GenAI4ELab/papercli-papers huggingface dataset:
CIAR: Interval-based Collaborative Decoding for Image Generation Acceleration
Authors: Keming Ye; Zhou Zhao; Fan Wu; Shengyu Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Auto-regressive (AR) models have recently made notable progress in image generation, achieving performance comparable to diffusion-based approaches. However, their computational intensity and sequential nature impede on-device deployment, causing disruptive latency. We address this via a cloud-device collaboration framework \textbf{CIAR}, which utilizes on-device self-verification to handle two key properties of visual synthesis: \textit{the vast token vocabulary} required for high-fidelity images and \textit{inherent spatial redundancy} which leads to extreme predictability in homogeneous regions, while object boundaries exhibit high uncertainty. Uniform verification wastes resources on such redundant tokens. Our solution centers on an on-device token uncertainty quantifier, which adopts continuous probability intervals to accelerate processing and make it feasible for large visual vocabularies instead of conventional discrete solution sets. Additionally, we incorporate a Interval-enhanced decoding module to further speed up decoding while maintaining visual fidelity and semantic consistency via a distribution alignment training strategy.
Extensive experiments demonstrate that CIAR achieves a 2.18× speed-up and reduces cloud requests by 70\%, while preserving image quality compared to existing methods.
ST-HHOL: Spatio-Temporal Hierarchical Hypergraph Online Learning for Crime Prediction
Authors: Keqing Du; Yufan Kang; Xinyu Yang; Wei Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Crime prediction is a critical yet challenging task in urban spatio-temporal forecasting. 
Sparse crime records alone are insufficient to capture latent high-order patterns shaped by heterogeneous contextual factors with spatial and criminal specificity, while high non-stationarity renders conventional offline models ineffective against concept drift. 
To tackle these challenges, we propose a Spatio-Temporal Hierarchical Hypergraph Online Learning framework named ST-HHOL. First, we propose a hierarchical hypergraph convolution network that integrates crime data with heterogeneous contextual factors to uncover dual-specific crime patterns and their co-occurrence relations. Second, we introduce an iterative online learning strategy to address concept drift by employing frequent fine-tuning for short-term dynamics and periodic retraining for long-term shifts. 
Moreover, we adopt a Partially-Frozen LLM that leverages pre-trained sequence priors while adapting its attention mechanisms to crime-specific dependencies, enhancing spatio-temporal reasoning under sparse supervision.
Extensive experiments on three real-world datasets demonstrate that ST-HHOL consistently outperforms state-of-the-art methods in terms of accuracy and robustness, while also providing enhanced interpretability.  Code is available at https://github.com/777Rebecca/ST-HHOL.
SMixer: Rethinking Efficient-Training and Event-Driven SNNs
Authors: Yijie Lu; Xinhao Luo; Yixing Zhang; Zhiyan Wang; Wentao Li; YanhanWang; Zhi Liu; Zhaokun Zhou; Guoqi Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking Neural Networks (SNNs) offer a promising, energy-efficient paradigm for computation, but their practical application is hindered by challenges in architecture design and training costs. For example, Spiking ResNet exhibits relatively low performance, whereas high-performance Spiking Transformers are not truly event driven and cannot be implemented on asynchronous chips. Moreover, the intrinsic time steps and neuron state dynamics result in a substantial computational overhead for training SNNs on GPUs. In response to these problems, we discuss rational architectural design for SNNs and argue that such designs should exhibit three key characteristics: operations fully supported by asynchronous scenarios, low training overhead and competitive performance. In light of this, we adopt the event-driven friendly Spiking Mixer (SMixer) as the foundational architecture and develop a spike feature Spatial-Temporal Pruning (STP) framework with a high pruning ratio and no trainable parameters to reduce the training overhead. Based on a statistical analysis of sparse spike features, STP eliminates redundant spike features across both spatial and temporal dimensions, thereby reducing the input features and computational load during training. It adaptively selects the most salient spike tokens spatially and dynamically constrains neuron firing rates temporally. By leveraging STP and architectural adaptation, SMixer accelerates training while ensuring a fully event-driven characteristics and maintaining competitive performance, offering valuable insights for the design of efficient, event-driven SNNs.
Online time series prediction using feature adjustment
Authors: Xiannan Huang; Shuhan Qiu; Jiayuan Du; Chao Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series forecasting is of significant importance across various domains. However, it faces significant challenges due to distribution shift. This issue becomes particularly pronounced in online deployment scenarios where data arrives sequentially, requiring models to adapt continually to evolving patterns. Current time series online learning methods focus on two main aspects: selecting suitable parameters to update (e.g., final layer weights or adapter modules) and devising suitable update strategies (e.g., using recent batches, replay buffers, or averaged gradients). We challenge the conventional parameter selection approach, proposing that distribution shifts stem from changes in underlying latent factors influencing the data. Consequently, updating the feature representations of these latent factors may be more effective. To address the critical problem of delayed feedback in multi-step forecasting (where true values arrive much later than predictions), we introduce ADAPT-Z (Automatic Delta Adjustment via Persistent Tracking in Z-space). ADAPT-Z utilizes an adapter module that leverages current feature representations combined with historical gradient information to enable robust parameter updates despite the delay. Extensive experiments demonstrate that our method consistently outperforms standard base models without adaptation and surpasses state-of-the-art online learning approaches across multiple datasets.
An Ensemble Framework for Unbiased Language Model Watermarking
Authors: Yihan Wu; Ruibo Chen; Georgios Milis; Heng Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models become increasingly capable and widely deployed, verifying the provenance of machine-generated content is critical to ensuring trust, safety, and accountability. Watermarking techniques have emerged as a promising solution by embedding imperceptible statistical signals into the generation process. Among them, unbiased watermarking is particularly attractive due to its theoretical guarantee of preserving the language model's output distribution, thereby avoiding degradation in fluency or detectability through distributional shifts. However, existing unbiased watermarking schemes often suffer from weak detection power and limited robustness, especially under short text lengths or distributional perturbations. In this work, we propose ENS, a novel ensemble framework that enhances the detectability and robustness of logits-based unbiased watermarks while strictly preserving their unbiasedness. ENS sequentially composes multiple independent watermark instances, each governed by a distinct key, to amplify the watermark signal. We theoretically prove that the ensemble construction remains unbiased in expectation and demonstrate how it improves the signal-to-noise ratio for statistical detectors. Empirical evaluations on multiple LLM families show that ENS substantially reduces the number of tokens needed for reliable detection and increases resistance to smoothing and paraphrasing attacks without compromising generation quality.
Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Authors: Justin Cui; Jie Wu; Ming Li; Tao Yang; Xiaojie Li; Rui Wang; Andrew Bai; Yuanhao Ban; Cho-Jui Hsieh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively high computational costs, particularly when extending generation to long videos. Recent work has explored autoregressive formulations for long video generation, typically by distilling from short-horizon bidirectional teachers. Nevertheless, given that teacher models cannot synthesize long videos, the extrapolation of student models beyond their training horizon often leads to pronounced quality degradation, arising from the compounding of errors within the continuous latent space. In this paper, we propose a simple yet effective approach to mitigate quality degradation in long-horizon video generation without requiring supervision from long-video teachers or retraining on long video datasets. Our approach centers on exploiting the rich knowledge of teacher models to provide guidance for the student model through sampled segments drawn from self-generated long videos. Our method maintains temporal consistency while scaling video length by up to 20$\times$ beyond teacher's capability, avoiding common issues such as over-exposure and error-accumuation without recomputing overlapping frames like previous methods. When scaling up the computation, our method shows the capability of generating videos up to 4 minutes and 15 seconds, 
equivalent to 99.9\% of the maximum span supported by our base model’s position embedding and more than 50x longer than that of our baseline model.  Experiments on standard benchmarks and our proposed improved benchmark demonstrate that our approach substantially outperforms  baseline methods in both fidelity and consistency. Our long-horizon videos demo can be found at http://self-forcing-plus-plus.github.io/
Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Authors: Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Masahiro Ikeda
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We derive a new Rademacher complexity bound for deep neural networks using Koopman operators, group representations, and reproducing kernel Hilbert spaces (RKHSs).
The proposed bound describes why the models with high-rank weight matrices generalize well.
Although there are existing bounds that attempt to describe this phenomenon, these existing bounds can be applied to limited types of models.
We introduce an algebraic representation of neural networks and a kernel function to construct an RKHS to derive a bound
for a wider range of realistic models.
This work paves the way for the Koopman-based theory for Rademacher complexity bounds to be valid for more practical situations.
PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling
Authors: Kai Yang; Yuqi Huang; Junheng Tao; Wanyu Wang; Qitian Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling 3D dynamics is a fundamental problem in multi-body systems across scientific and engineering domains and has important practical implications in object trajectory prediction and simulation. While recent GNN-based approaches have achieved strong performance by enforcing geometric symmetries, encoding high-order features or incorporating neural-ODE mechanics, they typically depend on explicitly observed structures and inherently fail to capture the unobserved interactions that are crucial to complex physical behaviors and dynamics mechanism. In this paper, we propose PAINET, a principled SE(3)-equivariant neural architecture for learning all-pair interactions in multi-body systems. The model comprises: (1) a novel physics-inspired attention network derived from the minimization trajectory of an energy function, and (2) a parallel decoder that preserves equivariance while enabling efficient inference. 
Empirical results on diverse real-world benchmarks, including human motion capture, molecular dynamics, and large-scale protein simulations, show that PAINET consistently outperforms recently proposed models, yielding 4.7% to 41.5% error reductions in 3D dynamics prediction with comparable computation costs in terms of time and memory. Our codes, baseline models and datasets are available at https://github.com/Icarus1411/PAINET.
PHyCLIP: $\ell_1$-Product of Hyperbolic Factors Unifies Hierarchy and Compositionality in Vision-Language Representation Learning
Authors: Daiki Yoshikawa; Takashi Matsubara
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language models have achieved remarkable success in multi-modal representation learning from large-scale pairs of visual scenes and linguistic descriptions. However, they still struggle to simultaneously express two distinct types of semantic structures: the hierarchy within a concept family (e.g., *dog* $\preceq$ *mammal* $\preceq$ *animal*) and the compositionality across different concept families (e.g., "a dog in a car" $\preceq$ *dog*, *car*). Recent works have addressed this challenge by employing hyperbolic space, which efficiently captures tree-like hierarchy, yet its suitability for representing compositionality remains unclear. To resolve this dilemma, we propose *PHyCLIP*, which employs an $\ell_1$-*P*roduct metric on a Cartesian product of *Hy*perbolic factors. With our design, intra-family hierarchies emerge within individual hyperbolic factors, and cross-family composition is captured by the $\ell_1$-product metric, analogous to a Boolean algebra. Experiments on zero-shot classification, retrieval, hierarchical classification, and compositional understanding tasks demonstrate that PHyCLIP outperforms existing single-space approaches and offers more interpretable structures in the embedding space.
SIGMA-Gen: Structure and Identity Guided Multi-Subject Assembly for Image Generation
Authors: Oindrila Saha; Vojtech Krs; Radomir Mech; Subhransu Maji; Kevin James Blackburn-Matzen; Matheus Gadelha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present SIGMA-Gen, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-Gen is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision — from coarse 2D or 3D boxes to pixel-level segmentations and depth — with a single model. To enable this, we introduce SIGMA-Set27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k images. Through extensive evaluation we demonstrate that SIGMA-Gen achieves state-of-the-art performance in identity preservation, image generation quality, and speed.
MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Authors: Siyoon Jin; Seongchan Kim; Jae Ho Lee; Dahyun Chung; Hyunwook Choi; Jisu Nam; Jiyoung Kim; Seungryong Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, 
a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether noun and verb tokens capture instances and their relations; and semantic propagation, via video-to-video attention, which assesses whether instance bindings persist across frames. We find both effects concentrate in a small subset of interaction-dominant layers. Motivated by this, we introduce MATRIX, a simple and effective regularization that aligns attention in specific layers of video DiTs with multi-instance mask tracks from the MATRIX-11K dataset, enhancing both grounding and propagation. We further propose InterGenEval, an evaluation protocol for interaction-aware video generation. In experiments, MATRIX improves both interaction fidelity and semantic alignment while reducing drift and hallucination. Extensive ablations validate our design choices. Codes and weights will be released.
EarthSE: A Benchmark Evaluating Earth Scientific Exploration Capability for Large Language Models
Authors: Wanghan Xu; Xiangyu Zhao; Yuhao Zhou; Xiaoyu Yue; Ben Fei; Fenghua Ling; Wenlong Zhang; LEI BAI
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advancements in Large Language Models (LLMs) drive interest in scientific applications, necessitating specialized benchmarks such as Earth science. Existing benchmarks either present a general science focus devoid of Earth science specificity or cover isolated subdomains, lacking holistic evaluation. Furthermore, current benchmarks typically neglect the assessment of LLMs' capabilities in open-ended scientific exploration. In this paper, we present a comprehensive and professional benchmark for the Earth sciences, designed to evaluate the capabilities of LLMs in scientific exploration within this domain, spanning from fundamental to advanced levels. Leveraging a corpus of 100,000 research papers, we first construct two Question Answering (QA) datasets: Earth-Iron, which offers extensive question coverage for broad assessment, and Earth-Silver, which features a higher level of difficulty to evaluate professional depth. These datasets encompass five Earth spheres, 114 disciplines, and 11 task categories, assessing foundational knowledge crucial for scientific exploration. Most notably, we introduce Earth-Gold with new metrics, a dataset comprising open-ended multi-turn dialogues specifically designed to evaluate the depth and diversity of LLMs in scientific exploration, including methodology induction, limitation analysis, and concept proposal. Extensive experiments reveal limitations in 11 leading LLMs across different domains and tasks, highlighting considerable room for improvement in their scientific exploration capabilities. The data is available on https://huggingface.co/ai-earth.
EntropyLong: Effective Long-Context Training via Predictive Uncertainty
Authors: Junlong Jia; Ziyang Chen; Xing W; Chaochen Gao; Zijia Lin; Songlin Hu; GuoBinghui
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training long-context language models to capture long-range dependencies requires specialized data construction. Current approaches, such as generic text concatenation or heuristic-based variants, frequently fail to guarantee genuine long-range dependencies. We propose \textbf{EntropyLong}, a novel data construction method that leverages predictive uncertainty to verify dependency quality. Our approach identifies high-entropy positions in documents, retrieves semantically relevant contexts from large corpora, and verifies their utility by assessing whether they reduce prediction entropy. This \textit{model-in-the-loop verification} ensures each dependency represents measurable information gain rather than spurious correlation. We construct training samples with long-range dependencies by combining original documents with these verified contextual supplements. Using FineWeb-Edu and Cosmopedia, we generate a dataset of 128K-length sequences with verified dependencies. Models trained on this data demonstrate significant improvements on RULER benchmarks, particularly in tasks requiring distant information. Following instruction fine-tuning, our models also achieve substantial gains on LongBench-v2, demonstrating enhanced long-context understanding. Extensive ablation studies further validate the necessity and effectiveness of entropy-based verification for long-context training.
Entropy-Monitored Kernelized Token Distillation for Audio-Visual Compression
Authors: Hyoungseob Park; Lipeng Ke; Pritish Mohapatra; Huajun Ying; sankar venkataraman; Alex Wong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a method for audio-visual knowledge distillation. Existing methods typically distill a student model from the latent embeddings or outputs of a teacher. The former requires matching feature dimensions, if not the same architecture, between teacher and student models while the latter supports any teacher-student pairing, but tends to be less performant. Unlike them, we do not explicitly distill from latent embeddings or outputs, but the pairwise relationships between embeddings across samples for each modality; this is realized as a kernel, which is the crux of our method, "Kernelized Token Distillation (KTD)". Specifically, we tokenize and embed the input for a given modality, and compute the Gram matrix across tokens, from which we distill. As audio and visual modalities afford different information for a task, we adaptively modulate distillation by measuring the entropy of each modality, leading to an Entropy-Monitored Kernelized Token Distillation (EM-KTD) scheme. Our method allows for flexibility in complexity of kernel function to model relationships across tokens, which are selectively distilled to ensure high-fidelity supervision for the student. We evaluate EM-KTD on VGGSound and AVS-Bench, where we use 94% fewer parameters than the teacher while preserving 96.9% in performance for audio-visual event recognition and 96.5% on audio-visual segmentation.
ORCaS: Unsupervised Depth Completion via Occluded Region Completion as Supervision
Authors: Hyoungseob Park; Runjian Chen; Patrick Rim; Dong Lao; Alex Wong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a method for inferring an egocentric dense depth map from an RGB image and a sparse point cloud. 
The crux of our method lies in modeling the 3D scene implicitly within the latent space and learning an inductive bias in an unsupervised manner through principles of Structure-from-Motion. To force the learning of this inductive bias, we propose to optimize for an ill-posed objective during training: predicting latent features that are not observed in the input view, but exist in the 3D scene. This is facilitated by means of rigid warping of latent features from the input view to a nearby or adjacent (co-visible) view of the same 3D scene. "Empty" regions in the latent space that correspond to regions occluded from the input view are completed by a Contextual eXtrapolation (ConteXt) mechanism based on features visible in input view. The learned inductive bias of ConteXt can be transferred to modulate the features of the input view to improve fidelity. We term our method "Occluded Region Completion as Supervision" or ORCaS. We evaluate ORCaS on VOID1500 and NYUv2 benchmark datasets, where we improve over the best existing method by 8.91% across all metrics. ORCaS also improves generalization from VOID1500 to ScanNet and NYUv2 by 15.7% and robustness to low density inputs by 31.2%.