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# DiscoveryBench with OpenHands
[DiscoveryBench](https://github.com/allenai/discoverybench/) [(Paper)](https://arxiv.org/abs/2407.01725v1) contains 264 tasks collected across 6 diverse domains, such as biology, economics, and sociology. It incorporates discovery workflows from published papers to approximate the real-world challenges faced by researchers.
<p align="center">
<a href="[https://github.com/allenai/discoverybench](https://github.com/allenai/discoverybench)">
<img src="https://raw.githubusercontent.com/allenai/discoverybench/refs/heads/main/assets/discoverybench-openhands-teaser.png" width="100%" alt="DiscoveryBench Background" />
</a>
</p>
## Setup Environment and LLM Configuration
1. Please follow instructions mentioned [here](https://github.com/openlocus/OpenHands/blob/discoverybench-openhands-integration/evaluation/README.md#setup) to setup OpenHands development environment and LLMs locally
2. Execute the bash script to start DiscoveryBench Evaluation
```
./evaluation/benchmarks/discoverybench/scripts/run_infer.sh [YOUR MODEL CONFIG]
```
Replace `[YOUR MODEL CONFIG]` with any model the model that you have set up in `config.toml`
## Run Inference on DiscoveryBench Instances
When the `run_infer.sh` script is started, it will automatically pull the latest DiscoveryBench instances & set up the agent environment. The OpenHands agent is invoked to process the task within this environment, producing a hypothesis. We then evaluate it against the “gold” hypothesis provided by DiscoveryBench. The evaluation result, along with the agent chat history is logged to `output.jsonl` under `evaluation_outputs`.
```
./evaluation/benchmarks/discoverybench/scripts/run_infer.sh [MODEL_CONFIG] [GIT_COMMIT] [AGENT] [EVAL_LIMIT] [NUM_WORKERS]
```
- `MODEL_CONFIG`: Name of the model you want to evaluate with
- `GIT_COMMIT`: This should be the git commit hash or release tag for OpenHands, e.g., HEAD or a specific tag like 0.6.2.
- `AGENT`: Use CoderActAgent, right now it only supports that.
- `EVAL_LIMIT`: Number of samples to evaluate.
- `NUM_WORKERS`: Number of workers to parallelize the evaluation process.