GenAI4ELab/papercli-papers huggingface dataset:
Test-Time Adaptation without Source Data for Out-of-Domain Bioactivity Prediction
Authors: Yiming Yang; Zhiyuan Zhou; Yueming Yin; Hoi-Yeung Li; Adams Wai-Kin Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate prediction of protein-ligand bioactivity is a cornerstone of modern drug discovery, yet current deep learning methods often struggle with out-of-domain (OOD) generalization. The existing methods rely on access to source data, making them impractical in scenarios where data cannot be accessed due to confidentiality, privacy concerns or intellectual property restrictions. In this paper, we provide the first exploration of a more realistic setting for bioactivity prediction, where models are expected to adapt to out-of-domain distributions without access to source data. Motivated by the critical role of binding-relevant interactions in determining ligand-protein bioactivity, we introduce an uncertainty-weighted consistency strategy, in which original samples with high confidence guide their augmented counterparts by minimizing feature distance. This encourages the model to focus on informative interaction regions while suppressing reliance on spurious or non-causal substructures. To further enhance representation discriminability and prevent feature collapse, we integrate a contrastive optimization objective that pulls together augmented views of the same complex and pushes away views from different complexes. Together, these two components enable the learning of invariant, bioactivity-aware representations, allowing robust adaptation under distribution shifts. Extensive experiments across DTIGN, SIU 0.6, and DrugOOD demonstrate that our framework consistently outperforms state-of-the-art baselines under scaffold, protein, and assay based OOD settings. Especially on the eight subsets of DTIGN, it improves Pearson’s $R$ by 8.2\% and Kendall’s Tau $\tau$ by 5.8\% on average over the best baseline, underscoring its effectiveness as a source data-absent solution for OOD bioactivity prediction.
MoCa: Modeling Object Consistency for 3D Camera Control in Video Generation
Authors: Zhijing Cheng; Xuancheng Zhang; Donglin Di; Chen Wei; Hao Li; Xun Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Camera control is important in text-to-video generation for achieving realistic scene navigation and view synthesis. 
This control is defined by parameters that describe movement through 3D space, thereby introducing 3D consistency into the generation process.
A core challenge for existing methods is achieving 3D consistency within the 2D pixel domain.  Strategies that directly integrate camera conditions into text-to-video models often produce artifacts, while those relying on explicit 3D supervision face challenges with generalization. 
Both limitations originate from the gap between the 2D pixel space and the underlying 3D world.
The key insight is that the projection of a smooth 3D camera movement produces consistency in object view, appearance, and motion across 2D frames. Inspired by this insight, we propose MoCa, a dual-branch framework that bridges this gap by modeling object consistency to implicitly learn 3D relationships between the camera and the scene.
To ensure view consistency, we design a Spatial-Temporal Camera Encoder with Plücker embedding, which encodes camera trajectories into a geometrically grounded latent representation. For appearance consistency, we introduce a semantic guidance strategy that leverages persistent vision-language features to maintain object identity and texture across frames. To address motion consistency, we propose an object-aware motion disentanglement mechanism that separates object dynamics from global camera movement, ensuring precise camera control and natural object motion.
Experiments show that MoCa achieves accurate camera control while preserving video quality, offering a practical and effective solution for camera-controllable video generation.
Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts
Authors: Yannis Montreuil; Axel Carlier; Lai Xing Ng; Wei Tsang Ooi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing _Learning-to-Defer_ (L2D) frameworks are limited to _single-expert deferral_, forcing each query to rely on only one expert and preventing the use of collective expertise. We introduce the first framework for _Top-$k$ Learning-to-Defer_, which allocates queries to the $k$ most cost-effective entities. Our formulation unifies and strictly generalizes prior approaches, including the _one-stage_ and _two-stage_ regimes, _selective prediction_, and classical cascades. In particular, it recovers the usual Top-1 deferral rule as a special case while enabling principled collaboration with multiple experts when $k>1$. We further propose _Top-$k(x)$ Learning-to-Defer_, an adaptive variant that learns the optimal number of experts per query based on input difficulty, expert quality, and consultation cost. To enable practical learning, we develop a novel surrogate loss that is Bayes-consistent, $\mathcal{H}_h$-consistent in the one-stage setting, and $(\mathcal{H}_r,\mathcal{H}_g)$-consistent in the two-stage setting. Crucially, this surrogate is independent of $k$, allowing a single policy to be learned once and deployed flexibly across $k$. Experiments across both regimes show that Top-$k$ and Top-$k(x)$ deliver superior accuracy–cost trade-offs, opening a new direction for multi-expert deferral in L2D.
GUIDE: Gated Uncertainty-Informed Disentangled Experts for Long-tailed Recognition
Authors: Yuan Dong; Zhe Zhao; Liheng Yu; Di Wu; Pengkun Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-Tailed Recognition (LTR) remains a significant challenge in deep learning. While multi-expert architectures are a prominent paradigm, we argue that their efficacy is fundamentally limited by a series of deeply entangled problems at the levels of representation, policy, and optimization. These entanglements induce homogeneity collapse among experts, suboptimal dynamic adjustments, and unstable meta-learning. In this paper, we introduce GUIDE, a novel framework conceived from the philosophy of Hierarchical Disentanglement. We systematically address these issues at three distinct levels. First, we disentangle expert representations and decisions through competitive specialization objectives to foster genuine diversity. Second, we disentangle policy-making from ambiguous signals by using online uncertainty decomposition to guide a dynamic expert refinement module, enabling a differentiated response to model ignorance versus data ambiguity. Third, we disentangle the optimization of the main task and the meta-policy via a two-timescale update mechanism, ensuring stable convergence. Extensive experiments on five challenging LTR benchmarks, including  ImageNet-LT, iNaturalist 2018, CIFAR-100-LT, CIFAR-10-LT and Places-LT, demonstrate that GUIDE establishes a new state of the art, validating the efficacy of our disentanglement approach. Code is available at Supplement.
Evolution of Concepts in Language Model Pre-Training
Authors: Xuyang Ge; Wentao Shu; Jiaxing Wu; Yunhua Zhou; Zhengfu He; Xipeng Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Language models obtain extensive capabilities through pre-training. However, the pre-training dynamics remains a black box. In this work, we track linear interpretable feature evolution across pre-training snapshots using a sparse dictionary learning method called crosscoders. We find that most features begin to form around a specific point, while more complex patterns emerge in later training stages. Feature attribution analyses reveal causal connections between feature evolution and downstream performance. Our feature-level observations are highly consistent with previous findings on Transformer's two-stage learning process, which we term a statistical learning phase and a feature learning phase. Our work opens up the possibility to track fine-grained representation progress during language model learning dynamics. Our code is available at https://github.com/OpenMOSS/Language-Model-SAEs.
Taming Hierarchical Image Coding Optimization: A Spectral Regularization Perspective
Authors: Wuyang Cong; Junqi Shi; Ming Lu; Xu Zhang; Zhan Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hierarchical coding offers distinct advantages for learned image compression by capturing multi-scale representations to support scale-wise modeling and enable flexible quality scalability, making it a promising alternative to single-scale models. However, its practical performance remains limited. Through spectral analysis of training dynamics, we reveal that existing hierarchical image coding approaches suffer from cross-scale energy dispersion and spectral aliasing, resulting in optimization inefficiency and performance bottlenecks. To address this, we propose explicit spectral regularization schemes for hierarchical image coding, consisting of (i) intra-scale frequency regularization, which encourages a smooth low‑to‑high frequency buildup as scales increase, and (ii) inter-scale similarity regularization, which suppresses spectral aliasing across scales. Both regularizers are applied only during training and impose no overhead at inference. Extensive experiments demonstrate that our method accelerates the training of the vanilla model by 2.3$\times$, delivers an average 20.65\% rate–distortion gain over the latest VTM-22.0 on public datasets, and outperforms existing single-scale approaches, thereby setting a new state of the art in learned image compression.
Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants
Authors: Yuhao Liu; Yu Chen; Rui Hu; Longbo Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Stochastic interpolants offer a robust framework for continuously transforming samples between arbitrary data distributions via ordinary or stochastic differential equations (ODEs/SDEs), holding significant promise for generative modeling. While previous studies have analyzed the finite-time convergence rate of discrete-time implementations for SDEs, the ODE counterpart remains largely unexplored. In this work, we bridge this gap by presenting a rigorous finite-time convergence analysis of numerical implementations for ODEs in the framework of stochastic interpolants. We establish novel discrete-time total variation error bounds for two widely used numerical solvers: the first-order forward Euler method and the second-order Heun's method. Our analysis also yields optimized iteration complexity results and step size schedules that enhance computational efficiency. Notably, when specialized to the diffusion model setting, our theoretical guarantees for the second-order method improve upon prior results in terms of both smoothness requirements and dimensional dependence. Our theoretical findings are corroborated by numerical and image generation experiments, which validate the derived error bounds and complexity analyses.
Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models
Authors: Jiaqi Liu; Lang Sun; Ronghao Fu; Bo Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) in remote sensing often fail at complex analytical tasks, a limitation stemming from their end-to-end training paradigm that bypasses crucial reasoning steps and leads to unverifiable outputs. To address this limitation, we introduce the Perceptually-Grounded Geospatial Chain-of-Thought (Geo-CoT), a framework that models remote sensing analysis as a verifiable, multi-step process. We instill this analytical process through a two-stage alignment strategy, leveraging Geo-CoT380k, the first large-scale dataset of structured Geo-CoT rationales. This strategy first employs supervised fine-tuning (SFT) to instill the foundational cognitive architecture, then leverages Group Reward Policy Optimization (GRPO) to refine the model’s reasoning policy towards factual correctness. The resulting model, RSThinker, outputs both a final answer and its justifying, verifiable analytical trace. This capability yields dominant performance, significantly outperforming state-of-the-art models across a comprehensive range of tasks. The public release of our Geo-CoT380k dataset and RSThinker model upon publication serves as a concrete pathway from opaque perception towards structured, verifiable reasoning for Earth Observation.
PSDNorm: Temporal Normalization for Deep Learning in Sleep Staging
Authors: Theo Gnassounou; Antoine Collas; Rémi Flamary; Alexandre Gramfort
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Distribution shift poses a significant challenge in machine learning, particularly in
    biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data.
    While existing normalization layers, BatchNorm, LayerNorm and InstanceNorm, help mitigate distribution shifts, when applied over the time dimension they ignore the dependencies and auto-correlation inherent to the vector coefficients they normalize.
    In this paper, we propose PSDNorm that leverages Monge mapping and temporal context to normalize feature maps in deep learning models for signals.
    Evaluations with architectures based on U-Net or transformer backbones trained on 10K subjects across 10 datasets,
    show that PSDNorm achieves state-of-the-art performance on unseen left-out datasets while being 4-times more data-efficient than BatchNorm.
DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving
Authors: Haisheng Su; Wei Wu; Feixiang Song; Junjie Zhang; Zhenjie Yang; Junchi Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances towards End-to-End Autonomous Driving (E2E-AD) focus on integrating modular designs into a unified framework for joint optimization. Most of these advances follow a sequential paradigm (i.e., perception-prediction-planning) based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design can inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Meanwhile, insufficient training of image backbone and quadratic-complexity of attention mechanism also hinder the scalability and efficiency of E2E-AD system to handle spatiotemporal input. To this end, we propose DriveMamba, a Task-Centric Scalable paradigm for efficient E2E-AD, which integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into a single-stage Unified Mamba decoder. Specifically, both extracted image features and expected task outputs are converted into token-level sparse representations in advance, which are then sorted by their instantiated positions in 3D space. The linear-complexity operator enables efficient long-context sequential token modeling to capture task-related inter-dependencies simultaneously. Additionally, a bidirectional trajectory-guided "local-to-global" scan method is designed to preserve spatial locality from ego-perspective, thus facilitating the ego-planning. Extensive experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superiority, generalizability and great efficiency of DriveMamba.
BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching
Authors: Hanshuai Cui; Zhiqing Tang; Zhifei Xu; Zhi Yao; Wenyi Zeng; Weijia Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in Diffusion Transformers (DiTs) have established them as the state-of-the-art method for video generation. However, their inherently sequential denoising process results in inevitable latency, limiting real-world applicability. Existing acceleration methods either compromise visual quality due to architectural modifications or fail to reuse intermediate features at proper granularity. Our analysis reveals that DiT blocks are the primary contributors to inference latency. Across diffusion timesteps, the feature variations of DiT blocks exhibit a U-shaped pattern with high similarity during intermediate timesteps, which suggests substantial computational redundancy. In this paper, we propose Block-Wise Caching (BWCache), a training-free method to accelerate DiT-based video generation. BWCache dynamically caches and reuses features from DiT blocks across diffusion timesteps. Furthermore, we introduce a similarity indicator that triggers feature reuse only when the differences between block features at adjacent timesteps fall below a threshold, thereby minimizing redundant computations while maintaining visual fidelity. Extensive experiments on several video diffusion models demonstrate that BWCache achieves up to 2.6$\times$ speedup with comparable visual quality.
TIGaussian: Disentangle Gaussians for Spatial-Awared Text-Image-3D Alignment
Authors: Jiarun Liu; Qifeng Chen; Yiru Zhao; Minghua Liu; Baorui Ma; Sheng Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While visual-language models have profoundly linked features between texts and images, the incorporation of 3D modality data, such as point clouds and 3D Gaussians, further enables pretraining for 3D-related tasks, e.g., cross-modal retrieval, zero-shot classification, and scene recognition.
As challenges remain in extracting 3D modal features and bridging the gap between different modalities,
we propose TIGaussian, a framework that harnesses 3D Gaussian Splatting (3DGS) characteristics to strengthen cross-modality alignment through multi-branch 3DGS tokenizer and modality-specific 3D feature alignment strategies. Specifically, our multi-branch 3DGS tokenizer decouples the intrinsic properties of 3DGS structures into compact latent representations, enabling more generalizable feature extraction. To further bridge the modality gap, we develop a bidirectional cross-modal alignment strategies: a multi-view feature fusion mechanism that leverages diffusion priors to resolve perspective ambiguity in image-3D alignment, while a text-3D projection module adaptively maps 3D features to text embedding space for better text-3D alignment. Extensive experiments on various datasets demonstrate the state-of-the-art performance of TIGaussian in multiple tasks. Code repository: https://github.com/RUiN-jiarun/TIGaussian.
Angle K-Means
Authors: Shenfei Pei; Ruiyu Huang; Yiqing Hu; Zhongqi Lin; Xudong Jiang; Zengwei Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose an accelerated exact $k$-means algorithm, Angle $k$-means.
As its name suggests, the algorithm mainly leverages angular relationships between data points and cluster centers 
to reduce computational overhead. Although grounded in straightforward geometric principles, 
it delivers substantial performance improvements in empirical evaluations.
In contrast to existing acceleration techniques, our model introduces no new hyperparameters,
preserving full compatibility with standard $k$-means.
Theoretical analysis shows that Angle $k$-means maintains linear time complexity 
with respect to both sample size and dimensionality, 
while empirical evaluations on diverse real-world datasets demonstrate 
significant speedup over state-of-the-art algorithms such as ball $k$-means and Exp-ns.
ResiliBench: Evaluating Agentic Workflow Adaptation in Stochastic Environments
Authors: Ruicheng Ao; Zeping Min; Tingyu Zhu; Wotao Yin; Xinshang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce ResiliBench, a benchmark that evaluates LLM workflow execution under simulated realistic conditions of instruction quality variability and tool execution uncertainty. Unlike existing benchmarks that encounter these challenges incidentally, our work makes uncertainty the primary focus of systematic study. The benchmark incorporates three key aspects: (1) modeling of probabilistic tool behaviors through parameterized error models that simulate real-world API failure patterns, (2) provision of MDP-derived workflows that maximize expected success rates, and (3) systematic evaluation of model robustness through controlled perturbations of workflow instruction quality. Our construction pipeline generates 5,040 tasks from a tool library of 30 APIs. The evaluation conducted across widely used large language models under conditions of probabilistic tool failures and varying instruction quality reveals notable performance differences. Specifically, MDP-optimal workflow prompts achieve an average success rate of 62.1\%, Chain-of-Thought prompts yield an average success rate of 50.8\%, and flawed workflow prompts result in an average success rate of 54.3\%. Our benchmark is available at https://github.com/Archer222arc/ResiliBench.
Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
Authors: Oscar Davis; Michael Samuel Albergo; Nicholas Matthew Boffi; Michael M. Bronstein; Joey Bose
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Geometric data 
and purpose-built generative models on them have become ubiquitous in high-impact deep learning application domains, ranging from protein backbone generation and computational chemistry to geospatial data. 
Current geometric generative models remain computationally expensive at inference---requiring many steps of complex numerical simulation---as they are derived from dynamical measure transport frameworks such as diffusion and flow-matching on Riemannian manifolds. In this paper, we propose Generalised Flow Maps (GFM), a new class of few-step generative models that generalises the Flow Map framework in Euclidean spaces
to arbitrary Riemannian manifolds. We instantiate GFMs with three self-distillation-based training methods: Generalised Lagrangian Flow Maps, Generalised Eulerian Flow Maps, and Generalised Progressive Flow Maps. We theoretically show that GFMs, under specific design decisions, unify and elevate existing Euclidean few-step generative models, such as consistency models, shortcut models, and meanflows, to the Riemannian setting. We benchmark GFMs against other geometric generative models on a suite of geometric datasets, including geospatial data, RNA torsion angles, and hyperbolic manifolds, and   
achieve state-of-the-art sample quality for single- and few-step evaluations, and superior or competitive log-likelihoods using the implicit probability flow.
AQuA: Toward Strategic Response Generation for Ambiguous Visual Questions
Authors: Jihyoung Jang; Hyounghun Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Visual Question Answering (VQA) is a core task for evaluating the capabilities of Vision–Language Models (VLMs). Existing VQA benchmarks primarily feature clear and unambiguous image–question pairs, whereas real-world scenarios often involve varying degrees of ambiguity that require nuanced reasoning and context-appropriate response strategies. Although recent studies have begun to address ambiguity in VQA, they lack (1) a systematic categorization of ambiguity levels and (2) datasets and models that support strategy-aware responses. In this paper, we introduce Ambiguous Visual Question Answering (AQuA), a fine-grained dataset that classifies ambiguous VQA instances into four levels according to the nature and degree of ambiguity, along with the optimal response strategy for each case. Our evaluation of diverse open-source and proprietary VLMs shows that most models fail to adapt their strategy to the ambiguity type, frequently producing overconfident answers rather than seeking clarification or acknowledging uncertainty. To address this challenge, we fine-tune VLMs on AQuA, enabling them to adaptively choose among multiple response strategies, such as directly answering, inferring intent from contextual cues, listing plausible alternatives, or requesting clarification. VLMs trained on AQuA achieve strategic response generation for ambiguous VQA, demonstrating the ability to recognize ambiguity, manage uncertainty, and respond with context-appropriate strategies, while outperforming both open-source and closed-source baselines.