GenAI4ELab/papercli-papers huggingface dataset:
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
Authors: Abdulkadir Gokce; Martin Schrimpf
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: When trained on large-scale object classification datasets, certain artificial neural network models begin to approximate core object recognition behaviors and neural response patterns in the primate brain. While recent machine learning advances suggest that scaling compute, model size, and dataset size improves task performance, the impact of scaling on brain alignment remains unclear. In this study, we explore scaling laws for modeling the primate visual ventral stream by systematically evaluating over 600 models trained under controlled conditions on benchmarks spanning V1, V2, V4, IT and behavior. We find that while behavioral alignment continues to scale with larger models, neural alignment saturates. This observation remains true across model architectures and training datasets, even though models with stronger inductive biases and datasets with higher-quality images are more compute-efficient. Increased scaling is especially beneficial for higher-level visual areas, where small models trained on few samples exhibit only poor alignment. Our results suggest that while scaling current architectures and datasets might suffice for alignment with human core object recognition behavior, it will not yield improved models of the brain's visual ventral stream, highlighting the need for novel strategies in building brain models.
Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport
Authors: Mingyang Sun; Pengxiang Ding; Weinan Zhang; Donglin Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Diffusion policies have shown promise in learning complex behaviors from demonstrations, particularly for tasks requiring precise control and long-term planning. However, they face challenges in robustness when encountering distribution shifts. This paper explores improving diffusion-based imitation learning models through online interactions with the environment. We propose OTPR (Optimal Transport-guided score-based diffusion Policy for Reinforcement learning fine-tuning), a novel method that integrates diffusion policies with RL using optimal transport theory. OTPR leverages the Q-function as a transport cost and views the policy as an optimal transport map, enabling efficient and stable fine-tuning. Moreover, we introduce masked optimal transport to guide state-action matching using expert keypoints and a compatibility-based resampling strategy to enhance training stability. Experiments on three simulation tasks demonstrate OTPR's superior performance and robustness compared to existing methods, especially in complex and sparse-reward environments. In sum, OTPR provides an effective framework for combining IL and RL, achieving versatile and reliable policy learning.
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
Authors: Alessandro Pierro; Steven Abreu; Jonathan Timcheck; Philipp Stratmann; Andreas Wild; Sumit Bam Shrestha
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. 
Unstructured sparsity offers a compelling solution, enabling substantial reductions in compute and memory requirements--when accelerated by compatible hardware platforms. 
In this paper, we conduct a scaling study to investigate the Pareto front of performance and efficiency across inference compute budgets.
We find that highly sparse linear RNNs *consistently* achieve better efficiency-performance trade-offs than dense baselines, with $2\times$ less compute and $36$\% less memory at iso-accuracy.
Our models achieve state-of-the-art results on a real-time streaming task for audio denoising.
By quantizing our sparse models to fixed-point arithmetic and deploying them on the Intel Loihi 2 neuromorphic chip for real-time processing, we translate model compression into tangible gains of $42\times$ lower latency and $149\times$ lower energy consumption compared to a dense model on an edge GPU.
Our findings showcase the transformative potential of unstructured sparsity, paving the way for highly efficient recurrent neural networks in real-world, resource-constrained environments.
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
Authors: Renhao Lu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent advancements in deep neural networks have significantly enhanced the performance of semantic segmentation. However, class imbalance and instance imbalance remain persistent challenges, where smaller instances and thin boundaries are often overshadowed by larger structures. To address the multiscale nature of segmented objects, various models have incorporated mechanisms such as spatial attention and feature pyramid networks. Despite these advancements, most loss functions are still primarily pixel-wise, while regional and boundary-focused loss functions often incur high computational costs or are restricted to small-scale regions. To address this limitation, we propose the complex wavelet mutual information (CWMI) loss, a novel loss function that leverages mutual information from subband images decomposed by a complex steerable pyramid. The complex steerable pyramid captures features across multiple orientations and preserves structural similarity across scales. Meanwhile, mutual information is well-suited to capturing high-dimensional directional features and offers greater noise robustness. Extensive experiments on diverse segmentation datasets demonstrate that CWMI loss achieves significant improvements in both pixel-wise accuracy and topological metrics compared to state-of-the-art methods, while introducing minimal computational overhead. Our code is available at https://github.com/lurenhaothu/CWMI
Balancing Model Efficiency and Performance: Adaptive Pruner for Long-tailed Data
Authors: Zhe Zhao; HaiBin Wen; Pengkun Wang; ShuangWang; Zhenkun Wang; Qingfu Zhang; Yang Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Long-tailed distribution datasets are prevalent in many machine learning tasks, yet existing neural network models still face significant challenges when handling such data. This paper proposes a novel adaptive pruning strategy, LTAP (Long-Tailed Adaptive Pruner), aimed at balancing model efficiency and performance to better address the challenges posed by long-tailed data distributions. LTAP introduces multi-dimensional importance scoring criteria and designs a dynamic weight adjustment mechanism to adaptively determine the pruning priority of parameters for different classes. By focusing on protecting parameters critical for tail classes, LTAP significantly enhances computational efficiency while maintaining model performance. This method combines the strengths of long-tailed learning and neural network pruning, overcoming the limitations of existing approaches in handling imbalanced data. Extensive experiments demonstrate that LTAP outperforms existing methods on various long-tailed datasets, achieving a good balance between model compression rate, computational efficiency, and classification accuracy. This research provides new insights into solving model optimization problems in long-tailed learning and is significant for improving the performance of neural networks on imbalanced datasets. The code is available at https://github.com/DataLab-atom/LT-VOTE.
Contour Integration Underlies Human-Like Vision
Authors: Ben Lonnqvist; Elsa Scialom; Abdulkadir Gokce; Zehra Merchant; Michael Herzog; Martin Schrimpf
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite the tremendous success of deep learning in computer vision, models still fall behind humans in generalizing to new input distributions. Existing benchmarks do not investigate the specific failure points of models by analyzing performance under many controlled conditions. Our study systematically dissects where and why models struggle with contour integration - a hallmark of human vision -- by designing an experiment that tests object recognition under various levels of object fragmentation. Humans (n=50) perform at high accuracy, even with few object contours present. This is in contrast to models which exhibit substantially lower sensitivity to increasing object contours, with most of the over 1,000 models we tested barely performing above chance. Only at very large scales ($\sim5B$ training dataset size) do models begin to approach human performance. Importantly, humans exhibit an integration bias - a preference towards recognizing objects made up of directional fragments over directionless fragments. We find that not only do models that share this property perform better at our task, but that this bias also increases with model training dataset size, and training models to exhibit contour integration leads to high shape bias. Taken together, our results suggest that contour integration is a hallmark of object vision that underlies object recognition performance, and may be a mechanism learned from data at scale.
Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings
Authors: Rong-Xi Tan; Ming Chen; Ke Xue; Yao Wang; Yaoyuan Wang; Fu Sheng; Chao Qian
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has driven generalization, progress in offline BBO remains hindered by the lack of unified representations for heterogeneous numerical spaces. Thus, existing offline BBO approaches are constrained to single-task and fixed-dimensional settings, failing to achieve cross-domain universal optimization. Recent advances in language models (LMs) offer a promising path forward: their embeddings capture latent relationships in a unifying way, enabling universal optimization across different data types possible. In this paper, we discuss multiple potential approaches, including an end-to-end learning framework in the form of next-token prediction, as well as prioritizing the learning of latent spaces with strong representational capabilities. To validate the effectiveness of these methods, we collect offline BBO tasks and data from open-source academic works for training. Experiments demonstrate the universality and effectiveness of our proposed methods. Our findings suggest that unifying language model priors and learning string embedding space can overcome traditional barriers in universal BBO, paving the way for general-purpose BBO algorithms. The code is provided at https://github.com/lamda-bbo/universal-offline-bbo.
Nonlinear transformers can perform inference-time feature learning
Authors: Naoki Nishikawa; Yujin Song; Kazusato Oko; Denny Wu; Taiji Suzuki
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pretrained transformers have demonstrated the ability to implement various algorithms at inference time without parameter updates. While theoretical works have established this capability through constructions and approximation guarantees, the optimization and statistical efficiency aspects remain understudied. In this work, we investigate how transformers learn features in-context -- a key mechanism underlying their inference-time adaptivity. We focus on the in-context learning of single-index models $y=\sigma_*(\langle \\boldsymbol{x},\\boldsymbol{\beta}\rangle)$, which are low-dimensional nonlinear functions parameterized by feature vector $\\boldsymbol\beta$. We prove that transformers pretrained by gradient-based optimization can perform *inference-time feature learning*, i.e., extract information of the target features $\\boldsymbol{\beta}$ solely from test prompts (despite $\\boldsymbol
{\beta}$ varying across different prompts), hence achieving an in-context statistical efficiency that surpasses any non-adaptive (fixed-basis) algorithms such as kernel methods. Moreover, we show that the inference-time sample complexity surpasses the Correlational Statistical Query (CSQ) lower bound, owing to nonlinear label transformations naturally induced by the Softmax self-attention mechanism.
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
Authors: Antonia Wüst; Tim Tobiasch; Lukas Helff; Inga Ibs; Wolfgang Stammer; Devendra Singh Dhami; Constantin A. Rothkopf; Kristian Kersting
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recently, newly developed Vision-Language Models (VLMs), such as OpenAI's o1, have emerged, seemingly demonstrating advanced reasoning capabilities across text and image modalities. However, the depth of these advances in language-guided perception and abstract reasoning remains underexplored, and it is unclear whether these models can truly live up to their ambitious promises. To assess the progress and identify shortcomings, we enter the wonderland of Bongard problems, a set of classic visual reasoning puzzles that require human-like abilities of pattern recognition and abstract reasoning. With our extensive evaluation setup, we show that while VLMs occasionally succeed in identifying discriminative concepts and solving some of the problems, they frequently falter. Surprisingly, even elementary concepts that may seem trivial to humans, such as simple spirals, pose significant challenges. Moreover, when explicitly asked to recognize ground truth concepts, they continue to falter, suggesting not only a lack of understanding of these elementary visual concepts but also an inability to generalize to unseen concepts. We compare the results of VLMs to human performance and observe that a significant gap remains between human visual reasoning capabilities and machine cognition.
Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres
Authors: Muskan Dosi; Chiranjeev Chiranjeev; Kartik Thakral; Mayank Vatsa; Richa Singh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Do contemporary diffusion models preserve the class geometry of hyperspherical data? Standard diffusion models rely on isotropic Gaussian noise in the forward process, inherently favoring Euclidean spaces. However, many real-world problems involve non-Euclidean distributions, such as hyperspherical manifolds, where class-specific patterns are governed by angular geometry within hypercones. When modeled in Euclidean space, these angular subtleties are lost, leading to suboptimal generative performance. To address this limitation, we introduce \textbf{HyperSphereDiff} to align hyperspherical structures with directional noise, preserving class geometry and effectively capturing angular uncertainty. We demonstrate both theoretically and empirically that this approach aligns the generative process with the intrinsic geometry of hyperspherical data, resulting in more accurate and geometry-aware generative models. We evaluate our framework on four object datasets and two face datasets, showing that incorporating angular uncertainty better preserves the underlying hyperspherical manifold.
Subgroups Matter for Robust Bias Mitigation
Authors: Anissa Alloula; Charles Jones; Ben Glocker; Bartlomiej Papiez
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite the constant development of new bias mitigation methods for machine learning, no method consistently succeeds, and a fundamental question remains unanswered: when and why do bias mitigation techniques fail? In this paper, we hypothesise that a key factor may be the often-overlooked but crucial step shared by many bias mitigation methods: the definition of subgroups. To investigate this, we conduct a comprehensive evaluation of state-of-the-art bias mitigation methods across multiple vision and language classification tasks, systematically varying subgroup definitions, including coarse, fine-grained, intersectional, and noisy subgroups. Our findings reveal that subgroup choice significantly impacts performance, with certain groupings paradoxically leading to worse outcomes than no mitigation at all. They suggest that observing a disparity between a set of subgroups is not a sufficient reason to use those subgroups for mitigation. Through theoretical analysis, we explain these phenomena and uncover a counter-intuitive insight that, in some cases, improving fairness with respect to a particular set of subgroups is best achieved by using a different set of subgroups for mitigation. Our work highlights the importance of careful subgroup definition in bias mitigation and presents it as an alternative lever for improving the robustness and fairness of machine learning models.
Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
Authors: Juwei Yue; Haikuo Li; Jiawei Sheng; Xiaodong Li; Taoyu Su; Tingwen Liu; Li Guo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph neural networks (GNNs) leverage message passing mechanisms to learn the topological features of graph data. Traditional GNNs learns node features in a spatial domain unrelated to the topology, which can hardly ensure topological features. In this paper, we formulates message passing as a system of hyperbolic partial differential equations (hyperbolic PDEs), constituting a dynamical system that explicitly maps node representations into a particular solution space. This solution space is spanned by a set of eigenvectors describing the topological structure of graphs. Within this system, for any moment in time, a node features can be decomposed into a superposition of the basis of eigenvectors. This not only enhances the interpretability of message passing but also enables the explicit extraction of fundamental characteristics about the topological structure. Furthermore, by solving this system of hyperbolic partial differential equations, we establish a connection with spectral graph neural networks (spectral GNNs), serving as a message passing enhancement paradigm for spectral GNNs.We further introduce polynomials to approximate arbitrary filter functions. Extensive experiments demonstrate that the paradigm of hyperbolic PDEs not only exhibits strong flexibility but also significantly enhances the performance of various spectral GNNs across diverse graph tasks.
Mind the Gap: A Practical Attack on GGUF Quantization
Authors: Kazuki Egashira; Robin Staab; Mark Vero; Jingxuan He; Martin Vechev
Venue: ICML 2025 (ICML 2025 poster)

Abstract: With the increasing size of frontier LLMs, post-training quantization has become the standard for memory-efficient deployment. Recent work has shown that basic rounding-based quantization schemes pose security risks, as they can be exploited to inject malicious behaviors into quantized models that remain hidden in full precision. However, existing attacks cannot be applied to more complex quantization methods, such as the GGUF family used in the popular ollama and llama.cpp frameworks. In this work, we address this gap by introducing the first attack on GGUF. Our key insight is that the quantization error -- the difference between the full-precision weights and their (de-)quantized version -- provides sufficient flexibility to construct malicious quantized models that appear benign in full precision. Leveraging this, we develop an attack that trains the target malicious LLM while constraining its weights based on quantization errors. We demonstrate the effectiveness of our attack on three popular LLMs across nine GGUF quantization data types on three diverse attack scenarios: insecure code generation ($\Delta$=$88.7\%$), targeted content injection ($\Delta$=$85.0\%$), and benign instruction refusal ($\Delta$=$30.1\%$). Our attack highlights that (1) the most widely used post-training quantization method is susceptible to adversarial interferences, and (2) the complexity of quantization schemes alone is insufficient as a defense.
Elucidating the design space of language models for image generation
Authors: Xuantong LIU; Shaozhe Hao; Xianbiao Qi; Tianyang Hu; Jun Wang; Rong Xiao; Yuan Yao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The success of large language models (LLMs) in text generation has inspired their application to image generation. However, existing methods either rely on specialized designs with inductive biases or adopt LLMs without fully exploring their potential in vision tasks. In this work, we systematically investigate the design space of LLMs for image generation and demonstrate that LLMs can achieve near state-of-the-art performance without domain-specific designs, simply by making proper choices in tokenization methods, modeling approaches, scan patterns, vocabulary design, and sampling strategies. We further analyze autoregressive models' learning and scaling behavior, revealing how larger models effectively capture more useful information than the smaller ones. Additionally, we explore the inherent differences between text and image modalities, highlighting the potential of LLMs across domains. The exploration provides valuable insights to inspire more effective designs when applying LLMs to other domains. With extensive experiments, our proposed model, **ELM** achieves an FID of 1.54 on 256$\times$256 ImageNet and an FID of 3.29 on 512$\times$512 ImageNet, demonstrating the powerful generative potential of LLMs in vision tasks.
Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders
Authors: Kuheli Pratihar; Debdeep Mukhopadhyay
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Variational Autoencoders (VAEs), a class of latent-variable generative models, have seen extensive use in high-fidelity synthesis tasks, yet their loss landscape remains poorly understood. Prior theoretical works on VAE loss analysis have focused on their latent-space representational capabilities, both in the optimal and limiting cases. Although these insights have guided better VAE designs, they also often restrict VAEs to problem settings where classical algorithms, such as Principal Component Analysis (PCA), can trivially guarantee globally optimal solutions. In this work, we push the boundaries of our understanding of VAEs beyond these traditional regimes to tackle NP-hard sparse inverse problems, for which no classical algorithms exist. Specifically, we examine the nontrivial Sparse Linear Regression (SLR) problem of recovering optimal sparse inputs in the presence of an ill-conditioned design matrix having correlated features. We provably show that, under a linear encoder-decoder architecture incorporating the product of the SLR design matrix with a trainable, sparsity-promoting diagonal matrix, any minimum of VAE loss is guaranteed to be an optimal solution. This property is especially useful for identifying (a) a preconditioning factor that reduces the eigenvalue spread, and (b) the corresponding optimal sparse representation. Lastly, our empirical analysis with different types of design matrices validates these findings and even demonstrates a higher recovery rate at low sparsity where traditional algorithms fail. Overall, this work highlights the flexible nature of the VAE loss, which can be adapted to efficiently solve computationally hard problems under specific constraints.