# AI use cases for Evo VLM (zzzz7788990213-ops/EvoVLM)

Repo: [zzzz7788990213-ops/EvoVLM](https://github.com/zzzz7788990213-ops/EvoVLM) | Use cases: 4 | Page: https://dedupedai.com/use-cases/zzzz7788990213-ops-evovlm/

EvoVLM is a research repo that uses LLM-guided evolutionary search to improve the inference programs for frozen Qwen3-VL-2B vision-language models on chart understanding.

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

- Verify Evo VLM's recorded results on a CPU: https://dedupedai.com/use-cases/zzzz7788990213-ops-evovlm/verify-recorded-evovlm-results/
- Smoke-test exported Evo VLM programs without weights: https://dedupedai.com/use-cases/zzzz7788990213-ops-evovlm/smoke-test-exported-evovlm-programs/
- Run the evolved Qwen3-VL-2B programs on your charts: https://dedupedai.com/use-cases/zzzz7788990213-ops-evovlm/run-evolved-programs-on-own-charts/
- Adapt the Evo VLM evolution loop to a new VLM task: https://dedupedai.com/use-cases/zzzz7788990213-ops-evovlm/adapt-evolution-loop-to-new-vlm-task/

## How to verify Evo VLM's recorded results on a CPU with Evo VLM

Repo: [zzzz7788990213-ops/EvoVLM](https://github.com/zzzz7788990213-ops/EvoVLM) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Replay the recorded evidence and run the final checks on a CPU, without model weights or new inference.

**Who it is for:** A research reviewer or lab member who needs to check the Dev128 and Speed32 numbers before citing Evo VLM.

**The problem:** Published accuracy and latency numbers are hard to trust without a way to confirm they match the underlying per-query evidence.

**The outcome:** The reviewer confirms that the recorded metrics recompute from the per-query evidence and that file hashes and checks pass.

### Steps

1. `git clone https://github.com/zzzz7788990213-ops/EvoVLM.git`
2. `cd EvoVLM/research/evolving-vlm-inference-main`
3. `python3 -B -m evaluation.portable_reproduce`
4. `bash reproduce.sh check`
5. Open the sealed metric trace at report/generated/report_number_trace.json and compare its values with the results table in the top-level README.

### Prompt to paste into your agent

```text
Clone https://github.com/zzzz7788990213-ops/EvoVLM, enter research/evolving-vlm-inference-main, run `python3 -B -m evaluation.portable_reproduce` and then `bash reproduce.sh check`. Report the pass or fail status of each command and whether the recomputed Dev128 correct counts match the results table in the README. Do not run GPU inference and do not use `bash reproduce.sh` without a mode.
```

### Caveat

These checks replay recorded evidence and do not rerun inference, so they cannot confirm that the numbers reproduce on new hardware, and the README says latency is specific to the recorded environment.

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/zzzz7788990213-ops-evovlm/verify-recorded-evovlm-results/

## How to smoke-test exported Evo VLM programs without weights with Evo VLM

Repo: [zzzz7788990213-ops/EvoVLM](https://github.com/zzzz7788990213-ops/EvoVLM) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Import the selected inference programs and check their interface on a CPU before any GPU setup.

**Who it is for:** A developer who wants to read the selected Qwen3-VL-2B inference programs and confirm they load before investing in a GPU environment.

**The problem:** Setting up model weights and GPU dependencies is costly, so a developer needs a cheap way to confirm the exported programs are usable.

**The outcome:** The developer sees the exported best_*.py programs import cleanly and their interface checks pass on CPU.

### Steps

1. `git clone https://github.com/zzzz7788990213-ops/EvoVLM.git`
2. `cd EvoVLM/research/evolving-vlm-inference-main`
3. `bash reproduce.sh smoke`
4. Open the selected best_*.py files in the same folder and read how each program builds its prompt, image preprocessing, and answer extraction.

### Prompt to paste into your agent

```text
Clone https://github.com/zzzz7788990213-ops/EvoVLM and enter research/evolving-vlm-inference-main. Run `bash reproduce.sh smoke` and list each best_*.py file with whether it imports and passes the interface check. Verify that no model weights were loaded and no inference ran.
```

### Caveat

The smoke check confirms that programs import and expose the expected interface, not that they answer charts correctly, which needs GPU inference with external model snapshots.

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/zzzz7788990213-ops-evovlm/smoke-test-exported-evovlm-programs/

## How to run the evolved Qwen3-VL-2B programs on your charts with Evo VLM

Repo: [zzzz7788990213-ops/EvoVLM](https://github.com/zzzz7788990213-ops/EvoVLM) | Difficulty: Advanced | Time: 3 hours | Basis: From the README

Set up the recorded GPU environment and model snapshots to run the selected programs on your own chart questions.

**Who it is for:** An ML engineer with a GPU who wants to test whether the evolved Instruct or Thinking program helps on chart questions in their own data.

**The problem:** The published results cover one benchmark subset, so the engineer needs to see how the selected programs behave on their own charts and latency budget.

**The outcome:** The engineer runs the selected programs with pinned Qwen3-VL-2B snapshots and records accuracy and latency on their own question set.

### Steps

1. Clone the repo and enter research/evolving-vlm-inference-main as in the quick start.
2. Read requirements.lock.txt in that folder and note that it lists Linux and CUDA-specific packages.
3. Follow the runtime guide README.md in that folder for pinned model downloads, local overrides, and offline behavior.
4. Run the example inference commands given in that runtime guide against your own chart questions.
5. Compare the output against the manual seed programs and the baseline on the same questions.

### Prompt to paste into your agent

```text
In the EvoVLM repo at research/evolving-vlm-inference-main, follow the runtime guide README.md to set up pinned Qwen3-VL-2B snapshots and run the Evolved Instruct program on <path to your chart question file>. Report the answers, median latency, and accuracy against your labels. Verify that the environment matches requirements.lock.txt before comparing numbers.
```

### Caveat

Model weights are not included in the repo and latency depends on the recorded hardware and software environment, so results on your setup may differ from the published Speed32 numbers.

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/zzzz7788990213-ops-evovlm/run-evolved-programs-on-own-charts/

## How to adapt the Evo VLM evolution loop to a new VLM task with Evo VLM

Repo: [zzzz7788990213-ops/EvoVLM](https://github.com/zzzz7788990213-ops/EvoVLM) | Difficulty: Advanced | Time: 1 hour 30 minutes | Basis: From the README

Reuse the typed mutation catalog, evaluation protocols, and Pareto selection for a different vision-language task.

**Who it is for:** A researcher who wants to search inference programs for another vision-language task using the same mutate, evaluate, and select pattern.

**The problem:** Hand-tuning prompts, image preprocessing, and generation budgets for each task is slow, and the researcher needs a constrained search instead of unrestricted code generation.

**The outcome:** The researcher has a working evolution setup for a new task, with mutations, evaluation subsets, and Pareto-selected programs tied to recorded evidence.

### Steps

1. Clone the repo and read the evolution/ folder to see the typed mutations, local provider, search, and selection code.
2. Review evaluation/ to see how protocols, identity checks, and semantic verification are defined.
3. Replace the CharXiv-specific data manifests in data/ with your own task subsets.
4. Adjust the mutation catalog to cover prompts, image preprocessing, generation budgets, and answer extraction for your task.
5. Run the search on small search and selection subsets, then keep the Pareto-selected programs and their evidence records.

### Prompt to paste into your agent

```text
Clone https://github.com/zzzz7788990213-ops/EvoVLM and read the evolution/ and evaluation/ folders in research/evolving-vlm-inference-main. Propose a plan to swap in <path to your task manifest> while keeping the typed mutation catalog and Pareto selection. Verify that every proposed mutation is listed in the existing catalog and does not add new code generation.
```

### Caveat

The README describes a deliberately small recorded search of two generations with at most six children per population, so a new task will need its own evaluation budget and cannot assume the published results transfer.

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/zzzz7788990213-ops-evovlm/adapt-evolution-loop-to-new-vlm-task/