GenAI4ELab/papercli-papers huggingface dataset:
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Authors: Thibault de Surrel; Fabien Lotte; Sylvain Chevallier; Florian Yger
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Circular and non-flat data distribution are prevalent across diverse domains of data science, yet their specific geometric structures often remain underutilized in machine learning frameworks.
A principled approach to accounting for the underlying geometry of such data is pivotal, particularly when extending statistical models, like the pervasive Gaussian distribution.
In this work, we tackle those issue by focusing on the manifold of symmetric positive definite matrices, a key focus in information geometry.
We introduced a non-isotropic wrapped Gaussian by leveraging the exponential map, we derive theoretical properties of this distribution and propose a maximum likelihood framework for parameter estimation. Furthermore, we reinterpret established classifiers on SPD through a probabilistic lens and introduce new classifiers based on the wrapped Gaussian model.
Experiments on synthetic and real-world datasets demonstrate the robustness and flexibility of this geometry-aware distribution, underscoring its potential to advance manifold-based data analysis.
This work lays the groundwork for extending classical machine learning and statistical methods to more complex and structured data.
BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation
Authors: Lian Liu; Xiandong Zhao; Guanchen Li; Dong Li; Mengdi Wang; Yinhe Han; Xiaowei Li; ying wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: One-shot post-training pruning enhances the deployment of billion-scale large language models (LLMs), with the pruning metric playing a pivotal role in determining which weights to remove. However, existing metrics underperform due to their reliance on a simple symbolic combination of weights and activations, overlooking imbalanced weight magnitudes and the disproportionate influence of activation outliers.
To overcome these limitations, we introduce \textbf{BaWA}, a novel pruning metric that systematically \underline{Ba}lances \underline{W}eight and \underline{A}ctivation distributions for more effective pruning.
BaWA introduces two key innovations: \textbf{magnitude normalization}, which mitigates weight imbalance across channels for fairer pruning decisions, and \textbf{outlier regularization}, which reduces the impact of activation outliers, ensuring more appropriate channel prioritization. 
To further enhance its effectiveness, BaWA incorporates an efficient and automatic framework for optimizing normalization and regularization hyperparameters. Extensive experiments validate BaWA as a state-of-the-art (SOTA) pruning metric. For instance, applying BaWA to induce 2:4 sparsity in Mistral-7B reduces perplexity in language comprehension by 2.49 and improves average downstream task accuracy by 3.08\%, outperforming the previous SOTA method Wanda.
Action Dubber: Timing Audible Actions via Inflectional Flow
Authors: Wenlong Wan; Weiying Zheng; Tianyi Xiang; Guiqing Li; Shengfeng He
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose $TA^{2}Net$, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. $TA^{2}Net$ also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, $Audible623$, derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on $Audible623$ and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at https://github.com/WenlongWan/Audible623.
Visual Autoregressive Modeling for Image Super-Resolution
Authors: Yunpeng Qu; Kun Yuan; Jinhua Hao; Kai Zhao; Qizhi Xie; Ming Sun; Chao Zhou
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models.
However, challenges such as the trade-off issues between fidelity and realism, as well as computational complexity, have also posed limitations on their application.
Building upon the tremendous success of autoregressive models in the language domain, we propose \textbf{VARSR}, a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction.
To effectively integrate and preserve semantic information in low-resolution images, we propose using prefix tokens to incorporate the condition.
Scale-aligned Rotary Positional Encodings are introduced to capture spatial structures and the diffusion refiner is utilized for modeling quantization residual loss to achieve pixel-level fidelity.
Image-based Classifier-free Guidance is proposed to guide the generation of more realistic images.
Furthermore, we collect large-scale data and design a training process to obtain robust generative priors.
Quantitative and qualitative results show that VARSR is capable of generating high-fidelity and high-realism images with more efficiency than diffusion-based methods.
Our codes are released at \url{https://github.com/quyp2000/VARSR}.
Gamma Distribution PCA-Enhanced Feature Learning for Angle-Robust SAR Target Recognition
Authors: Chong Zhang; Peng Zhang; Mengke Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Scattering characteristics of synthetic aperture radar (SAR) targets are typically related to observed azimuth and depression angles. 
However, in practice, it is difficult to obtain adequate training samples at all observation angles, which probably leads to poor robustness of deep networks. In this paper, we first propose a Gamma-Distribution Principal Component Analysis ($\Gamma$PCA) model that fully accounts for the statistical characteristics of SAR data. The $\Gamma$PCA derives consistent convolution kernels to effectively capture the angle-invariant features of the same target at various attitude angles, thus alleviating deep models' sensitivity to angle changes in SAR target recognition task. We validate $\Gamma$PCA model based on two commonly used backbones, ResNet and ViT, and conduct multiple robustness experiments on the MSTAR benchmark dataset. The experimental results demonstrate that $\Gamma$PCA effectively enables the model to withstand substantial distributional discrepancy caused by angle changes. Additionally, $\Gamma$PCA convolution kernel is designed to require no parameter updates, introducing no extra computational burden to the network. The source code is available at \href{https://github.com/ChGrey/GammaPCA}{https://github.com/ChGrey/GammaPCA}.
Scaling Laws for Floating–Point Quantization Training
Authors: Xingwu Sun; Shuaipeng Li; Ruobing Xie; Weidong Han; Kan Wu; Zhen Yang; Yixing Li; An Wang; SHUAI LI; Jinbao Xue; Yu Cheng; Yangyu Tao; Zhanhui Kang; Cheng-zhong Xu; Di Wang; Jie Jiang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer quantization, which pay less attention to the constituents in floating-point (FP) quantization, and thus cannot well fit the LLM losses in this scenario. In contrast, while FP quantization training is more commonly implemented in production, it's research has been relatively superficial. In this paper, we thoroughly explore the effects of FP quantization targets, exponent bits, mantissa bits, and the calculation granularity of the scaling factor in FP quantization training performance of LLM models.
In addition to an accurate FP quantization unified scaling law, we also provide valuable suggestions for the community: (1) Exponent bits contribute slightly more to the model performance than mantissa bits. We provide the optimal exponent-mantissa bit ratio for different bit numbers, which is available for future reference by hardware manufacturers; (2) We discover the formation of the critical data size in low-precision LLM training. Too much training data exceeding the critical data size will inversely bring in degradation of LLM performance; (3) The optimal FP quantization precision is directly proportional to the computational power, but within a wide computational power range. We estimate that the best cost-performance precision should lie between 4-8 bits.
Enhancing Target-unspecific Tasks through a Features Matrix
Authors: Fangming Cui; Yonggang Zhang; Xuan Wang; Xinmei Tian; Jun Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent developments in prompt learning of large Vision-Language Models (VLMs) have significantly improved performance in target-specific tasks. However, these prompting methods often struggle to tackle the target-unspecific or generalizable tasks effectively. 
It may be attributed to the fact that overfitting training causes the model to forget its general knowledge. The general knowledge has a strong promotion on target-unspecific tasks. To alleviate this issue, we propose a novel Features Matrix (FM) approach designed to enhance these models on target-unspecific tasks. Our method extracts and leverages general knowledge, shaping a Features Matrix (FM). Specifically, the FM captures the semantics of diverse inputs from a deep and fine perspective, preserving essential general knowledge, which mitigates the risk of overfitting. Representative evaluations demonstrate that: 1) the FM is compatible with existing frameworks as a generic and flexible module, and 2)  the FM significantly showcases its effectiveness in enhancing target-unspecific tasks (base-to-novel generalization, domain generalization, and cross-dataset generalization), achieving state-of-the-art performance.
On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning
Authors: Yi-Rui Yang; Chang-Wei Shi; Wu-Jun Li
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Byzantine-robust distributed learning (BRDL), which refers to distributed learning that can work with potential faulty or malicious workers (also known as Byzantine workers), has recently attracted much research attention. Robust aggregators are widely used in existing BRDL methods to obtain robustness against Byzantine workers. However, Byzantine workers do not always exist in applications. As far as we know, there is almost no existing work theoretically investigating the effect of using robust aggregators when there are no Byzantine workers. To bridge this knowledge gap, we theoretically analyze the aggregation error for robust aggregators when there are no Byzantine workers. Specifically, we show that the worst-case aggregation error without Byzantine workers increases with the increase of the number of Byzantine workers that a robust aggregator can tolerate. The theoretical result reveals the tension between Byzantine robustness and no-attack accuracy, which refers to accuracy without faulty workers and malicious workers in this paper. Furthermore, we provide lower bounds for the convergence rate of gradient descent with robust aggregators for non-convex objective functions and objective functions that satisfy the Polyak-Lojasiewicz (PL) condition, respectively. We also prove the tightness of the lower bounds. The lower bounds for convergence rate reveal similar tension between Byzantine robustness and no-attack accuracy. Empirical results further support our theoretical findings.
Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
Authors: Jiazhong Cen; Xudong Zhou; Jiemin Fang; Changsong Wen; Lingxi Xie; XIAOPENG ZHANG; Wei Shen; Qi Tian
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent advancements in 3D Gaussian Splatting (3D-GS) enable high-quality 3D scene reconstruction from RGB images. Many studies extend this paradigm for language-driven open-vocabulary scene understanding. However, most of them simply project 2D semantic features onto 3D Gaussians and overlook a fundamental gap between 2D and 3D understanding: a 3D object may exhibit various semantics from different viewpoints—a phenomenon we term **view-dependent semantics**. To address this challenge, we propose **LaGa** (**La**nguage **Ga**ussians), which establishes cross-view semantic connections by decomposing the 3D scene into objects. Then, it constructs view-aggregated semantic representations by clustering semantic descriptors and reweighting them based on multi-view semantics. Extensive experiments demonstrate that LaGa effectively captures key information from view-dependent semantics, enabling a more comprehensive understanding of 3D scenes. Notably, under the same settings, LaGa achieves a significant improvement of **+18.7\% mIoU** over the previous SOTA on the LERF-OVS dataset. Our code is available at: https://github.com/https://github.com/SJTU-DeepVisionLab/LaGa.
Rethinking Time Encoding via Learnable Transformation Functions
Authors: Xi Chen; Yateng Tang; Jiarong Xu; Jiawei Zhang; Siwei Zhang; Sijia Peng; Xuehao Zheng; Yun Xiong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Effectively modeling time information and incorporating it into applications or models involving chronologically occurring events is crucial. Real-world scenarios often involve diverse and complex time patterns, which pose significant challenges for time encoding methods. While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity. This narrow focus on single-pattern modeling makes them less effective in handling the diversity and complexities of real-world time patterns. In this paper, we investigate to improve the existing commonly used time encoding methods and introduce **Learnable Transformation-based Generalized Time Encoding (LeTE)**. We propose using deep function learning techniques to parameterize nonlinear transformations in time encoding, making them learnable and capable of modeling generalized time patterns, including diverse and complex temporal dynamics. By enabling learnable transformations, LeTE encompasses previous methods as specific cases and allows seamless integration into a wide range of tasks. Through extensive experiments across diverse domains, we demonstrate the versatility and effectiveness of LeTE.
Delta Decompression  for MoE-based LLMs Compression
Authors: Hao Gu; Wei Li; Lujun Li; Zhu Qiyuan; Mark G. Lee; Shengjie Sun; Wei Xue; Yike Guo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mixture-of-Experts (MoE) architectures in large language models (LLMs) achieve exceptional performance, but face prohibitive storage and memory requirements. To address these challenges, we present $D^2$-MoE, a new delta decompression compressor for reducing the parameters of MoE LLMs. Based on observations of expert diversity, we decompose their weights into a shared base weight and unique delta weights.  Specifically, our method first merges each expert's weight into the base weight using the Fisher information matrix to capture shared components.  Then, we compress delta weights through Singular Value Decomposition (SVD) by exploiting their low-rank properties.
Finally, we introduce a semi-dynamical structured pruning strategy for the base weights, combining static and dynamic redundancy analysis to achieve further parameter reduction while maintaining input adaptivity. In this way, our $D^2$-MoE successfully compacts MoE LLMs to high compression ratios without additional training. Extensive experiments highlight the superiority of our approach, with over 13\% performance gains than other compressors on Mixtral|Phi-3.5|DeepSeek|Qwen2 MoE  LLMs at 40$\sim$60\% compression rates. Codes are available in https://github.com/lliai/D2MoE.
MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition
Authors: Wei Li; Lujun Li; Hao Gu; You-Liang Huang; Mark G. Lee; Shengjie Sun; Wei Xue; Yike Guo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mixture of Experts (MoE) architecture improves Large Language Models (LLMs) with better scaling, but its higher parameter counts and memory demands create challenges for deployment. In this paper, we present MoE-SVD, a new decomposition-based compression framework tailored for MoE LLMs without any extra training. By harnessing the power of Singular Value Decomposition (SVD), MoE-SVD addresses the critical issues of decomposition collapse and matrix redundancy in MoE architectures.   Specifically, we first decompose experts into compact low-rank matrices, resulting in accelerated inference and memory optimization. In particular, we propose selective decomposition strategy by measuring sensitivity metrics based on weight singular values and activation statistics to automatically identify decomposable expert layers. Then, we share a single V-matrix across all experts and employ a top-k selection for U-matrices. This low-rank matrix sharing and trimming scheme allows for significant parameter reduction while preserving diversity among experts.  Comprehensive experiments on Mixtral, Phi-3.5, DeepSeek, and Qwen2 MoE LLMs show MoE-SVD outperforms other compression methods, achieving a 60\% compression ratio and 1.5× faster inference with minimal performance loss.
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Authors: Haoquan Fang; Markus Grotz; Wilbert Pumacay; Yi Ru Wang; Dieter Fox; Ranjay Krishna; Jiafei Duan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce **SAM2Act**, a multi-view robotic transformer-based policy that leverages multi-resolution upsampling with visual representations from large-scale foundation model. SAM2Act achieves a state-of-the-art average success rate of **86.8% across 18 tasks** in the RLBench benchmark, and demonstrates robust generalization on *The Colosseum* benchmark, with only a **4.3% performance gap** under diverse environmental perturbations. Building on this foundation, we propose **SAM2Act+**, a memory-based architecture inspired by SAM2, which incorporates a memory bank, an encoder, and an attention mechanism to enhance spatial memory. To address the need for evaluating memory-dependent tasks, we introduce ***MemoryBench***, a novel benchmark designed to assess spatial memory and action recall in robotic manipulation. SAM2Act+ achieves an average success rate of **94.3% on memory-based tasks** in *MemoryBench*, significantly outperforming existing approaches and pushing the boundaries of memory-based robotic systems.
Project page: [sam2act.github.io](https://sam2act.github.io/).
COExpander: Adaptive Solution Expansion for Combinatorial Optimization
Authors: Jiale Ma; Wenzheng Pan; Yang Li; Junchi Yan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite rapid progress in neural combinatorial optimization (NCO) for solving CO problems (COPs), as the problem scale grows, several bottlenecks persist: 1) solvers in the Global Prediction (GP) paradigm struggle in long-range decisions where the overly smooth intermediate heatmaps impede effective decoding, and 2) solvers in the Local Construction (LC) paradigm are time-consuming and incapable of tackling large instances due to the onerous auto-regressive process. Observing these challenges, we propose a new paradigm named Adaptive Expansion AE with its instantiation COExpander, positioned to leverage both advantages of GP and LC. COExpander utilizes informative heatmaps generated by a global predictor, which is learned under the guidance of locally determined partial solutions, to in turn direct the expansion of determined decision variables with adaptive step-sizes. To ensure transparent evaluation, we further take the lead to canonicalize 29 benchmarks spanning 6 popular COPs (MIS, MCl, MVC, MCut, TSP, ATSP) and various scales (50-10K nodes), upon which experiments demonstrate concrete SOTA performance of COExpander over these tasks. Source code and our standardized datasets will be made public.
BECAME: Bayesian Continual Learning with Adaptive Model Merging
Authors: Mei Li; Yuxiang Lu; Qinyan Dai; Suizhi Huang; Yue Ding; Hongtao Lu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Continual Learning (CL) strives to learn incrementally across tasks while mitigating catastrophic forgetting. A key challenge in CL is balancing stability (retaining prior knowledge) and plasticity (learning new tasks). While representative gradient projection methods ensure stability, they often limit plasticity. Model merging techniques offer promising solutions, but prior methods typically rely on empirical assumptions and carefully selected hyperparameters. In this paper, we explore the potential of model merging to enhance the stability-plasticity trade-off, providing theoretical insights that underscore its benefits. Specifically, we reformulate the merging mechanism using Bayesian continual learning principles and derive a closed-form solution for the optimal merging coefficient that adapts to the diverse characteristics of tasks. To validate our approach, we introduce a two-stage framework named BECAME, which synergizes the expertise of gradient projection and adaptive merging. Extensive experiments show that our approach outperforms state-of-the-art CL methods and existing merging strategies https://github.com/limei0818/BECAME.
Targeted control of fast prototyping through domain-specific interface
Authors: Yu-Zhe Shi; Mingchen Liu; Hanlu Ma; Qiao Xu; Huamin Qu; Kun He; Lecheng Ruan; Qining Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Industrial designers have long sought a natural and intuitive way to achieve the targeted control of prototype models---using simple natural language instructions to configure and adjust the models seamlessly according to their intentions, without relying on complex modeling commands. While Large Language Models have shown promise in this area, their potential for controlling prototype models through language remains partially underutilized. This limitation stems from gaps between designers' languages and modeling languages, including mismatch in abstraction levels, fluctuation in semantic precision, and divergence in lexical scopes. To bridge these gaps, we propose an interface architecture that serves as a medium between the two languages. Grounded in design principles derived from a systematic investigation of fast prototyping practices, we devise the interface's operational mechanism and develop an algorithm for its automated domain specification. Both machine-based evaluations and human studies on fast prototyping across various product design domains demonstrate the interface's potential to function as an auxiliary module for Large Language Models, enabling precise and effective targeted control of prototype models.