GenAI4ELab/papercli-papers huggingface dataset:
Self-Guided Low Light Object Detection Framework
Authors: Gwangik Shin; Jaeha Song; Soonmin Hwang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Object detection in low-light environments is inherently challenging due to limited contrast and heavy noise, both of which significantly degrade feature representations. In this paper, we propose a novel self-guided low-light object detection framework that effectively addresses these issues without introducing additional parameters or increasing inference time. Our method incorporates a detachable auxiliary pipeline during training, consisting of an image enhancement module and a denoising module, followed by a Fourier-domain fusion block. This pipeline improves the feature representation of the detector's backbone, enhancing its robustness under low-light conditions. Importantly, at inference time, our method incurs no additional computational cost compared to the baseline detector while achieving substantial performance improvements. Extensive experiments on widely used low-light object detection benchmarks, such as DARK FACE and ExDark, demonstrate that our method achieves state-of-the-art performance. Notably, experiments on the nuImages dataset show that our approach can outperform domain adaptation methods—especially when a large domain gap between source and target domains is inevitable in the real-world applications—highlighting its practical effectiveness. Code is available at https://github.com/gw-shin/SGLDet.
PointRePar : SpatioTemporal Point Relation Parsing for Robust Category-Unified 3D Tracking
Authors: Juntao Liu; Zikun Zhou; Zhuotao Tian; Guangming Lu; Jun Yu; Wenjie Pei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 3D single object tracking (SOT) remains a highly challenging task due to the inherent crux in learning representations from point clouds to effectively capture both spatial shape features and temporal motion features. Most existing methods employ a category-specific optimization paradigm, training the tracking model individually for each object category to enhance tracking performance, albeit at the expense of generalizability across different categories. In this work, we propose a robust category-unified 3D SOT model, referred to as SpatioTemporal Point Relation Parsing model (*PointRePar*), which is capable of joint training across multiple categories while excelling in unified feature learning for both spatial shapes and temporal motions. Specifically, the proposed *PointRePar* captures and parses the latent point relations across both spatial and temporal domains to learn superior shape and motion characteristics for robust tracking. On the one hand, it models the multi-scale spatial point relations using a Mamba-based U-Net architecture with adaptive point-wise feature refinement. On the other hand, it captures both the point-level and box-level temporal relations to exploit the latent motion features. Extensive experiments across three benchmarks demonstrate that our *PointRePar* not only outperforms the existing category-unified 3D SOT methods significantly, but also compares favorably against the state-of-the-art category-specific methods. Codes will be released.
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Authors: Wenzhe Yin; Zehao Xiao; Pan Zhou; Shujian Yu; Jiayi Shen; Jan-Jakob Sonke; Stratis Gavves
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize mutual information, primarily aligning pairwise samples across modalities while overlooking distributional differences.  In addition, InfoNCE has inherent conflict in terms of alignment and uniformity in multimodality, leading to suboptimal alignment with modality gaps. To overcome the limitations, we propose CS-Aligner, a novel framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz (CS) divergence with mutual information. CS-Aligner captures both the global distribution information of each modality and the pairwise semantic relationships. We find that the CS divergence seamlessly addresses the InfoNCE's alignment-uniformity conflict and serves complementary roles with InfoNCE, yielding tighter and more precise alignment. Moreover, by introducing distributional alignment, CS-Aligner enables incorporating additional information from unpaired data and token-level representations, enhancing flexible and fine-grained alignment in practice. Experiments on text-to-image generation and cross-modality retrieval tasks demonstrate the effectiveness of our method on vision-language alignment.
CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild
Authors: Balamurugan Thambiraja; Omid Taheri; Radek Danecek; Giorgio Becherini; Gerard Pons-Moll; Justus Thies
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hands play a central role in daily life, yet modeling natural hand motions remains
underexplored. Existing methods that tackle text-to-hand-motion generation or
hand animation captioning rely on studio-captured datasets with limited actions
and contexts, making them costly to scale to “in-the-wild” settings. Further,
contemporary models and their training schemes struggle to capture animation
fidelity with text–motion alignment. To address this, we (1) introduce ‘3D Hands
in the Wild’ (3D-HIW), a dataset of 32K 3D hand-motion sequences and aligned
text, and (2) propose CLUTCH, an LLM-based hand animation system with two
critical innovations: (a) SHIFT, a novel VQ-VAE architecture to tokenize hand
motion, and (b) a geometric refinement stage to finetune the LLM. To build 3D-
HIW, we propose a data annotation pipeline that combines vision–language models
(VLMs) and state-of-the-art 3D hand trackers, and apply it to a large corpus of
egocentric action videos covering a wide range of scenarios. To fully capture
motion in-the-wild, CLUTCH employs SHIFT, a part–modality decomposed VQ-
VAE, which improves generalization and reconstruction fidelity. Finally, to improve
animation quality, we introduce a geometric refinement stage, where CLUTCH is
co-supervised with a reconstruction loss applied directly to decoded hand motion
parameters. Experiments demonstrate state-of-the-art performance on text-to-
motion and motion-to-text tasks, establishing the first benchmark for scalable
in-the-wild hand motion modelling. Code, data and models will be released.
Fostering Video Reasoning via Next-Event Prediction
Authors: Haonan Wang; Hongfu Liu; Xiangyan Liu; Chao Du; Kenji Kawaguchi; Ye Wang; Tianyu Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Next-token prediction serves as the foundational learning task that enables reasoning in LLMs. But what should the learning task be when aiming to equip MLLMs with temporal reasoning capabilities over video inputs? Existing tasks such as video captioning primarily promote modality alignment, while video question answering typically relies on annotations from humans or much stronger MLLMs. To address this gap, we propose next-event prediction (NEP), a learning task that harnesses future video segments as a rich, self-supervised signal to foster temporal reasoning. We segment each video into past and future frames: the MLLM takes the past frames as input and predicts events in the future, thereby encouraging the model to reason temporally in order to complete the task. To study this learning task, we curate V1-33K, a dataset comprising 33,000 automatically extracted videos spanning diverse real-world scenarios. Using the same videos, we further explore a range of video instruction-tuning tasks data to provide controlled comparisons and isolate the effect of NEP. To evaluate progress, we introduce FutureBench to assess coherence in predicting unseen future events. Experiments validate that NEP offers a scalable and effective training task for fostering temporal reasoning in MLLMs.
3DSMT: A Hybrid Spiking Mamba-Transformer for Point Cloud Analysis
Authors: Zhiming Zhou; Yong He; Qiaoyun Wu; Chaoxu Mu; Ajmal Saeed Mian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The sparse unordered structure of point clouds causes unnecessary computation and energy consumption in deep models. 
Conventionally, the Transformer architecture is leveraged to model global relationships in point clouds, however, its quadratic complexity restricts scalability. Although the Mamba architecture enables efficient global modeling with linear complexity, it lacks natural adaptability to unordered point clouds. 
Spiking Neural Network (SNN) is an energy-efficient alternative to Artificial Neural Network (ANN), offering an ultra low-power event-driven paradigm. 
The inherent sparsity and event-driven characteristics of SNN are highly compatible with the sparse distribution of point clouds. To balance efficiency and performance, we propose a hybrid spiking Mamba-Transformer (3DSMT) model for point cloud analysis. 3DSMT integrates a Spiking Local Offset Attention module to efficiently capture fine-grained local geometric features with a spiking Mamba block designed for unordered point clouds to achieve global feature integration with linear complexity. Experiments show that 3DSMT achieves state-of-the-art performance among SNN-based methods in shape classification, few-shot classification, and part segmentation tasks, significantly reducing computational energy consumption while also outperforming numerous ANN-based models.
Our source code is in supplementary material and will be made publicly available
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
Authors: Marcel Wienöbst; Leonard Henckel; Sebastian Weichwald
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in standard settings. This performance calls for revisiting discrete search over graphs as a reasonable approach to causal discovery.
PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs
Authors: Yixuan Wu; Yang Zhang; Jian Wu; Philip Torr; Jindong Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable performance in vision-language tasks, such as image captioning and visual question answering. However, these models often struggle with fine-grained visual understanding and are prone to hallucinations, primarily due to over-reliance on linguistic priors that distract them from leveraging actual visual information. This results in outputs that are often unanchored in the visual content, leading to errors. To address these challenges, we introduce MMGrounded-PostAlign, a post-multimodal alignment framework designed to enhance the visual understanding capabilities of MLLMs and mitigate hallucinations. In the framework, the visual grounding module identifies the referred objects in the image, while the textual grounding module generates the rationale for the final answer. This dual grounding approach ensures that outputs are firmly anchored in both visual and textual evidence. In particular, we incorporate a negative rejection mechanism within the visual grounding module to distinguish between grounded entities and non-existent objects influenced by linguistic biases. Moreover, we propose a selective reasoning mechanism within the textual grounding module to adjust the model’s reasoning strategy based on the complexity of the query. These innovations together work to resolve the issues associated with hallucinations and enhance the overall alignment between visual and textual modalities. Extensive evaluations on benchmarks such as POPE, HaloQuest, ReasonSeg, MME, and MMBench demonstrate significant improvements in fine-grained visual understanding and hallucination suppression, showcasing the effectiveness of our approach in real-world multimodal tasks.
wd1:  Weighted Policy Optimization for Reasoning in Diffusion Language Models
Authors: Xiaohang Tang; Rares Dolga; Sangwoong Yoon; Ilija Bogunovic
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Improving the reasoning capabilities of diffusion-based large language models (dLLMs) through reinforcement learning (RL) remains an open problem. The intractability of dLLMs likelihood function necessitates approximating the current, old, and reference policy likelihoods at each policy optimization step. This reliance introduces additional computational overhead, and can lead to large variance and estimation error in RL objective -- particularly in computing the policy ratio for importance sampling. To mitigate these issues, we introduce wd1, a novel ratio-free policy optimization approach that reformulates the RL objective as a weighted log-likelihood, requiring only a single approximation for the current parametrized policy likelihood. We formally show that our proposed method can be interpreted as energy-guided discrete diffusion training combined with negative sample unlearning, thereby confirming its theoretical soundness. In experiments on LLaDA-8B model, wd1 outperforms diffusion-based GRPO (d1) while requiring lower computational cost, achieving up to a +59\% improvement in accuracy. Furthermore, we extend wd1 to denoising-stepwise weighted policy optimization (wd1++), achieving state-of-the-art math performance of 44.2\% on MATH500 and 84.5\% on GSM8K with only 20 RL training steps.
HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models
Authors: Seyedmorteza Sadat; Farnood Salehi; Romann M. Weber
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While diffusion models have made remarkable progress in image generation, their outputs can still appear unrealistic and lack fine details, especially when using fewer number of neural function evaluations (NFEs) or lower guidance scales. To address this issue, we propose a novel momentum-based sampling technique, termed history-guided sampling (HiGS), which enhances quality and efficiency of diffusion sampling by integrating recent model predictions into each inference step. Specifically, HiGS leverages the difference between the current prediction and a weighted average of past predictions to steer the sampling process toward more realistic outputs with better details and structure. Our approach introduces practically no additional computation and integrates seamlessly into existing diffusion frameworks, requiring neither extra training nor fine-tuning. Extensive experiments show that HiGS consistently improves image quality across diverse models and architectures and under varying sampling budgets and guidance scales. Moreover, using a pretrained SiT model, HiGS achieves a new state-of-the-art FID of 1.61 for unguided ImageNet generation at 256$\times$256 with only 30 sampling steps (instead of the standard 250). We thus present HiGS as a plug-and-play enhancement to standard diffusion sampling that enables faster generation with higher fidelity.
GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent System
Authors: Yiqin Yang; Xu Yang; Yuhua Jiang; Ni Mu; Hao Hu; Runpeng Xie; Ziyou Zhang; Siyuan Li; Yuan-Hua Ni; Qianchuan Zhao; Bo XU
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the realm of multi-agent systems, the challenge of partial observability is a critical barrier to effective coordination and decision-making. Existing approaches, such as belief state estimation and inter-agent communication, often fall short. Belief-based methods are limited by their focus on past experiences without fully leveraging global information, while communication methods often lack a robust model to effectively utilize the auxiliary information they provide.
To solve this issue, we propose Global State Diffusion Algorithm to infer the global state based on the local observations.
By formulating the state inference process as a multi-modal diffusion process, GlobeDiff overcomes ambiguities in state estimation while simultaneously inferring the global state with high fidelity.
We prove that the estimation error of GlobeDiff under both unimodal and multi-modal distributions can be bounded.
Extensive experimental results demonstrate that GlobeDiff achieves superior performance and is capable of accurately inferring the global state.
DeRaDiff: Denoising Time Realignment of Diffusion Models
Authors: Ratnavibusena Don Shahain Manujith; Teoh Tze Tzun; Kenji Kawaguchi; Yang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances align diffusion models with human preferences to increase aesthetic appeal and mitigate artifacts and biases. Such methods aim to maximize a conditional output distribution aligned with higher rewards whilst not drifting far from a pretrained prior. This is commonly enforced by KL (Kullback–Leibler) regularization. As such, a central issue still remains: how does one choose the right regularization strength? Too high of a strength leads to limited alignment and too low of a strength leads to "reward hacking". This renders the task of choosing the correct regularization strength highly non-trivial. Existing approaches sweep over this hyperparameter by aligning a pretrained model at multiple regularization strengths and then choose the best strength. Unfortunately, this is prohibitively expensive. We introduce _DeRaDiff_, a _denoising-time realignment_ procedure that, after aligning a pretrained model once, modulates the regularization strength _during sampling_ to emulate models trained at other regularization strengths—_without any additional training or fine-tuning_. Extending decoding-time realignment from language to diffusion models, DeRaDiff operates over iterative predictions of continuous latents by replacing the reverse-step reference distribution by a geometric mixture of an aligned and reference posterior, thus giving rise to a closed-form update under common schedulers and a single tunable parameter, $\lambda$, for on-the-fly control. Our experiments show that across multiple text–image alignment and image-quality metrics, our method consistently provides a strong approximation for models aligned entirely from scratch at different regularization strengths. Thus, by enabling very precise inference-time control of the regularization strength, our method yields an efficient way to search for the optimal strength, eliminating the need for expensive alignment sweeps and thereby substantially reducing computational costs. The official implementation is available at https://github.com/itsShahain/DeRaDiff.
Learnable Sparsity for Vision Generative Models
Authors: Yang Zhang; Er Jin; Wenzhong Liang; Yanfei Dong; Ashkan Khakzar; Philip Torr; Johannes Stegmaier; Kenji Kawaguchi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which raises computational complexity and memory demands. The increased computational demand poses challenges for deployment, elevates inference costs, and impacts the environment. While some studies have explored pruning techniques to improve the memory efficiency of diffusion models, most existing methods require extensive retraining to maintain model performance. Retraining a large model is extremely costly and resource-intensive, which limits the practicality of pruning methods. In this work, we achieve low-cost pruning by proposing a general pruning framework for vision generative models that learns a differentiable mask to sparsify the model. To learn a mask that minimally deteriorates the model, we design a novel end-to-end pruning objective that spans the entire generation process over all steps. Since end-to-end pruning is memory-intensive, we further design a time step gradient checkpointing technique for the end-to-end pruning, a technique that significantly reduces memory usage during optimization, enabling end-to-end pruning within a limited memory budget. Results on the state-of-the-art U-Net diffusion models Stable Diffusion XL (SDXL) and DiT flow models (FLUX) show that our method efficiently prunes 20% of parameters in just 10 A100 GPU hours, outperforming previous pruning approaches.
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
Authors: Dmitry Bylinkin; Mikhail Aleksandrov; Savelii Chezhegov; Aleksandr Beznosikov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Physics-informed neural networks (PINNs) have gained prominence in recent years and are now effectively used in a number of applications. However, their performance remains unstable due to the complex landscape of the loss function. To address this issue, we reformulate PINN training as a nonconvex-strongly concave saddle-point problem. After establishing the theoretical foundation for this approach, we conduct an extensive experimental study, evaluating its effectiveness across various tasks and architectures. Our results demonstrate that the proposed method outperforms the current state-of-the-art techniques.
Precise and Interpretable Editing of Code Knowledge in Large Language Models
Authors: Min Xue; Nikolai Bolik; Lennart Stöpler; Erik Imgrund; Janik Schmid; Artur Andrzejak
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have demonstrated outstanding capabilities in various code-related tasks, including code completion, translation, or summarization. However, these pretrained models are static, posing a challenge to incorporate new knowledge into an LLM to correct erroneous behavior. Approaches such as retraining or fine-tuning demand extensive labeled datasets and might be computationally expensive, while prompt engineering fails to change models permanently. Knowledge Editing (KE) techniques offer a more efficient alternative, enabling model updates with minimal data, even just a single example. Nevertheless, existing KE methods often manipulate parameters within the Transformer's multi-layer perceptrons (MLPs), where neuronal polysemanticity hinders both the precision and interpretability of the edits. To address these limitations, we exploit TransCoder, an MLP-like model component with a wide and sparsely activated hidden feature vector. Specifically, we introduce **TransCoder-based Precise Editing** (**TCPE**), a novel method that leverages the sparsity and monosemanticity of the TransCoder’s neurons for highly localized knowledge editing. TCPE exhibits neuron-level mechanistic interpretability characteristics, revealing the correspondence between the edited neurons and the specific code-related knowledge. Furthermore, we present KECode, a new evaluation benchmark for code-to-code translation based on functional equivalence. Using KECode, we conduct a systematic evaluation of representative KE methods in the context of code-to-code translation. Our experimental results demonstrate that TCPE outperforms existing KE methods, achieving a substantial improvement of translation accuracy of CodeLlama-7b-Instruct from 57.5% to 64.0% in a low-resource scenario of Java-to-D translation.