Nemotron CORTEXA

Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity

This is the official codebase for the ICML 2025 paper: [Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity](https://openreview.net/forum?id=k6p8UKRdH7). Please see this [blogpost](https://research.nvidia.com/labs/adlr/cortexa/) for a high-level overview.

Requirements:

• Python 3.11 or higher

Installation

Download the repo and install the repo with

pip install -e .

Localization Stage

There are two steps in code localization: file localization and entity localization.

File Localization

We have developed, NV-EmbedCode, a code embedding model that specializes in mapping bug descriptions to faulty codes. The model is available on [HuggingFace](https://huggingface.co/nvidia/NV-EmbedCode-7b-v1) and as a [NIM]((https://build.nvidia.com/nvidia/nv-embedcode-7b-v1)).

The following command runs the file localization using [NV-EmbedCode's NIM](https://build.nvidia.com/nvidia/nv-embedcode-7b-v1):

python -m cortexa.retrieval.embed_retrieve \
        --model_name nvidia/nv-embedcode-7b-v1 \
        --base_url https://integrate.api.nvidia.com/v1  \
        --log_dir ./logs \
        --repo_playground ./repos \
        --batch_size 16 \
        --max_length 450 \
        --dataset_name_or_path princeton-nlp/SWE-bench_Verified \
        --query_type llmsummary \
        --instance_id astropy__astropy-12907

The instance_id argument accepts a comma-separated list of instances to run. To run the entire benchmark, omit the argument entirely.

You can measure the accuracy of retrieval by running:

python -m cortexa.retrieval.file_retrieval_eval \
        --log_dir ./logs \
        --dataset_name_or_path princeton-nlp/SWE-bench_Verified \
        --query_type llmsummary

For SWE-bench Lite and Verified sets, we generated file localization results and mad