Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design

• URL: http://arxiv.org/abs/2511.23120v1
• Date: Fri, 28 Nov 2025 12:07:38 GMT
• Title: Freeze, Diffuse, Decode: Geometry-Aware Adaptation of Pretrained Transformer Embeddings for Antimicrobial Peptide Design
• Abstract summary: We introduce Freeze, Diffuse, Decode (FDD), a novel diffusion-based framework that adapts pre-trained embeddings to downstream tasks.<n>FDD propagates supervised signal along the intrinsic manifold intrinsic frozen embeddings, enabling a geometry-aware adaptation of the embedding space.
• Score: 11.120840203434915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies: fine-tuning and probing, either distort the pretrained geometric structure of the embeddings or lack sufficient expressivity to capture task-relevant signals. These issues become even more pronounced when supervised data are scarce. Here, we introduce Freeze, Diffuse, Decode (FDD), a novel diffusion-based framework that adapts pre-trained embeddings to downstream tasks while preserving their underlying geometric structure. FDD propagates supervised signal along the intrinsic manifold of frozen embeddings, enabling a geometry-aware adaptation of the embedding space. Applied to antimicrobial peptide design, FDD yields low-dimensional, predictive, and interpretable representations that support property prediction, retrieval, and latent-space interpolation.

Related papers

• Multimodal Foundation Models Adaptation based on Domain-Aware Relaxed Orthogonal Subspace for Remote Sensing [16.45880716714667]
  Domain-aware Relaxed Orthogonal Subspace adaptation (DROS)<n>DROS reformulates low-rank adaptation as data-conditioned subspace learning and flexible subspace adaptation.<n>Experiments on multiple remote sensing benchmarks demonstrate that DROS achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-09-12T02:19:11Z)
• Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers [93.3976834364707]
  We propose sREPA, a structural REPresentation Alignment framework to enforce consistency in the relational geometry of feature maps.<n>By encouraging the model to internalize holistic spatial layouts and structural correlations from pre-trained features, sREPA achieves faster and more stable convergence.
  arXiv  Detail & Related papers  (2026-05-16T12:01:04Z)
• REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning [5.853890728403084]
  Recent text models are often adapted to specialized domains via contrastive pre-finetuning (PFT) on a collection of scattered, heterogeneous tasks.<n>This approach often introduces task-induced bias alongside domain knowledge, leading to uncontrolled representation shifts.<n>We propose R, a representation regularization framework that explicitly controls representation shift during embedding pre-finetuning.
  arXiv  Detail & Related papers  (2026-04-19T04:41:55Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• Structural Action Transformer for 3D Dexterous Manipulation [80.07649565189035]
  Cross-embodiment skill transfer is a challenge for high-DoF robotic hands.<n>Existing methods, often relying on 2D observations and temporal-centric action representation, struggle to capture 3D spatial relations.<n>This paper proposes a new 3D dexterous manipulation policy that challenges this paradigm by introducing a structural-centric perspective.
  arXiv  Detail & Related papers  (2026-03-04T11:38:12Z)
• Low-Dimensional Execution Manifolds in Transformer Learning Dynamics: Evidence from Modular Arithmetic Tasks [0.0]
  We investigate the structure of learning dynamics in transformer models through carefully controlled arithmetic tasks.<n>Our results suggest a unifying geometric framework for understanding transformer learning.
  arXiv  Detail & Related papers  (2026-02-11T03:57:46Z)
• Riemannian Flow Matching for Disentangled Graph Domain Adaptation [51.98961391065951]
  Graph Domain Adaptation (GDA) typically uses adversarial learning to align graph embeddings in Euclidean space.<n>DisRFM is a geometry-aware GDA framework that unifies embedding and flow-based transport.
  arXiv  Detail & Related papers  (2026-01-31T11:05:35Z)
• Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective [55.919842734983156]
  CoSA is a transferable attack framework that operates within a shared low-dimensional semantic space.<n>CoSA consistently outperforms state-of-the-art transferable attacks.
  arXiv  Detail & Related papers  (2026-01-30T15:48:11Z)
• Latent Iterative Refinement Flow: A Geometric-Constrained Approach for Few-Shot Generation [5.062604189239418]
  We introduce Latent Iterative Refinement Flow (LIRF), a novel approach to few-shot generation.<n>LIRF establishes a stable latent space using an autoencoder trained with our novel textbfmanifold-preservation loss.<n>Within this cycle, candidate samples are refined by a geometric textbfcorrection operator, a provably contractive mapping.
  arXiv  Detail & Related papers  (2025-09-24T08:57:21Z)
• Diffusion Bridge Variational Inference for Deep Gaussian Processes [31.082191748525137]
  Diffusion Bridge Variational Inference (DBVI) is a principled extension of Denoising diffusion variational inference (DDVI)<n>DBVI initiates the reverse diffusion from a learnable, data-dependent initial distribution.<n>It consistently outperforms DDVI and other variational baselines in predictive accuracy, convergence speed, and posterior quality.
  arXiv  Detail & Related papers  (2025-09-23T14:36:47Z)
• GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters [61.51810815162003]
  We propose an SE(3)-equivariant adapter framework ( GeoAda) that enables flexible and parameter-efficient fine-tuning for controlled generative tasks.<n>GeoAda preserves the model's geometric consistency while mitigating overfitting and catastrophic forgetting.<n>We demonstrate the wide applicability of GeoAda across diverse geometric control types, including frame control, global control, subgraph control, and a broad range of application domains.
  arXiv  Detail & Related papers  (2025-07-02T18:44:03Z)
• SeqPE: Transformer with Sequential Position Encoding [76.22159277300891]
  SeqPE represents each $n$-dimensional position index as a symbolic sequence and employs a lightweight sequential position encoder to learn their embeddings.<n> Experiments across language modeling, long-context question answering, and 2D image classification demonstrate that SeqPE not only surpasses strong baselines in perplexity, exact match (EM) and accuracy--but also enables seamless generalization to multi-dimensional inputs without requiring manual architectural redesign.
  arXiv  Detail & Related papers  (2025-06-16T09:16:40Z)
• Geometry-Editable and Appearance-Preserving Object Compositon [67.98806888489385]
  General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties.<n>Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation.<n>We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion model that first leverages semantic embeddings to implicitly capture desired geometric transformations.
  arXiv  Detail & Related papers  (2025-05-27T09:05:28Z)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.