Revisit: synthetic multi-stream dataset generator

## Background

In feature [001-ms-tcm-impl](../tree/main/specs/001-ms-tcm-impl), the original plan included a seeded synthetic dataset generator (per-storyline cluster-center feature vectors, configurable K/events/m/trials, bit-exact Parquet output across macOS/Ubuntu/Windows). After `/speckit.clarify`, we decided to replace the synthetic worked example with a real dataset (the category condition of Manning et al. 2023's FRFR study) so model fitting could target real behavior instead of simulated data.

## Why we might want to come back to this

- The FRFR dataset is a single real experiment with K=4 categories per list. It cannot exercise MS-TCM's distinguishing predictions at parameter regimes that aren't present in the data (e.g., large K, varied m, the §4.4 numerical regime at β_G = β_S = 0.5, w_G = 0.2, w_S = 0.8, m = 3).
- A synthetic generator lets us run the §4.4 numerical anchor end-to-end (as an integration test, not just a unit test of the equations).
- Fast offline CI without network dependencies.
- Parameter-recovery experiments: generate data at known parameters, fit, verify the MLE recovers them — the single best diagnostic of whether the fitter works.

## Proposed scope (when we revisit)

- Seeded `numpy.random.Generator(PCG64)` based generator with configurable K, events/storyline, bridge m-range, trials/condition, feature dimensionality, jitter σ.
- Output in the same Parquet schema used for FRFR-category; validation routine accepts both with no special-casing.
- Unit tests pinning the §4.4 numerical anchor (composite similarity 0.866 vs 0.806 at β_G = β_S = 0.5, w_G = 0.2, w_S = 0.8, m = 3) to four decimals.
- Parameter-recovery test: generate at a known (β_G, β_S, w_G, γ, λ), fit, assert recovery within two bootstrap SEs.

## Design artifacts already written

The spec/plan/tasks in `specs/001-ms-tcm-impl/` (before the FRFR switch) contain detailed decisions on Parquet conventions, bit-exactness across platforms (zstd level 1, no dictionary, no statistics, sorted rows), and the §4.4 numerical anchor. When we revisit this issue, those decisions can be lifted with minimal rework.

## Priority

Follow-on. Not blocking for the current FRFR-category feature.