GenAI4ELab/papercli-papers huggingface dataset:
LLM-Assisted Semantically Diverse Teammate Generation for Efficient Multi-agent Coordination
Authors: Lihe Li; Lei Yuan; Pengsen Liu; Tao Jiang; Yang Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Training with diverse teammates is the key for learning generalizable agents. Typical approaches aim to generate diverse teammates by utilizing techniques like randomization, designing regularization terms, or reducing policy compatibility, etc. However, such teammates lack semantic information, resulting in inefficient teammate generation and poor adaptability of the agents. To tackle these challenges, we propose Semantically Diverse Teammate Generation (SemDiv), a novel framework leveraging the capabilities of large language models (LLMs) to discover and learn diverse coordination behaviors at the semantic level. In each iteration, SemDiv first generates a novel coordination behavior described in natural language, then translates it into a reward function to train a teammate policy. Once the policy is verified to be meaningful, novel, and aligned with the behavior, the agents train a policy for coordination. Through this iterative process, SemDiv efficiently generates a diverse set of semantically grounded teammates, enabling agents to develop specialized policies, and select the most suitable ones through language-based reasoning to adapt to unseen teammates. Experiments show that SemDiv generates teammates covering a wide range of coordination behaviors, including those unreachable by baseline methods. Evaluation across four MARL environments, each with five unseen representative teammates, demonstrates SemDiv's superior coordination and adaptability. Our code is available at https://github.com/lilh76/SemDiv.
PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model
Authors: Baijiong Lin; Weisen Jiang; Yuancheng Xu; Hao Chen; Ying-Cong Chen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-objective test-time alignment aims to adapt large language models (LLMs) to diverse multi-dimensional user preferences during inference while keeping LLMs frozen. 
Recently, GenARM (Xu et al., 2025) first independently trains Autoregressive Reward Models (ARMs) for each preference dimension without awareness of each other, then combines their outputs based on user-specific preference vectors during inference to achieve multi-objective test-time alignment, leading to two key limitations: the need for *multiple* ARMs increases the inference cost, and the *separate* training of ARMs causes the misalignment between the guided generation and the user preferences.
To address these issues, we propose Preference-aware ARM (PARM), a *single* unified ARM trained across *all* preference dimensions. 
PARM uses our proposed Preference-Aware Bilinear Low-Rank Adaptation (PBLoRA), which employs a bilinear form to condition the ARM on preference vectors, enabling it to achieve precise control over preference trade-offs during inference.
Experiments demonstrate that PARM reduces inference costs and achieves better alignment with preference vectors compared with existing methods. Additionally, PARM enables weak-to-strong guidance, allowing a smaller PARM to guide a larger frozen LLM without expensive training, making multi-objective alignment accessible with limited computing resources.
The code is available at https://github.com/Baijiong-Lin/PARM.
Projection Pursuit Density Ratio Estimation
Authors: Meilin Wang; Wei Huang; Mingming Gong; Zheng Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: *Density ratio estimation* (DRE) is a paramount task in machine learning, for its broad applications across multiple domains, such as covariate shift adaptation, causal inference, independence tests and beyond. Parametric methods for estimating the density ratio possibly lead to biased results if models are misspecified, while conventional non-parametric methods suffer from the curse of dimensionality when the dimension of data is large. To address these challenges, in this paper, we propose a novel approach for DRE based on the projection pursuit (PP) approximation. The proposed method leverages PP to mitigate the impact of high dimensionality while retaining the model flexibility needed for the accuracy of DRE. We establish the consistency and the convergence rate for the proposed estimator.  Experimental results demonstrate that our proposed method outperforms existing alternatives in various applications.
Test-time Adapted Reinforcement Learning with Action Entropy Regularization
Authors: Shoukai Xu; ZihaoLian; Mingkui Tan; Liu Liu; Zhong Zhang; Peilin Zhao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Offline reinforcement learning is widely applied in multiple fields due to its advantages in efficiency and risk control. However, a major problem it faces is the distribution shift between offline datasets and online environments. This mismatch leads to out-of-distribution (OOD) state-action pairs that fall outside the scope of the training data. Therefore, existing conservative training policies may not provide reliable decisions when the test environment deviates greatly from the offline dataset. In this paper, we propose Test-time Adapted Reinforcement Learning (TARL) to address this problem. TARL constructs unsupervised test-time optimization objectives for discrete and continuous control tasks, using test data without depending on environmental rewards. In discrete control tasks, it minimizes the entropy of predicted action probabilities to decrease uncertainty and avoid OOD state-action pairs. For continuous control tasks, it represents and minimizes action uncertainty based on the normal distribution of policy network outputs. Moreover, to prevent model bias caused by overfitting and error accumulation during the test-time update process, TARL enforces a KL divergence constraint between the fine-tuned policy and the original policy. For efficiency, TARL only updates the layer normalization layer parameters during testing. Extensive experiments on popular Atari game benchmarks and the D4RL dataset demonstrate the superiority of our method. Our method achieved a significant improvement over CQL, with a 13.6% episode return relative increase on the hopper-expert-v2 task.
Discovering a Zero  (Zero-Vector Class of Machine Learning)
Authors: Harikrishna Metta; Venkatesh Babu Radhakrishnan
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: In Machine learning, separating data into classes is a very fundamental problem. A mathematical framework around the classes is presented in this work to deepen the understanding of classes. The classes are defined as vectors in a Vector Space, where addition corresponds to the union of classes, and scalar multiplication resembles set complement of classes. The Zero-Vector in the vector space corresponds to a class referred to as the Metta-Class. This discovery enables numerous applications. One such application, termed 'clear learning' in this work, focuses on learning the true nature (manifold) of the data instead of merely learning a boundary sufficient for classification. Another application, called 'unary class learning', involves learning a single class in isolation rather than learning by comparing two or more classes. Additionally, 'set operations on classes' is another application highlighted in this work. Furthermore, Continual Learning of classes is facilitated by smaller networks. The Metta-Class enables neural networks to learn only the data manifold; therefore, it can also be used for generation of new data. Results for the key applications are shown using the MNIST dataset. To further strengthen the claims, some results are also produced using the CIFAR-10 and ImageNet-1k embeddings. The code supporting these applications is publicly available at: github.com/hm-4/Metta-Class.
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
Authors: Gabriel Tseng; Anthony Fuller; Marlena Reil; Henry Herzog; Patrick Beukema; Favyen Bastani; James R Green; Evan Shelhamer; Hannah Kerner; David Rolnick
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for diverse remote sensing tasks, such as crop mapping and flood detection. However, learning shared representations of remote sensing data is challenging, given the diversity of relevant data modalities, and because objects of interest vary massively in scale, from small boats (1-2 pixels and fast) to glaciers (thousands of pixels and slow). We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling. Our dual global and local contrastive losses differ in their targets (deep representations vs. shallow input projections) and masking strategies (structured vs. not). Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Authors: Ning Lu; Shengcai Liu; Jiahao Wu; Weiyu Chen; Zhirui Zhang; Yew-Soon Ong; Qi Wang; Ke Tang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many companies provide fine-tuning API services, enabling users to upload their own data for LLM customization. However, fine-tuning services introduce a new safety threat: user-uploaded data, whether harmful or benign, can break the model’s alignment, leading to unsafe outputs. Moreover, existing defense methods struggle to address the diversity of fine-tuning datasets (e.g., varying sizes, tasks), often sacrificing utility for safety or vice versa. To address this issue, we propose Safe Delta, a safety-aware post-training defense method that adjusts the delta parameters (i.e., the parameter change before and after fine-tuning). Specifically, Safe Delta estimates the safety degradation, selects delta parameters to maximize utility while limiting overall safety loss, and applies a safety compensation vector to mitigate residual safety loss. Through extensive experiments on four diverse datasets with varying settings, our approach consistently preserves safety while ensuring that the utility gain from benign datasets remains unaffected.
Multi-View Graph Clustering via Node-Guided Contrastive Encoding
Authors: Yazhou Ren; Junlong Ke; Zichen Wen; Tianyi Wu; Yang Yang; Xiaorong Pu; Lifang He
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-view clustering has gained significant attention for integrating multi-view information in multimedia applications. With the growing complexity of graph data, multi-view graph clustering (MVGC) has become increasingly important. Existing methods primarily use Graph Neural Networks (GNNs) to encode structural and feature information, but applying GNNs within contrastive learning poses specific challenges, such as integrating graph data with node features and handling both homophilic and heterophilic graphs. To address these challenges, this paper introduces Node-Guided Contrastive Encoding (NGCE), a novel MVGC approach that leverages node features to guide embedding generation. NGCE enhances compatibility with GNN filtering, effectively integrates homophilic and heterophilic information, and strengthens contrastive learning across views. Extensive experiments demonstrate its robust performance on six homophilic and heterophilic multi-view benchmark datasets.
Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
Authors: Yusong Wang; Shiyin Tan; Jialun Shen; Yicheng Xu; Haobo Song; Qi Xu; Prayag Tiwari; Mingkun Xu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph Contrastive Learning (GCL) improves Graph Neural Network (GNN)-based protein representation learning by enhancing its generalization and robustness. Existing GCL approaches for protein representation learning rely on 2D topology, where graph augmentation is solely based on topological features, ignoring the intrinsic biological properties of proteins. Besides, 3D structure-based protein graph augmentation remains unexplored, despite proteins inherently exhibiting 3D structures. To bridge this gap, we propose novel biology-aware graph augmentation strategies from the perspective of invariance and integrate them into the protein GCL framework. Specifically, we introduce Functional Community Invariance (FCI)-based graph augmentation, which employs spectral constraints to preserve topology-driven community structures while incorporating residue-level chemical similarity as edge weights to guide edge sampling and maintain functional communities. Furthermore, we propose 3D Protein Structure Invariance (3-PSI)-based graph augmentation, leveraging dihedral angle perturbations and secondary structure rotations to retain critical 3D structural information of proteins while diversifying graph views.
Extensive experiments on four different protein-related tasks demonstrate the superiority of our proposed GCL protein representation learning framework.
ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
Authors: Zhehan Zhao; Lu Bai; Lixin Cui; Ming Li; Ziyu Lyu; Lixiang Xu; Yue Wang; Edwin Hancock
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools for graph learning, and one key challenge arising in GNNs is the development of effective pooling operations for learning meaningful graph representations. In this paper, we propose a novel Edge-Node Attention-based Hierarchical Pooling (ENAHPool) operation for GNNs. Unlike existing cluster-based pooling methods that suffer from ambiguous node assignments and uniform edge-node information aggregation, ENAHPool assigns each node exclusively to a cluster and employs attention mechanisms to perform weighted aggregation of both node features within clusters and edge connectivity strengths between clusters, resulting in more informative hierarchical representations. To further enhance the model performance, we introduce a Multi-Distance Message Passing Neural Network (MD-MPNN) that utilizes edge connectivity strength information to enable direct and selective message propagation across multiple distances, effectively mitigating the over-squashing problem in classical MPNNs. Experimental results demonstrate the effectiveness of the proposed method.
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
Authors: Mao-Lin Luo; Zi-Hao Zhou; Tong Wei; Min-Ling Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Continual learning with vision-language models like CLIP offers a pathway toward scalable machine learning systems by leveraging its transferable representations. Existing CLIP-based methods adapt the pre-trained image encoder by adding multiple sets of learnable parameters, with each task using a partial set of parameters. This requires selecting the expected parameters for input images during inference, which is prone to error that degrades performance. To address this problem, we introduce LADA (**L**abel-specific **ADA**pter). Instead of partitioning parameters across tasks, LADA appends lightweight, label-specific memory units to the frozen CLIP image encoder, enabling discriminative feature generation by aggregating task-agnostic knowledge. To prevent catastrophic forgetting, LADA employs feature distillation for seen classes, preventing their features from being interfered with by new classes. Positioned after the image encoder, LADA prevents gradient flow to the frozen CLIP parameters, ensuring efficient training. Extensive results show that LADA achieves state-of-the-art performance in continual learning settings.  The implementation code is available at [https://github.com/MaolinLuo/LADA](https://github.com/MaolinLuo/LADA).
Scaling Laws for Upcycling Mixture-of-Experts Language Models
Authors: Seng Pei Liew; Takuya Kato; Sho Takase
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pretraining large language models (LLMs) is resource-intensive, often requiring months of training time even with high-end GPU clusters.
 There are two approaches of mitigating such computational demands: reusing smaller models to train larger ones (upcycling), and training computationally efficient models like mixture-of-experts (MoE).
 In this paper, we study the upcycling of LLMs to MoE models, of which the scaling behavior remains underexplored.
 Through extensive experiments, we identify empirical scaling laws that describe how performance depends on dataset size and model configuration.
 Particularly, we show that, while scaling these factors improves performance, there is a novel interaction term between the dense and upcycled training dataset that limits the efficiency of upcycling at large computational budgets.  
 Based on these findings, we provide guidance to scale upcycling, and establish conditions under which upcycling outperforms from-scratch trainings within budget constraints.
L-Diffusion: Laplace Diffusion for Efficient Pathology Image Segmentation
Authors: Weihan Li; Linyun Zhou; YangJian; Shengxuming Zhang; Xiangtong Du; Xiuming Zhang; Jing Zhang; ChaoqingXu; Mingli Song; Zunlei Feng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pathology image segmentation plays a pivotal role in artificial digital pathology diagnosis and treatment. Existing approaches to pathology image segmentation are hindered by labor-intensive annotation processes and limited accuracy in tail-class identification, primarily due to the long-tail distribution inherent in gigapixel pathology images. In this work, we introduce the Laplace Diffusion Model, referred to as L-Diffusion, an innovative framework tailored for efficient pathology image segmentation. L-Diffusion utilizes multiple Laplace distributions, as opposed to Gaussian distributions, to model distinct components—a methodology supported by theoretical analysis that significantly enhances the decomposition of features within the feature space. A sequence of feature maps is initially generated through a series of diffusion steps. Following this, contrastive learning is employed to refine the pixel-wise vectors derived from the feature map sequence.  By utilizing these highly discriminative pixel-wise vectors, the segmentation module achieves a harmonious balance of precision and robustness with remarkable efficiency. Extensive experimental evaluations demonstrate that L-Diffusion attains improvements of up to  7.16\%,  26.74\%,  16.52\%, and  3.55\% on tissue segmentation datasets, and  20.09\%,  10.67\%,  14.42\%, and  10.41\% on cell segmentation datasets, as quantified by DICE, MPA, mIoU, and FwIoU metrics. The source are available at https://github.com/Lweihan/LDiffusion.
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Authors: Lincan Cai; Jingxuan Kang; Shuang Li; Wenxuan Ma; Binhui Xie; Zhida Qin; Jian Liang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmentation techniques, like random cropping, in alignment with fine-grained class descriptions generated by large language models (LLMs), significantly enhancing zero-shot performance by incorporating multi-view information. However, the inherent randomness of these augmentations can inevitably introduce background artifacts and cause models to overly focus on local details, compromising global semantic understanding. To address these issues, we propose an **A**ttention-**B**ased **S**election (**ABS**) method from local details to global context, which applies attention-guided cropping in both raw images and feature space, supplement global semantic information through strategic feature selection. Additionally, we introduce a soft matching technique to effectively filter LLM descriptions for better alignment. **ABS** achieves state-of-the-art performance on out-of-distribution generalization and zero-shot classification tasks. Notably, **ABS** is training-free and even rivals few-shot and test-time adaptation methods.
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
Authors: Aleksandr Gushchin; Khaled Abud; Georgii Bychkov; Ekaterina Shumitskaya; Anna Chistyakova; Sergey Lavrushkin; Bader Rasheed; Kirill Malyshev; Dmitriy S. Vatolin; Anastasia Antsiferova
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Modern neural-network-based Image Quality Assessment (IQA) metrics are vulnerable to adversarial attacks, which can be exploited to manipulate search engine rankings, benchmark results, and content quality assessments, raising concerns about the reliability of IQA metrics in critical applications. This paper presents the first comprehensive study of IQA defense mechanisms in response to adversarial attacks on these metrics to pave the way for safer use of IQA metrics. We systematically evaluated 30 defense strategies, including purification, training-based, and certified methods --- and applied 14 adversarial attacks in adaptive and non-adaptive settings to compare these defenses on 9 no-reference IQA metrics. Our proposed benchmark aims to guide the development of IQA defense methods and is open to submissions; the latest results and code are at https://msu-video-group.github.io/adversarial-defenses-for-iqa/.
AMPO: Active Multi Preference Optimization for Self-play Preference Selection
Authors: Taneesh Gupta; Rahul Madhavan; Xuchao Zhang; Chetan Bansal; Saravan Rajmohan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-preference optimization enriches language-model alignment beyond pairwise preferences by contrasting entire sets of helpful and undesired responses, enabling richer training signals for large language models. During self-play alignment, these models often produce numerous candidate answers per query, making it computationally infeasible to include all of them in the training objective. We propose Active Multi-Preference Optimization (AMPO), which combines on-policy generation, a multi-preference group-contrastive loss, and active subset selection. Specifically, we score and embed large candidate pools of responses, then pick a small but informative subset—covering reward extremes and distinct semantic clusters—for preference optimization.The resulting contrastive-training scheme identifies not only the best and worst answers but also subtle, underexplored modes crucial for robust alignment. Theoretically, we provide guarantees of expected reward maximization using our active selection method. Empirically, AMPO achieves state-of-the-art results on AlpacaEval with Llama 8B and Mistral 7B. We release our datasets [here](https://huggingface.co/Multi-preference-Optimization).