AMix-2: Establishing Protein as a Native Modality in Large Language Models

Bowen Zhou; Changze Lv; Dahua Lin; Dongyu Xue; Hao Wang; Hao Zhou; Jiangtao Feng; Jixiang Yu; Ka-Chun Wong; Keyue Qiu

arxiv: 2605.30963 · v1 · pith:KVRLZ3M6new · submitted 2026-05-29 · 🧬 q-bio.BM · cs.AI

AMix-2: Establishing Protein as a Native Modality in Large Language Models

Keyue Qiu , Yixin Wu , Lihao Wang , Yawen Ouyang , Jixiang Yu , Zihan Zhou , Changze Lv , Dongyu Xue

show 14 more authors

Yuxuan Song Xinbo Zhang Hao Wang Jiangtao Feng Zhiqiang Gao Lijun Wu Xiaoqing Zheng Ka-Chun Wong Lei Bai Ya-Qin Zhang Wei-Ying Ma Dahua Lin Bowen Zhou Hao Zhou

This is my paper

Pith reviewed 2026-06-28 20:02 UTC · model grok-4.3

classification 🧬 q-bio.BM cs.AI

keywords protein foundation modelprotein-text modelblock-wise diffusionsequence designprotein understandingunified modalityProteinArenadiffusion language modeling

0 comments

The pith

AMix-2 unifies protein understanding and design in one foundation model by sharing token space with text and using block-wise diffusion modeling.

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

The paper presents AMix-2 as a way to make proteins a native part of large language models rather than a separate domain. It does this through a single formulation that puts protein sequences and natural language into the same token space so the model can both interpret biological data and generate new sequences based on text instructions. Instead of generating proteins strictly from left to right, the model uses block-wise diffusion that allows bidirectional information within blocks and refinement over iterations. This is evaluated on a new benchmark called ProteinArena that uses time-aware splits to test real generalization, where the model beats general LLMs and matches specialized protein tools. Experiments show the diffusion approach works better than standard autoregressive training for this task.

Core claim

AMix-2 establishes protein as a native modality in large language models by unifying protein understanding and sequence design within a single foundation model via a unified protein-text formulation and a block-wise diffusion language modeling backbone that combines causal generation across blocks with bidirectional context and iterative refinement within blocks.

What carries the argument

Block-wise diffusion language modeling backbone that enables causal generation across blocks while allowing bidirectional context and iterative refinement within each block, paired with a shared token space for protein sequences and text.

If this is right

One foundation model can replace multiple task-specific protein models for both understanding and design tasks.
Generation order flexibility from diffusion better suits the non-sequential nature of protein folding and function than strict autoregression.
Time-aware and homology-aware evaluation protocols reveal whether models truly generalize beyond training data patterns.
Controlled comparisons confirm that diffusion-based training outperforms autoregressive training on protein tasks.

Where Pith is reading between the lines

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

This setup might allow direct text-to-protein generation for applications like custom enzyme design without separate pipelines.
The approach could extend to other sequence modalities such as DNA or RNA in the same model.
ProteinArena may serve as a standard testbed for future protein foundation models to ensure fair comparisons.
Scaling the shared modality to larger models could lead to emergent capabilities in multi-step biological reasoning.

Load-bearing premise

Embedding natural language and protein sequences in a shared token space plus using block-wise diffusion rather than strict autoregressive factorization will produce performance on realistic generalization tasks that cannot be explained by training data or benchmark choices alone.

What would settle it

Training an autoregressive version of the same model on identical data and showing it matches or exceeds AMix-2 performance on ProteinArena under the time-aware and homology-aware splits.

read the original abstract

We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein understanding and sequence design within a single foundation model. AMix-2 is built upon two key ideas: (1) a unified protein-text formulation that embeds natural language and protein sequence in a shared token space, enabling one model to perform biological reasoning and conditional design instead of separate downstream task-specialized models; and (2) a block-wise diffusion language modeling backbone that combines causal generation across blocks with bidirectional context and iterative refinement within blocks. This scheme better matches the intrinsic nature of proteins than a strict left-to-right factorization. To evaluate protein foundation models under realistic generalization settings, we further introduce ProteinArena, a comprehensive benchmark with time-aware and homology-aware protocols across various understanding and design tasks, and with baselines covering classical bioinformatics tools, protein-specialized models and LLMs. On ProteinArena, AMix-2 outperforms frontier LLMs and demonstrates competitive performance to task-specific protein models. Controlled experiments further show that the diffusion-based paradigm generally surpasses its autoregressive counterpart, highlighting the advantage of flexible generation order for protein sequences. We release both AMix-2 and ProteinArena to facilitate open research in protein foundation models.

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

AMix-2 tries to fold protein sequences into the same LLM token space as text and swaps autoregressive generation for block-wise diffusion, plus a new time- and homology-aware benchmark, but the performance numbers are still needed to judge the claims.

read the letter

The main thing here is that AMix-2 puts protein sequences into the same token space as natural language and uses block-wise diffusion instead of strict left-to-right prediction so one model can handle both understanding tasks and conditional sequence design.

What is new is the specific pairing of the shared protein-text token space with the block diffusion backbone, plus the ProteinArena benchmark that applies time-aware and homology-aware splits across a range of tasks. The controlled experiments comparing diffusion to autoregressive versions are also a concrete addition.

The paper does a reasonable job laying out why block diffusion might better match protein properties than pure causal factorization, and releasing the model and benchmark is helpful for others who want to test similar ideas.

The soft spots are in the evidence. The abstract states outperformance over frontier LLMs and competitive results against task-specific protein models, but supplies no scores, ablations, or details on how baselines were implemented. Without those tables it is hard to tell whether the gains survive the benchmark splits or data choices. The assumption that the shared embedding plus diffusion will deliver generalization not explained by training data alone still needs the full results to check.

This is aimed at groups building or using multimodal protein models who want fewer task-specific tools. Readers working on protein design pipelines or LLM extensions to biology could get value from the benchmark construction even if they treat the model claims cautiously.

I would send it to peer review because the ideas are concrete and the benchmark addresses a real evaluation gap, though the results section will need close attention.

Referee Report

2 major / 0 minor

Summary. The manuscript presents AMix-2, a protein-text foundation model that establishes protein as a native modality in LLMs by unifying protein understanding and sequence design within a single model. It relies on a unified protein-text formulation embedding natural language and protein sequences in a shared token space, combined with a block-wise diffusion language modeling backbone that enables causal generation across blocks with bidirectional context and iterative refinement within blocks. The work introduces ProteinArena, a benchmark with time-aware and homology-aware protocols across understanding and design tasks, and reports that AMix-2 outperforms frontier LLMs while remaining competitive with task-specific protein models; controlled experiments indicate the diffusion paradigm generally surpasses its autoregressive counterpart.

Significance. If the performance claims hold under the stated evaluation protocols, the work would be significant for integrating protein sequences as a native modality in LLMs, enabling a single model for both biological reasoning and conditional design tasks rather than task-specialized models. The introduction of ProteinArena provides a standardized, realistic generalization benchmark, and the open release of both the model and benchmark facilitates reproducibility and community research in protein foundation models.

major comments (2)

[Abstract] Abstract: the central claim of outperformance on ProteinArena and competitiveness with task-specific models is asserted without any quantitative metrics, ablation tables, error bars, or baseline numbers; this prevents verification of whether the reported advantage survives controls for data splits, homology leakage, or benchmark construction details.
[Abstract] The weakest assumption—that shared token space plus block-wise diffusion yields generalization not explained by training data or benchmark construction—requires explicit evidence in the results; without reported numbers or controls isolating these factors, the diffusion advantage cannot be confirmed as load-bearing rather than data-dependent.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the abstract would be strengthened by including key quantitative results and will revise it accordingly in the next version. We address each major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim of outperformance on ProteinArena and competitiveness with task-specific models is asserted without any quantitative metrics, ablation tables, error bars, or baseline numbers; this prevents verification of whether the reported advantage survives controls for data splits, homology leakage, or benchmark construction details.

Authors: We agree that the abstract currently lacks specific numbers. The main text (Sections 4 and 5, Tables 1-4, and Figure 3) reports the full quantitative results on ProteinArena, including comparisons to baselines, ablations, and controls for time-aware/homology-aware splits. In revision we will add concise performance deltas (e.g., average improvement over frontier LLMs and competitiveness metrics vs. task-specific models) plus a reference to the benchmark protocols directly into the abstract. revision: yes
Referee: [Abstract] The weakest assumption—that shared token space plus block-wise diffusion yields generalization not explained by training data or benchmark construction—requires explicit evidence in the results; without reported numbers or controls isolating these factors, the diffusion advantage cannot be confirmed as load-bearing rather than data-dependent.

Authors: The controlled diffusion-vs-autoregressive comparison is presented in Section 4.3 with the same training data and benchmark construction for both paradigms; the results show consistent gains for the block-wise diffusion approach across tasks. We will add a brief clause in the revised abstract that explicitly references these controlled experiments and the ProteinArena protocols (Section 3) to make the supporting evidence visible at the abstract level. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents AMix-2 as an empirical construction: a shared protein-text token space plus block-wise diffusion backbone, trained and evaluated on the newly introduced ProteinArena benchmark with time/homology-aware splits. No equations, fitted parameters, or self-citations are described that would reduce reported performance or the unification claim to a definition or tautology. The central results are controlled comparisons against external baselines (bioinformatics tools, specialized models, frontier LLMs), making the derivation self-contained rather than circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, training details, or modeling assumptions; therefore the ledger cannot enumerate free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5837 in / 1247 out tokens · 22359 ms · 2026-06-28T20:02:57.262861+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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