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library_name: pytorch
base_model: microsoft/deberta-v3-large
tags:
- retrieval-augmented-generation
- reranking
- robust-retrieval
- evidence-critic
- corm-rag
- arxiv:2605.01302
---
# CoRM-RAG Evidence Critic
This repository hosts the released Evidence Critic checkpoint for **CoRM-RAG**:
**Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation**
Peiyang Liu, Qiang Yan, Ziqiang Cui, Di Liang, Xi Wang, Wei Ye
arXiv: <https://arxiv.org/abs/2605.01302>
Code: <https://github.com/PeiYangLiu/CoRM-RAG>
## Model Description
CoRM-RAG aligns retrieval with decision safety rather than semantic similarity alone. The Evidence Critic is a lightweight reranking model trained to score whether a document remains useful under cognitively biased query perturbations, such as false premises, confirmation bias, and distracting assumptions.
The released checkpoint uses a `microsoft/deberta-v3-large` backbone and outputs a robustness score for a `(query, document)` pair. It is intended to be used inside the CoRM-RAG pipeline for evidence reranking and risk-aware retrieval.
## Files
```text
critic-v12-mixed/checkpoint-latest/state.pt
```
This file is a PyTorch checkpoint consumed by the CoRM-RAG codebase.
## Usage
Install the code from GitHub and download the checkpoint:
```bash
git clone https://github.com/PeiYangLiu/CoRM-RAG.git
cd CoRM-RAG
huggingface-cli download PeiyangLiu/CoRM-RAG \
critic-v12-mixed/checkpoint-latest/state.pt \
--local-dir checkpoints/hf
```
Run evaluation by pointing `CRITIC_PATH` to the downloaded checkpoint:
```bash
CRITIC_PATH=checkpoints/hf/critic-v12-mixed/checkpoint-latest/state.pt bash src/run_eval.sh
```
For training-data construction, critic training, and end-to-end evaluation details, see the GitHub repository.
## Intended Use
This checkpoint is intended for research on robust retrieval-augmented generation, evidence reranking, and risk-aware retrieval under biased or perturbed user queries. It is not a standalone generative model.
## Limitations
The critic score reflects robustness patterns learned from the CoRM-RAG training pipeline and should be interpreted within that retrieval setting. Performance may vary across domains, corpora, retrievers, and perturbation distributions.
## Citation
```bibtex
@misc{liu2026cormrag,
title={Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation},
author={Peiyang Liu and Qiang Yan and Ziqiang Cui and Di Liang and Xi Wang and Wei Ye},
year={2026},
eprint={2605.01302},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2605.01302}
}
```
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