OmicsBench: A Benchmark for Distinguishing Multi-Omics Reasoning from Shortcut Learning in Large Models

OmicsBench is a benchmark developed by the SeedScientist team, focusing on evaluating the genuine reasoning ability of large language models on multi-omics data rather than relying on surface pattern matching. It aims to help researchers identify whether models have true biological reasoning capabilities and avoid scientific research misdirection caused by pseudo-reasoning. This benchmark detects shortcut learning through strategies such as adversarial sample design, multi-omics integration tasks, and interpretability evaluation, which is of great significance to the biomedical AI field.

As large language models continue to improve their capabilities in general tasks, researchers have begun applying them to the biomedical field, especially multi-omics data analysis. Multi-omics involves the integration of biological data from multiple layers such as genomics, transcriptomics, proteomics, and metabolomics, which places extremely high demands on the reasoning ability of models. However, a long-standing problem plaguing researchers is: Are large models truly performing scientific reasoning, or have they merely learned to use surface patterns in data (Shortcut Learning) to give seemingly correct answers? This "pseudo-reasoning" phenomenon is particularly dangerous in the biomedical field, as incorrect conclusions may lead to serious scientific research misdirection.

OmicsBench is a benchmark developed by the SeedScientist team, specifically designed to distinguish between the genuine reasoning ability and shortcut learning behavior of large language models in multi-omics tasks. The core goal of this project is to establish a rigorous testing framework to reveal whether models truly understand biological concepts or merely rely on statistical correlations in training data. The project repository provides complete evaluation code and datasets, supporting researchers to reproduce results and test their own models. Through carefully designed test cases, OmicsBench can identify models that perform well on the surface but actually lack true understanding capabilities.

OmicsBench uses multiple strategies to distinguish between genuine reasoning and shortcut learning:

Adversarial Sample Design

The evaluation set includes specially designed adversarial samples that maintain biological rationality while altering the surface features that models may rely on. If a model only depends on shortcuts, its performance will drop significantly on these samples.

Multi-Omics Integration Tasks

True biological understanding requires integrating information from different omics layers. OmicsBench designs complex tasks that require cross-omics reasoning, testing whether models can establish causal relationships between genes, proteins, and metabolites.

Interpretability Evaluation

In addition to the correctness of the final answer, OmicsBench also focuses on the model's reasoning process. By analyzing the model's intermediate outputs and explanations, we can determine whether it reasons based on correct biological principles.

OmicsBench is implemented based on Python with a clear code structure that is easy to extend. Users can use it through the following steps:

1. Clone the repository and install dependencies
2. Prepare the large model API to be evaluated or local deployment
3. Run the evaluation script to get a detailed report
4. Analyze the indicators in the report to identify the model's strengths and weaknesses

The evaluation results not only include overall accuracy but also provide fine-grained error analysis to help developers locate specific defects of the model.

The launch of OmicsBench is of great significance to the biomedical AI field:

For model developers, it provides a rigorous testing standard to help identify and improve the model's reasoning ability, rather than just pursuing superficial benchmark scores.

For biomedical researchers, it provides a screening tool to help determine whether a large model is suitable for real scientific research tasks. In key applications such as disease diagnosis and drug discovery, ensuring that the model has true understanding capabilities is crucial.

For the entire field, OmicsBench promotes the transformation of large model evaluation from "score competition" to "ability understanding", encouraging researchers to pay more attention to the model's internal mechanisms rather than external performance.

Although OmicsBench has made important progress in detecting shortcut learning, there are still some limitations. The current evaluation set may not cover all types of biological reasoning tasks, and the design of adversarial samples needs to be continuously updated to cope with the improvement of model capabilities.

Future development directions may include: expanding to more omics types (such as epigenomics, single-cell sequencing data), introducing temporal dynamic analysis, and developing dedicated evaluation subsets for specific disease fields.

OmicsBench represents an important progress in the field of large model evaluation—shifting from focusing on "what can be done" to "how it is done". In fields like biomedicine that require extremely high accuracy, this distinction is particularly important.

For developers who want to apply large models to scientific research, OmicsBench provides a valuable tool to help establish a true understanding of model capabilities and avoid being misled by superficial high performance. As large models are increasingly applied in the scientific field, similar "de-shortcutting" evaluation benchmarks will become key infrastructure to ensure AI reliability.