSpec(
         name="synthetic_xor_loop",
         bpmn=REPO_ROOT / "samples" / "dev-samples" / "synthetic_xor_loop.bpmn",
diff --git a/evaluation/state_metrics/perturb.py b/evaluation/state_metrics/perturb.py
index 5b2e972..212bf84 100644
--- a/evaluation/state_metrics/perturb.py
+++ b/evaluation/state_metrics/perturb.py
@@ -642,6 +642,172 @@ def _is_split(entry: dict) -> bool:
     }
 
 
+def build_branch_automation_params(
+    base_json_path: str | Path,
+    *,
+    automate_task_ids: list[str],
+    level: int,
+    floor_seconds: float = 1.0,
+    out_json_path: str | Path,
+) -> dict:
+    """Automate (shrink) the durations of the non-critical branch tasks.
+
+    Designed for the *parallel-automation* synthetic (see
+    ``tools/generate_parallel_auto.py``): the listed ``automate_task_ids`` are
+    the non-critical AND branch, whose total duration is dominated by the
+    critical branch. Each task's duration params are scaled by
+    ``(1 - level/100)``, with the location parameter floored at
+    ``floor_seconds`` so the activity still appears once per case (keeping the
+    bigram histogram and per-case activity counts identical, hence ``ngd_n2``
+    blind). Because the critical branch sets the cycle time and runs on a
+    separate pool, ``cycle_time`` is blind too — only the time-weighted state
+    metric sees the non-critical branch shrink out of the concurrent active set.
+
+    ``level == 0`` is a no-op (factor 1.0). ``level`` is read as a percentage in
+    ``[0, 100]``; ``level == 100`` collapses every automated task to the floor.
+
+    Returns a manifest with the scale factor and which tasks were touched.
+    """
+    if level < 0:
+        raise ValueError("level must be >= 0")
+    if level > 100:
+        raise ValueError("level must be <= 100")
+    if not automate_task_ids:
+        raise ValueError("automate_task_ids must be non-empty")
+    if floor_seconds <= 0:
+        raise ValueError("floor_seconds must be > 0")
+
+    params = _load_params(base_json_path)
+    factor = 1.0 - level / 100.0
+    targets = set(automate_task_ids)
+    touched: list[str] = []
+    for task in params.get("task_resource_distribution", []):
+        if task.get("task_id") not in targets:
+            continue
+        touched.append(task["task_id"])
+        for r in task.get("resources", []):
+            dparams = r.get("distribution_params", [])
+            new_params = []
+            for i, p in enumerate(dparams):
+                scaled = p["value"] * factor
+                # Floor only the location/value parameter (index 0) so the
+                # activity keeps a strictly positive duration and stays in the
+                # trace; scale any shape/min/max params by the same factor.
+                if i == 0:
+                    scaled = max(scaled, floor_seconds)
+                new_params.append({"value": scaled})
+            r["distribution_params"] = new_params
+
+    missing = targets - set(touched)
+    if missing:
+        raise ValueError(
+            f"automate_task_ids not found in task_resource_distribution: {sorted(missing)}"
+        )
+
+    _write_params(params, out_json_path)
+    return {
+        "automate_task_ids": list(automate_task_ids),
+        "level": level,
+        "factor": factor,
+        "floor_seconds": floor_seconds,
+        "tasks_touched": touched,
+    }
+
+
+def build_front_back_load_params(
+    base_json_path: str | Path,
+    *,
+    chain_task_ids: list[str],
+    shift: int,
+    out_json_path: str | Path,
+) -> dict:
+    """Redistribute duration mass along a sequential chain, total-invariant.
+
+    Designed for the *linear-chain* synthetic (see
+    ``tools/generate_linear_chain.py``): the ordered ``chain_task_ids`` form a
+    sequence of comparable-duration activities on one pool. This builder
+    reweights their mean durations by position so the per-case **total**
+    duration (and hence cycle time and aggregate utilisation) is held constant,
+    but the duration mass moves toward the front or the back of the chain.
+
+    Signed ``shift`` (a percentage):
+      * ``shift > 0`` *front-loads* — early tasks longer, late tasks shorter.
+      * ``shift < 0`` *back-loads* — late tasks longer, early tasks shorter.
+      * ``shift == 0`` is a no-op.
+
+    For a chain of ``n`` tasks at ordered positions ``p_i = i/(n-1) ∈ [0, 1]``
+    the per-task weight is ``w_i = 1 + (shift/100)·(1 - 2·p_i)`` — an
+    antisymmetric ramp about the chain midpoint. New means are ``m_i·w_i``,
+    then globally renormalised by ``Σm_i / Σ(m_i·w_i)`` so the total is held
+    **exactly** regardless of any base-mean asymmetry.
+
+    Because only *where* the duration sits changes (not the per-case total, the
+    activities, the bigrams, or aggregate ρ), ``cycle_time`` and ``ngd_n2`` are
+    blind, while the time-weighted state metric localises the moved mass.
+    ``|shift|`` must be < 100 so every weight stays positive.
+
+    Returns a manifest with the per-task factors actually applied.
+    """
+    if abs(shift) >= 100:
+        raise ValueError("|shift| must be < 100 so all task weights stay positive")
+    if len(chain_task_ids) < 2:
+        raise ValueError("chain_task_ids must list at least two tasks (ordered)")
+
+    params = _load_params(base_json_path)
+    trd_by_id = {t.get("task_id"): t for t in params.get("task_resource_distribution", [])}
+    missing = [tid for tid in chain_task_ids if tid not in trd_by_id]
+    if missing:
+        raise ValueError(f"chain_task_ids not found in params: {missing}")
+
+    n = len(chain_task_ids)
+    s = shift / 100.0
+
+    def _task_mean(task: dict) -> float:
+        # Representative mean = first resource's location parameter.
+        resources = task.get("resources", [])
+        if not resources or not resources[0].get("distribution_params"):
+            raise ValueError(f"task {task.get('task_id')!r} has no distribution params")
+        return float(resources[0]["distribution_params"][0]["value"])
+
+    if shift == 0:
+        _write_params(params, out_json_path)
+        return {
+            "chain_task_ids": list(chain_task_ids), "shift": 0,
+            "factors": {tid: 1.0 for tid in chain_task_ids},
+            "total_invariant": True,
+        }
+
+    weights: dict[str, float] = {}
+    for i, tid in enumerate(chain_task_ids):
+        p_i = i / (n - 1)
+        weights[tid] = 1.0 + s * (1.0 - 2.0 * p_i)
+
+    base_total = sum(_task_mean(trd_by_id[tid]) for tid in chain_task_ids)
+    new_total = sum(_task_mean(trd_by_id[tid]) * weights[tid] for tid in chain_task_ids)
+    g = base_total / new_total if new_total > 0 else 1.0
+
+    factors: dict[str, float] = {}
+    for tid in chain_task_ids:
+        f = weights[tid] * g
+        factors[tid] = f
+        for r in trd_by_id[tid].get("resources", []):
+            r["distribution_params"] = [
+                {"value": p["value"] * f} for p in r.get("distribution_params", [])
+            ]
+
+    _write_params(params, out_json_path)
+    return {
+        "chain_task_ids": list(chain_task_ids),
+        "shift": shift,
+        "factors": factors,
+        "base_total_mean": base_total,
+        "renormalised_total_mean": sum(
+            _task_mean(trd_by_id[tid]) for tid in chain_task_ids
+        ),
+        "total_invariant": True,
+    }
+
+
 def build_arrival_burstier_params(
     base_json_path: str | Path,
     *,
diff --git a/evaluation/state_metrics/pipeline.py b/evaluation/state_metrics/pipeline.py
index ec068f4..88ceec1 100644
--- a/evaluation/state_metrics/pipeline.py
+++ b/evaluation/state_metrics/pipeline.py
@@ -60,9 +60,11 @@
 from evaluation.state_metrics.perturb import (
     build_all_calendars_shifted_params,
     build_arrival_burstier_params,
+    build_branch_automation_params,
     build_calendar_shifted_params,
     build_case_route_params,
     build_duration_scaled_params,
+    build_front_back_load_params,
     build_gateway_biased_params,
     build_perturbed_params,
     build_role_swap_params,
@@ -146,6 +148,15 @@ class PipelineConfig:
     # `level` is then the percentage of case_type tags swapped on that
     # reference (the pairing-unique attack on a genuinely attribute-routed log).
     case_route_ruled: int | None = None
+    # parallel_auto kwargs (scenario #2). The non-critical AND-branch tasks to
+    # automate; `level` is read as the percent shrink toward `automation_floor`.
+    automate_task_ids: tuple[str, ...] | None = None
+    automation_floor_seconds: float = 1.0
+    # front_back_load kwargs (scenario #4). The ordered chain tasks whose mean
+    # durations are reweighted total-invariantly; `load_direction` decides the
+    # sign so the (non-negative) `level` ladder stays monotone for ranking.
+    chain_task_ids: tuple[str, ...] | None = None
+    load_direction: str = "front"           # "front" or "back"
     # Windowing controls (added 2026-05 to enable cross-utilization comparison).
     # cutoff_strategy:
     #   "p90_wip"   — middle of top-decile WIP band (legacy default).
@@ -804,6 +815,37 @@ def _prepare_params_for_level(
             cv2_multiplier=cv2_mult,
             out_json_path=out_params,
         )
+    elif cfg.perturbation == "parallel_auto":
+        if cfg.automate_task_ids is None:
+            raise ValueError("parallel_auto requires cfg.automate_task_ids")
+        if level < 0:
+            raise ValueError("parallel_auto levels must be >= 0 (percent automated)")
+        manifest = build_branch_automation_params(
+            cfg.params_path,
+            automate_task_ids=list(cfg.automate_task_ids),
+            level=level,
+            floor_seconds=cfg.automation_floor_seconds,
+            out_json_path=out_params,
+        )
+    elif cfg.perturbation == "front_back_load":
+        if cfg.chain_task_ids is None:
+            raise ValueError("front_back_load requires cfg.chain_task_ids")
+        if level < 0:
+            raise ValueError(
+                "front_back_load levels must be >= 0; direction is set by "
+                "cfg.load_direction"
+            )
+        if cfg.load_direction not in ("front", "back"):
+            raise ValueError("load_direction must be 'front' or 'back'")
+        # Non-negative ladder stays monotone for the c-index; the sign of the
+        # shift is taken from the configured direction.
+        signed = level if cfg.load_direction == "front" else -level
+        manifest = build_front_back_load_params(
+            cfg.params_path,
+            chain_task_ids=list(cfg.chain_task_ids),
+            shift=signed,
+            out_json_path=out_params,
+        )
     else:
         raise ValueError(f"unknown perturbation type {cfg.perturbation!r}")
     manifest = {"perturbation": cfg.perturbation, "level": level, **manifest}
@@ -1136,6 +1178,71 @@ def flip(row_case_id: str, row_type: str) -> str:
     return out
 
 
+def drift_case_types(
+    log: pd.DataFrame,
+    *,
+    strength: float,
+    rng: np.random.Generator,
+) -> pd.DataFrame:
+    """Return a copy of ``log`` whose ``case_type`` tags are made to *drift*
+    along the arrival timeline, with the global marginal preserved exactly.
+
+    A fraction ``strength`` of the cases (chosen at random, by arrival
+    position) have their tags re-sorted so that — among the chosen positions —
+    the lexicographically-smaller value lands on the earlier arrivals and the
+    larger value on the later ones. Because the re-sort only permutes the tags
+    *already present* on the chosen positions, the per-value counts (and hence
+    the global red/blue marginal) are unchanged; only the temporal arrangement
+    shifts. At ``strength == 1`` the chosen set is the whole log and the tags
+    are fully time-sorted; at ``strength == 0`` the output is identical.
+
+    This is the controlled, synthetic analogue of a real log whose case-type
+    mix drifts over time (scenario #6). Unlike ``label_swap_case_types`` (random
+    marginal-preserving swaps → only the *paired* ``activity_type`` projection
+    moves), a temporal drift skews the active case_type composition inside any
+    sub-window, so the marginal-blind baselines stay flat while BOTH the
+    ``case_type`` and ``activity_type`` state projections detect it.
+
+    The event stream (activities, timestamps, resources, case ids) is untouched,
+    so cycle_time / ngd / red / rtd and the type-agnostic state projections are
+    invariant by construction.
+    """
+    out = log.copy()
+    if "case_type" not in out.columns:
+        raise ValueError("log has no 'case_type' column to drift")
+    if not 0.0 <= strength <= 1.0:
+        raise ValueError("strength must be in [0, 1]")
+    if strength <= 0:
+        return out
+
+    first_start = out.groupby("case_id")["start_time"].min().sort_values()
+    case_order = list(first_start.index)
+    tag_by_case = out.groupby("case_id")["case_type"].first().to_dict()
+    n = len(case_order)
+    k = int(round(strength * n))
+    if k <= 1:
+        return out
+
+    # Choose k arrival positions to re-sort; keep them in arrival order.
+    positions = np.sort(rng.choice(n, size=k, replace=False))
+    chosen_cases = [case_order[p] for p in positions]
+    chosen_tags = [tag_by_case[c] for c in chosen_cases]
+    distinct = sorted(set(t for t in chosen_tags if t is not None and t == t))
+    if len(distinct) < 2:
+        return out  # only one value among the chosen — nothing to drift
+    early_val = distinct[0]
+    n_early = sum(t == early_val for t in chosen_tags)
+    # early_val first (earliest arrivals), everything else after.
+    resorted = [early_val] * n_early + [
+        t for t in chosen_tags if t != early_val
+    ]
+    new_tag = dict(tag_by_case)
+    for case, nt in zip(chosen_cases, resorted):
+        new_tag[case] = nt
+    out["case_type"] = out["case_id"].map(new_tag)
+    return out
+
+
 def _run_label_swap_levels(
     cfg: PipelineConfig,
     run_dir: Path,
@@ -1437,6 +1544,87 @@ def _run_case_route_levels(
             ))
 
 
+def _run_case_type_drift_levels(
+    cfg: PipelineConfig,
+    run_dir: Path,
+    *,
+    prefix_csv: Path,
+    cutoff: pd.Timestamp,
+    horizon: pd.Timedelta,
+    horizon_end: pd.Timestamp,
+    ongoing_ids: set[str],
+    results: list[dict],
+    util_rows: list[dict],
+) -> None:
+    """Case-type-drift oracle (scenario #6, controlled synthetic form).
+
+    Like ``_run_case_route_levels`` the reference is a REAL short-term sim in
+    which ``case_type`` genuinely drives XOR routing (red -> A-branch, blue ->
+    B-branch) via Prosimos branch_rules. Each ``level`` then makes the
+    ``case_type`` tags *drift* along the arrival timeline at strength
+    ``level%`` (``drift_case_types``), holding the global red/blue marginal
+    fixed.
+
+    Because the event stream is untouched and the marginal is preserved,
+    cycle_time / ngd_n2 / red / rtd and the type-agnostic state projections are
+    blind. Unlike the random label_swap, a *temporal* drift skews the active
+    case_type composition inside the evaluation window, so BOTH the
+    ``case_type`` and the joint ``activity_type`` state projections detect it —
+    the data-attribute, drift-over-time win.
+    """
+    n_splits = _count_split_gateways(Path(cfg.params_path))
+    n_ruled = cfg.case_route_ruled if cfg.case_route_ruled is not None else n_splits
+    ruled_params = run_dir / "case_route_ruled.json"
+    route_manifest = build_case_route_params(
+        cfg.params_path, n_gateways_ruled=n_ruled, out_json_path=ruled_params,
+    )
+    with open(ruled_params, encoding="utf-8") as f:
+        level_params = json.load(f)
+    role_map = _resource_to_profile(level_params)
+
+    ref_logs: list[pd.DataFrame] = []
+    for k in range(1, cfg.runs + 1):
+        print(f"[case_type_drift] reference (real attribute-routed) sim k={k}/{cfg.runs}")
+        ref = _run_short_term(
+            prefix_csv=prefix_csv, bpmn=cfg.bpmn_path, params=ruled_params,
+            cutoff=cutoff, horizon_end=horizon_end,
+            total_cases=cfg.sim_total_cases,
+            out_dir=run_dir / "reference" / f"k_{k}",
+            seed=10_000 + k,
+        )
+        if "case_type" not in ref.columns:
+            raise RuntimeError(
+                "reference sim has no case_type column; the params must declare "
+                "a case_type case attribute"
+            )
+        ref_logs.append(ref)
+
+    (run_dir / "case_type_drift_manifest.json").write_text(json.dumps({
+        **route_manifest,
+        "levels_are_percent_drift_strength": True,
+        "reference": "real short-term sim with case_type-driven branch_rules",
+    }, indent=2))
+
+    for level in cfg.levels:
+        strength = level / 100.0
+        for k in range(1, cfg.runs + 1):
+            print(f"[case_type_drift] drift level={level}% k={k}/{cfg.runs}")
+            ref = ref_logs[k - 1]
+            rng = np.random.default_rng(70_000 + 1_000 * level + k)
+            perturbed = drift_case_types(ref, strength=strength, rng=rng)
+            base_A = filter_to_cases_in_window(ref, ongoing_ids, cutoff, horizon)
+            sim_A = filter_to_cases_in_window(perturbed, ongoing_ids, cutoff, horizon)
+            util_rows.extend(_compute_utilization_rows(
+                sim_log=ref, params=level_params,
+                cutoff=cutoff, horizon_end=horizon_end, level=level, k_sim=k,
+            ))
+            results.extend(_compute_metrics_row(
+                baseline_continuation=base_A, sim_continuation=sim_A,
+                level=level, k_baseline=k, k_sim=k, scope="A_ongoing",
+                window=(cutoff, horizon_end), role_map=role_map,
+            ))
+
+
 def run_pipeline(cfg: PipelineConfig) -> Path:
     """Run the end-to-end pipeline; return path to the results.csv."""
     run_id = generate_short_uuid()
@@ -1523,6 +1711,13 @@ def run_pipeline(cfg: PipelineConfig) -> Path:
             results=results, util_rows=util_rows,
         )
         return _finalize_pipeline_outputs(run_dir, results, util_rows)
+    if cfg.perturbation == "case_type_drift":
+        _run_case_type_drift_levels(
+            cfg, run_dir, prefix_csv=prefix_csv, cutoff=cutoff,
+            horizon=horizon, horizon_end=horizon_end, ongoing_ids=ongoing_ids,
+            results=results, util_rows=util_rows,
+        )
+        return _finalize_pipeline_outputs(run_dir, results, util_rows)
 
     # Step 3: K short-term BASELINE runs at level 0 (shared across all levels).
     # The level-0 short-term sim is the locked baseline: zero-perturbation
diff --git a/evaluation/state_metrics/run_pipeline.py b/evaluation/state_metrics/run_pipeline.py
index 6930dbb..2aab0ee 100644
--- a/evaluation/state_metrics/run_pipeline.py
+++ b/evaluation/state_metrics/run_pipeline.py
@@ -52,7 +52,9 @@ def main() -> None:
                    choices=["resources", "duration", "role_swap",
                             "calendar_shift", "calendar_shift_all", "gateway",
                             "arrival_burst", "relabel", "rephase",
-                            "mix_ratio", "label_swap", "case_route"],
+                            "mix_ratio", "label_swap", "case_route",
+                            "parallel_auto", "front_back_load",
+                            "case_type_drift"],
                    default="resources",
                    help="which perturbation family to apply")
     p.add_argument("--remove-from-profile", default=None,
@@ -82,8 +84,11 @@ def main() -> None:
                    help="mix_ratio: baseline fraction of green cases (the "
                         "reference mix). Default 0.5.")
     p.add_argument("--case-route-ruled", type=int, default=None,
-                   help="case_route: number of XOR splits the reference sim "
-                        "routes by case_type (default: all splits)")
+                   help="case_route / case_type_drift: number of XOR splits the "
+                        "reference sim routes by case_type (default: all splits)")
+    p.add_argument("--load-direction", choices=["front", "back"], default="front",
+                   help="front_back_load: which end of the chain gets the "
+                        "duration mass (default: front)")
     p.add_argument("--gt-total-cases", type=int, default=2000)
     p.add_argument("--sim-total-cases", type=int, default=2000)
     p.add_argument("--outputs-root", type=Path,
@@ -150,6 +155,15 @@ def main() -> None:
     elif args.perturbation == "case_route":
         # Percentage of case_type tags swapped on a real attribute-routed sim.
         levels = (0, 10, 20, 30, 40)
+    elif args.perturbation == "case_type_drift":
+        # Percentage drift strength of case_type tags along the timeline.
+        levels = (0, 25, 50, 75, 100)
+    elif args.perturbation == "parallel_auto":
+        # Percentage automation (duration shrink) of the non-critical branch.
+        levels = spec.default_levels
+    elif args.perturbation == "front_back_load":
+        # Percentage of duration mass moved toward the chosen chain end.
+        levels = spec.default_levels
     else:
         levels = spec.default_levels
 
@@ -177,6 +191,9 @@ def main() -> None:
         mix_red_params=args.mix_red_params,
         mix_baseline_green=args.mix_baseline_green,
         case_route_ruled=args.case_route_ruled,
+        automate_task_ids=spec.automate_task_ids or None,
+        chain_task_ids=spec.chain_task_ids or None,
+        load_direction=args.load_direction,
     )
     run_pipeline(cfg)
 
diff --git a/samples/dev-samples/synthetic_linear_chain.bpmn b/samples/dev-samples/synthetic_linear_chain.bpmn
new file mode 100644
index 0000000..fb42828
--- /dev/null
+++ b/samples/dev-samples/synthetic_linear_chain.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_linear_chain" targetNamespace="http://bpmn.io/schema/bpmn"><bpmn:process id="Process_linear_chain" isExecutable="false"><bpmn:startEvent id="StartEvent_1" name="Start"><bpmn:outgoing>f_start</bpmn:outgoing></bpmn:startEvent><bpmn:task id="t_step1" name="Step_1"><bpmn:incoming>f_start</bpmn:incoming><bpmn:outgoing>f_step1</bpmn:outgoing></bpmn:task><bpmn:task id="t_step2" name="Step_2"><bpmn:incoming>f_step1</bpmn:incoming><bpmn:outgoing>f_step2</bpmn:outgoing></bpmn:task><bpmn:task id="t_step3" name="Step_3"><bpmn:incoming>f_step2</bpmn:incoming><bpmn:outgoing>f_step3</bpmn:outgoing></bpmn:task><bpmn:task id="t_step4" name="Step_4"><bpmn:incoming>f_step3</bpmn:incoming><bpmn:outgoing>f_step4</bpmn:outgoing></bpmn:task><bpmn:task id="t_step5" name="Step_5"><bpmn:incoming>f_step4</bpmn:incoming><bpmn:outgoing>f_end</bpmn:outgoing></bpmn:task><bpmn:endEvent id="EndEvent_1" name="End"><bpmn:incoming>f_end</bpmn:incoming></bpmn:endEvent><bpmn:sequenceFlow id="f_start" sourceRef="StartEvent_1" targetRef="t_step1" /><bpmn:sequenceFlow id="f_step1" sourceRef="t_step1" targetRef="t_step2" /><bpmn:sequenceFlow id="f_step2" sourceRef="t_step2" targetRef="t_step3" /><bpmn:sequenceFlow id="f_step3" sourceRef="t_step3" targetRef="t_step4" /><bpmn:sequenceFlow id="f_step4" sourceRef="t_step4" targetRef="t_step5" /><bpmn:sequenceFlow id="f_end" sourceRef="t_step5" targetRef="EndEvent_1" /></bpmn:process></bpmn:definitions>
\ No newline at end of file
diff --git a/samples/dev-samples/synthetic_linear_chain.json b/samples/dev-samples/synthetic_linear_chain.json
new file mode 100644
index 0000000..d760387
--- /dev/null
+++ b/samples/dev-samples/synthetic_linear_chain.json
@@ -0,0 +1,2438 @@
+{
+  "task_resource_distribution": [
+    {
+      "task_id": "t_step1",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_12"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_13"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_14"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_15"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_16"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_17"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_18"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_19"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_20"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_21"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_22"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_23"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_24"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_25"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_26"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_27"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_28"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_29"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_30"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_31"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_32"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_33"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_34"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_35"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_36"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_37"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_38"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_39"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_40"
+        }
+      ]
+    },
+    {
+      "task_id": "t_step2",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_12"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_13"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_14"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_15"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_16"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_17"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_18"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 180.0
+            }
+          ],
+          "resource_id": "Worker_19"
+        },
+        {
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+            "t_step4",
+            "t_step5"
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+            "t_step4",
+            "t_step5"
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+            "t_step2",
+            "t_step3",
+            "t_step4",
+            "t_step5"
+          ]
+        },
+        {
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+            "t_step2",
+            "t_step3",
+            "t_step4",
+            "t_step5"
+          ]
+        }
+      ]
+    }
+  ],
+  "event_distribution": [],
+  "batch_processing": [],
+  "case_attributes": [],
+  "prioritisation_rules": []
+}
\ No newline at end of file
diff --git a/samples/dev-samples/synthetic_parallel_auto.bpmn b/samples/dev-samples/synthetic_parallel_auto.bpmn
new file mode 100644
index 0000000..5da1833
--- /dev/null
+++ b/samples/dev-samples/synthetic_parallel_auto.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_parallel_auto" targetNamespace="http://bpmn.io/schema/bpmn"><bpmn:process id="Process_parallel_auto" 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_split</bpmn:outgoing></bpmn:task><bpmn:parallelGateway id="g_split" name="AND-split"><bpmn:incoming>f_reg_split</bpmn:incoming><bpmn:outgoing>f_split_crit</bpmn:outgoing><bpmn:outgoing>f_split_nc0</bpmn:outgoing></bpmn:parallelGateway><bpmn:task id="t_crit" name="Critical"><bpmn:incoming>f_split_crit</bpmn:incoming><bpmn:outgoing>f_crit_join</bpmn:outgoing></bpmn:task><bpmn:task id="t_nc1" name="NonCritical_1"><bpmn:incoming>f_split_nc0</bpmn:incoming><bpmn:outgoing>f_nc1</bpmn:outgoing></bpmn:task><bpmn:task id="t_nc2" name="NonCritical_2"><bpmn:incoming>f_nc1</bpmn:incoming><bpmn:outgoing>f_nc2</bpmn:outgoing></bpmn:task><bpmn:task id="t_nc3" name="NonCritical_3"><bpmn:incoming>f_nc2</bpmn:incoming><bpmn:outgoing>f_nc_join</bpmn:outgoing></bpmn:task><bpmn:parallelGateway id="g_join" name="AND-join"><bpmn:incoming>f_crit_join</bpmn:incoming><bpmn:incoming>f_nc_join</bpmn:incoming><bpmn:outgoing>f_join_dec</bpmn:outgoing></bpmn:parallelGateway><bpmn:task id="t_dec" name="Decide"><bpmn:incoming>f_join_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_split" sourceRef="t_reg" targetRef="g_split" /><bpmn:sequenceFlow id="f_split_crit" sourceRef="g_split" targetRef="t_crit" /><bpmn:sequenceFlow id="f_split_nc0" sourceRef="g_split" targetRef="t_nc1" /><bpmn:sequenceFlow id="f_nc1" sourceRef="t_nc1" targetRef="t_nc2" /><bpmn:sequenceFlow id="f_nc2" sourceRef="t_nc2" targetRef="t_nc3" /><bpmn:sequenceFlow id="f_crit_join" sourceRef="t_crit" targetRef="g_join" /><bpmn:sequenceFlow id="f_nc_join" sourceRef="t_nc3" targetRef="g_join" /><bpmn:sequenceFlow id="f_join_dec" sourceRef="g_join" 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_parallel_auto.json b/samples/dev-samples/synthetic_parallel_auto.json
new file mode 100644
index 0000000..78eea1a
--- /dev/null
+++ b/samples/dev-samples/synthetic_parallel_auto.json
@@ -0,0 +1,2763 @@
+{
+  "task_resource_distribution": [
+    {
+      "task_id": "t_reg",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_12"
+        }
+      ]
+    },
+    {
+      "task_id": "t_dec",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 60.0
+            }
+          ],
+          "resource_id": "Common_12"
+        }
+      ]
+    },
+    {
+      "task_id": "t_crit",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_12"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_13"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_14"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_15"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_16"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_17"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_18"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_19"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_20"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_21"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_22"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_23"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_24"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_25"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_26"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_27"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_28"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_29"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
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+              "value": 600.0
+            }
+          ],
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+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
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+            }
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+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
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+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_32"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_33"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_34"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_35"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_36"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_37"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_38"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_39"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 600.0
+            }
+          ],
+          "resource_id": "Critical_40"
+        }
+      ]
+    },
+    {
+      "task_id": "t_nc1",
+      "resources": [
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_1"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_2"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_3"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_4"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_5"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_6"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_7"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_8"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_9"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_10"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_11"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_12"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_13"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_14"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_15"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_16"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
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+              "value": 120.0
+            }
+          ],
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+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_18"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_19"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_20"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_21"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_22"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_23"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_24"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_25"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_26"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_27"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_28"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_29"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_30"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_31"
+        },
+        {
+          "distribution_name": "fix",
+          "distribution_params": [
+            {
+              "value": 120.0
+            }
+          ],
+          "resource_id": "NonCritical_32"
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+          "amount": 1,
+          "calendar": "cal_247",
+          "assignedTasks": [
+            "t_nc1",
+            "t_nc2",
+            "t_nc3"
+          ]
+        }
+      ]
+    }
+  ],
+  "event_distribution": [],
+  "batch_processing": [],
+  "case_attributes": [],
+  "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 8de29e5..ff9f3f3 100644
--- a/tests/evaluation/state_metrics/test_perturb.py
+++ b/tests/evaluation/state_metrics/test_perturb.py
@@ -5,19 +5,31 @@
 
 from evaluation.state_metrics.perturb import (
     build_arrival_burstier_params,
+    build_branch_automation_params,
     build_calendar_shifted_params,
     build_case_route_params,
+    build_front_back_load_params,
     build_gateway_biased_params,
     build_perturbed_params,
     build_role_swap_params,
 )
 
 
-XOR_JSON = Path(__file__).resolve().parents[3] / "samples" / "dev-samples" / "synthetic_xor_loop.json"
+_DEV = Path(__file__).resolve().parents[3] / "samples" / "dev-samples"
+XOR_JSON = _DEV / "synthetic_xor_loop.json"
 LOAN_JSON = Path(__file__).resolve().parents[3] / "samples" / "icpm-2025" / "synthetic" / "Loan-stable.json"
-CASE_ROUTE_JSON = (
-    Path(__file__).resolve().parents[3] / "samples" / "dev-samples" / "synthetic_case_route.json"
-)
+CASE_ROUTE_JSON = _DEV / "synthetic_case_route.json"
+PARALLEL_AUTO_JSON = _DEV / "synthetic_parallel_auto.json"
+LINEAR_CHAIN_JSON = _DEV / "synthetic_linear_chain.json"
+
+
+def _task_mean(data, task_id):
+    t = next(t for t in data["task_resource_distribution"] if t["task_id"] == task_id)
+    return t["resources"][0]["distribution_params"][0]["value"]
+
+
+def _chain_total_mean(data, task_ids):
+    return sum(_task_mean(data, t) for t in task_ids)
 
 
 @pytest.mark.skipif(not CASE_ROUTE_JSON.exists(), reason="synthetic_case_route.json missing")
@@ -438,3 +450,130 @@ def test_arrival_burst_rejects_non_gamma(tmp_path):
         build_arrival_burstier_params(
             src, cv2_multiplier=2.0, out_json_path=tmp_path / "dst.json",
         )
+
+
+# ---------------------------------------------------------------------------
+# parallel_auto: branch automation (scenario #2)
+# ---------------------------------------------------------------------------
+
+@pytest.mark.skipif(not PARALLEL_AUTO_JSON.exists(), reason="synthetic_parallel_auto.json missing")
+def test_branch_automation_zero_is_noop(tmp_path):
+    out = tmp_path / "p.json"
+    m = build_branch_automation_params(
+        PARALLEL_AUTO_JSON, automate_task_ids=["t_nc1", "t_nc2", "t_nc3"],
+        level=0, out_json_path=out,
+    )
+    assert m["factor"] == 1.0
+    src = json.loads(PARALLEL_AUTO_JSON.read_text())
+    dst = json.loads(out.read_text())
+    assert src["task_resource_distribution"] == dst["task_resource_distribution"]
+
+
+@pytest.mark.skipif(not PARALLEL_AUTO_JSON.exists(), reason="synthetic_parallel_auto.json missing")
+def test_branch_automation_shrinks_only_targets(tmp_path):
+    out = tmp_path / "p.json"
+    build_branch_automation_params(
+        PARALLEL_AUTO_JSON, automate_task_ids=["t_nc1", "t_nc2", "t_nc3"],
+        level=50, out_json_path=out,
+    )
+    src = json.loads(PARALLEL_AUTO_JSON.read_text())
+    dst = json.loads(out.read_text())
+    # Non-critical tasks halved; critical and common tasks untouched.
+    for tid in ("t_nc1", "t_nc2", "t_nc3"):
+        assert _task_mean(dst, tid) == pytest.approx(_task_mean(src, tid) * 0.5)
+    for tid in ("t_crit", "t_reg", "t_dec"):
+        assert _task_mean(dst, tid) == _task_mean(src, tid)
+
+
+@pytest.mark.skipif(not PARALLEL_AUTO_JSON.exists(), reason="synthetic_parallel_auto.json missing")
+def test_branch_automation_floors_at_full_level(tmp_path):
+    out = tmp_path / "p.json"
+    build_branch_automation_params(
+        PARALLEL_AUTO_JSON, automate_task_ids=["t_nc1"], level=100,
+        floor_seconds=2.0, out_json_path=out,
+    )
+    dst = json.loads(out.read_text())
+    # The activity must still carry a strictly-positive (floored) duration so it
+    # stays in the trace -> ngd stays blind.
+    assert _task_mean(dst, "t_nc1") == 2.0
+
+
+def test_branch_automation_rejects_bad_args(tmp_path):
+    base = tmp_path / "base.json"
+    base.write_text(json.dumps({"task_resource_distribution": [
+        {"task_id": "t_nc1", "resources": [
+            {"distribution_name": "fix", "distribution_params": [{"value": 100.0}], "resource_id": "R1"}]},
+    ]}))
+    with pytest.raises(ValueError):
+        build_branch_automation_params(base, automate_task_ids=[], level=10, out_json_path=tmp_path / "o.json")
+    with pytest.raises(ValueError):
+        build_branch_automation_params(base, automate_task_ids=["t_nc1"], level=-1, out_json_path=tmp_path / "o.json")
+    with pytest.raises(ValueError):
+        build_branch_automation_params(base, automate_task_ids=["t_nc1"], level=101, out_json_path=tmp_path / "o.json")
+    with pytest.raises(ValueError, match="not found"):
+        build_branch_automation_params(base, automate_task_ids=["nope"], level=10, out_json_path=tmp_path / "o.json")
+
+
+# ---------------------------------------------------------------------------
+# front_back_load: total-invariant duration redistribution (scenario #4)
+# ---------------------------------------------------------------------------
+
+_CHAIN = ["t_step1", "t_step2", "t_step3", "t_step4", "t_step5"]
+
+
+@pytest.mark.skipif(not LINEAR_CHAIN_JSON.exists(), reason="synthetic_linear_chain.json missing")
+def test_front_back_load_zero_is_noop(tmp_path):
+    out = tmp_path / "p.json"
+    m = build_front_back_load_params(
+        LINEAR_CHAIN_JSON, chain_task_ids=_CHAIN, shift=0, out_json_path=out,
+    )
+    assert all(f == 1.0 for f in m["factors"].values())
+    src = json.loads(LINEAR_CHAIN_JSON.read_text())
+    dst = json.loads(out.read_text())
+    assert src["task_resource_distribution"] == dst["task_resource_distribution"]
+
+
+@pytest.mark.skipif(not LINEAR_CHAIN_JSON.exists(), reason="synthetic_linear_chain.json missing")
+@pytest.mark.parametrize("shift", [30, 60, -30, -60])
+def test_front_back_load_preserves_total(tmp_path, shift):
+    out = tmp_path / "p.json"
+    build_front_back_load_params(
+        LINEAR_CHAIN_JSON, chain_task_ids=_CHAIN, shift=shift, out_json_path=out,
+    )
+    src = json.loads(LINEAR_CHAIN_JSON.read_text())
+    dst = json.loads(out.read_text())
+    # Per-case total duration (= cycle time, aggregate ρ) held exactly.
+    assert _chain_total_mean(dst, _CHAIN) == pytest.approx(_chain_total_mean(src, _CHAIN))
+
+
+@pytest.mark.skipif(not LINEAR_CHAIN_JSON.exists(), reason="synthetic_linear_chain.json missing")
+def test_front_back_load_direction(tmp_path):
+    src = json.loads(LINEAR_CHAIN_JSON.read_text())
+    front = tmp_path / "front.json"
+    back = tmp_path / "back.json"
+    build_front_back_load_params(LINEAR_CHAIN_JSON, chain_task_ids=_CHAIN, shift=60, out_json_path=front)
+    build_front_back_load_params(LINEAR_CHAIN_JSON, chain_task_ids=_CHAIN, shift=-60, out_json_path=back)
+    fd, bd = json.loads(front.read_text()), json.loads(back.read_text())
+    # Front-load: early step longer than late step. Back-load: mirror image.
+    assert _task_mean(fd, "t_step1") > _task_mean(fd, "t_step5")
+    assert _task_mean(bd, "t_step1") < _task_mean(bd, "t_step5")
+    # Midpoint is the fixed pivot of the antisymmetric ramp.
+    assert _task_mean(fd, "t_step3") == pytest.approx(_task_mean(src, "t_step3"))
+    # Front(+s) and back(-s) are reflections about the chain midpoint.
+    assert _task_mean(fd, "t_step1") == pytest.approx(_task_mean(bd, "t_step5"))
+
+
+def test_front_back_load_rejects_bad_args(tmp_path):
+    base = tmp_path / "base.json"
+    base.write_text(json.dumps({"task_resource_distribution": [
+        {"task_id": "t_step1", "resources": [
+            {"distribution_name": "fix", "distribution_params": [{"value": 100.0}], "resource_id": "R1"}]},
+        {"task_id": "t_step2", "resources": [
+            {"distribution_name": "fix", "distribution_params": [{"value": 100.0}], "resource_id": "R1"}]},
+    ]}))
+    with pytest.raises(ValueError, match="weights stay positive"):
+        build_front_back_load_params(base, chain_task_ids=["t_step1", "t_step2"], shift=100, out_json_path=tmp_path / "o.json")
+    with pytest.raises(ValueError, match="at least two"):
+        build_front_back_load_params(base, chain_task_ids=["t_step1"], shift=10, out_json_path=tmp_path / "o.json")
+    with pytest.raises(ValueError, match="not found"):
+        build_front_back_load_params(base, chain_task_ids=["t_step1", "nope"], shift=10, out_json_path=tmp_path / "o.json")
diff --git a/tests/evaluation/state_metrics/test_pipeline.py b/tests/evaluation/state_metrics/test_pipeline.py
index ea6860c..7e17e81 100644
--- a/tests/evaluation/state_metrics/test_pipeline.py
+++ b/tests/evaluation/state_metrics/test_pipeline.py
@@ -14,6 +14,7 @@
     _select_n_ongoing_cutoff,
     _write_prefix_csv,
     filter_to_cases_in_window,
+    drift_case_types,
     get_ongoing_case_ids,
     label_swap_case_types,
     merge_logs_at_ratio,
@@ -487,3 +488,67 @@ def test_compute_utilization_rows_partial_busy():
     assert by_role["Team A"]["utilization"] == pytest.approx(9000 / (2 * 14400))
     # Team B never used → 0.
     assert by_role["Team B"]["utilization"] == 0.0
+
+
+# ---------------------------------------------------------------------------
+# drift_case_types: temporal case-type drift (scenario #6)
+# ---------------------------------------------------------------------------
+
+def _typed_timeline(n, rng_tags=None):
+    """n cases, one event each, arrival-ordered; alternating red/blue tags."""
+    rows = []
+    base = pd.Timestamp("2025-01-01", tz="UTC")
+    for i in range(n):
+        tag = "red" if i % 2 == 0 else "blue"
+        rows.append((f"c{i}", "A", base + pd.Timedelta(hours=i),
+                     base + pd.Timedelta(hours=i + 1), "R1", tag))
+    return _mklog_typed(rows)
+
+
+def test_drift_zero_is_identity():
+    log = _typed_timeline(10)
+    out = drift_case_types(log, strength=0.0, rng=np.random.default_rng(0))
+    assert out.equals(log)
+
+
+def test_drift_preserves_marginal():
+    log = _typed_timeline(20)
+    for s in (0.25, 0.5, 0.75, 1.0):
+        out = drift_case_types(log, strength=s, rng=np.random.default_rng(1))
+        assert (out["case_type"] == "red").sum() == 10
+        assert (out["case_type"] == "blue").sum() == 10
+
+
+def test_drift_full_strength_is_time_sorted():
+    log = _typed_timeline(20)
+    out = drift_case_types(log, strength=1.0, rng=np.random.default_rng(2))
+    ordered = out.sort_values("start_time")["case_type"].tolist()
+    # Lexicographically-smaller value ("blue") lands on the earliest arrivals.
+    assert ordered == ["blue"] * 10 + ["red"] * 10
+
+
+def test_drift_leaves_event_stream_untouched():
+    log = _typed_timeline(12)
+    out = drift_case_types(log, strength=1.0, rng=np.random.default_rng(3))
+    for col in ("case_id", "activity", "start_time", "end_time", "resource"):
+        assert (out[col].values == log[col].values).all()
+
+
+def test_drift_requires_case_type_column():
+    log = _mklog([("c1", "A", "2025-01-01 10:00", "2025-01-01 11:00", "R1")])
+    with pytest.raises(ValueError):
+        drift_case_types(log, strength=0.5, rng=np.random.default_rng(0))
+
+
+def test_drift_increases_temporal_sortedness_monotonically():
+    # As strength rises, the first-half red fraction falls toward 0 (blue early).
+    log = _typed_timeline(40)
+    n = 40
+    prev = None
+    for s in (0.0, 0.5, 1.0):
+        out = drift_case_types(log, strength=s, rng=np.random.default_rng(7))
+        ordered = out.sort_values("start_time")["case_type"].tolist()
+        first_half_red = sum(t == "red" for t in ordered[: n // 2])
+        if prev is not None:
+            assert first_half_red <= prev
+        prev = first_half_red
diff --git a/tools/generate_linear_chain.py b/tools/generate_linear_chain.py
new file mode 100644
index 0000000..3c5d665
--- /dev/null
+++ b/tools/generate_linear_chain.py
@@ -0,0 +1,171 @@
+"""Generate the synthetic *linear-chain* dataset (BPMN + Prosimos params).
+
+A purely sequential process of ``n_tasks`` comparable-duration activities on a
+single resource pool:
+
+    Start -> Step_1 -> Step_2 -> ... -> Step_N -> End
+
+No gateways, no branching: every case runs the same activity sequence with the
+same per-task service distribution, so the baseline is symmetric and the only
+thing a perturbation can move is *where in the case* duration mass sits.
+
+The state-metrics ``front_back_load`` perturbation
+(``evaluation.state_metrics.perturb.build_front_back_load_params``) reweights
+the per-task means by chain position while holding the per-case **total**
+duration constant: front-loading makes early steps longer and late steps
+shorter (back-loading is the mirror). Because the per-case total (cycle time),
+the activity set, the bigrams, and the aggregate utilisation are all unchanged,
+``cycle_time`` and ``ngd_n2`` are blind; only the time-weighted state metric
+localises the moved duration mass — the timeline-localisation story.
+
+Run::
+
+    python -m tools.generate_linear_chain
+    python -m tools.generate_linear_chain --tasks 5 --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 _flow(fid: str, src: str, tgt: str) -> str:
+    return f'<bpmn:sequenceFlow id="{fid}" sourceRef="{src}" targetRef="{tgt}" />'
+
+
+def build_model(
+    n_tasks: int,
+    *,
+    workers: int = 40,
+    arrival_mean: float = 90.0,
+    service: float = 180.0,
+) -> tuple[str, dict, list[str]]:
+    """Return (bpmn_xml, prosimos_params, chain_task_ids).
+
+    All ``n_tasks`` steps share the same ``service`` mean, so the unperturbed
+    chain is duration-symmetric — front/back-loading is a pure redistribution.
+    The pool is sized so contention is minimal (the win is timeline, not
+    throughput).
+    """
+    if n_tasks < 2:
+        raise ValueError("n_tasks must be >= 2")
+
+    nodes: list[str] = []
+    flows: list[str] = []
+    task_ids: list[str] = []
+
+    nodes.append('<bpmn:startEvent id="StartEvent_1" name="Start">'
+                 '<bpmn:outgoing>f_start</bpmn:outgoing></bpmn:startEvent>')
+    prev_out = "f_start"
+    prev_src = "StartEvent_1"
+    for i in range(1, n_tasks + 1):
+        tid = f"t_step{i}"
+        task_ids.append(tid)
+        out_flow = "f_end" if i == n_tasks else f"f_step{i}"
+        nodes.append(_task(tid, f"Step_{i}", prev_out, out_flow))
+        flows.append(_flow(prev_out, prev_src, tid))
+        prev_out = out_flow
+        prev_src = tid
+    nodes.append('<bpmn:endEvent id="EndEvent_1" name="End">'
+                 '<bpmn:incoming>f_end</bpmn:incoming></bpmn:endEvent>')
+    flows.append(_flow("f_end", prev_src, "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_linear_chain" '
+        'targetNamespace="http://bpmn.io/schema/bpmn">'
+        '<bpmn:process id="Process_linear_chain" isExecutable="false">'
+        f"{process_body}"
+        "</bpmn:process></bpmn:definitions>"
+    )
+
+    params = _build_params(task_ids, workers=workers,
+                           arrival_mean=arrival_mean, service=service)
+    return bpmn, params, task_ids
+
+
+def _build_params(
+    task_ids: list[str], *, workers: int, arrival_mean: float, service: 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)]
+
+    def fix_service(rid: str) -> dict:
+        return {
+            "distribution_name": "fix",
+            "distribution_params": [{"value": service}],
+            "resource_id": rid,
+        }
+
+    trd = [
+        {"task_id": tid, "resources": [fix_service(r) for r 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
+        ],
+    }]
+
+    return {
+        "task_resource_distribution": trd,
+        "resource_calendars": [{"id": cal_id, "name": "24/7", "time_periods": periods}],
+        "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": [],
+        "prioritisation_rules": [],
+    }
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Generate the linear-chain synthetic dataset")
+    ap.add_argument("--tasks", type=int, default=5)
+    ap.add_argument("--workers", type=int, default=40)
+    ap.add_argument("--arrival-mean", type=float, default=90.0)
+    ap.add_argument("--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_linear_chain")
+    args = ap.parse_args()
+
+    bpmn, params, ids = build_model(args.tasks, workers=args.workers,
+                                    arrival_mean=args.arrival_mean, service=args.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")
+    print(f"wrote {bpmn_path}")
+    print(f"wrote {json_path}")
+    print(f"  chain tasks (ordered): {ids}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/generate_parallel_auto.py b/tools/generate_parallel_auto.py
new file mode 100644
index 0000000..2d4fd12
--- /dev/null
+++ b/tools/generate_parallel_auto.py
@@ -0,0 +1,263 @@
+"""Generate the synthetic *parallel-automation* dataset (BPMN + Prosimos params).
+
+The process has one AND (parallel) block with a *critical* and a *non-critical*
+branch running concurrently:
+
+    Start -> Register
+          -> AND-split
+                -> Critical                     (long; sets the cycle time)
+                -> NonCritical_1 -> ... -> NonCritical_M   (short chain)
+          -> AND-join
+          -> Decide -> End
+
+The critical branch's duration strictly dominates the non-critical chain
+(``D_crit`` >> ``Σ D_nc``) and the two branches run on SEPARATE resource pools.
+Consequently:
+
+* the per-case cycle time is set by the critical branch, so shrinking the
+  non-critical work leaves cycle time unchanged;
+* automating the non-critical branch does not touch the critical pool, so the
+  critical resource utilisation is unchanged.
+
+The state-metrics ``parallel_auto`` perturbation
+(``evaluation.state_metrics.perturb.build_branch_automation_params``) scales the
+non-critical task durations toward a small floor. Because the non-critical
+activities still appear once per case (the floor keeps them in the trace) but
+occupy the active-instance set for progressively less time, the per-instant
+active multiset shifts while:
+
+* ``cycle_time`` stays blind (critical path unchanged), and
+* ``ngd_n2`` stays blind (same activities, same bigrams, same per-case counts).
+
+Only the time-weighted state metric sees the non-critical branch vanishing from
+the concurrent active set. This is Marlon's parallel-branch-automation example.
+
+Run::
+
+    python -m tools.generate_parallel_auto
+    python -m tools.generate_parallel_auto --nc-tasks 3 --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 _parallel(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:parallelGateway id="{gid}" name="{escape(name)}">{inc}{out}</bpmn:parallelGateway>'
+
+
+def _flow(fid: str, src: str, tgt: str) -> str:
+    return f'<bpmn:sequenceFlow id="{fid}" sourceRef="{src}" targetRef="{tgt}" />'
+
+
+def build_model(
+    n_nc_tasks: int,
+    *,
+    common_workers: int = 12,
+    critical_workers: int = 40,
+    nc_workers: int = 40,
+    arrival_mean: float = 30.0,
+    common_service: float = 60.0,
+    critical_service: float = 600.0,
+    nc_service: float = 120.0,
+) -> tuple[str, dict, list[str]]:
+    """Return (bpmn_xml, prosimos_params, nc_task_ids).
+
+    ``critical_service`` must dominate ``n_nc_tasks * nc_service`` so the
+    critical branch sets the cycle time; the defaults give 600 vs 360.
+    Pools are sized generously so contention is minimal and the win is a pure
+    composition/timeline effect rather than a throughput effect.
+    """
+    if n_nc_tasks < 1:
+        raise ValueError("n_nc_tasks must be >= 1")
+    nc_total = n_nc_tasks * nc_service
+    if critical_service <= nc_total:
+        raise ValueError(
+            f"critical_service ({critical_service}) must exceed the non-critical "
+            f"chain total ({nc_total}) so the critical branch sets cycle time"
+        )
+
+    nodes: list[str] = []
+    flows: list[str] = []
+
+    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_split"))
+    flows.append(_flow("f_start", "StartEvent_1", "t_reg"))
+
+    # AND-split: one incoming (from Register), two outgoing (critical / nc).
+    f_split_crit = "f_split_crit"
+    f_split_nc = "f_split_nc0"
+    flows.append(_flow("f_reg_split", "t_reg", "g_split"))
+    nodes.append(_parallel("g_split", "AND-split", ["f_reg_split"],
+                           [f_split_crit, f_split_nc]))
+
+    # Critical branch: single long task into the join.
+    f_crit_join = "f_crit_join"
+    nodes.append(_task("t_crit", "Critical", f_split_crit, f_crit_join))
+    flows.append(_flow(f_split_crit, "g_split", "t_crit"))
+
+    # Non-critical branch: a chain of short tasks into the join.
+    nc_task_ids: list[str] = []
+    prev_out = f_split_nc
+    prev_src = "g_split"
+    for i in range(1, n_nc_tasks + 1):
+        tid = f"t_nc{i}"
+        nc_task_ids.append(tid)
+        is_last = i == n_nc_tasks
+        out_flow = "f_nc_join" if is_last else f"f_nc{i}"
+        nodes.append(_task(tid, f"NonCritical_{i}", prev_out, out_flow))
+        flows.append(_flow(prev_out, prev_src, tid))
+        prev_out = out_flow
+        prev_src = tid
+
+    # AND-join: two incoming (critical / nc), one outgoing (to Decide).
+    nodes.append(_parallel("g_join", "AND-join", [f_crit_join, "f_nc_join"],
+                           ["f_join_dec"]))
+    flows.append(_flow(f_crit_join, "t_crit", "g_join"))
+    flows.append(_flow("f_nc_join", prev_src, "g_join"))
+
+    nodes.append(_task("t_dec", "Decide", "f_join_dec", "f_dec_end"))
+    flows.append(_flow("f_join_dec", "g_join", "t_dec"))
+    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_parallel_auto" '
+        'targetNamespace="http://bpmn.io/schema/bpmn">'
+        '<bpmn:process id="Process_parallel_auto" isExecutable="false">'
+        f"{process_body}"
+        "</bpmn:process></bpmn:definitions>"
+    )
+
+    params = _build_params(
+        nc_task_ids,
+        common_workers=common_workers, critical_workers=critical_workers,
+        nc_workers=nc_workers, arrival_mean=arrival_mean,
+        common_service=common_service, critical_service=critical_service,
+        nc_service=nc_service,
+    )
+    return bpmn, params, nc_task_ids
+
+
+def _pool(name: str, prefix: str, n: int, cal_id: str, tasks: list[str]) -> dict:
+    return {
+        "id": f"{name}_profile",
+        "name": name,
+        "resource_list": [
+            {"id": f"{prefix}_{i}", "name": f"{prefix}_{i}", "cost_per_hour": 0,
+             "amount": 1, "calendar": cal_id, "assignedTasks": list(tasks)}
+            for i in range(1, n + 1)
+        ],
+    }
+
+
+def _build_params(
+    nc_task_ids: list[str],
+    *,
+    common_workers: int,
+    critical_workers: int,
+    nc_workers: int,
+    arrival_mean: float,
+    common_service: float,
+    critical_service: float,
+    nc_service: float,
+) -> dict:
+    cal_id = "cal_247"
+    periods = [{"from": "MONDAY", "to": "SUNDAY",
+                "beginTime": "00:00:00", "endTime": "23:59:59.999"}]
+
+    common_tasks = ["t_reg", "t_dec"]
+    critical_tasks = ["t_crit"]
+
+    common_pool = _pool("Common", "Common", common_workers, cal_id, common_tasks)
+    critical_pool = _pool("Critical", "Critical", critical_workers, cal_id, critical_tasks)
+    nc_pool = _pool("NonCritical", "NonCritical", nc_workers, cal_id, nc_task_ids)
+
+    def fix(rid: str, service: float) -> dict:
+        return {
+            "distribution_name": "fix",
+            "distribution_params": [{"value": service}],
+            "resource_id": rid,
+        }
+
+    trd: list[dict] = []
+    for tid in common_tasks:
+        trd.append({"task_id": tid,
+                    "resources": [fix(r["id"], common_service)
+                                  for r in common_pool["resource_list"]]})
+    for tid in critical_tasks:
+        trd.append({"task_id": tid,
+                    "resources": [fix(r["id"], critical_service)
+                                  for r in critical_pool["resource_list"]]})
+    for tid in nc_task_ids:
+        trd.append({"task_id": tid,
+                    "resources": [fix(r["id"], nc_service)
+                                  for r in nc_pool["resource_list"]]})
+
+    return {
+        "task_resource_distribution": trd,
+        "resource_calendars": [{"id": cal_id, "name": "24/7", "time_periods": periods}],
+        # Parallel (AND) gateways take all branches -> no 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": [common_pool, critical_pool, nc_pool],
+        "event_distribution": [],
+        "batch_processing": [],
+        "case_attributes": [],
+        "prioritisation_rules": [],
+    }
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Generate the parallel-automation synthetic dataset")
+    ap.add_argument("--nc-tasks", type=int, default=3,
+                    help="number of tasks in the non-critical chain")
+    ap.add_argument("--arrival-mean", type=float, default=30.0)
+    ap.add_argument("--critical-service", type=float, default=600.0)
+    ap.add_argument("--nc-service", type=float, default=120.0)
+    ap.add_argument("--out-dir", type=Path,
+                    default=Path(__file__).resolve().parents[1] / "samples" / "dev-samples")
+    ap.add_argument("--name", default="synthetic_parallel_auto")
+    args = ap.parse_args()
+
+    bpmn, params, nc_ids = build_model(
+        args.nc_tasks, arrival_mean=args.arrival_mean,
+        critical_service=args.critical_service, nc_service=args.nc_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")
+    print(f"wrote {bpmn_path}")
+    print(f"wrote {json_path}")
+    print(f"  non-critical tasks (automation targets): {nc_ids}")
+
+
+if __name__ == "__main__":
+    main()