# SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understanding

arXiv: https://arxiv.org/abs/2601.12805 · HTML: https://mlsanity.com/paper/2601.12805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 82 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 29 of 29)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery](https://mlsanity.com/paper/2405.09783/) (2024)
- [Benchmarking large language models for biomedical natural language processing applications and recommendations](https://mlsanity.com/paper/2305.16326/) (2023)
- [BioRAG: A RAG-LLM Framework for Biological Question Reasoning](https://mlsanity.com/paper/2408.01107/) (2024)
- [Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges](https://mlsanity.com/paper/2412.11427/) (2024)
- [SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://mlsanity.com/paper/2401.07950/) (2024)
- [The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review](https://mlsanity.com/paper/2409.04600/) (2024)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification](https://mlsanity.com/paper/2506.04592/) (2025)
- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification](https://mlsanity.com/paper/2109.06661/) (2021)
- [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2309.03251/) (2023)
- [Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification](https://mlsanity.com/paper/2209.13519/) (2022)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training](https://mlsanity.com/paper/2501.15108/) (2025)
- [FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens](https://mlsanity.com/paper/2510.15729/) (2025)
- [GeneSUM: Large Language Model-based Gene Summary Extraction](https://mlsanity.com/paper/2412.18154/) (2024)
- [Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2504.17356/) (2025)
- [Reinforcement Learning-based Feature Generation Algorithm for Scientific Data](https://mlsanity.com/paper/2507.03498/) (2025)

## Built on by (most cited first, 1 of 1)

- [Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale](https://mlsanity.com/paper/2608.19625/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. However, their ability to reliably reason from gene-level knowledge to functional understanding, a core requirement for knowledge-enhanced cell atlas interpretation, remains largely underexplored. To address this gap, we introduce SciHorizon-GENE, a large-scale gene-centric benchmark constructed from authoritative biological databases. The benchmark integrates curated knowledge for over 190K human genes and comprises more than 540K questions covering diverse gene-to-function reasoning scenarios relevant to cell type annotation, functional interpretation, and mechanism-oriented analysis. Motivated by behavioral patterns observed in preliminary examinations, SciHorizon-GENE evaluates LLMs along four biologically critical perspectives: research attention sensitivity, hallucination tendency, answer completeness, and literature influence, explicitly targeting failure modes that limit the safe adoption of LLMs in biological interpretation pipelines. We systematically evaluate a wide range of state-of-the-art general-purpose and biomedical LLMs, revealing substantial heterogeneity in gene-level reasoning capabilities and persistent challenges in generating faithful, complete, and literature-grounded functional interpretations. Our benchmark establishes a systematic foundation for analyzing LLM behavior at the gene scale and offers insights for model selection and development, with direct relevance to knowledge-enhanced biological interpretation.