GenAI4ELab/papercli-papers huggingface dataset:
Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
Authors: Aditya Shankar; Yuandou Wang; Rihan Hai; Lydia Y. Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process. Existing methods rely on training-time strategies that do not generalise to unseen constraints during inference, and struggle to handle conditional tasks beyond tabular imputation. While manifold theory offers a principled way to guide generation, current formulations are tied to specific inference-time objectives and are limited to continuous domains. We extend manifold theory to tabular data and expand its scope to handle diverse inference-time objectives. On this foundation, we introduce Harpoon, a  tabular diffusion method that guides unconstrained samples along the manifold geometry to satisfy diverse tabular conditions at inference. We validate our theoretical contributions empirically on tasks such as imputation and enforcing inequality constraints, demonstrating Harpoon's strong performance across diverse datasets and the practical benefits of manifold-aware guidance for tabular data. Code URL: https://github.com/adis98/Harpoon
Context and Diversity Matter: The Emergence of In-Context Learning in World Models
Authors: Fan Wang; ZHIYUAN CHEN; YUXUAN ZHONG; Sunjian Zheng; Pengtao Shao; Bo Yu; Shaoshan Liu; Jianan Wang; Ning Ding; Yang Cao; Yu Kang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learning (ICL) of world models, shifting attention from zero-shot performance to the growth and asymptotic limits of the world model. Our contributions are three-fold: (1) we formalize ICL of a world model and identify two core mechanisms: environment recognition (ER) and environment learning (EL); (2) we derive error upper-bounds for both mechanisms that expose how the mechanisms emerge; and (3) we empirically confirm that distinct ICL mechanisms exist in the world model, and we further investigate how data distribution and model architecture affect ICL in a manner consistent with theory. These findings demonstrate the potential of self-adapting world models and highlight the key factors behind the emergence of EL/ER, most notably the necessity of long context and diverse environments. The codes are available at https://github.com/airs-cuhk/airsoul/tree/main/projects/MazeWorld.
Inlier-Centric Post-Training Quantization for Object Detection Models
Authors: Minsu Kim; Dongyeun Lee; Jaemyung Yu; Jiwan Hur; Giseop Kim; Junmo Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Object detection is pivotal in computer vision, yet its immense computational demands make deployment slow and power-hungry, motivating quantization. However, task-irrelevant morphologies such as background clutter and sensor noise induce redundant activations (or anomalies). These anomalies expand activation ranges and skew activation distributions toward task-irrelevant responses, complicating bit allocation and weakening the preservation of informative features. Without a clear criterion to distinguish anomalies, suppressing them can inadvertently discard useful information. To address this, we present InlierQ, an inlier-centric post-training quantization approach that separates anomalies from informative inliers. InlierQ computes gradient-aware volume saliency scores, classifies each volume as an inlier or anomaly, and fits a posterior distribution over these scores using the Expectation-Maximization (EM) algorithm. This design suppresses anomalies while preserving informative features. InlierQ is label-free, drop-in, and requires only 64 calibration samples. Experiments on the COCO and nuScenes benchmarks show consistent reductions in quantization error for camera-based (2D and 3D) and LiDAR-based (3D) object detection.
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Authors: Bac Nguyen; Yuhta Takida; Naoki Murata; Chieh-Hsin Lai; Toshimitsu Uesaka; Stefano Ermon; Yuki Mitsufuji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content. We propose Contrastive Object-centric Diffusion Alignment (CODA), a simple extension that (i) employs register slots to absorb residual attention and reduce interference between object slots, and (ii) applies a contrastive alignment loss to explicitly encourage slot–image correspondence. The resulting training objective serves as a tractable surrogate for maximizing mutual information (MI) between slots and inputs, strengthening slot representation quality. On both synthetic (MOVi-C/E) and real-world datasets (VOC, COCO), CODA improves object discovery (e.g., +6.1% FG-ARI on COCO), property prediction, and compositional image generation over strong baselines. Register slots add negligible overhead, keeping CODA efficient and scalable. These results indicate potential applications of CODA as an effective framework for robust OCL in complex, real-world scenes. Code and pretrained models are available at https://github.com/sony/coda.
Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
Authors: Arnaud Vadeboncoeur; Gregory Duthé; Mark Girolami; Eleni Chatzi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of physical systems from a small number of noisy observations, a usually highly ill-posed problem. Sharing information from multiple distinct yet related physical systems can alleviate this ill-posedness. Critically, engineering systems often have complicated variable geometries prohibiting the use of standard multi-system Bayesian UQ. In this work, we introduce Geometric Autoencoders for Bayesian Inversion (GABI), a framework for learning geometry-aware generative models of physical responses that serve as highly informative geometry-conditioned priors for Bayesian inversion. Following a ''learn first, observe later'' paradigm, GABI distills information from large datasets of systems with varying geometries, without requiring knowledge of governing PDEs, boundary conditions, or observation processes, into a rich latent prior. At inference time, this prior is seamlessly combined with the likelihood of a specific observation process, yielding a geometry-adapted posterior distribution. Our proposed framework is architecture-agnostic. A creative use of Approximate Bayesian Computation (ABC) sampling yields an efficient implementation that utilizes modern GPU hardware. We test our method on: steady-state heat over rectangular domains; Reynolds-Averaged Navier-Stokes (RANS) flow around airfoils; Helmholtz resonance and source localization on 3D car bodies; RANS airflow over terrain. We find: the predictive accuracy to be comparable to deterministic supervised learning approaches in the restricted setting where supervised learning is applicable; UQ to be well calibrated and robust on challenging problems with complex geometries.
LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models
Authors: Ruilin Yao; Bo Zhang; Jirui Huang; Xinwei Long; Yifang Zhang; Tianyu Zou; Shili Xiong; Yi Rong; Yufei Wu; Shichao Su; Yifan Xu; Wenxi Zeng; Zhaoyu Yang; Guoyou Li; Shilan Zhang; Zichan Li; Yaxiong Chen; Shengwu Xiong; Peng Xu; Jiajun Zhang; Bowen Zhou; David A. Clifton; Luc Van Gool
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to reason about complex and real-world scenarios remains limited.
Existing benchmarks are usually constructed in a task-oriented manner, without a guarantee that different task samples come from the same data distribution. Therefore, they often fall short in evaluating the synergistic effects of lower-level perceptual capabilities on higher-order reasoning. To lift this limitation, we contribute Lens, a multi-level evaluation benchmark of multimodal reasoning with with 3.4K contemporary images and 60K+ human-authored questions covering eight tasks and 12 daily scenarios, forming three progressive task tiers, i.e., perception, understanding, and reasoning. One feature is that each image is equipped with rich annotations for all tasks. Thus, this data set intrinsically supports evaluating MLLMs to handle image-invariable prompts, from basic perception to compositional reasoning. In addition, our images have been   collected manually from social media, with $53$% published after Jan. 2025. We evaluate 15+ frontier MLLMs such as  Qwen2.5-VL,  InternVL3,  GPT-4o  and two reasoning models  QVQ-Max and Kimi-VL. Most models were released in 2025, and none of them achieve an accuracy beyond $60$% in the reasoning tasks. Furthermore, we propose the Self-Driven Multi-Expert Collaborative Framework (SMEC), a framework designed for MLLMs that simulates a panel of experts discussing and exchanging viewpoints via self-generated role-specific prompts. The experimental results confirm the existence of synergistic effects in a hierarchical task structure, where low-level tasks facilitate the reasoning of MLLMs on more complex, high-level tasks. Statistical analysis and ablation studies further demonstrate the comprehensiveness of our dataset and the superiority of our methodology. Project page:  https://github.com/Lens4MLLMs/lens. We conducted the ICCV 2025 MARS2 Multimodal Reasoning Challenge on Lens. https://mars2workshop.github.io/iccv2025/
Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation
Authors: Wei Chen; Xingyu Guo; Shuang Li; Zhao Zhang; Yan Zhong; Fuzhen Zhuang; deqing wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs but is challenged by complex, multi-faceted distributional shifts. Existing methods attempt to reduce distributional shifts by aligning manually selected graph elements (e.g., node attributes or structural statistics), which typically require manually designed graph filters to extract relevant features before alignment. However, such approaches are inflexible: they rely on scenario-specific heuristics, and struggle when dominant discrepancies vary across transfer scenarios.
To address these limitations,
we propose \textbf{ADAlign}, an Adaptive Distribution Alignment framework for GDA. Unlike heuristic methods, ADAlign requires no manual specification of alignment criteria. It automatically identifies the most relevant discrepancies in each transfer and aligns them jointly, capturing the interplay between attributes, structures, and their dependencies. This makes ADAlign flexible, scenario-aware, and robust to diverse and dynamically evolving shifts.
To enable this adaptivity, we introduce the Neural Spectral Discrepancy (NSD), a theoretically principled parametric distance that provides a unified view of cross-graph shifts. NSD leverages neural characteristic function in the spectral domain to encode feature-structure dependencies of all orders, while a learnable frequency sampler adaptively emphasizes the most informative spectral components for each task via minimax paradigm.
Extensive experiments on 10 datasets and 16 transfer tasks show that ADAlign not only outperforms state-of-the-art baselines but also achieves efficiency gains with lower memory usage and faster training.
How does the optimizer implicitly bias the model merging loss landscape?
Authors: Chenxiang Zhang; Alexander Theus; Damien Teney; Antonio Orvieto; Jun Pang; Sjouke Mauw
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. 
Two popular approaches are _linear interpolation_, which simply averages multiple model weights, and _task arithmetic_, which combines task vectors obtained by the difference between finetuned and base models. 
While useful in practice, what properties make merging effective are poorly understood. This paper explores how the optimization dynamics affect the loss landscape geometry and its impact on merging success.
We show that a single quantity -- the _effective noise scale_ -- unifies the impact of different optimizer components on model merging. Across architectures and datasets, merging success is a non-monotonic function of the effective noise scale, with a distinct optimum. Decomposing this quantity, we find that larger learning rates, stronger weight decay, smaller batch sizes, and data augmentation all independently modulate the effective noise scale and exhibit the same qualitative trend.
Unlike prior work connecting optimizer noise to the flatness or generalization of _individual_ minima, we show that it also affects the _global_ loss landscape, predicting when independently trained solutions can be successfully merged. Our findings broaden the understanding of how optimization shapes the loss landscape geometry and its consequences for model merging, suggesting that training dynamics could be further manipulated to improve model merging.
SpatiaLab: Can Vision–Language Models Perform Spatial Reasoning in the Wild?
Authors: Azmine Toushik Wasi; Wahid Faisal; Abdur Rahman; Mahfuz Ahmed Anik; Munem Shahriar; Mohsin Mahmud Topu; Sadia Tasnim Meem; Rahatun Nesa Priti; Sabrina Afroz Mitu; Md. Iqramul Hoque; Shahriyar Zaman Ridoy; Mohammed Eunus Ali; Majd Hawasly; Mohammad Raza; Md Rizwan Parvez
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision–language models (VLMs). Prior work largely relied on synthetic or LLM-generated environments with limited task designs and puzzle-like setups, failing to capture the real-world complexity, visual noise, and diverse spatial relationships that VLMs encounter. To address this, we introduce **_SpatiaLab_**, a comprehensive benchmark for evaluating VLMs’ spatial reasoning in realistic, unconstrained contexts.
**_SpatiaLab_** comprises 1,400 visual question–answer pairs across six major categories: *Relative Positioning, Depth & Occlusion, Orientation, Size & Scale, Spatial Navigation,* and *3D Geometry*, each with five subcategories, yielding 30 distinct task types. Each subcategory contains at least 25 questions, and each main category includes at least 200 questions, supporting both multiple-choice and open-ended evaluation.
Experiments across diverse state-of-the-art VLMs, including open- and closed-source models, reasoning-focused, and specialized spatial reasoning models, reveal a substantial gap in spatial reasoning capabilities compared with humans. In the multiple-choice setup, InternVL3.5-72B achieves 54.93% accuracy versus 87.57% for humans. In the open-ended setting, all models show a performance drop of around 10–25%, with GPT-5-mini scoring highest at 40.93% versus 64.93% for humans. These results highlight key limitations in handling complex spatial relationships, depth perception, navigation, and 3D geometry.
By providing a diverse, real-world evaluation framework, **_SpatiaLab_** exposes critical challenges and opportunities for advancing VLMs’ spatial reasoning, offering a benchmark to guide future research toward robust, human-aligned spatial understanding. **_SpatiaLab_** is available at: https://spatialab-reasoning.github.io/.
CatalystBench: A Comprehensive Multi-Task Benchmark for Advancing Language Models in Catalysis Science
Authors: Xueqing Chen; Jian Xu; Ludi Wang; Yang Gao; Huihan Zhu; Yuanchun Zhou; Yi Du; Cheng-Lin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The discovery of novel catalytic materials is a cornerstone of chemical engineering and sustainable energy, yet it remains a complex, knowledge-intensive process. While Large Language Models (LLMs) have demonstrated remarkable potential in various scientific domains, their application to catalysis is hindered by the lack of specialized, multi-dimensional benchmarks to guide their development and evaluation. To bridge the critical gap, we introduce CatalystBench, a comprehensive and challenging benchmark meticulously constructed from scientific literature and public datasets, specifically designed to assess the capabilities of LLMs in the nuanced domain of catalyst design. The tasks covered by this benchmark dataset encompass the entire closed-loop process of catalyst development, including reading comprehension, experimental analysis and scheme reasoning. Based on this benchmark, we propose a Multi-head Full-task (MFT) domain-specific fine-tuning method that employs coupling task-specific output heads. We systematically compare with other three distinct fine-tuning strategies: Single-Task (ST), Full-Task (FT) and Multi-head Single-Task (MST). The extensive experiments demonstrate that the MFT strategy consistently achieves the most substantial performance improvements across all tasks, underscoring the effectiveness of explicit multi-task architectures in complex scientific reasoning. The resulting CatalystLLM significantly outperforms a wide array of state-of-the-art open-source and closed-source models on CatalystBench. We will publicly release both the CatalystBench benchmark and the CatalystLLM model, providing the community with a robust evaluation framework and a powerful new tool to accelerate AI-driven research in catalytic materials science.
FreeAdapt: Unleashing Diffusion Priors for Ultra-High-Definition Image Restoration
Authors: Xiaoan Liu; Xinyi Liu; Yongjun Zhang; Yi Wan; Li Tingyun; Dongdong Yue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Latent Diffusion Models (LDMs) have recently shown great potential for image restoration owing to their powerful generative priors. However, directly applying them to ultra-high-definition image restoration (UHD-IR) often results in severe global inconsistencies and loss of fine-grained details, primarily caused by patch-based inference and the information bottleneck of the VAE. To overcome these issues, we present FreeAdapt, a plug-and-play framework that unleashes the capability of diffusion priors for UHD-IR. The core of FreeAdapt is a training-free Frequency Feature Synergistic Guidance (FFSG) mechanism, which introduces guidance at each denoising step during inference time. It consists of two modules: 1) Frequency Guidance (FreqG) selectively fuses phase information from a reference image in the frequency domain to enforce structural consistency across the entire image; 2) Feature Guidance (FeatG) injects global contextual information into the self-attention layers of the U-Net, effectively suppressing unrealistic textures in smooth regions and preserving local detail fidelity. In addition, FreeAdapt includes an optional VAE fine-tuning module, where skip connection further enhances the reconstruction of fine-grained textures. Extensive experiments demonstrate that our method achieves superior quantitative performance and visual quality compared to state-of-the-art UHD-IR approaches, and consistently delivers strong gains across multiple LDM-based backbones.
Exposing Mixture and Annotating Confusion for Active Universal Test-Time Adaptation
Authors: Jiayao Tan; Fan Lyu; Chenggong Ni; Fuyuan Hu; Wei Feng; Rui Yao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Universal Test-Time Adaptation (UTTA) tackles the challenge of handling both class and domain shifts in unsupervised settings with stream testing data. Currently, most UTTA methods can only deal with minor shifts and heavily rely on heuristic approaches. To advance UTTA under dual shifts, we propose a novel Active Universal Test-Time Adaptation (AUTTA) framework, Exposing Mixture and Annotating Confusion (EMAC), which incorporates active human annotation into the UTTA setting. To select appropriate samples for annotation in AUTTA, we first identify the mixed regions of target domain samples under dual shifts, highlighting potential candidate samples. We then design a reward-guided active selection strategy to prioritize annotating the most representative samples within this set, maximizing annotation effectiveness. Additionally, to balance the use of pseudo-labels with the limited number of annotations, we propose an adaptation objective designed to address the adaptation imbalance caused by annotation scarcity. Extensive experiments show that the proposed AUTTA approach significantly improves performance and achieves state-of-the-art.
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
Authors: Yuki Takezawa; Anastasia Koloskova; Xiaowen Jiang; Sebastian U Stich
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, a new optimization method based on the linear minimization oracle (LMO), called Muon, has been attracting increasing attention since it can train neural networks faster than the existing adaptive optimization methods, such as Adam.
In this paper, we study how Muon can be utilized in federated learning.
We first show that straightforwardly using Muon as the local optimizer of FedAvg does not work since the LMO is a biased operator.
We then propose FedMuon, which can mitigate this issue and can converge to the stationary point.
We also analyze how solving the LMO approximately affects the convergence rate and find that, surprisingly, FedMuon can converge for any number of Newton-Schulz iterations, while it can converge faster as we solve the LMO more accurately.
Through experiments, we demonstrated that FedMuon can outperform the state-of-the-art federated learning methods.
Play to Generalize: Learning to Reason Through Game Play
Authors: Yunfei Xie; Yinsong Ma; Shiyi Lan; Alan Yuille; Junfei Xiao; Chen Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Developing reasoning capabilities in multimodal large language models (MLLMs) remains challenging. Motivated by literature suggesting that gameplay promotes transferable reasoning skills, we propose a novel post-training method, Visual Game Learning (ViGaL), where MLLMs develop generalizable reasoning skills through playing arcade-like games. Specifically, we show that training a 7B-parameter MLLM via reinforcement learning (RL) on simple games like Snake significantly enhances the downstream performance on multimodal math benchmarks like MathVista, and on multi-discipline questions like MMMU, without seeing any worked solutions, equations, or diagrams during RL.
Remarkably, our model outperforms specialist models post-trained on benchmark-oriented multimodal reasoning data, while preserving the model’s performance on general visual benchmarks, a challenge where specialist models often fall short.
Our findings suggest that multimodal reasoning can emerge from gameplay, pointing to a promising strategy of designing surrogate tasks for RL post-training. The code is available at https://yunfeixie233.github.io/ViGaL.