arxiv: 2605.06728 · v1 · submitted 2026-05-07 · 🧬 q-bio.GN · cs.AI· q-bio.CB

Recognition: no theorem link

OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

Joanna Krawczyk, {\L}ukasz Smoli\'nski, Maciej Sypetkowski, Przemys{\l}aw Pietrzak, Rafa{\l} Powalski, Remigiusz Kinas, Tomasz Jetka

Pith reviewed 2026-05-11 01:10 UTC · model grok-4.3

classification 🧬 q-bio.GN cs.AIq-bio.CB

keywords multimodal LLMtranscriptomicsomics reasoninggene expressionbiological question answeringGEO benchmarkinstruction tuningmulti-sample analysis

0 comments

The pith

OmicsLM integrates quantitative transcriptomic profiles directly into an LLM context to handle both profile predictions and language-guided reasoning over multiple samples.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces OmicsLM to connect raw gene expression numbers with natural-language biological questions in one model. Each profile becomes a compact continuous vector placed inside the LLM so the system can read quantitative signals while also processing instructions, gene names, and several samples at once. It trains on more than 5.5 million examples across seventy task types that mix data and text. The result is a single model that works on standard profile tasks and on a new benchmark of real multi-sample questions drawn from GEO studies.

Core claim

OmicsLM represents each transcriptomic profile as a compact continuous representation within the LLM context. This interface preserves quantitative expression signal while allowing natural-language instructions, explicit gene mentions, and multiple interleaved biological samples to be processed together in one model context. Trained on more than 5.5 million instruction-following examples spanning over 70 task types, OmicsLM performs comparably to specialized omics models on profile-level tasks and outperforms both omics-specialized models and general LLMs on language-guided biological reasoning over expression data.

What carries the argument

The continuous embedding of each transcriptomic profile as a vector inside the LLM context, which lets quantitative data interleave with language instructions and multiple samples.

If this is right

The model can directly consume expression profiles for cell type annotation, perturbation prediction, and clinical tasks without separate preprocessing steps.
Language instructions can reference specific genes and compare multiple real expression samples within a single forward pass.
A new benchmark called GEO-OmicsQA measures language-guided multi-sample reasoning on actual Gene Expression Omnibus studies.
One unified model can now cover both quantitative profile prediction and open-ended biological question answering.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same continuous-embedding approach could be tested on other high-dimensional biological measurements such as proteomics or metabolomics.
Biologists might use conversational queries to explore large public datasets without writing code or running separate analysis pipelines.
Scaling the training mixture to include spatial or single-cell data could extend the same interface to finer-grained biological questions.

Load-bearing premise

Representing each transcriptomic profile as a compact continuous representation preserves enough of the original quantitative expression values to support accurate multi-sample reasoning guided by natural language.

What would settle it

Replacing the continuous profile embeddings with random vectors or text-only summaries and observing that performance on the GEO-OmicsQA benchmark drops to match general LLMs without data access would falsify the claim that the representation carries sufficient signal.

Figures

Figures reproduced from arXiv: 2605.06728 by Joanna Krawczyk, {\L}ukasz Smoli\'nski, Maciej Sypetkowski, Przemys{\l}aw Pietrzak, Rafa{\l} Powalski, Remigiusz Kinas, Tomasz Jetka.

**Figure 1.** Figure 1: OmicsLM overview. Top: example questions from diverse omics datasets are formatted as multi-task conversations for OmicsLM, with each <omics> placeholder denoting a biological profile and gene symbols treated as explicit tokens. Bottom: each placeholder is built by mapping a single-cell or bulk expression profile, including profiles from perturbation screens, to a continuous vector and projecting it into t… view at source ↗

read the original abstract

Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profiles without producing natural-language biological explanations, or reason in language without direct access to quantitative omics measurements. We introduce OmicsLM, a multimodal LLM that connects quantitative omics profiles with natural-language biological tasks. OmicsLM represents each transcriptomic profile as a compact continuous representation within the LLM context. This interface preserves quantitative expression signal while allowing natural-language instructions, explicit gene mentions, and multiple interleaved biological samples to be processed together in one model context. We train OmicsLM on more than 5.5 million instruction-following examples spanning over 70 task types, combining continuous transcriptomic inputs, experimental data rendered through diverse language templates, and free-text biological knowledge and question-answering data. This mixture covers cell type annotation, perturbation prediction, clinical prediction, pathway reasoning, and open-ended biological question answering. Existing benchmarks evaluate either profile-level prediction or text-only biological QA, leaving language-guided, multi-sample reasoning over real expression profiles unmeasured. To close this gap, we introduce GEO-OmicsQA, a benchmark for multi-sample biological question answering built from real Gene Expression Omnibus (GEO) studies. We demonstrate that OmicsLM can use expression profiles directly and perform comparably to specialized omics models on profile-level tasks, while outperforming both omics-specialized models and general LLMs on language-guided biological reasoning over expression data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

OmicsLM adds continuous omics embeddings to an LLM plus a new multi-sample GEO benchmark, but the abstract supplies no metrics or ablations so the performance claims cannot be checked.

read the letter

The paper's core move is to embed each transcriptomic profile as a compact continuous vector that sits inside the LLM context alongside text, letting the model handle multiple samples, gene mentions, and natural-language instructions in one pass. They train on a large mixture of 5.5 million examples that mixes those vectors with templated experimental data and free-text biology knowledge, then release GEO-OmicsQA to test language-guided reasoning over real GEO studies. That benchmark is the clearest addition; prior evaluations split between pure profile tasks and text-only QA, so this one actually measures the combined setting the authors care about. The training scale and task coverage look reasonable on paper. The soft spot is the complete absence of numbers. The abstract claims the model matches specialized omics tools on profile tasks and beats both omics and general LLMs on reasoning, yet it gives no accuracies, no baselines, no statistical tests, and no error breakdown. The embedding dimension, encoder details, and any reconstruction or ablation results are also missing, which leaves the stress-test concern standing: if the continuous vectors collapse too much quantitative signal, the reported gains on multi-sample reasoning could come from the language templates alone. Without those checks it is impossible to know whether the interface actually works as described. This is the sort of paper a bioinformatics group or multimodal-model lab might want to read to see one concrete attempt at bridging the two worlds. A serious referee could usefully press for the missing methods, results, and controls, so I would send it to review rather than desk-reject.

Referee Report

2 major / 0 minor

Summary. The manuscript introduces OmicsLM, a multimodal LLM that processes transcriptomic profiles as compact continuous representations alongside natural language instructions for various biological reasoning tasks. It describes training on a large mixture of instruction data including omics profiles, language templates, and free-text knowledge, and proposes the GEO-OmicsQA benchmark for evaluating multi-sample, language-guided reasoning over real GEO expression data. The central claim is that OmicsLM achieves performance comparable to specialized omics models on profile-level tasks and superior performance on language-guided biological reasoning tasks.

Significance. If substantiated, this work could meaningfully advance integrative biology by enabling direct multimodal reasoning over quantitative expression data with natural-language outputs. The scale of the training mixture (>5.5M examples across 70 tasks) and the introduction of a dedicated benchmark for multi-sample GEO-based QA are constructive contributions that address a genuine evaluation gap. The approach of interleaving continuous profile embeddings with explicit gene mentions and language templates is a reasonable architectural direction for the claimed use cases.

major comments (2)

[Abstract] Abstract: The abstract asserts that OmicsLM 'perform[s] comparably to specialized omics models on profile-level tasks' and 'outperform[s] both omics-specialized models and general LLMs on language-guided biological reasoning over expression data,' yet supplies no quantitative metrics, baseline details, statistical tests, error bars, or ablation results. Without these, the central claim of effective multimodal integration cannot be evaluated and the reported outperformance on GEO-OmicsQA remains unverifiable.
[Abstract] Abstract (representation paragraph): The statement that each transcriptomic profile is mapped to a 'compact continuous representation' that 'preserves quantitative expression signal' is load-bearing for the multi-sample reasoning claim, but the manuscript provides no embedding dimension, encoder architecture, reconstruction loss, or ablation that isolates the continuous channel from the language templates also present in training. This omission directly engages the concern that high-dimensional counts may collapse into an insufficient summary, undermining the assertion of true quantitative multimodal reasoning.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and constructive suggestions. We have revised the abstract to address the concerns about missing quantitative details and technical specifications. Our point-by-point responses are below.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract asserts that OmicsLM 'perform[s] comparably to specialized omics models on profile-level tasks' and 'outperform[s] both omics-specialized models and general LLMs on language-guided biological reasoning over expression data,' yet supplies no quantitative metrics, baseline details, statistical tests, error bars, or ablation results. Without these, the central claim of effective multimodal integration cannot be evaluated and the reported outperformance on GEO-OmicsQA remains unverifiable.

Authors: We agree that the abstract would be strengthened by including specific quantitative evidence. In the revised version of the manuscript, we have updated the abstract to report key performance metrics from our experiments, including the specific scores on profile-level tasks where OmicsLM matches specialized models, and the percentage improvements on the GEO-OmicsQA benchmark over both omics-specialized and general LLMs. We also reference the statistical tests and ablations detailed in the main text and supplementary materials. This makes the claims more verifiable while keeping the abstract concise. revision: yes
Referee: [Abstract] Abstract (representation paragraph): The statement that each transcriptomic profile is mapped to a 'compact continuous representation' that 'preserves quantitative expression signal' is load-bearing for the multi-sample reasoning claim, but the manuscript provides no embedding dimension, encoder architecture, reconstruction loss, or ablation that isolates the continuous channel from the language templates also present in training. This omission directly engages the concern that high-dimensional counts may collapse into an insufficient summary, undermining the assertion of true quantitative multimodal reasoning.

Authors: We acknowledge this point and have revised the abstract to include a concise description of the representation. The full details of the encoder architecture, embedding dimension, training with reconstruction loss, and the ablation study isolating the continuous embeddings (which demonstrates their contribution to preserving quantitative signal beyond language templates) are provided in the Methods section and Supplementary Information. We believe this addresses the concern about potential collapse of information. revision: yes

Circularity Check

0 steps flagged

No significant circularity; training mixture and new benchmark are independent of claimed results

full rationale

The paper describes training OmicsLM on a stated mixture of >5.5M instruction examples (continuous profiles + language templates + free-text knowledge) and evaluates on the newly introduced GEO-OmicsQA benchmark built from real GEO studies. No load-bearing step reduces by construction to its own inputs: the continuous embedding is an architectural choice whose fidelity is tested rather than presupposed, the benchmark is external to training data, and performance claims rest on standard held-out evaluation rather than self-referential fitting or self-citation chains. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unverified assumption that compact continuous embeddings retain enough quantitative information for reasoning tasks and that the large mixed training set produces generalizable behavior; no independent evidence for these is supplied in the abstract.

free parameters (1)

omics profile embedding dimension
The size of the continuous vector used to represent each transcriptomic profile is a design choice that must balance information preservation against context length and compute.

axioms (1)

domain assumption Continuous embeddings can preserve sufficient quantitative signal from expression profiles for downstream reasoning tasks
Invoked by the choice of interface that converts profiles into LLM-compatible continuous representations.

invented entities (1)

GEO-OmicsQA benchmark no independent evidence
purpose: To measure language-guided multi-sample reasoning over real expression profiles
Newly introduced evaluation set; no external validation or prior existence claimed.

pith-pipeline@v0.9.0 · 5617 in / 1325 out tokens · 51190 ms · 2026-05-11T01:10:06.489381+00:00 · methodology

discussion (0)

Reference graph

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