> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/more-details/digital-twin/version-history.md).

# Version History

A [Digital Twin](/more-details/digital-twin.md) is not a single fixed model. Every time you train it — on a new Data View, a changed configuration, or a refreshed dataset — RootCause keeps the result as a numbered **version**. Each version bundles a configuration and the model trained from it, so you can compare versions, return to an earlier one, and see how the twin has evolved.

One version is **current** at any time: the one your [simulations](/more-details/digital-twin/simulation-types.md) run against. The others stay on record, ready to be switched back to.

***

## The Version History view

Open a twin and, from its Home, open the **Versions** card. The page lists every version with its status, creation time, type, and model fit, with the current version marked; click a row to see its details, what changed compared to the version before it, and to switch to it.

<figure><img src="/files/jQrrKfd53cIQIPIe7tPO" alt="The Version History page listing a single version, v1.0.0, as the Current trained Static Model with a 69.6% model fit"><figcaption><p>Version History. Here v1.0.0 is the only version, Trained and Current, with a 69.6% model fit; select a row to inspect a version or switch back to it.</p></figcaption></figure>

Each version carries a **status**:

* **Current** — the version simulations run against. Exactly one version is current at a time.
* **Trained** — a completed version, ready to be switched to.
* **Failed** — training did not complete. The version is kept on record but cannot be used; retrain or delete it.

A version mid-training shows its progress in place until it resolves to Trained or Failed.

***

## How versions are numbered

Versions follow a three-part **major.minor.patch** scheme, and the kind of change you make decides which number advances:

* **Major** version (e.g. 1.0.0 → 2.0.0) — the **data** changed: a different Data View, a change in the underlying data itself, or a different set of included fields. A model built on different data is a different model, so it earns a new major number.
* **Minor** version (e.g. 1.0.0 → 1.1.0) — a configuration-only change: algorithm versions, training options, variable roles, prior knowledge, or ordering constraints. A **Re-discover & train** run also advances the minor version even when the configuration is unchanged — a fresh discovery pass can produce a different graph, so it is more than a plain retrain.
* **Patch** version (e.g. 1.0.0 → 1.0.1) — a plain **Retrain model** with no data or configuration changes at all.

See [Configuration for Existing Twin › What happens when you run](/more-details/digital-twin/configuration.md#what-happens-when-you-run).

***

## Creating a new version

From a twin's Config tab, click **Modify model**. This opens the twin builder — the same wizard used to [create the twin in the first place](/user-guide/creating-digital-twin.md) — seeded with the current version's configuration: data, type, fields, roles, prior knowledge, training options, and algorithm versions.

<figure><img src="/files/8qhaO1pkOzldq71qnfCt" alt="The Modify model screen: a choice between Re-discover with new priors and Edit the graph &#x26; retrain, the data inherited from v1.0.0, and the domain-knowledge step seeded with the current model"><figcaption><p>Modifying a twin. The builder opens seeded with the current version's configuration; the method chosen in step 1 decides how the next version is built.</p></figcaption></figure>

The builder's final step launches the work with one of two actions, depending on the method you chose:

* **Re-discover & train** — re-run causal discovery with your updated priors, then train. Use this when the data or your prior knowledge changed.
* **Retrain model** — retrain directly on your edited graph, with no discovery step. Use this when you adjusted the graph by hand.

***

## Training and the result

While a version trains, the twin shows a live status banner — **Discovering relationships…**, then **Training model…** — and a list of processing stages — preparing data, causal discovery, then, when the options are enabled, confounder modeling and equation discovery, and finally building the model. The configuration cannot be edited until training finishes.

<figure><img src="/files/lbbEXPkINVufMZrvexAV" alt="A twin mid-training: the Training Model stage fitting equations node by node, with the stage progress bar and the Versions card showing v1.0.0 as Training"><figcaption><p>A version in training. Each stage reports progress; the run can be cancelled.</p></figcaption></figure>

On success the banner turns to **Model trained & ready**, the version's status becomes Trained (and Current if it is the first), and simulations can run against it. The Config tab now shows the version's configuration, and **Modify model** starts the next iteration.

<figure><img src="/files/CCJ5zAG1UhA84Pp1xuIw" alt="A successfully trained twin: a Model trained &#x26; ready banner, the causal graph, and cards reporting v1.0.0 as Trained and the model fit score"><figcaption><p>A finished version. The model fit score reports how well this version fits the data — see Reviewing Model Quality.</p></figcaption></figure>

***

## Seeing every version at a glance

The Digital Twins list shows each twin with its training status and owner — the fastest way to scan what exists across a workspace. Per-version detail lives in each twin's **Versions** page.

<figure><img src="/files/nJsYET3HIQtU5lx1TyKa" alt="The Digital Twins list showing a trained twin with its status and owner"><figcaption><p>The management list. Open a twin's Versions card for the per-version breakdown.</p></figcaption></figure>

To compare two versions side by side — their graphs, metrics, and relationships — use [Model Comparison](/more-details/digital-twin/model-comparison.md).

***

## Other Working with a Digital Twin pages

* [Exploring the Causal Model](/more-details/digital-twin/exploring-causal-model.md) — graph layouts and variable details.
* [Reviewing Model Quality](/more-details/digital-twin/model-quality.md) — predictive accuracy and per-variable metrics.
* [Inspecting Causal Relationships](/more-details/digital-twin/causal-relationships.md) — individual edges and their statistics.
* [Configuration for Existing Twin](/more-details/digital-twin/configuration.md) — the fields behind every version.

See [Digital Twin overview](/more-details/digital-twin.md) — general overview.