Author: Denis Avetisyan
A new benchmark assesses how well artificial intelligence can understand and predict biomolecular relationships from scientific literature.
Researchers introduce BIOME-Bench, a resource for evaluating large language models on biomolecular interaction inference and multi-omics pathway mechanism elucidation.
Despite advances in multi-omics data analysis, interpreting complex biological changes remains challenging due to limitations in existing pathway resources and evaluation benchmarks. To address this, we introduce BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature, a novel, literature-grounded benchmark designed to rigorously evaluate large language models’ ability to infer biomolecular interactions and elucidate pathway mechanisms. Our results reveal substantial deficiencies in current models’ capacity to accurately discern nuanced biological relationships and generate robust, faithful mechanistic explanations at the pathway level. Can these findings catalyze the development of more effective AI systems for reasoning about and ultimately understanding the intricacies of biological systems?
The Biology of Complexity: A Modeling Quagmire
Biological systems present a unique modeling challenge due to their inherent complexity and deeply interconnected nature. Unlike many engineered systems with clearly defined components and linear pathways, cellular processes involve thousands of interacting molecules – proteins, RNAs, metabolites – forming intricate networks with feedback loops and redundancies. Current computational approaches, often relying on simplified representations or focusing on isolated pathways, struggle to capture this holistic behavior. This limitation hinders a true mechanistic understanding – the ability to predict how changes in one part of the system will propagate and affect others. Consequently, models frequently fail to accurately reflect biological reality, particularly when confronted with novel conditions or perturbations, and often serve as descriptive rather than predictive tools. Addressing this requires developing modeling frameworks capable of representing and reasoning about the full spectrum of biological interactions and their dynamic interplay.
The sheer volume of biomedical literature presents a significant hurdle in discerning true causal relationships within biological systems. Natural language, while flexible, is inherently ambiguous; a single gene or protein may be described with multiple synonyms, and interactions can be reported with varying degrees of experimental certainty. This ‘noise’ – encompassing vague terminology, conflicting results, and implicit assumptions – requires sophisticated natural language processing techniques to resolve. Current approaches must move beyond simple keyword matching to understand the context of each statement, assess the reliability of the source, and differentiate between correlation and causation. Successfully navigating this complexity is crucial for building accurate mechanistic models and ultimately, for translating biomedical knowledge into effective interventions.
Current evaluations of artificial intelligence in biology largely focus on whether a system can detect relationships between genes, proteins, or other biological entities. However, true biological understanding demands more than simple identification; it requires mechanistic explanation. A critical gap exists in benchmarks capable of rigorously assessing an AI’s ability to not only pinpoint interactions, but also to articulate how and why those interactions occur, detailing the underlying causal chain of events. Developing such benchmarks necessitates moving beyond correlative assessments to tests that demand detailed, step-by-step explanations grounded in established biological principles, pushing AI systems toward genuine biological reasoning and predictive capability. This shift will require benchmarks incorporating diverse data types, complex scenarios, and quantifiable measures of explanatory power, ultimately enabling a more nuanced evaluation of AI’s potential to unravel the intricacies of life.
BIOME-Bench: A Grounded Approach to Biological Reasoning
BIOME-Bench is a newly developed benchmark dataset assembled through a systematic literature curation process. Data acquisition prioritized comprehensiveness by leveraging the PubMed database and utilizing Medical Subject Headings (MeSH) terms to broaden search queries and ensure relevant publications were identified. This approach facilitated the collection of a diverse and representative corpus of biomedical literature, serving as the foundation for constructing a robust and challenging evaluation resource for mechanism elucidation and biomolecular interaction inference tasks.
The BIOME-Bench construction pipeline leverages Large Language Models (LLMs) for automated Information Extraction (IE) from scientific literature. This process involves utilizing LLMs to identify and categorize key biological entities – such as genes, proteins, and metabolites – and to subsequently extract the mechanistic relationships between these entities as described in the text. Specifically, the LLMs are employed to parse sentences, recognize relevant biological terms, and determine the type of interaction or mechanism being described, enabling the creation of a structured knowledge base from unstructured scientific text. This automated approach facilitates the large-scale curation of data for the benchmark, reducing reliance on manual annotation and increasing reproducibility.
BIOME-Bench comprises a total of 12,925 instances designed for evaluating performance on biological reasoning tasks. Specifically, the benchmark features 1,347 instances focused on multi-omics mechanism elucidation, requiring models to infer causal relationships between different omics layers. Complementing this, BIOME-Bench includes 11,578 instances dedicated to biomolecular interaction inference, assessing a model’s ability to predict physical or functional associations between biomolecules. This dual focus, encompassing both mechanistic and interaction-based reasoning, positions BIOME-Bench as a comprehensive resource for evaluating the capabilities of biological language models.
Evaluating Mechanistic Reasoning: Beyond Simple Detection
BIOME-Bench represents a shift in mechanistic reasoning evaluation from identifying simple interactions between biological entities to elucidating the complete pathway mechanisms driving observed phenomena. Traditional benchmarks often assess whether a model can predict a relationship, while BIOME-Bench necessitates that models articulate how a biological process unfolds, demanding an explanation of the underlying steps and dependencies. This focus on mechanism elucidation requires models to move beyond correlation and demonstrate a causal understanding of multi-omics data, integrating information across different biological levels to construct a coherent and biologically plausible explanation of observed changes.
The BIOME-Bench task employs differential expression data as input, representing changes in gene expression levels under specified conditions. Models are then required to integrate this data with established pathway context – information detailing known biological pathways and the interactions between genes and proteins within them. Successful completion of the task necessitates the generation of coherent mechanistic explanations, detailing how observed changes in expression relate to pathway activity and ultimately, to the biological phenomenon being investigated. This integration process assesses a model’s ability to move beyond simple correlation and demonstrate an understanding of underlying biological mechanisms.
Evaluation of Large Language Models (LLMs) on the BIOME-Bench dataset employed a dual assessment strategy consisting of automated metrics and Human Expert Verification. Automated metrics provided a quantitative assessment of model outputs, while Human Expert Verification – conducted by domain specialists – ensured the biological plausibility and accuracy of the generated mechanistic explanations. Critically, the dataset demonstrated a 100% pass rate under Human Expert Verification, indicating a high degree of reliability in the dataset’s construction and annotation, and establishing a robust benchmark for evaluating mechanistic reasoning capabilities in LLMs.
Towards Robust and Explainable AI: The Illusion of Understanding
Traditional evaluations of large language models (LLMs) in biology often prioritize surface-level accuracy, such as correctly predicting a protein’s function given its sequence. However, BIOME-Bench introduces a paradigm shift by employing structured supervision – a framework demanding LLMs not just what is correct, but why. This approach utilizes carefully designed prompts and evaluation criteria that dissect biological reasoning into distinct steps, assessing the model’s ability to justify its conclusions with mechanistic explanations. By focusing on the process of reasoning, rather than solely the final answer, BIOME-Bench offers a more robust and nuanced understanding of an LLM’s capabilities, revealing whether the model genuinely ‘understands’ biological principles or is simply recognizing patterns. This detailed assessment moves beyond easily gamed metrics and provides a more trustworthy foundation for deploying AI in critical biological research and applications.
The evaluation of large language models in complex biological domains traditionally demands significant time and expertise; however, the BIOME-Bench framework introduces a novel approach by leveraging another large language model as an automated judge. This ‘LLM-as-a-Judge’ system doesn’t replace expert evaluation, but rather complements it, offering a scalable means to assess the mechanistic explanations generated by the evaluated model. By automating the initial filtering and scoring of responses based on pre-defined criteria, the system dramatically accelerates the research process, allowing human experts to focus on the most promising and nuanced explanations. This automated assessment not only increases efficiency but also introduces a level of consistency and reproducibility often challenging to achieve with purely manual review, ultimately fostering a more robust and rapidly evolving field of AI-driven biological discovery.
The BIOME-Bench framework prioritizes the development of biologically plausible AI by demanding mechanistic explanations, not simply accurate predictions. This approach shifts the focus from ‘black box’ models – which may correctly identify patterns without revealing how they arrived at that conclusion – to systems that articulate the underlying biological reasoning. By requiring AI to justify its inferences with established biological principles, BIOME-Bench fosters trustworthiness and allows researchers to validate whether the model’s reasoning aligns with current scientific understanding. This emphasis on interpretability is crucial for translating AI-driven discoveries into actionable insights, as it enables scientists to identify potential flaws in the model’s logic and build confidence in its predictions, ultimately accelerating progress in biological research.
The pursuit of automated reasoning over scientific literature, as demonstrated by BIOME-Bench, inevitably invites a certain skepticism. It’s a valiant attempt to quantify understanding of biomolecular interactions and pathway mechanisms, but the benchmark itself will become another layer in the complexity. As David Hilbert famously stated, “We must be able to answer every question posed by mathematics.” The same ambition drives this work – to provide definitive answers from the chaos of scientific papers. Yet, the reality is, production data – the endless stream of experimental results and contradictory findings – will always present edge cases the model hasn’t seen. BIOME-Bench is a snapshot in time; a beautifully constructed model, but one that will require constant maintenance as the underlying science evolves. If code looks perfect, no one has deployed it yet.
What’s Next?
The construction of BIOME-Bench feels less like a culmination and more like a detailed map of everything that will inevitably break. A benchmark, however meticulously crafted, merely formalizes the ways in which large language models will fail to grasp the exquisitely messy reality of biology. The current focus on interaction inference and pathway elucidation is a reasonable starting point, but it addresses only a sliver of the problem. True biological reasoning demands not just knowledge retrieval, but a capacity for error, for dealing with incomplete data, and for acknowledging the inherent ambiguity of experimental results.
Future iterations will undoubtedly expand the scope, incorporating more complex scenarios and challenging models with tasks requiring genuine hypothesis generation, not just pattern matching. Expect a proliferation of adversarial examples designed to expose the limits of these systems, quickly followed by equally sophisticated methods for masking those limitations. The cycle continues.
Ultimately, the value of BIOME-Bench-and benchmarks like it-may not lie in achieving ever-higher scores, but in providing a persistent, granular record of what these models cannot do. A detailed catalog of failure. A legacy, if one exists, will be a better understanding of the gap between statistical mimicry and genuine biological insight. It’s a long road, and production will always find a way to prolong the suffering.
Original article: https://arxiv.org/pdf/2512.24733.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-01-04 07:29