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

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.

This file is a PyTorch checkpoint consumed by the CoRM-RAG codebase.

Install the code from GitHub and download the checkpoint:

Run evaluation by pointing CRITIC_PATH to the downloaded checkpoint:

For training-data construction, critic training, and end-to-end evaluation details, see the GitHub repository.

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.

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.