Title: BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Biological Reasoning

DNA Foundation Models

Large Language Models (LLMs)

Reinforcement Learning

Multimodal
Abstract: Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DNA foundation model with a large language model (LLM), enabling the LLM to directly interpret and reason over genomic information. Through supervised fine-tuning and reinforcement learning, BioReason learns to produce logical, biologically coherent deductions. It achieves major performance gains, boosting KEGG-based disease pathway prediction accuracy from 86% to 98% and improving variant effect prediction by an average of 15% over strong baselines. BioReason can reason over unseen biological entities and explain its decisions step by step, offering a transformative framework for interpretable, mechanistic AI in biology. All data, code, and checkpoints are available at [https://github.com/bowang-lab/BioReason](https://github.com/bowang-lab/BioReason).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents BioReason, a model architecture that integrates a DNA foundation model with a LLM to improve prediction accuracy and enable natural language reasoning for each prediction. The training process combines supervised fine-tuning with reinforcement learning to encourage multi-step reasoning. As part of this work, the authors also developed and curated new benchmark datasets for biological reasoning. BioReason is evaluated using two different DNA foundation models and two LLMs across these datasets and shows marked accuracy improvements as well as useful step-by-step reasoning.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Clarity: This is a very well-written paper—clear, well-organized, and easy to follow. The motivations are explicitly laid out, arguments are well supported, and the structure flows naturally, almost telling a story. The writing is polished, concise, and approachable. Thank you for a very enjoyable read.\n- Significance/Originality/Quality: The introduction of curated datasets and benchmarking for DNA LLM reasoning represents a valuable contribution to the field.\n- Quality: The inclusion of stratification to ensure balanced disease representation is a thoughtful design choice.\n- Quality/Significance: The modular design, in which $\\mathcal{f}\\_{\\text{LLM}}$ and $\\mathcal{f}\\_{\\text{DNA}}$ can adopt different backbones allows for flexibility in model size and the potential for performance improvements as new architectures emerge.\n- Quality: The pairing of two SOTA $\\mathcal{f}\\_{\\text{LLM}}$ and two SOTA $\\mathcal{f}\\_{\\text{DNA}}$ models adds to the rigor to the evaluation, with results presented clearly using standard evaluation metrics.\n\nWeakness:\n- The term interpretability is overloaded in the field, so clearly defining it at the outset would help set expectations and clarify the nature of the claims. For instance, the approach does not offer inherent interpretability. Moreover, the LLM-generated explanations may not accurately reflect the true reasoning behind the model’s predictions—LLMs are known to produce plausible-sounding justifications for incorrect outputs and nonsensical rationales for correct ones. Without clearer framing at the outset, the scope and limitations of interpretability in this work only become apparent after a close and thorough reading of the paper.\n\nQUESTIONS:\n1. Line 131: What are the effects of truncation?\n2. Suggestion: The notation is clear with a detailed read, but including labels for major components (eg $\\textbf{E}^\\prime_{\\text{DNA},k}$ vs $\\textbf{E}^\\prime_{\\text{DNA}}$ in Figure 1 could help ease the comprehension, especially for the steps described in 3.3. “Stacking” along which dimension? \n3. In section 4.2, what was the reasoning for the chosen data split by chromosome? \n4. What was the performance like across different disease representations? \n5. How do models of similar size (ie $\\mathcal{f}\\_{LargeLLM} \\approx \\text{BioReason}(\\mathcal{f}_{SmallLLM}$ , $\\mathcal{f}\\_{DNA})$ compare? Could improved performance just be a factor of a larger joint model? \n6. Suggestion: Reference Figure 3 in section 5.5, perhaps at the end of the first sentence.\n\nLIMITATIONS:\n1. Since GPT was used in the dataset creation process, there is a non-negligible risk of errors or hallucinations in the generated data. This introduces potential noise that could affect model training or evaluation, and it would be valuable to include an error analysis or quality check to quantify this risk.\n2. A user study could further explore the interpretability claims—specifically, by evaluating whether domain experts find BioReason’s outputs more trustworthy, understandable, or useful compared to a typical classifier. If experts tend to agree with BioReason’s explanations, it would also be important to examine how they respond when the model is wrong—do the explanations still seem convincing, and could they mislead users?", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper introduces BioReason, a DNA-LLM model that can reason about the effects of sequence mutations on downstream pathways and diseases. It merges two types of language models: genomic language models (such as EVO2 and NT) and large language models (such as Qwen3). Each types of these models are good for their own designed tasks. The gLMs are great at understanding DNA and its syntaxes and LLM are great for natural language. There is a disconnect between these two family of models, and BioReason tries to bridge the gap and make a native model that gets input from both DNA sequences and text query. The authors have benchmarked it on KEGG-derived biological reasoning datasets and variant effect prediction tasks and shown that it outperforms any other model (gLM and LLM) individually. This clearly shows a potential of merging these two types of models in order to boost biological reasoning.

STRENGTHS AND WEAKNESSES:
Strengths:

- Merging gLMs and LLMs is an innovative and interesting idea to enhance reasoning.

- The benchmarks are convincing and clearly show the advantages of such hybrid models. 

- The multi-step reasoning associating variants to downstream pathways and diseases is very helpful and has great use cases.

Weaknesses:

- The only gLMs used here is EVO and NT which are unsupervised models. It would be great to also show the performance of supervised models such as Enformer, Borzoi, and alphaGenome.

- Other than sequence to disease reasoning, it would be great to show how BioReason can handle sequence to gene expression and chromatin accessibility as well.

QUESTIONS:
- Why have you only used unsupervised gLMs? I wonder what would be the performance of a supervised model like Borzoi and alphaGenome rather than EVO2.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces BIOREASON, a groundbreaking multimodal AI framework that integrates a DNA foundation model with a large language model (LLM) to enable deep, interpretable biological reasoning. Unlike traditional DNA models that act as black boxes, BIOREASON allows the LLM to directly process genomic sequences and generate step-by-step, biologically coherent explanations.\n\nKey highlights include:\n\n1. A novel architecture that fuses genomic and textual data for multi-step reasoning.\n\n2. Training with supervised fine-tuning and reinforcement learning to enhance logical deduction.\n\n3. Superior performance on tasks like disease pathway prediction and variant effect analysis, with up to 15% improvement over single-modality baselines.\n\n4. Transparent reasoning traces that explain predictions in a biologically meaningful way.\n\nBIOREASON represents a major step forward in AI-driven biology, offering both accuracy and interpretability for complex genomic analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Multimodal Innovation: BIOREASON is the first architecture to deeply integrate a DNA foundation model with a large language model (LLM), enabling direct reasoning over genomic sequences.\n\n2. Interpretable Reasoning: Unlike traditional “black-box” models, BIOREASON generates step-by-step, biologically coherent explanations using special tokens like <think>, enhancing transparency and scientific trust.\n\n3. Strong Empirical Performance: Achieves up to 97.24% accuracy and 86.30% F1-score on KEGG-based reasoning tasks, outperforming both DNA-only and LLM-only baselines by significant margins.\n\n4. Benchmark Contributions: Introduces new biological reasoning benchmarks, including a curated KEGG-derived dataset with mechanistic variant-to-disease pathways.\n\n5. Reinforcement Learning Enhancement: Uses Group Relative Policy Optimization (GRPO) to further refine reasoning quality beyond supervised fine-tuning.\n\n6. Case Study Validation: Demonstrates real-world utility by accurately predicting disease outcomes (e.g., ALS) with detailed mechanistic justifications.\n\n7. Generalization Across Tasks: Performs well on both mechanistic reasoning and variant effect prediction, showing versatility across biological domains.\n\nWeaknesses\n1. Dataset Bias: Heavy reliance on curated datasets like KEGG may introduce biases and limit generalizability to under-characterized genomic regions.\n\n2. Computational Overhead: Integrating long DNA sequences and applying reinforcement learning increases training and inference time, which may hinder scalability.\n\n3. Lack of Uncertainty Quantification: The model does not currently provide confidence estimates, which is a limitation for clinical or high-stakes applications.\n\n4. Implementation Complexity: The architecture and training pipeline (e.g., multimodal input fusion, GRPO) are technically complex and may be difficult to reproduce or deploy without significant resources.\n\n5. Limited Modal Diversity: Currently focused on DNA; future extensions to RNA, proteins, or multi-omics data are needed to broaden its biological utility.\n\nQUESTIONS:\nrefer to Strengths And Weaknesses\n\nLIMITATIONS:\nrefer to Strengths And Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}