paper_id uint32 0 5.29k | title stringlengths 14 183 | authors listlengths 1 36 | abstract large_stringlengths 246 3.59k | type stringclasses 3
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0 | Feature-aware Modulation for Learning from Temporal Tabular Data | [
"Haorun Cai",
"Han-Jia Ye"
] | While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between features and labels continuously evolve. Static models assume fixed mappings to ensure generalization, whereas adaptive models may overfit to tr... | poster | null | null | null | [] | [] | [] | [
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1 | Multimodal Tabular Reasoning with Privileged Structured Information | [
"Jun-Peng Jiang",
"Yu Xia",
"Hai-Long Sun",
"Shiyin Lu",
"Qingguo Chen",
"Weihua Luo",
"Kaifu Zhang",
"De-Chuan Zhan",
"Han-Jia Ye"
] | Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically app... | poster | 2506.04088 | null | null | [] | [] | [] | [
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2 | Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation | [
"Zhi-Kai Chen",
"Jun-Peng Jiang",
"Han-Jia Ye",
"De-Chuan Zhan"
] | Autoregressive (AR) image generation models can produce high-fidelity images but often struggle with slow inference due to their token-by-token, sequential decoding. Speculative decoding, which employs a draft model to approximate the AR model’s output, offers a promising way to reduce inference time. While this techni... | poster | null | null | null | [] | [] | [] | [
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3 | AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments | [
"Weijie Zhou",
"Xuantang Xiong",
"Yi Peng",
"Manli Tao",
"Chaoyang Zhao",
"Honghui Dong",
"Ming Tang",
"Jinqiao Wang"
] | Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environments where information is often incomplete due to occlusion or limited field of view. Humans, in contrast, actively explore and interact with t... | poster | null | null | null | [] | [] | [] | [
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4 | StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold | [
"Zhizhong Li",
"Sina Sajadmanesh",
"Jingtao Li",
"Lingjuan Lyu"
] | Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning in performance, partly due to its insufficient exploitation of the geometric structure underlying low-rank manifolds. In this paper, we int... | spotlight | 2510.01938 | null | null | [] | [] | [] | [
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5 | Continuous Subspace Optimization for Continual Learning | [
"Quan Cheng",
"Yuanyu Wan",
"Lingyu Wu",
"Chenping Hou",
"Lijun Zhang"
] | Continual learning aims to learn multiple tasks sequentially while preserving prior knowledge, but faces the challenge of catastrophic forgetting when acquiring new knowledge. Recently, approaches leveraging pre-trained models have gained increasing popularity to mitigate this issue, due to the strong generalization ab... | poster | 2505.11816 | null | null | [] | [] | [] | [
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6 | Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings | [
"Xingguang Wei",
"Haomin Wang",
"Shenglong Ye",
"Ruifeng Luo",
"Zhang",
"Lixin Gu",
"Jifeng Dai",
"Yu Qiao",
"Wenhai Wang",
"Hongjie Zhang"
] | We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable \textit{things} and the semantic regions of uncountable \textit{stuff} in computer-aided design (CAD) drawings composed of vector graphical primitives.Existing methods typically rely on image rasterization, ... | poster | 2505.23395 | null | null | [] | [] | [] | [
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7 | HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations | [
"Shuaicheng Zhang",
"Haohui Wang",
"Junhong Lin",
"Xiaojie Guo",
"Yada Zhu",
"Si Zhang",
"Dongqi Fu",
"Dawei Zhou"
] | Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily ... | poster | 2510.10864 | null | null | [] | [] | [] | [
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8 | Learning to Plan Like the Human Brain via Visuospatial Perception and Semantic-Episodic Synergistic Decision-Making | [
"Tianyuan Jia",
"Ziyu Li",
"Qing Li",
"Xiuxing Li",
"Xiang Li",
"Chen Wei",
"Li Yao",
"Xia Wu"
] | Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and... | poster | null | null | null | [] | [] | [] | [
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9 | Cognitive Predictive Processing: A Human-like Framework for Adaptive Exploration in Open-World Reinforcement Learning | [
"boheng liu",
"Ziyu Li",
"Chenghua Duan",
"YuTian Liu",
"Zhuo Wang",
"Xiuxing Li",
"Qing Li",
"Xia Wu"
] | Open-world reinforcement learning challenges agents to develop intelligent behavior in vast exploration spaces. Recent approaches like LS-Imagine have advanced the field by extending imagination horizons through jumpy state transitions, yet remain limited by fixed exploration mechanisms and static jump thresholds that ... | poster | null | null | null | [] | [] | [] | [
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10 | FlexWorld: Progressively Expanding 3D Scenes for Flexible-View Exploration | [
"Luxi Chen",
"Zihan Zhou",
"Min Zhao",
"Yikai Wang",
"Ge Zhang",
"Wenhao Huang",
"Hao Sun",
"Ji-Rong Wen",
"Chongxuan LI"
] | Generating flexible-view 3D scenes, including 360° rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework that progressively constructs a persistent 3D Gaussian splatting representation by synthesizing and integrating new 3D content. To h... | poster | null | null | null | [] | [] | [] | [
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11 | Learning Efficient Fuse-and-Refine for Feed-Forward 3D Gaussian Splatting | [
"Yiming Wang",
"Lucy Chai",
"Xuan Luo",
"Michael Niemeyer",
"Manuel Lagunas",
"Stephen Lombardi",
"Siyu Tang",
"Tiancheng Sun"
] | Recent advances in feed-forward 3D Gaussian Splatting have led to rapid improvements in efficient scene reconstruction from sparse views. However, most existing approaches construct Gaussian primitives directly aligned with the pixels in one or more of the input images. This leads to redundancies in the representation ... | poster | null | null | null | [] | [] | [] | [
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12 | Implicit Modeling for Transferability Estimation of Vision Foundation Models | [
"Yaoyan Zheng",
"Huiqun Wang",
"Nan Zhou",
"Di Huang"
] | Transferability estimation identifies the best pre-trained models for downstream tasks without incurring the high computational cost of full fine-tuning. This capability facilitates deployment and advances the pre-training and fine-tuning paradigm. However, existing methods often struggle to accurately assess transfera... | poster | null | null | null | [] | [] | [] | [
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13 | Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection | [
"Yu Guo",
"Shengfeng He",
"Yuxu Lu",
"Haonan An",
"Yihang Tao",
"Huilin Zhu",
"Jingxian Liu",
"Yuguang Fang"
] | Maritime object detection is essential for navigation safety, surveillance, and autonomous operations, yet constrained by two key challenges: the scarcity of annotated maritime data and poor generalization across various maritime attributes (e.g., object category, viewpoint, location, and imaging environment). To addre... | spotlight | null | null | null | [] | [] | [] | [
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14 | Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples | [
"Suqin Yuan",
"Lei Feng",
"Bo Han",
"Tongliang Liu"
] | Sample selection is a prevalent approach in learning with noisy labels, aiming to identify confident samples for training. Although existing sample selection methods have achieved decent results by reducing the noise rate of the selected subset, they often overlook that not all mislabeled examples harm the model's perf... | poster | 2502.08227 | null | null | [] | [] | [] | [
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15 | SAM2Flow: Interactive Optical Flow Estimation with Dual Memory for in vivo Microcirculation Analysis | [
"Luojie Huang",
"Ryan Zhang",
"Marisa Morakis",
"Michaela Taylor-Williams",
"Gregory McKay",
"Nicholas Durr"
] | Analysis of noninvasive microvascular blood flow can improve the diagnosis, prognosis, and management of many medical conditions, including cardiovascular, peripheral vascular, and sickle cell disease. This paper introduces SAM2Flow, an interactive optical flow estimation model to analyze long Oblique Back-illumination... | poster | null | null | null | [] | [] | [] | [
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16 | FastLongSpeech: Enhancing Large Speech-Language Models for Efficient Long-Speech Processing | [
"Shoutao Guo",
"Shaolei Zhang",
"Qingkai Fang",
"Zhengrui Ma",
"Min Zhang",
"Yang Feng"
] | The rapid advancement of Large Language Models (LLMs) has spurred significant progress in Large Speech-Language Models (LSLMs), enhancing their capabilities in both speech understanding and generation. While existing LSLMs often concentrate on augmenting speech generation or tackling a diverse array of short-speech tas... | poster | 2507.14815 | null | null | [] | [] | [] | [
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17 | Towards Human-Like Language Comprehension: Incremental and Wrap-Up Based fMRI-to-Text Decoding | [
"Wentao Lu",
"Dong Nie",
"Pengcheng Xue",
"Zheng Cui",
"Piji Li",
"Daoqiang Zhang",
"Xuyun Wen"
] | Decoding natural language text from non-invasive brain signals, such as functional magnetic resonance imaging (fMRI), remains a central challenge in brain-computer interface research. While recent advances in large language models (LLMs) have enabled open-vocabulary fMRI-to-text decoding, existing frameworks typically ... | spotlight | null | null | null | [] | [] | [] | [
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18 | Value Gradient Guidance for Flow Matching Alignment | [
"Zhen Liu",
"Tim Xiao",
"Carles Domingo i Enrich",
"Weiyang Liu",
"Dinghuai Zhang"
] | While methods exist for aligning flow matching models -- a popular and effective class of generative models -- with human preferences, existing approaches fail to achieve both adaptation efficiency and probabilistically sound prior preservation. In this work, we leverage the theory of optimal control and propose VGG-Fl... | poster | null | null | null | [] | [] | [] | [
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19 | Metric Automata Theory: A Theory of Recurrent Neural Networks | [
"Adam Dankowiakowski",
"Alessandro Ronca"
] | We propose Metric Automata Theory, an elegant generalisation of classic Automata Theory to continuous dynamical systems, that constitutes a unifying theory of all kinds of Recurrent Neural Networks (RNNs), including widely-adopted architectures such as xLSTM and State Space Models (SSMs). The theory allows one to analy... | poster | null | null | null | [] | [] | [] | [
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20 | Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models | [
"Xiaoyu Wu",
"Yifei Pang",
"Terrance Liu",
"Steven Wu"
] | Large language models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of these, exact unlearning--... | poster | null | null | null | [] | [] | [] | [
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21 | Continuous Thought Machines | [
"Luke Darlow",
"Ciaran Regan",
"Sebastian Risi",
"Jeffrey Seely",
"Llion Jones"
] | Biological brains demonstrate complex neural activity, where neural dynamics are critical to how brains process information. Most artificial neural networks simplify neurons by abstracting away dynamics. We challenge that paradigm. By incorporating neuron-level processing and synchronization, we reintroduce neural timi... | spotlight | 2505.05522 | https://github.com/SakanaAI/continuous-thought-machines | https://pub.sakana.ai/ctm/ | [] | [
"SakanaAI/ctm-maze-large",
"SakanaAI/ctm-imagenet"
] | [] | [
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22 | Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point | [
"Hongjie Chen",
"Jingqiu Ding",
"Yiding Hua",
"Stefan Tiegel"
] | We study the problem of robustly estimating the edge density of Erdos Renyi random graphs $\mathbb{G}(n, d^\circ/n)$ when an adversary can arbitrarily add or remove edges incident to an $\eta$-fraction of the nodes.We develop the first polynomial-time algorithm for this problem that estimates $d^\circ$ up to an additiv... | poster | null | null | null | [] | [] | [] | [
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23 | Flash Invariant Point Attention | [
"Andrew Liu",
"Axel Elaldi",
"Nicholas Franklin",
"Nathan Russell",
"Gurinder Atwal",
"Yih-En Ban",
"Olivia Viessmann"
] | Invariant Point Attention (IPA) is a key algorithm for geometry-aware modeling in structural biology, central to many protein and RNA models. However, its quadratic complexity limits the input sequence length. We introduce FlashIPA, a factorized reformulation of IPA that leverages hardware-efficient FlashAttention to a... | spotlight | 2505.11580 | https://github.com/flagshippioneering/flash_ipa | null | [] | [] | [] | [
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24 | Factor Decorrelation Enhanced Data Removal from Deep Predictive Models | [
"Wenhao Yang",
"Lin Li",
"Xiaohui Tao",
"Kaize Shi"
] | The imperative of user privacy protection and regulatory compliance necessitates sensitive data removal in model training, yet this process often induces distributional shifts that undermine model performance-particularly in out-of-distribution (OOD) scenarios. We propose a novel data removal approach that enhances dee... | poster | 2509.23443 | null | null | [] | [] | [] | [
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25 | Accurately Predicting Protein Mutational Effects via a Hierarchical Many-Body Attention Network | [
"Dahao Xu",
"Jiahua Rao",
"Mingming Zhu",
"Jixian Zhang",
"Wei Lu",
"Shuangjia Zheng",
"Yuedong Yang"
] | Predicting changes in binding free energy ($\Delta\Delta G$) is essential for understanding protein-protein interactions, which are critical in drug design and protein engineering. However, existing methods often rely on pre-trained knowledge and heuristic features, limiting their ability to accurately model complex mu... | poster | null | null | null | [] | [] | [] | [
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26 | RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees | [
"Eilon Vaknin Laufer",
"Boaz Nadler"
] | Recovering a low rank matrix from a subset of its entries, some of which may be corrupted, is known as the robust matrix completion (RMC) problem.Existing RMC methods have several limitations: they require a relatively large number of observed entries; they may fail under overparametrization, when their assumed rank i... | poster | 2505.12919 | null | null | [] | [] | [] | [
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27 | Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis | [
"Hengyuan Cao",
"Yutong Feng",
"Biao Gong",
"Yijing Tian",
"Yunhong Lu",
"Chuang Liu",
"Bin Wang"
] | Video generative models can be regarded as world simulators due to their ability to capture dynamic, continuous changes inherent in real-world environments. These models integrate high-dimensional information across visual, temporal, spatial, and causal dimensions, enabling predictions of subjects in various status. A ... | poster | 2505.23325 | https://github.com/Kunbyte-AI/DRA-Ctrl | https://dra-ctrl-2025.github.io/DRA-Ctrl/ | [] | [] | [] | [
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28 | Reconstruction and Secrecy under Approximate Distance Queries | [
"Shay Moran",
"Elizaveta Nesterova"
] | Consider the task of locating an unknown target point using approximate distance queries: in each round, a reconstructor selects a reference point and receives a noisy version of its distance to the target. This problem arises naturally in various contexts—from localization in GPS and sensor networks to privacy-aware d... | spotlight | null | null | null | [] | [] | [] | [
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29 | Multi-Agent Reinforcement Learning with Communication-Constrained Priors | [
"Guang Yang",
"Jingwen Qiao",
"Tianpei Yang",
"Yanqing Wu",
"Jing Huo",
"Yang Gao",
"Xingguo Chen"
] | Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a prevalent issue. Existing multi-agent reinforcement learning with communication, due to their limited scalability and robustness, struggles to... | poster | null | null | null | [] | [] | [] | [
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30 | OmniTry: Virtual Try-On Anything without Masks | [
"Yutong Feng",
"Linlin Zhang",
"Hengyuan Cao",
"Yiming Chen",
"Xiaoduan Feng",
"Jian Cao",
"Yuxiong Wu",
"Bin Wang"
] | Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON beyond garment to encompass any wearable objects, e.g., jewelries and accessories, with mask-free setting for more practical application. Wh... | poster | 2508.13632 | https://github.com/Kunbyte-AI/OmniTry | https://omnitry.github.io/ | [] | [] | [] | [
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31 | RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching | [
"Runze Ma",
"Zhongyue Zhang",
"Zichen Wang",
"Chenqing Hua",
"Jiahua Rao",
"Zhuomin Zhou",
"Shuangjia Zheng"
] | Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA that target specific ligands face limitations in capturing RNA’s conformational flexibility, ensuring structural validity, and overcoming data ... | poster | 2503.17007 | null | null | [] | [] | [] | [
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32 | FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion | [
"Akide Liu",
"Zeyu Zhang",
"Zhexin Li",
"Xuehai Bai",
"Yizeng Han",
"Jiasheng Tang",
"Yuanjie Xing",
"Jichao Wu",
"Mingyang Yang",
"Weihua Chen",
"Jiahao He",
"Yuanyu He",
"Fan Wang",
"Reza Haffari",
"Bohan Zhuang"
] | Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder practical deployment. Although both quantization and sparsity can independently accelerate inference while maintaining generation quality, naivel... | spotlight | 2506.04648 | null | https://fps.ziplab.co | [] | [] | [] | [
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33 | Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism | [
"Mete Erdogan",
"Cengiz Pehlevan",
"Alper Erdogan"
] | We introduce *Error Broadcast and Decorrelation* (EBD), a novel learning framework for neural networks that addresses credit assignment by directly broadcasting output errors to individual layers, circumventing weight transport of backpropagation. EBD is rigorously grounded in the stochastic orthogonality property of M... | spotlight | 2504.11558 | null | null | [] | [] | [] | [
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34 | ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS | [
"Weijie Wang",
"Donny Y. Chen",
"Zeyu Zhang",
"Duochao Shi",
"Akide Liu",
"Bohan Zhuang"
] | Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their encoders, leading to degraded pe... | poster | 2505.23734 | https://github.com/ziplab/ZPressor | https://lhmd.top/zpressor/ | [] | [
"lhmd/ZPressor"
] | [] | [
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35 | Can Dependencies Induced by LLM-Agent Workflows Be Trusted? | [
"Yu Yao",
"Yiliao (Lia) Song",
"Yian Xie",
"Mengdan Fan",
"Mingyu Guo",
"Tongliang Liu"
] | LLM-agent systems often decompose a high-level task objective into a subtask-dependency graph, assuming each subtask’s response is conditionally independent of others given its parent responses. However, we find the inaccessible ground-truth responses will violate this assumption during execution, leading to inter-agen... | poster | null | null | null | [] | [] | [] | [
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36 | Pareto Optimal Risk-Agnostic Distributional Bandits with Heavy-Tail Rewards | [
"Kyungjae Lee",
"Dohyeong Kim",
"Taehyun Cho",
"Chaeyeon Kim",
"Yunkyung Ko",
"Seungyub Han",
"Seokhun Ju",
"Dohyeok Lee",
"Sungbin Lim"
] | This paper addresses the problem of multi-risk agnostic multi-armed bandits in heavy-tailed reward settings. We propose a framework that leverages novel deviation inequalities for the $1$-Wasserstein distance to construct confidence intervals for Lipschitz risk measures. The distributional LCB (DistLCB) algorithm is in... | poster | null | null | null | [] | [] | [] | [
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37 | Isotropic Noise in Stochastic and Quantum Convex Optimization | [
"Annie Marsden",
"Liam O'Carroll",
"Aaron Sidford",
"Chenyi Zhang"
] | We consider the problem of minimizing a $d$-dimensional Lipschitz convex function using a stochastic gradient oracle. We introduce and motivate a setting where the noise of the stochastic gradient is \emph{isotropic} in that it is bounded in every direction with high probability. We then develop an algorithm for this s... | poster | null | null | null | [] | [] | [] | [
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38 | MixPrompt: Efficient Mixed Prompting for Multimodal Semantic Segmentation | [
"Zhiwei Hao",
"Zhongyu Xiao",
"Jianyuan Guo",
"Li Shen",
"Yong Luo",
"Han Hu",
"Dan Zeng"
] | Recent advances in multimodal semantic segmentation show that incorporating auxiliary inputs—such as depth or thermal images—can significantly improve performance over single-modality (RGB-only) approaches. However, most existing solutions rely on parallel backbone networks and complex fusion modules, greatly increasin... | poster | null | null | null | [] | [] | [] | [
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39 | Balancing Gradient and Hessian Queries in Non-Convex Optimization | [
"Deeksha Adil",
"Brian Bullins",
"Aaron Sidford",
"Chenyi Zhang"
] | We develop optimization methods which offer new trade-offs between the number of gradient and Hessian computations needed to compute the critical point of a non-convex function. We provide a method that for a twice-differentiable $f\colon \mathbb{R}^d \rightarrow \mathbb{R}$ with $L_2$-Lipschitz Hessian, and input init... | poster | null | null | null | [] | [] | [] | [
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40 | SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models | [
"Emil Biju",
"Shayan Talaei",
"Zhemin Huang",
"Mohammadreza Pourreza",
"Azalia Mirhoseini",
"Amin Saberi"
] | Large reasoning models (LRMs) excel at complex reasoning tasks but typically generate lengthy sequential chains-of-thought, resulting in long inference times before arriving at the final answer. To address this challenge, we introduce SPRINT, a novel post-training and inference-time framework designed to enable LRMs to... | poster | 2506.05745 | null | null | [] | [] | [] | [
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41 | Visual Sync: Multi‑Camera Synchronization via Cross‑View Object Motion | [
"Shaowei Liu",
"David Yao",
"Saurabh Gupta",
"Shenlong Wang"
] | Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross‑camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correcti... | poster | null | null | null | [] | [] | [] | [
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42 | CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects | [
"Huaijin Pi",
"Zhi Cen",
"Zhiyang Dou",
"Taku Komura"
] | Synthesizing whole-body manipulation of articulated objects, including body motion, hand motion, and object motion, is a critical yet challenging task with broad applications in virtual humans and robotics.The core challenges are twofold.First, achieving realistic whole-body motion requires tight coordination between t... | poster | 2505.21437 | https://github.com/phj128/CoDA | https://phj128.github.io/page/CoDA/index.html | [] | [] | [] | [
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43 | Context-Aware Hierarchical Learning: A Two-Step Paradigm towards Safer LLMs | [
"Tengyun Ma",
"Damon Yao",
"Daojing He",
"Shihao Peng",
"YU LI",
"Shaohui Liu",
"Zhuotao Tian"
] | Large Language Models (LLMs) have emerged as powerful tools for diverse applications. However, their uniform token processing paradigm introduces critical vulnerabilities in instruction handling, particularly when exposed to adversarial scenarios. In this work, we identify and propose a novel class of vulnerabilities, ... | poster | null | null | null | [] | [] | [] | [
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44 | Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought | [
"Chao Huang",
"Benfeng Wang",
"Jie Wen",
"Chengliang Liu",
"Wei Wang",
"Li Shen",
"Xiaochun Cao"
] | Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based Video Anomaly Detection (VAD) methods remain limited to shallow anomaly descriptions without deep reasoning. In this paper, we propose a ne... | poster | null | null | null | [] | [] | [] | [
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45 | MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching | [
"Liang Yue",
"Yihong Tang",
"Kehai Chen",
"Jie Liu",
"Min Zhang"
] | Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MAST... | poster | 2506.02689 | null | null | [] | [] | [] | [
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46 | A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers | [
"Zhixiao Wu",
"Yao Lu",
"Jie Wen",
"Hao Sun",
"Qi Zhou",
"Guangming Lu"
] | Poison-only Clean-label Backdoor Attacks (PCBAs) aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple triggers are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additi... | poster | 2509.19947 | null | null | [] | [] | [] | [
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... |
47 | Confidence-Aware With Prototype Alignment for Partial Multi-label Learning | [
"Weijun Lv",
"Yu Chen",
"Xuhuan Zhu",
"Jie Wen",
"Guoxu Zhou",
"Sixian Chan",
"Xiaozhao Fang"
] | Label prototype learning has emerged as an effective paradigm in Partial Multi-Label Learning (PML), providing a distinctive framework for modeling structured representations of label semantics while naturally filtering noise through prototype-based label confidence estimation. However, existing prototype-based methods... | poster | null | null | null | [] | [] | [] | [
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0.01... |
48 | Online locally differentially private conformal prediction via binary inquiries | [
"Qiangqiang Zhang",
"Chenfei Gu",
"Xinwei Feng",
"Jinhan Xie",
"Ting Li"
] | We propose an online conformal prediction framework under local differential privacy to address the emerging challenge of privacy-preserving uncertainty quantification in streaming data environments. Our method constructs dynamic, model-free prediction sets based on randomized binary inquiries, ensuring rigorous privac... | poster | null | null | null | [] | [] | [] | [
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49 | Online robust locally differentially private learning for nonparametric regression | [
"Chenfei Gu",
"Qiangqiang Zhang",
"Ting Li",
"Jinhan Xie",
"Niansheng Tang"
] | The growing prevalence of streaming data and increasing concerns over data privacy pose significant challenges for traditional nonparametric regression methods, which are often ill-suited for real-time, privacy-aware learning. In this paper, we tackle these issuesby first proposing a novel one-pass online functional st... | poster | null | null | null | [] | [] | [] | [
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50 | Learning from Disjoint Views: A Contrastive Prototype Matching Network for Fully Incomplete Multi-View Clustering | [
"Yiming Wang",
"Qun Li",
"Dongxia Chang",
"Jie Wen",
"Hua Dai",
"Fu Xiao",
"Yao Zhao"
] | Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, existing methods typically assume that instances are present in most or all views, which is impractical in real-world scenarios. This paper focuses on the understudied problem of fully incomplete multi-... | poster | null | null | null | [] | [] | [] | [
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51 | New Parallel and Streaming Algorithms for Directed Densest Subgraph | [
"Slobodan Mitrovic",
"Theodore Pan",
"Mahdi Qaempanah",
"Mohammad Amin Raeisi"
] | Finding dense subgraphs is a fundamental problem with applications to community detection, clustering, and data mining. Our work focuses on finding approximate densest subgraphs in directed graphs in computational models for processing massive data. We consider two such models: Massively Parallel Computation (MPC) and ... | poster | 2509.21729 | null | null | [] | [] | [] | [
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52 | Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease Diagnosis | [
"Chengliang Liu",
"Que Yuanxi",
"Qihao Xu",
"Yabo Liu",
"Jie Wen",
"Jinghua Wang",
"Xiaoling Luo"
] | Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehensive joint diagnosis... | poster | null | null | null | [] | [] | [] | [
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53 | NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis | [
"Shengrong Li",
"Qi Zhu",
"Chunwei Tian",
"Xinyang Zhang",
"WEI SHAO",
"Jie Wen",
"Daoqiang Zhang"
] | Dynamic functional brain networks (DFBNs) are powerful tools in neuroscience research. Recent studies reveal that DFBNs contain heterogeneous neural nodes with more extensive connections and more drastic temporal changes, which play pivotal roles in coordinating the reorganization of the brain. Moreover, the spatio-tem... | poster | null | null | null | [] | [] | [] | [
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54 | Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs | [
"Amirmohammad Farzaneh",
"Osvaldo Simeone"
] | The selection of hyperparameters, such as prompt templates in large language models (LLMs), must often strike a balance between reliability and cost. In many cases, structural relationships between the expected reliability levels of the hyperparameters can be inferred from prior information and held-out data -- e.g., l... | poster | 2501.13018 | null | null | [] | [] | [] | [
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55 | Backdoor Cleaning without External Guidance in MLLM Fine-tuning | [
"Xuankun Rong",
"Wenke Huang",
"Jian Liang",
"Jinhe Bi",
"Xun Xiao",
"Yiming Li",
"Bo Du",
"Mang Ye"
] | Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downstream tasks. This flexibility, however, introduces serious security risks, as malicious fine-tuning can implant backdoors into MLLMs with mi... | poster | 2505.16916 | https://github.com/xuankunrong/bye | null | [] | [] | [] | [
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56 | NormFit: A Lightweight Solution for Few-Shot Federated Learning with Non-IID Data | [
"Azadeh Motamedi",
"Jae-Mo Kang",
"Il-Min Kim"
] | Vision–Language Models (VLMs) have recently attracted considerable attention in Federated Learning (FL) due to their strong and robust performance. In particular, few-shot adaptation with pre-trained VLMs like CLIP enhances the performance of downstream tasks. However, existing methods still suffer from substantial com... | spotlight | null | null | null | [] | [] | [] | [
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57 | ECO: Evolving Core Knowledge for Efficient Transfer | [
"Fu Feng",
"Yucheng Xie",
"Ruixiao Shi",
"Jianlu Shen",
"Jingq Wang",
"Xin Geng"
] | Knowledge in modern neural networks is often entangled and structurally opaque, making current transfer methods—typically based on reusing entire parameter sets—inefficient and inflexible. Efforts to improve flexibility by reusing partial parameters frequently depend on handcrafted heuristics or rigid structural assump... | poster | null | null | null | [] | [] | [] | [
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58 | Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning | [
"Lei Wang",
"Jieming Bian",
"Letian Zhang",
"Jie Xu"
] | Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, but fine-tuning them for domain-specific applications often requires substantial domain-specific data that may be distributed across multiple organizations. Federated Learning (FL) offers a privacy-preserving solution, but face... | poster | 2509.15087 | null | null | [] | [] | [] | [
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59 | OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions | [
"Yuanhao Cai",
"HE Zhang",
"Xi Chen",
"Jinbo Xing",
"Yiwei Hu",
"Yuqian Zhou",
"Kai Zhang",
"Zhifei Zhang",
"Soo Ye Kim",
"Tianyu Wang",
"Yulun Zhang",
"Xiaokang Yang",
"Zhe Lin",
"Alan Yuille"
] | Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging problem that how to use the signals such as depth, mask, camera, and text prompts to control and edit the subject in the cust... | poster | 2506.23361 | https://github.com/caiyuanhao1998/Open-OmniVCus | https://caiyuanhao1998.github.io/project/OmniVCus/ | [] | [] | [] | [
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60 | LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss | [
"Pau Rodriguez",
"Michal Klein",
"Eleonora Gualdoni",
"Valentino Maiorca",
"Arno Blaas",
"Luca Zappella",
"Marco Cuturi",
"Xavier Suau"
] | provide users with tools to explore style changes. Ideally, such mechanisms should require low volume of unpaired data (i.e., without explicit preference), and should be cheap, both at train and inference time, while preserving output quality. Recent research has shown that such mechanisms can be obtained by intervenin... | poster | 2503.10679 | null | null | [] | [] | [] | [
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61 | Geometry-Aware Collaborative Multi-Solutions Optimizer for Model Fine-Tuning with Parameter Efficiency | [
"Van-Anh Nguyen",
"Trung Le",
"Mehrtash Harandi",
"Ehsan Abbasnejad",
"Thanh-Toan Do",
"Dinh Phung"
] | We propose a framework grounded in gradient flow theory and informed by geometric structure that provides multiple diverse solutions for a given task, ensuring collaborative results that enhance performance and adaptability across different tasks. This framework enables flexibility, allowing for efficient task-specific... | poster | null | null | null | [] | [] | [] | [
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62 | FedEL: Federated Elastic Learning for Heterogeneous Devices | [
"Letian Zhang",
"Bo Chen",
"Jieming Bian",
"Lei Wang",
"Jie Xu"
] | Federated learning (FL) enables distributed devices to collaboratively train machine learning (ML) models while maintaining data privacy. However, the heterogeneous hardware capabilities of participating devices often result in significant training delays, as straggler clients with limited resources prolong the aggrega... | poster | 2509.16902 | null | null | [] | [] | [] | [
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63 | TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning | [
"Sheng Wang",
"Pengan CHEN",
"Jingqi Zhou",
"Qintong Li",
"Jingwei Dong",
"Jiahui Gao",
"Boyang XUE",
"Jiyue Jiang",
"Lingpeng Kong",
"Chuan Wu"
] | Model customization necessitates high-quality and diverse datasets, but acquiring such data remains time-consuming and labor-intensive. Despite the great potential of large language models (LLMs) for data synthesis, current approaches are constrained by limited seed data, model biases and low-variation prompts, resulti... | spotlight | 2503.17195 | https://github.com/cpa2001/TreeSynth | null | [] | [] | [] | [
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64 | Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives | [
"Geng Zhang",
"Jiangrong Shen",
"Kaizhong Zheng",
"Liangjun Chen",
"Badong Chen"
] | Brain disorders have been consistently associated with abnormalities in specific brain regions or neural circuits. Identifying key brain regional activities and functional connectivity patterns is essential for discovering more precise neurobiological biomarkers. However, previous studies have primarily emphasized alte... | poster | null | null | null | [] | [] | [] | [
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65 | RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models | [
"Yilang Zhang",
"Bingcong Li",
"Georgios Giannakis"
] | Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates in... | poster | 2505.18877 | null | null | [] | [] | [] | [
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66 | Recurrent Self-Attention Dynamics: An Energy-Agnostic Perspective from Jacobians | [
"Akiyoshi Tomihari",
"Ryo Karakida"
] | The theoretical understanding of self-attention (SA) has been steadily progressing. A prominent line of work studies a class of SA layers that admit an energy function decreased by state updates. While it provides valuable insights into inherent biases in signal propagation, it often relies on idealized assumptions or ... | poster | 2505.19458 | null | null | [] | [] | [] | [
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67 | Embodied Crowd Counting | [
"Runling Long",
"Yunlong Wang",
"Jia Wan",
"Xiang Deng",
"Xinting Zhu",
"Weili Guan",
"Antoni Chan",
"Liqiang Nie"
] | Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets on which the methods are trained are based on passive cameras, re... | poster | 2503.08367 | null | null | [] | [] | [] | [
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68 | Compress, Gather, and Recompute: REFORMing Long-Context Processing in Transformers | [
"Woomin Song",
"Sai Muralidhar Jayanthi",
"Srikanth Ronanki",
"Kanthashree Mysore Sathyendra",
"Jinwoo Shin",
"Aram Galstyan",
"Shubham Katiyar",
"Sravan Babu Bodapati"
] | As large language models increasingly gain popularity in real-world applications, processing extremely long contexts, often exceeding the model’s pre-trained context limits, has emerged as a critical challenge. While existing approaches to efficient long-context processing show promise, recurrent compression-based meth... | poster | 2506.01215 | null | null | [] | [] | [] | [
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69 | Curriculum Model Merging: Harmonizing Expert Chemical LLMs for Enhanced Cross-Task Generalization | [
"Baoyi He",
"Luotian Yuan",
"Ying Wei",
"Fei Wu"
] | The emergence of large language models (LLMs) prompts fine-tuning foundation LLMs to solve real-world chemical problems. However, these chemical LLMs are tailored to specific task formats or narrow content domains, which limits their capacity for comprehensive knowledge integration and cross-task generalization. Mod... | poster | null | null | null | [] | [] | [] | [
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70 | Rebalancing Contrastive Alignment with Learnable Semantic Gaps in Text-Video Retrieval | [
"Jian Xiao",
"Zijie Song",
"Jialong Hu",
"Hao Cheng",
"Jia Li",
"Zhenzhen Hu",
"Richang Hong"
] | Recent advances in text-video retrieval have been largely driven by contrastive learning frameworks. However, existing methods overlook a key source of optimization tension: the separation between text and video distributions in the representation space—referred to as the modality gap—and the prevalence of false negati... | poster | null | null | null | [] | [] | [] | [
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71 | Non-convex entropic mean-field optimization via Best Response flow | [
"Razvan-Andrei Lascu",
"Mateusz Majka"
] | We study the problem of minimizing non-convex functionals on the space of probability measures, regularized by the relative entropy (KL divergence) with respect to a fixed reference measure, as well as the corresponding problem of solving entropy-regularized non-convex-non-concave min-max problems. We utilize the Best ... | poster | 2505.22760 | null | null | [] | [] | [] | [
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72 | OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain | [
"Wenzhen Yue",
"Yong Liu",
"Haoxuan Li",
"Hao Wang",
"Xianghua Ying",
"Ruohao Guo",
"Bowei Xing",
"Ji Shi"
] | This paper presents $\mathbf{OLinear}$, a $\mathbf{linear}$-based multivariate time series forecasting model that operates in an $\mathbf{o}$rthogonally transformed domain. Recent forecasting models typically adopt the temporal forecast (TF) paradigm, which directly encode and decode time series in the time domain. How... | poster | 2505.08550 | https://github.com/jackyue1994/OLinear | null | [] | [] | [] | [
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73 | Noise Hypernetworks: Moving Test-Time Compute in Diffusion Models to Training | [
"Luca Eyring",
"Shyamgopal Karthik",
"Alexey Dosovitskiy",
"Nataniel Ruiz",
"Zeynep Akata"
] | The new paradigm of test-time scaling has yielded remarkable breakthroughs in Large Language Models (LLMs) (e.g.~reasoning models) and in generative vision models, allowing models to allocate additional computation during inference to effectively tackle increasingly complex problems. Despite the improvements of this ap... | poster | null | null | null | [] | [] | [] | [
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74 | Selective Learning for Deep Time Series Forecasting | [
"Yisong Fu",
"Zezhi Shao",
"Chengqing Yu",
"Yujie Li",
"Zhulin An",
"Qi Wang",
"Yongjun Xu",
"Fei Wang"
] | Benefiting from high capacity for capturing complex temporal patterns, deep learning (DL) has significantly advanced time series forecasting (TSF). However, deep models tend to suffer from severe overfitting due to the inherent vulnerability of time series to noise and anomalies. The prevailing DL paradigm uniformly op... | poster | null | null | null | [] | [] | [] | [
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75 | CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning | [
"Ronghao Lin",
"Qiaolin He",
"Sijie Mai",
"Ying Zeng",
"Aolin Xiong",
"Li Huang",
"Yap-peng Tan",
"Haifeng Hu"
] | Multimodal machine learning, mimicking the human brain’s ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real‑world deployments, however, the presence of modality is highly variable and unpre... | poster | null | null | null | [] | [] | [] | [
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76 | Interaction-Centric Knowledge Infusion and Transfer for Open Vocabulary Scene Graph Generation | [
"Lin Li",
"Chuhan ZHANG",
"Dong Zhang",
"Chong Sun",
"Chen Li",
"Long Chen"
] | Open-vocabulary scene graph generation (OVSGG) extends traditional SGG by recognizing novel objects and relationships beyond predefined categories, leveraging the knowledge from pre-trained large-scale models. Existing OVSGG methods always adopt a two-stage pipeline: 1) Infusing knowledge into large-scale models via pr... | poster | null | null | null | [] | [] | [] | [
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77 | On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting | [
"Yisong Fu",
"Fei Wang",
"Zezhi Shao",
"Boyu Diao",
"Lin Wu",
"Zhulin An",
"Chengqing Yu",
"Yujie Li",
"Yongjun Xu"
] | Transformers have gained attention in atmospheric time series forecasting (ATSF) for their ability to capture global spatial-temporal correlations. However, their complex architectures lead to excessive parameter counts and extended training times, limiting their scalability to large-scale forecasting. In this paper, w... | poster | 2408.09695 | null | null | [] | [] | [] | [
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78 | Path-specific effects for pulse-oximetry guided decisions in critical care | [
"Kevin Zhang",
"Yonghan Jung",
"Divyat Mahajan",
"Karthikeyan Shanmugam",
"Shalmali Joshi"
] | Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent supplemental oxygen needs. M... | poster | 2506.12371 | null | null | [] | [] | [] | [
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79 | InfMasking: Unleashing Synergistic Information \\ by Contrastive Multimodal Interactions | [
"Liangjian Wen",
"Qun Dai",
"Yong Dai",
"Jianzhuang Liu",
"Jiangtao Zheng",
"Dongkai Wang",
"Zhao Kang",
"Jun Wang",
"Zenglin Xu",
"Jiang Duan"
] | In multimodal representation learning, synergistic interactions between modalities not only provide complementary information but also create unique outcomes through specific interaction patterns that no single modality could achieve alone. Existing methods may struggle to effectively capture the full spectrum of syner... | spotlight | 2509.25270 | https://github.com/brightest66/InfMasking | null | [] | [] | [] | [
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80 | UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation | [
"Rui Tian",
"Mingfei Gao",
"Mingze Xu",
"Jiaming Hu",
"Jiasen Lu",
"Zuxuan Wu",
"Yinfei Yang",
"Afshin Dehghan"
] | We introduce UniGen, a unified multimodal large language model (MLLM) capable of image understanding and generation. We study the full training pipeline of UniGen from a data-centric perspective, including multi-stage pre-training, supervised fine-tuning, and direct preference optimization. More importantly, we propose... | poster | 2505.14682 | null | null | [] | [] | [] | [
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81 | Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach | [
"Swetha Ganesh",
"Vaneet Aggarwal"
] | Actor-Critic methods are widely used for their scalability, yet existing theoretical guarantees for infinite-horizon average-reward Markov Decision Processes (MDPs) often rely on restrictive ergodicity assumptions. We propose NAC-B, a Natural Actor-Critic with Batching, that achieves order-optimal regret of \$\tilde{O}... | poster | 2505.19986 | null | null | [] | [] | [] | [
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82 | TreeSplat: Mergeable Tree for Deformable Gaussian Splatting | [
"Qiuhong Shen",
"Xingyi Yang",
"Xinchao Wang"
] | Dynamic 3D scene reconstruction from multi-view videos demands representation to model complex deformations at scale. Current Gaussian Splatting based methods often either suffer from significant computation cost due to dense MLP-based modeling or explicit modeling deformation of each Gaussian independently. However, t... | poster | null | null | null | [] | [] | [] | [
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83 | 1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering | [
"Yuheng Yuan",
"Qiuhong Shen",
"Xingyi Yang",
"Xinchao Wang"
] | 4D Gaussian Splatting (4DGS) has recently gained considerable attention as a method for reconstructing dynamic scenes. Despite achieving superior quality, 4DGS typically requires substantial storage and suffers from slow rendering speed. In this work, we delve into these issues and identify two key sources of temporal ... | poster | 2503.16422 | null | https://4dgs-1k.github.io/ | [] | [] | [] | [
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84 | Test3R: Learning to Reconstruct 3D at Test Time | [
"Yuheng Yuan",
"Qiuhong Shen",
"Shizun Wang",
"Xingyi Yang",
"Xinchao Wang"
] | Dense matching methods like DUST3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce \textbf{Test3R}, a surprisingly simple test-time learning technique... | poster | 2506.13750 | https://github.com/nopQAQ/Test3R | null | [] | [] | [] | [
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85 | Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data | [
"Harsh Poonia",
"Felix Divo",
"Kristian Kersting",
"Devendra Singh Dhami"
] | Causality in time series can be difficult to determine, especially in the presence of non-linear dependencies. The concept of Granger causality helps analyze potential relationships between variables, thereby offering a method to determine whether one time series can predict—Granger cause—future values of another. Alth... | poster | 2502.09981 | null | null | [] | [] | [] | [
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86 | Angles Don’t Lie: Unlocking Training‑Efficient RL Through the Model’s Own Signals | [
"Qinsi Wang",
"Jinghan Ke",
"Hancheng Ye",
"Yueqian Lin",
"Yuzhe Fu",
"Jianyi Zhang",
"Kurt Keutzer",
"Chenfeng Xu",
"Yiran Chen"
] | Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models (LLMs) suffer from sample inefficiency due to the redundant exposure of identical queries under uniform data sampling. While previous work has explored curriculum learning via heuristic difficulty metrics, these strategies exhibit limitations b... | spotlight | null | null | null | [] | [] | [] | [
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87 | Image Editing As Programs with Diffusion Models | [
"Yujia Hu",
"Songhua Liu",
"Zhenxiong Tan",
"Xingyi Yang",
"Xinchao Wang"
] | While diffusion models have achieved remarkable success in text-to-image generation, they encounter significant challenges with instruction-driven image editing. Our research highlights a key challenge: these models particularly struggle with structurally-inconsistent edits that involve substantial layout changes. To a... | poster | 2506.04158 | https://github.com/YujiaHu1109/IEAP | https://yujiahu1109.github.io/IEAP/ | [
"Cicici1109/IEAP"
] | [
"Cicici1109/IEAP"
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88 | Aura Attention: $\mathcal O(n \log n)$ Sparse Attention for Long Video Generation | [
"XINGYANG LI",
"Muyang Li",
"Tianle Cai",
"Haocheng Xi",
"Shuo Yang",
"Yujun Lin",
"Lvmin Zhang",
"Songlin Yang",
"Jinbo Hu",
"Kelly Peng",
"Maneesh Agrawala",
"Ion Stoica",
"Kurt Keutzer",
"Song Han"
] | Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we call \textit{Spatiotemporal Energy Decay} in... | poster | null | null | null | [] | [] | [] | [
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89 | Influence Functions for Edge Edits in Non-Convex Graph Neural Networks | [
"Jaeseung Heo",
"Kyeongheung Yun",
"Seokwon Yoon",
"MoonJeong Park",
"Jungseul Ok",
"Dongwoo Kim"
] | Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence functions have emerged as promising tools to efficiently estimate the effects of edge deletions without retraining. However, existing influence pred... | poster | 2506.04694 | null | null | [] | [] | [] | [
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90 | Elastic ViTs from Pretrained Models without Retraining | [
"Walter Simoncini",
"Michael Dorkenwald",
"Tijmen Blankevoort",
"Cees Snoek",
"Yuki Asano"
] | Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deployment choices under real-world constraints. We introduce a novel post-pretraining structured pruning method that enables elastic inference across a continuum of compute budge... | poster | null | null | null | [] | [] | [] | [
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91 | SGAR: Structural Generative Augmentation for 3D Human Motion Retrieval | [
"Jiahang Zhang",
"Lilang Lin",
"Shuai Yang",
"Jiaying Liu"
] | 3D human motion-text retrieval is essential for accurate motion understanding, targeted at cross-modal alignment learning. Existing methods typically align the global motion-text concepts directly, suffering from sub-optimal generalization due to the uncertainty of correspondence learning between multiple motion concep... | poster | null | null | null | [] | [] | [] | [
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92 | T-REGS: Minimum Spanning Tree Regularization for Self-Supervised Learning | [
"Julie Mordacq",
"David Loiseaux",
"Vicky Kalogeiton",
"Steve OUDOT"
] | Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data, often by enforcing invariance to input transformations such as rotations or blurring.Recent studies have highlighted two pivotal properties for effective representations: (i) avoiding dimensional collaps... | spotlight | null | null | null | [] | [] | [] | [
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93 | Noise Matters: Optimizing Matching Noise for Diffusion Classifiers | [
"Yanghao Wang",
"Long Chen"
] | Although today's pretrained discriminative vision-language models (e.g., CLIP) have demonstrated strong perception abilities, such as zero-shot image classification, they also suffer from the bag-of-words problem and spurious bias. To mitigate these problems, some pioneering studies leverage powerful generative models ... | poster | 2508.11330 | null | null | [] | [] | [] | [
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94 | Quantifying the Potential of Control Algorithms through Large Language Models | [
"Lianchen Jia",
"Chaoyang Li",
"Qian Houde",
"Tianchi Huang",
"Jiangchuan Liu",
"Lifeng Sun"
] | Control algorithms in production environments typically require domain experts to tune them for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of tuning potential. To bridge this gap, we introduce \te... | poster | null | null | null | [] | [] | [] | [
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95 | Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations | [
"David Burt",
"Renato Berlinghieri",
"Stephen Bates",
"Tamara Broderick"
] | Estimating associations between spatial covariates and responses — rather than merely predicting responses — is central to environmental science, epidemiology, and economics. For instance, public health officials might be interested in whether air pollution has a strictly positive association with a health outcome, and... | poster | 2502.06067 | null | null | [] | [] | [] | [
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96 | The quest for the GRAph Level autoEncoder (GRALE) | [
"Paul Krzakala",
"Gabriel Melo",
"Charlotte Laclau",
"Florence d'Alché-Buc",
"Rémi Flamary"
] | Although graph-based learning has attracted a lot of attention, graph representation learning is still a challenging task whose resolution may impact key application fields such as chemistry or biology. To this end, we introduce GRALE, a novel graph autoencoder that encodes and decodes graphs of varying sizes into a sh... | poster | 2505.22109 | null | null | [] | [] | [] | [
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97 | Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis | [
"Tianrui Wang",
"Haoyu Wang",
"Meng Ge",
"Cheng Gong",
"Chunyu Qiang",
"Ziyang Ma",
"Zikang Huang",
"Guanrou Yang",
"Xiaobao Wang",
"Eng-Siong Chng",
"Xie Chen",
"Longbiao Wang",
"Jianwu Dang"
] | While emotional text-to-speech (TTS) has made significant progress, most existing research remains limited to utterance-level emotional expression and fails to support word-level control. Achieving word-level expressive control poses fundamental challenges, primarily due to the complexity of modeling multi-emotion tran... | spotlight | 2509.24629 | null | null | [] | [] | [] | [
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98 | Structure Matters: Dynamic Policy Gradient | [
"Sara Klein",
"Xiangyuan Zhang",
"Tamer Basar",
"Simon Weissmann",
"Leif Döring"
] | In this work, we study $\gamma$-discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework directly integrates dynamic programming with (any) policy gradient method, explicitly leveraging the Markovian property of the environment.... | poster | 2411.04913 | null | null | [] | [] | [] | [
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99 | Spectral Compressive Imaging via Chromaticity-Intensity Decomposition | [
"Xiaodong Wang",
"Zijun He",
"Ping Wang",
"Lishun Wang",
"Yanan Hu",
"Xin Yuan"
] | In coded aperture snapshot spectral imaging (CASSI), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreover, the captured radiance inherently depends on scene illumination, making it difficult to recover t... | poster | 2509.16690 | null | null | [] | [] | [] | [
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-0... |
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