DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
- URL: http://arxiv.org/abs/2605.24841v1
- Date: Sun, 24 May 2026 03:17:20 GMT
- Title: DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
- Abstract summary: We introduce DriftingMol, a two-stage framework that adapts drifting models to a SELFIES latent molecular space.<n>In decoder-coupled drift, drift gradients are backpropagated through the decoder feature map to a DiT generator, inducing a pullback metric.<n>Across 15 controlled variants, models that preserve the gradient path through decoder features achieve higher correlations than the tested latent-space, random-feature, and external-feature drift variants.
- Score: 8.59642525668345
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We introduce DriftingMol, a two-stage framework that adapts drifting models to a SELFIES latent molecular space. A frozen SELFIES beta-VAE provides the latent space, and the hidden representation of its decoder serves as the drift feature map. In decoder-coupled drift, decoder weights remain fixed, but drift gradients are backpropagated through the decoder feature map to a DiT generator, inducing a pullback metric aligned with molecular decoding. On ZINC250K, the default setting achieves QED Spearman correlation 0.493 with 94.7% uniqueness, while the strongest decoder-coupled condition reaches 0.510. Under protocol-matched four-property conditioning, decoder-coupled drift reaches mean Spearman correlation up to 0.598. Across 15 controlled variants, models that preserve the gradient path through decoder features achieve higher correlations than the tested latent-space, random-feature, and external-feature drift variants, while detached or stop-gradient decoder controls yield near-zero QED correlation and very low uniqueness. These results indicate that decoder-coupled drift is a useful low-cost mechanism for property-biased molecular generation, requiring one generator evaluation and one frozen decoder pass.
Related papers
- Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detection [10.264738258468254]
Cross-scale coupling mismatch is a new forensic signal.<n>In natural videos, macro-level temporal dynamics and micro-level residual patterns are intrinsically coupled by the unified imaging physics pipeline.<n>But AI generators, whose training objectives do not explicitly preserve this joint distribution, systematically violate this coupling.<n>Detecting such mismatch is challenging because it requires independently extracting information at both scales.
arXiv Detail & Related papers (2026-09-01T05:23:48Z) - Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning [80.73561460934809]
multimodal object tracking has achieved remarkable robustness by leveraging complementary sensor data.<n>We identify the prediction head as a critical but often overlooked efficiency bottleneck.<n>By strategically streamlining the decoder architecture, we unlock the potential for real-time inference.
arXiv Detail & Related papers (2026-08-02T20:41:33Z) - LLM-Empowered Multimodal Fusion Framework for Autonomous Driving: Semantic Enhancement and Channel-Adaptive Design [56.76240343283953]
We propose a Large Language Model-centric Semantic-layer Channel-aware Integrated Perception framework.<n>It fuses a local visual stream with a quality-varying external radar stream used to cover perception-blind spots.<n>Experiments on nuScenes and VIRAT validate our approach.
arXiv Detail & Related papers (2026-07-02T06:44:02Z) - Ghost Attractor Networks: Basin-Structured Dynamical Decoders for Closed-Loop Sequential Generation [15.734979840846568]
This article proposes Ghost Attractor Networks, a theoretically derived dynamical decoder.<n>It evolves under a learned potential with drift and produces a basin-attractor structure by construction.
arXiv Detail & Related papers (2026-06-16T11:23:30Z) - Distilling Drifting Transformers with Representation Autoencoders [23.990618033500695]
Representation Autoencoders (RAEs) have improved diffusion and flow models by semantically richer latent space.<n>In this work, we argue that the RAE latent space is compatible with distillation via the newly proposed Drifting Models.
arXiv Detail & Related papers (2026-06-14T02:46:50Z) - Metric--Phase Fields: Decoupling Distance and Sign for Thin-Structure Reconstruction from Unoriented Point Clouds [67.30236447078187]
We introduce Metric-Phase Fields (MPFs), a decoupled implicit representation that separates metric proximity from topological phase.<n>Experiments on both synthetic and scanned thin-shell and thin-plate shapes demonstrate that MPFs preserve thin and layered structures more faithfully than recent SDF-based methods.
arXiv Detail & Related papers (2026-05-25T07:08:57Z) - Tippett-minimum Fusion of Representation-space Diffusion Models for Multi-Encoder Out-of-Distribution Detection [1.6921396880325779]
We statistically identify each encoder's sensitivity to specific shift types from ID data alone.<n>We introduce EncMin2L -- an encoder-a two-level $min(cdot)$-gate that combines and calibrates per-encoder diffusion-based likelihood detectors.
arXiv Detail & Related papers (2026-05-19T21:18:49Z) - Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models [0.0]
Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.<n>We replace the Gaussian decoder with a Phase-Type (PH) distribution based on Markov chains.<n>We show that the PH-based model reduces tail Kolmogorov-Smirnov distance by up to x6 and extreme quantile error by up to x10.
arXiv Detail & Related papers (2026-05-18T14:21:46Z) - DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving [65.7087560656003]
Generative diffusion models for end-to-end autonomous driving often suffer from mode collapse.<n>We propose DiffusionDriveV2, which leverages reinforcement learning to constrain low-quality modes and explore for superior trajectories.<n>This significantly enhances the overall output quality while preserving the inherent multimodality of its core Gaussian Mixture Model.
arXiv Detail & Related papers (2025-12-08T17:29:52Z) - Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion.
In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
arXiv Detail & Related papers (2023-07-17T07:12:29Z) - Think Twice before Driving: Towards Scalable Decoders for End-to-End
Autonomous Driving [74.28510044056706]
Existing methods usually adopt the decoupled encoder-decoder paradigm.
In this work, we aim to alleviate the problem by two principles.
We first predict a coarse-grained future position and action based on the encoder features.
Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly.
arXiv Detail & Related papers (2023-05-10T15:22:02Z) - String-based Molecule Generation via Multi-decoder VAE [56.465033997245776]
We investigate the problem of string-based molecular generation via variational autoencoders (VAEs)
We propose a simple, yet effective idea to improve the performance of VAE for the task.
In our experiments, the proposed VAE model particularly performs well for generating a sample from out-of-domain distribution.
arXiv Detail & Related papers (2022-08-23T03:56:30Z) - Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input [54.82369261350497]
We propose a CTC-enhanced NAR transformer, which generates target sequence by refining predictions of the CTC module.
Experimental results show that our method outperforms all previous NAR counterparts and achieves 50x faster decoding speed than a strong AR baseline with only 0.0 0.3 absolute CER degradation on Aishell-1 and Aishell-2 datasets.
arXiv Detail & Related papers (2020-10-28T15:00:09Z) - Variance Constrained Autoencoding [0.0]
We show that for encoders, simultaneously attempting to enforce a distribution constraint and minimising an output distortion leads to a reduction in generative and reconstruction quality.
We propose the variance-constrained autoencoder (VCAE), which only enforces a variance constraint on the latent distribution.
Our experiments show that VCAE improves upon Wasserstein Autoencoder and the Variational Autoencoder in both reconstruction and generative quality on MNIST and CelebA.
arXiv Detail & Related papers (2020-05-08T00:50:50Z)
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.