GenAI4ELab/papercli-papers huggingface dataset:
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
Authors: Kulin Shah; Alkis Kalavasis; Adam Klivans; Giannis Daras
Venue: ICML 2025 (ICML 2025 poster)

Abstract: There is strong empirical evidence that the stateof-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small. Prior methods to mitigate the memorization problem often lead to decrease in image quality. Is it possible to obtain strong and creative generative models, i.e., models that achieve high generation quality and low memorization? Despite the current pessimistic landscape of results, we make significant progress in pushing the trade-off between fidelity and memorization. We first provide theoretical evidence that memorization in diffusion models is only necessary for denoising problems at low noise scales (usually used in generating high-frequency details). Using this theoretical insight, we propose a simple, principled method to train the diffusion models using noisy data at large noise scales. We show that our method significantly reduces memorization without decreasing the image quality, for both text-conditional and unconditional models and for a variety of data availability settings.
Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs
Authors: Ravi Ghadia; Avinash Kumar; Gaurav Jain; Prashant J. Nair; Poulami Das
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Autoregressive Transformers rely on Key-Value (KV) caching to accelerate inference. However, the linear growth of the KV cache with context length leads to excessive memory consumption and bandwidth constraints. Existing methods drop distant tokens or compress states in a lossy manner, sacrificing accuracy by discarding vital context or introducing bias.

We propose ${MorphKV}$, an inference-time technique that maintains a constant-sized KV cache while preserving accuracy. MorphKV balances long-range dependencies and local coherence during text generation. It eliminates early-token bias while retaining high-fidelity context by adaptively ranking tokens through correlation-aware selection. Unlike heuristic retention or lossy compression, MorphKV iteratively refines the KV cache via lightweight updates guided by attention patterns of recent tokens. This approach captures inter-token correlation with greater accuracy, which is crucial for tasks like content creation and code generation. Our studies on long-response tasks show 52.9\% memory savings and 18.2\% higher accuracy on average compared to state-of-the-art prior works, enabling efficient deployment.
Test-Time Graph Neural Dataset Search With Generative Projection
Authors: Xin Zheng; Wei Huang; Chuan Zhou; Ming Li; Shirui Pan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this work, we address the test-time adaptation challenge in graph neural networks (GNNs), focusing on overcoming the limitations in flexibility and generalization inherent in existing data-centric approaches. To this end, we propose a novel research problem, test-time graph neural dataset search, which seeks to learn a parameterized test-time graph distribution to enhance the inference performance of unseen test graphs on well-trained GNNs. Specifically, we propose a generative Projection based test-time Graph Neural Dataset Search method, named PGNDS, which maps the unseen test graph distribution back to the known training distribution through a generation process guided by well-trained GNNs. The proposed PGNDS framework consists of three key modules: (1) dual conditional diffusion for GNN-guided generative projection through test-back-to-training distribution mapping; (2) dynamic search from the generative sampling space to select the most expressive test graphs; (3) ensemble inference to aggregate information from original and adapted test graphs. Extensive experiments on real-world graphs demonstrate the superior ability of our proposed PGNDS for improved test-time GNN inference.
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Authors: Keyon Vafa; Peter G. Chang; Ashesh Rambachan; Sendhil Mullainathan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the discovery of Newtonian mechanics. However, evaluating whether these models truly capture deeper structure remains a challenge. We develop a technique for evaluating foundation models that examines how they adapt to synthetic datasets generated from some postulated world model. Our technique measures whether the foundation model's inductive bias aligns with the world model, and so we refer to it as an inductive bias probe. Across multiple domains, we find that foundation models can excel at their training tasks yet fail to develop inductive biases towards the underlying world model when adapted to new tasks. We particularly find that foundation models trained on orbital trajectories consistently fail to apply Newtonian mechanics when adapted to new physics tasks. Further analysis reveals that these models behave as if they develop task-specific heuristics that fail to generalize.
AlphaPO: Reward Shape Matters for LLM Alignment
Authors: Aman Gupta; Shao Tang; Qingquan Song; Sirou Zhu; Jiwoo Hong; Ankan Saha; Viral Gupta; Noah Lee; Eunki Kim; Siyu Zhu; Parag Agrawal; Natesh S. Pillai; Sathiya Keerthi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and reflect human values. More recently, Direct Alignment Algorithms (DAAs) have emerged in which the reward modeling stage of RLHF is skipped by characterizing the reward directly as a function of the policy being learned. Some popular examples of DAAs include Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO). These methods often suffer from likelihood displacement, a phenomenon by which the probabilities of preferred responses are often reduced undesirably. In this paper, we argue that, for DAAs the reward (function) shape matters. We introduce \textbf{AlphaPO}, a new DAA method that leverages an $\alpha$-parameter to help change the shape of the reward function beyond the standard log reward. AlphaPO helps maintain fine-grained control over likelihood displacement and over-optimization. Compared to SimPO, one of the best performing DAAs, AlphaPO leads to about 7\% to 10\% relative improvement in alignment performance for the instruct versions of Mistral-7B and Llama3-8B while achieving 15\% to 50\% relative improvement over DPO on the same models. The analysis and results presented highlight the importance of the reward shape and how one can systematically change it to affect training dynamics, as well as improve alignment performance.
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Authors: Emiliano Penaloza; Tianyue H. Zhang; Laurent Charlin; Mateo Espinosa Zarlenga
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Concept Bottleneck Models (CBMs) propose to
enhance the trustworthiness of AI systems by
constraining their decisions on a set of human
understandable concepts. However, CBMs typically rely on datasets with assumedly accurate
concept labels—an assumption often violated in
practice which we show can significantly degrade
performance. To address this, we introduce the
Concept Preference Optimization (CPO) objective, a new loss function based on Direct Preference Optimization, which effectively mitigates
the negative impact of concept mislabeling on
CBM performance. We provide an analysis on
some key properties of the CPO objective showing it directly optimizes for the concept’s posterior
distribution, and contrast it against Binary Cross
Entropy (BCE) where we show CPO is inherently
less sensitive to concept noise. We empirically
confirm our analysis finding that CPO consistently
outperforms BCE in three real-world datasets with
and without added label noise
Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks
Authors: Maricela Best Mckay; Avleen Kaur; Chen Greif; Brian Wetton
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Natural gradient methods for PINNs have achieved state-of-the-art performance with errors several orders of magnitude smaller than those achieved by standard optimizers such as ADAM or L-BFGS. However, computing natural gradients for PINNs is prohibitively computationally costly and memory-intensive for all but small neural network architectures. We develop a randomized algorithm for natural gradient descent for PINNs that uses sketching to approximate the natural gradient descent direction. We prove that the change of coordinate Gram matrix used in a natural gradient descent update has rapidly-decaying eigenvalues for a one-layer, one-dimensional neural network and empirically demonstrate that this structure holds for four different example problems. Under this structure, our sketching algorithm is guaranteed to provide a near-optimal low-rank approximation of the Gramian. Our algorithm dramatically speeds up computation time and reduces memory overhead. Additionally, in our experiments, the sketched natural gradient outperforms the original natural gradient in terms of accuracy, often achieving an error that is an order of magnitude smaller. Training time for a network with around 5,000 parameters is reduced from several hours to under two minutes. Training can be practically scaled to large network sizes; we optimize a PINN for a network with over a million parameters within a few minutes, a task for which the full Gram matrix does not fit in memory.
KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Authors: Benson Chen; Tomasz Danel; Gabriel H. S. Dreiman; Patrick J. McEnaney; Nikhil Jain; Kirill Novikov; Spurti Umesh Akki; Joshua L. Turnbull; Virja Atul Pandya; Boris P. Belotserkovskii; Jared Bryce Weaver; Ankita Biswas; Dat Nguyen; Kent Gorday; Mohammad Sultan; Nathaniel Stanley; Daniel M Whalen; Divya Kanichar; Christoph Klein; Emily Fox; R. Edward Watts
Venue: ICML 2025 (ICML 2025 poster)

Abstract: DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally,  we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs.
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Authors: Shravan Nayak; Xiangru Jian; Kevin Qinghong Lin; Juan A. Rodriguez; Montek Kalsi; Nicolas Chapados; M. Tamer Özsu; Aishwarya Agrawal; David Vazquez; Christopher Pal; Perouz Taslakian; Spandana Gella; Sai Rajeswar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Autonomous agents that navigate Graphical User Interfaces (GUIs) to automate tasks like document editing and file management can greatly enhance computer workflows. While existing research focuses on online settings, desktop environments, critical for many professional and everyday tasks, remain underexplored due to data collection challenges and licensing issues. We introduce UI-Vision, the first comprehensive, license-permissive benchmark for offline, fine-grained evaluation of computer use agents in real-world desktop environments. Unlike online benchmarks, UI-Vision provides: (i) dense, high-quality annotations of human demonstrations, including bounding boxes, UI labels, and action trajectories (clicks, drags, and keyboard inputs) across 83 software applications, and (ii) three fine-to-coarse grained tasks—Element Grounding, Layout Grounding, and Action Prediction—with well-defined metrics to rigorously evaluate agents' performance in desktop environments. Our evaluation reveals critical limitations in state-of-the-art models like UI-TARS-72B, including issues with understanding professional software, spatial reasoning, and complex actions like drag-and-drop. These findings highlight the challenges in developing fully autonomous computer-use agents. With UI-Vision, we aim to advance the development of more capable agents for real-world desktop tasks.
Online Laplacian-Based Representation Learning in Reinforcement Learning
Authors: Maheed H. Ahmed; Jayanth Bhargav; Mahsa Ghasemi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Representation learning plays a crucial role in reinforcement learning, especially in complex environments with high-dimensional and unstructured states. Effective representations can enhance the efficiency of learning algorithms by improving sample efficiency and generalization across tasks. This paper considers the Laplacian-based framework for representation learning, where the eigenvectors of the Laplacian matrix of the underlying transition graph are leveraged to encode meaningful features from raw sensory observations of the states. Despite the promising algorithmic advances in this framework, it remains an open question whether the Laplacian-based representations can be learned online and with theoretical guarantees along with policy learning. We address this by formulating an online optimization approach using the Asymmetric Graph Drawing Objective (AGDO) and analyzing its convergence via online projected gradient descent under mild assumptions. Our extensive simulation studies empirically validate the convergence guarantees to the true Laplacian representation. Furthermore, we provide insights into the compatibility of different reinforcement learning algorithms with online representation learning.
Modulated Diffusion:  Accelerating Generative Modeling with Modulated Quantization
Authors: Weizhi Gao; Zhichao Hou; Junqi Yin; Feiyi Wang; Linyu Peng; Xiaorui Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Diffusion models have emerged as powerful generative models, but their high computational cost in iterative sampling remains a significant bottleneck. In this work, we present an in-depth and insightful study of state-of-the-art acceleration techniques for diffusion models, including caching and quantization, and reveal their limitations in computation error and generation quality. To break these limits, this work introduces Modulated Diffusion (MoDiff), an innovative, rigorous, and principled framework that accelerates generative modeling through modulated quantization and error compensation. MoDiff not only inherits the advantages of existing caching and quantization methods but also serves as a general framework to accelerate all diffusion models. The advantages of MoDiff are supported by solid theoretical insight and analysis. In addition, extensive experiments on CIFAR-10 and LSUN demonstrate that MoDiff significantly reduces activation quantization from 8 bits to 3 bits without performance degradation in post-training quantization (PTQ). Our code implementation is available at https://github.com/WeizhiGao/MoDiff.
Memorization Sinks: Isolating Memorization during LLM Training
Authors: Gaurav Rohit Ghosal; Pratyush Maini; Aditi Raghunathan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models are susceptible to memorizing repeated sequences, posing privacy and copyright concerns. A popular mitigation strategy is to remove memorized information from specific neurons post-hoc. However, such approaches have shown limited success so far. In a controlled setting, we show that the memorization of *natural* sequences (those that resemble linguistically plausible text) become *mechanistically entangled* with general language abilities, thereby becoming challenging to remove post-hoc. In this work, we put forward a new paradigm of MemSinks that promotes isolation of memorization by design. We leverage a sequence identifier to activate a unique set of memorization neurons for each sequence across repetitions. By analyzing the dynamics of learning and forgetting, we argue that MemSinks facilitates clean isolation of memorized content, making it easier to remove without compromising general language capabilities. We implement MemSinks at the billion-parameter and billion-token scale, and observe both effective isolation and strong generalization. To our knowledge, this is the first proof-of-concept on real data demonstrating that simultaneous generalization and isolation is achievable. We open-source our code at http://github.com/grghosal/MemSinks.
Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data
Authors: Krzysztof Kacprzyk; Julianna Piskorz; Mihaela van der Schaar
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While black-box approaches are commonly used for data-driven modeling of dynamical systems, they often obscure a system's underlying behavior and properties, limiting adoption in areas such as medicine and pharmacology. A two-step process of discovering ordinary differential equations (ODEs) and their subsequent mathematical analysis can yield insights into the system's dynamics. However, this analysis may be infeasible for complex equations, and refining the ODE to meet certain behavioral requirements can be challenging. Direct semantic modeling has recently been proposed to address these issues by predicting the system's behavior, such as the trajectory's shape, directly from data, bypassing post-hoc mathematical analysis. In this work, we extend the original instantiation, limited to one-dimensional trajectories and inputs, to accommodate multi-dimensional trajectories with additional personalization, allowing evolution to depend on auxiliary static features (e.g., patient covariates). In a series of experiments, we show how our approach enables practitioners to integrate prior knowledge, understand the dynamics, ensure desired behaviors, and revise the model when necessary.
Return Capping: Sample Efficient CVaR Policy Gradient Optimisation
Authors: Harry Mead; Clarissa Costen; Bruno Lacerda; Nick Hawes
Venue: ICML 2025 (ICML 2025 poster)

Abstract: When optimising for conditional value at risk (CVaR) using policy gradients (PG), current methods rely on discarding a large proportion of trajectories, resulting in poor sample efficiency.  We propose a reformulation of the CVaR optimisation problem by capping the total return of trajectories used in training, rather than simply discarding them, and show that this is equivalent to the original problem if the cap is set appropriately. We show, with empirical results in an number of environments, that this reformulation of the problem results in consistently improved performance compared to baselines. We have made all our code available here: \url{https://github.com/HarryMJMead/cvar-return-capping}.
LensLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
Authors: Xinyue Zeng; Haohui Wang; Junhong Lin; Jun Wu; Tyler Cody; Dawei Zhou
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: *how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks?* In this work, we propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs, thereby enabling accurate and efficient LLM selection for downstream applications. In particular, we first derive a *PAC-Bayesian Generalization Bound* that unveils fine-tuning dynamics of LLMs and then introduce *LensLLM*, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions across diverse tasks while maintaining computational efficiency. Extensive empirical results on 3 large-scale benchmarks demonstrate that our model achieves up to 91.1% accuracy and reduces up to 88.5% computational cost in LLM selection, outperforming 5 state-of-the-art methods. We open-source our proposed *LensLLM* model and corresponding results at [LensLLM.io](https://github.com/Susan571/LENSLLM.git).
A New Concentration Inequality for Sampling Without Replacement and Its Application for Transductive Learning
Authors: Yingzhen Yang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce a new tool, Transductive Local Complexity (TLC), to analyze the generalization performance of transductive learning methods and motivate new transductive learning algorithms. Our work extends the idea of the popular Local Rademacher Complexity (LRC) to the transductive setting with considerable and novel changes compared to the analysis of typical LRC methods in the inductive setting. While LRC has been widely used as a powerful tool in the analysis of inductive models  with sharp generalization bounds for classification and minimax rates for nonparametric regression, it remains an open problem whether a localized version of Rademacher complexity based tool can be designed and applied to transductive learning and gain sharp bound for transductive learning which is consistent with the inductive excess risk bound by (LRC). We give a confirmative answer to this open problem by TLC. Similar to the development of LRC, we build TLC by first establishing a novel and sharp concentration inequality for supremum of empirical processes for the gap between test and training loss in the setting of sampling uniformly without replacement. Then a peeling strategy and a new surrogate variance operator are used to derive the following excess risk bound in the transductive setting, which is consistent with that of the classical LRC based excess risk bound in the inductive setting.
As an application of TLC, we use the new TLC tool to analyze the Transductive Kernel Learning (TKL) model, and derive sharper excess risk bound than that by the current state-of-the-art. As a result of independent interest, the concentration inequality for the test-train process is used to derive a sharp concentration inequality for the general supremum of empirical process involving random variables in the setting of sampling uniformly without replacement, with comparison to current concentration inequalities.