# AI use cases for CLSR MDia (Plab-Lead/CLSR_MDia)

Repo: [Plab-Lead/CLSR_MDia](https://github.com/Plab-Lead/CLSR_MDia) | Use cases: 3 | Page: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/

MDia is a Python research framework for creating, evolving, profiling, and routing reusable machine reasoning dialects between language models.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Run the offline MDia lifecycle on fixtures: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/run-offline-dialect-pipeline/
- Reproduce the released MDia benchmark tables offline: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/reproduce-mdia-benchmark-tables/
- Build a dialect routing study on your own task: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/build-custom-dialect-routing-study/

## How to run the offline MDia lifecycle on fixtures with CLSR MDia

Repo: [Plab-Lead/CLSR_MDia](https://github.com/Plab-Lead/CLSR_MDia) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Run the deterministic toy pipeline to see every MDia stage without API keys or model downloads.

**Who it is for:** A researcher or developer new to MDia who wants to see the full collect-to-report lifecycle before using real models or data.

**The problem:** Reading about dialect cards, frozen banks, and route plans is hard without seeing the artifacts they produce.

**The outcome:** A versioned run directory with split manifests, dialect generations, profiles, route plans, predictions, token accounting, and a reproducibility report.

### Steps

1. Clone the repository at https://github.com/Plab-Lead/CLSR_MDia and open a terminal in its folder.
2. `python -m venv .venv`
3. `source .venv/bin/activate`
4. `python -m pip install -e .`
5. `mdia pipeline --config configs/toy_mdia.yaml`

### Prompt to paste into your agent

```text
In the CLSR_MDia repository, create a virtual environment, install the package with `python -m pip install -e .`, and run `mdia pipeline --config configs/toy_mdia.yaml`. Report the run directory and confirm that manifest.json, report.md, and selection/frozen_dialect_bank.json exist under it.
```

### Caveat

The toy pipeline uses redistributable fixtures to exercise the lifecycle; its outputs show the mechanics and are not the benchmark results reported in the paper.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/run-offline-dialect-pipeline/

## How to reproduce the released MDia benchmark tables offline with CLSR MDia

Repo: [Plab-Lead/CLSR_MDia](https://github.com/Plab-Lead/CLSR_MDia) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Verify the release package and regenerate the tables and figures from saved scalar records.

**Who it is for:** A reviewer, auditor, or researcher who needs to check the reported MDia accuracy and generated-token numbers without rerunning model inference.

**The problem:** Published accuracy and token savings are hard to trust unless the numbers can be recomputed from the source records.

**The outcome:** A fresh output directory with recomputed aggregates, paired intervals, frontiers, a VALIDATION.json file, and exported PNG and SVG figures.

### Steps

1. Clone the repository and open a terminal in its folder.
2. `python3 -m venv .venv-evidence`
3. `source .venv-evidence/bin/activate`
4. `python -m pip install -r requirements-evidence.lock.txt`
5. `python scripts/verify_mdia_release.py`
6. `python scripts/reproduce_mdia_release.py --output runs/mdia-results-20261005`

### Prompt to paste into your agent

```text
In the CLSR_MDia repository, set up the .venv-evidence environment from requirements-evidence.lock.txt, run scripts/verify_mdia_release.py, then run scripts/reproduce_mdia_release.py with a fresh output directory. Report whether all checks pass by reading VALIDATION.json, and list the exported table and figure files.
```

### Caveat

This command recomputes analyses from released scalar records and runs offline; it does not run new model inference, which needs the original benchmark inputs and providers.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/reproduce-mdia-benchmark-tables/

## How to build a dialect routing study on your own task with CLSR MDia

Repo: [Plab-Lead/CLSR_MDia](https://github.com/Plab-Lead/CLSR_MDia) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Adapt the MDia lifecycle to your own dataset and model providers to test budgeted dialect routing.

**Who it is for:** An ML engineer or researcher with a task and access to several LLMs who wants to know whether compact reasoning dialects can cut tokens without losing accuracy.

**The problem:** Shorter reasoning traces can drop state a listener needs, so savings must be checked per speaker, listener, and task.

**The outcome:** A profiled dialect bank and budgeted route plans for your task, with accuracy, token, and parse-risk results recorded per run.

### Steps

1. Clone the repository and complete the toy pipeline setup with `python -m pip install -e .`.
2. Read the extension interfaces in docs/extending.md and write dataset and provider adapters for your task and models.
3. Copy configs/toy_mdia.yaml to a new config file and point it at your adapters and held-out splits. (inferred, check the README)
4. `mdia collect --config <your-config.yaml>` (inferred, check the README)
5. `mdia create --config <your-config.yaml>` (inferred, check the README)
6. `mdia evolve --config <your-config.yaml>` (inferred, check the README)
7. `mdia profile --config <your-config.yaml>` (inferred, check the README)

### Prompt to paste into your agent

```text
Using the CLSR_MDia repository, read docs/extending.md and implement a dataset and provider adapter for <target task and model>. Run the mdia collect, create, evolve, and profile stages with a new config file, then report the transfer profiles and confirm the frozen dialect bank was written before any held-out execution.
```

### Caveat

The README says the frozen dialects and route policies should be selected from validation evidence before held-out execution, so the study must keep splits separate or the results will not be valid.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/plab-lead-clsr-mdia/build-custom-dialect-routing-study/