GenAI4ELab/papercli-papers huggingface dataset:
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Authors: Yafu Li; Xuyang Hu; Xiaoye Qu; Linjie Li; Yu Cheng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have presented impressive performance but often lack the flexibility to adapt to human preferences quickly without retraining. Inspired by the recent efforts on test-time scaling, we make the first attempt to propose Test-time Preference Optimization (TPO), a framework that aligns LLM outputs with human preferences during inference, eliminating the need to update model parameters. Instead of relying on purely numerical rewards, TPO translates reward signals into \emph{textual} critiques and uses them as textual rewards to iteratively refine its response. Evaluations on benchmarks covering instruction following, preference alignment, safety, and mathematics reveal that TPO progressively improves alignment with human preferences. Notably, after only a few TPO steps, the initially unaligned Llama-3.1-70B-SFT model can surpass the aligned counterpart, Llama-3.1-70B-Instruct. Furthermore, TPO scales efficiently with both the search width and depth of the inference process. Through case studies, we illustrate how TPO exploits the innate capacity of LLM to interpret and act upon reward signals. Our findings establish TPO as a practical, lightweight alternative for test-time preference optimization, achieving alignment on the fly.
Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models
Authors: Xingyu Chen; Jiahao Xu; Tian Liang; Zhiwei He; Jianhui Pang; Dian Yu; Linfeng Song; Qiuzhi Liu; Mengfei Zhou; Zhuosheng Zhang; Rui Wang; Zhaopeng Tu; Haitao Mi; Dong Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The remarkable performance of long reasoning models can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended chain-of-thought (CoT) processes, exploring multiple strategies to enhance problem-solving capabilities. However, a critical question remains: How to intelligently and efficiently scale computational resources during testing. This paper presents the first comprehensive study on the prevalent issue of overthinking in these models, where long reasoning models generate redundant solutions that contribute minimally to accuracy and diversity, thereby wasting computational resources on simple problems with minimal benefit. We introduce novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by long reasoning models. Using a self-training paradigm, we propose strategies to mitigate overthinking, simplifying reasoning processes without compromising accuracy.  Experimental results show that our approach successfully reduces computational overhead while preserving model performance across a range of testsets with varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME. Our code is open-source and available at https://github.com/galaxyChen/overthinking.
EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
Authors: Kaicheng Fu; Changde Du; Jie Peng; Kunpeng Wang; Shuangchen Zhao; Xiaoyu Chen; Huiguang He
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Emotion recognition systems face significant challenges in real-world applications, where novel emotion categories continually emerge and multiple emotions often co-occur. This paper introduces multi-label fine-grained class incremental emotion decoding, which aims to develop models capable of incrementally learning new emotion categories while maintaining the ability to recognize multiple concurrent emotions. We propose an Augmented Emotional Semantics Learning (AESL) framework to address two critical challenges: past- and future-missing partial label problems. AESL incorporates an augmented Emotional Relation Graph (ERG) for reliable soft label generation and affective dimension-based knowledge distillation for future-aware feature learning. We evaluate our approach on three datasets spanning brain activity and multimedia domains, demonstrating its effectiveness in decoding up to 28 fine-grained emotion categories. Results show that AESL significantly outperforms existing methods while effectively mitigating catastrophic forgetting. Our code is available at https://github.com/ChangdeDu/EmoGrowth.
Graph Minimum Factorization Distance and Its Application to Large-Scale Graph Data Clustering
Authors: Jicong Fan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Measuring the distance or similarity between graphs is the foundation of many graph analysis tasks, such as graph classification and clustering, but remains a challenge on large datasets. In this work, we treat the adjacency matrices of two graphs as two kernel matrices given by some unknown indefinite kernel function performed on two discrete distributions and define the distance between the two distributions as a measure, called MMFD, of the dissimilarity between two graphs. We show that MMFD is a pseudo-metric. Although the initial definition of MMFD seems complex, we show that it has a closed-form solution with extremely simple computation. To further improve the efficiency of large-scale clustering, we propose an MMFD-KM with linear space and time complexity with respect to the number of graphs. We also provide a generalization of MMFD, called MFD, which is more effective in exploiting the information of factors of adjacency matrices. The experiments on simulated graphs intuitively show that our methods are effective in comparing graphs. The experiments on real-world datasets demonstrate that, compared to the competitors, our methods have much better clustering performance in terms of three evaluation metrics and time cost.
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM’s Reasoning Capability
Authors: Zicheng Lin; Tian Liang; Jiahao Xu; Qiuzhi Liu; Xing Wang; Ruilin Luo; Chufan Shi; Siheng Li; Yujiu Yang; Zhaopeng Tu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we introduce the concept of critical tokens -- elements within reasoning trajectories that significantly influence incorrect outcomes. We present a novel framework for identifying these tokens through rollout sampling and demonstrate their substantial divergence from traditional error tokens. Through extensive experiments on datasets such as GSM8K and MATH500, we show that identifying and replacing critical tokens significantly improves model accuracy. We propose an efficient methodology for pinpointing these tokens in large-scale datasets using contrastive estimation and extend this framework to enhance model training processes with direct preference optimization (DPO). Experimental results on GSM8K and MATH500 benchmarks with the widely used models Llama-3 (8B and 70B) and Deepseek-math (7B) demonstrate the effectiveness of the proposed approach, cDPO. Our results underscore the potential of leveraging critical tokens to reduce errors in reasoning tasks, advancing the development of AI systems capable of robust logical deduction.
UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control
Authors: Kaizhen Zhu; Mokai Pan; Yuexin Ma; Yanwei Fu; Jingyi Yu; Jingya Wang; Ye Shi
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Recent advances in diffusion bridge models leverage Doob’s $h$-transform to establish fixed endpoints between distributions, demonstrating promising results in image translation and restoration tasks. However, these approaches frequently produce blurred or excessively smoothed image details and lack a comprehensive theoretical foundation to explain these shortcomings. To address these limitations, we propose UniDB, a unified framework for diffusion bridges based on Stochastic Optimal Control (SOC). UniDB formulates the problem through an SOC-based optimization and derives a closed-form solution for the optimal controller, thereby unifying and generalizing existing diffusion bridge models. We demonstrate that existing diffusion bridges employing Doob’s $h$-transform constitute a special case of our framework, emerging when the terminal penalty coefficient in the SOC cost function tends to infinity. By incorporating a tunable terminal penalty coefficient, UniDB achieves an optimal balance between control costs and terminal penalties, substantially improving detail preservation and output quality. Notably, UniDB seamlessly integrates with existing diffusion bridge models, requiring only minimal code modifications. Extensive experiments across diverse image restoration tasks validate the superiority and adaptability of the proposed framework. Our code is available at https://github.com/UniDB-SOC/UniDB/.
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
Authors: Lorenz Kummer; Samir Moustafa; Anatol Ehrlich; Franka Bause; Nikolaus Suess; Wilfried N. Gansterer; Nils Morten Kriege
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The lottery ticket hypothesis (LTH) is well-studied for convolutional neural networks but has been validated only empirically for graph neural networks (GNNs), for which theoretical findings are largely lacking. In this paper, we identify the expressivity of sparse subnetworks, i.e. their ability to distinguish non-isomorphic graphs, as crucial for finding winning tickets that preserve the predictive performance.
We establish conditions under which the expressivity of a sparsely initialized GNN matches that of the full network, particularly when compared to the Weisfeiler-Leman test, and in that context put forward and prove a Strong Expressive Lottery Ticket Hypothesis. We subsequently show that an increased expressivity in the initialization potentially accelerates model convergence and improves generalization. Our findings establish novel theoretical foundations for both LTH and GNN research, highlighting the importance of maintaining expressivity in sparsely initialized GNNs. We illustrate our results using examples from drug discovery.
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
Authors: Fangwen Wu; Lechao Cheng; Shengeng Tang; Xiaofeng Zhu; Chaowei Fang; Dingwen Zhang; Meng Wang
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown. To address this, our study reveals that the gap in feature distribution between novel and existing tasks is primarily driven by differences in mean and covariance moments. Building on this insight, we propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration. Specifically, we calculate each class's mean by averaging its sample embeddings and estimate task shifts using weighted embedding changes based on their proximity to the previous mean, effectively capturing mean shifts for all learned classes with each new task. We also apply Mahalanobis distance constraint for covariance calibration, aligning class-specific embedding covariances between old and current networks to mitigate the covariance shift. Additionally, we integrate a feature-level self-distillation approach to enhance generalization. Comprehensive experiments on commonly used datasets demonstrate the effectiveness of our approach. The source code is available at https://github.com/fwu11/MACIL.git.
Algorithms and Hardness for Active Learning on Graphs
Authors: Vincent Cohen-Addad; Silvio Lattanzi; Simon Meierhans
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We study the offline active learning problem on graphs. In this problem, one seeks to select k vertices whose labels are best suited for predicting the labels of all the other vertices in the graph.
Guillory and Bilmes (Guillory & Bilmes, 2009) introduced a natural theoretical model motivated by a label smoothness assumption. Prior to our work, algorithms with theoretical guarantees were only known for restricted graph types such as trees (Cesa-Bianchi et al., 2010) despite the models simplicity. We present the first O(log n)-resource augmented algorithm for general weighted graphs. To complement our algorithm, we show constant hardness of approximation.
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
Authors: Thibaut Boissin; Franck Mamalet; Thomas Fel; Agustin Martin Picard; Thomas Massena; Mathieu Serrurier
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Orthogonal convolutional layers are valuable components in multiple areas of machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitz-constrained models. Their ability to preserve norms and ensure stable gradient propagation makes them valuable for a large range of problems. Despite their promise, the deployment of orthogonal convolution in large-scale applications is a significant challenge due to computational overhead and limited support for modern features like strides, dilations, group convolutions, and transposed convolutions. In this paper, we introduce **AOC** (Adaptive Orthogonal Convolution), a scalable method that extends a previous method (BCOP), effectively overcoming existing limitations in the construction of orthogonal convolutions. This advancement unlocks the construction of architectures that were previously considered impractical. We demonstrate through our experiments that our method produces expressive models that become increasingly efficient as they scale. To foster further advancement, we provide an open-source python package implementing this method, called **Orthogonium**.
Unified K-Means Clustering with Label-Guided Manifold Learning
Authors: Qianqian Wang; Mengping Jiang; Zhengming Ding; Quanxue Gao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: K-Means clustering is a classical and effective unsupervised learning method attributed to its simplicity and efficiency. However, it faces notable challenges, including sensitivity to random initial centroid selection, a limited ability to discover the intrinsic manifold structures within nonlinear datasets, and difficulty in achieving balanced clustering in practical scenarios. To overcome these weaknesses, we introduce a novel framework for K-Means that leverages manifold learning. This approach eliminates the need for centroid calculation and utilizes a cluster indicator matrix to align the manifold structures, thereby enhancing clustering accuracy. Beyond the traditional Euclidean distance, our model incorporates Gaussian kernel distance, K-nearest neighbor distance, and low-pass filtering distance to effectively manage data that is not linearly separable. Furthermore, we introduce a balanced regularizer to achieve balanced clustering results. The detailed experimental results demonstrate the efficacy of our proposed methodology.
Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective
Authors: Yunzhen Yao; Lie He; Michael Gastpar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper considers the sample-efficiency of preference learning, which models and predicts human choices based on comparative judgments. The minimax optimal estimation error rate $\Theta(d/n)$ in classical estimation theory requires that the number of samples $n$ scales linearly with the dimensionality of the feature space $d$. However, the high dimensionality of the feature space and the high cost of collecting human-annotated data challenge the efficiency of traditional estimation methods. To remedy this, we leverage sparsity in the preference model and establish sharp error rates. We show that under the sparse random utility model, where the parameter of the reward function is $k$-sparse, the minimax optimal rate can be reduced to $\Theta(k/n \log(d/k))$. Furthermore, we analyze the $\ell_{1}$-regularized estimator and show that it achieves near-optimal rate under mild assumptions on the Gram matrix. 
Experiments on synthetic data and LLM alignment data validate our theoretical findings, showing that sparsity-aware methods significantly reduce sample complexity and improve prediction accuracy.
Generative Intervention Models for Causal Perturbation Modeling
Authors: Nora Schneider; Lars Lorch; Niki Kilbertus; Bernhard Schölkopf; Andreas Krause
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this enables us to predict the distribution shifts of unseen perturbation features while gaining insights about their mechanistic effects in the underlying data-generating process. On synthetic data and scRNA-seq drug perturbation data, GIMs achieve robust out-of-distribution predictions on par with unstructured approaches, while effectively inferring the underlying perturbation mechanisms, often better than other causal inference methods.
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
Authors: Jincheng Huang; Yujie Mo; Xiaoshuang Shi; Lei Feng; Xiaofeng Zhu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to reach more unlabeled neighbors, thereby increasing the utilization of labels. However, the additional label information does not always contribute positively to the GCN. To address this issue, we propose a new two-step framework called ELU-GCN. In the first stage,  ELU-GCN conducts graph learning to learn a new graph structure (i.e., ELU-graph), which allows the additional label information to positively influence the predictions of GCN. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning by exploring the consistency and mutually exclusive information between the learned ELU graph and the original graph.  Moreover, we theoretically demonstrate that the proposed method can ensure the generalization ability of GCNs. Extensive experiments validate the superiority of our method.
Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion
Authors: Xingpei Ma; Jiaran Cai; Yuansheng Guan; Shenneng Huang; Qiang Zhang; Shunsi Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent diffusion-based talking face generation models have demonstrated impressive potential in synthesizing videos that accurately match a speech audio clip with a given reference identity. However, existing approaches still encounter significant challenges due to uncontrollable factors, such as inaccurate lip-sync, inappropriate head posture and the lack of fine-grained control over facial expressions. In order to introduce more face-guided conditions beyond speech audio clips, a novel two-stage training framework Playmate is proposed to generate more lifelike facial expressions and talking faces. In the first stage, we introduce a decoupled implicit 3D representation along with a meticulously designed motion-decoupled module to facilitate more accurate attribute disentanglement and generate expressive talking videos directly from audio cues. Then, in the second stage, we introduce an emotion-control module to encode emotion control information into the latent space, enabling fine-grained control over emotions and thereby achieving the ability to generate talking videos with desired emotion. Extensive experiments demonstrate that Playmate not only outperforms existing state-of-the-art methods in terms of video quality, but also exhibits strong competitiveness in lip synchronization while offering improved flexibility in controlling emotion and head pose. The code will be available at https://github.com/Playmate111/Playmate.
Preference Optimization for Combinatorial Optimization Problems
Authors: Mingjun Pan; Guanquan Lin; You-Wei Luo; Bin Zhu; Zhien Dai; Lijun Sun; Chun Yuan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Reinforcement Learning (RL) has emerged as a powerful tool for neural combinatorial optimization, enabling models to learn heuristics that solve complex problems without requiring expert knowledge. Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast combinatorial action spaces, leading to inefficiency. In this paper, we propose **Preference Optimization**, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling, emphasizing the superiority among sampled solutions. Methodologically, by reparameterizing the reward function in terms of policy and utilizing preference models, we formulate an entropy-regularized RL objective that aligns the policy directly with preferences while avoiding intractable computations. Furthermore, we integrate local search techniques into the fine-tuning rather than post-process to generate high-quality preference pairs, helping the policy escape local optima. Empirical results on various benchmarks, such as the Traveling Salesman Problem (TSP), the Capacitated Vehicle Routing Problem (CVRP) and the Flexible Flow Shop Problem (FFSP), demonstrate that our method significantly outperforms existing RL algorithms, achieving superior convergence efficiency and solution quality.
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Authors: Xiang Zhang; Run He; Chen Jiao; Di Fang; Ming Li; Ziqian Zeng; Cen Chen; Huiping Zhuang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-world scenario where each sample may belong to multiple classes, introducing several challenges: label absence, which leads to incomplete historical information due to missing labels, and class imbalance, which results in the model bias toward majority classes. To address these challenges, we propose Label-Augmented Analytic Adaptation (L3A), an exemplar-free approach without storing past samples. L3A integrates two key modules. The pseudo-label (PL) module implements label augmentation by generating pseudo-labels for current phase samples, addressing the label absence problem. The weighted analytic classifier (WAC) derives a closed-form solution for neural networks. It introduces sample-specific weights to adaptively balance the class contribution and mitigate class imbalance. Experiments on MS-COCO and PASCAL VOC datasets demonstrate that L3A outperforms existing methods in MLCIL tasks. Our code is available at https://github.com/scut-zx/L3A.