GenAI4ELab/papercli-papers huggingface dataset:
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
Authors: Yingying Deng; Xiangyu He; Changwang Mei; Peisong Wang; Fan Tang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Though Rectified Flows (ReFlows) with distillation offer a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, an embarrassingly simple yet effective zero-shot approach that inherits the startling capacity of ReFlow-based models (such as FLUX) in generation while extending its capabilities to accurate inversion and editing in **8** steps. 
We first demonstrate that a carefully designed numerical solver is pivotal for ReFlow inversion, enabling accurate inversion and reconstruction with the precision of a second-order solver while maintaining the practical efficiency of a first-order Euler method. This solver achieves a $3\times$ runtime speedup compared to state-of-the-art ReFlow inversion and editing techniques while delivering smaller reconstruction errors and superior editing results in a training-free mode. The code is available at [this-URL](https://github.com/HolmesShuan/FireFlow-Fast-Inversion-of-Rectified-Flow-for-Image-Semantic-Editing).
RelGNN: Composite Message Passing for Relational Deep Learning
Authors: Tianlang Chen; Charilaos Kanatsoulis; Jure Leskovec
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Predictive tasks on relational databases are critical in real-world applications spanning e-commerce, healthcare, and social media. To address these tasks effectively, Relational Deep Learning (RDL) encodes relational data as graphs, enabling Graph Neural Networks (GNNs) to exploit relational structures for improved predictions. However, existing RDL methods often overlook the intrinsic structural properties of the graphs built from relational databases, leading to modeling inefficiencies, particularly in handling many-to-many relationships. Here we introduce RelGNN, a novel GNN framework specifically designed to leverage the unique structural characteristics of the graphs built from relational databases. At the core of our approach is the introduction of atomic routes, which are simple paths that enable direct single-hop interactions between the source and destination nodes. Building upon these atomic routes, RelGNN designs new composite message passing and graph attention mechanisms that reduce redundancy, highlight key signals, and enhance predictive accuracy. RelGNN is evaluated on 30 diverse real-world tasks from Relbench (Fey et al., 2024), and achieves state-of-the-art performance on the vast majority of tasks, with improvements of up to 25%.
Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
Authors: Shaokun Zhang; Ming Yin; Jieyu Zhang; Jiale Liu; Zhiguang Han; Jingyang Zhang; Beibin Li; Chi Wang; Huazheng Wang; Yiran Chen; Qingyun Wu
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Failure attribution in LLM multi-agent systems—identifying the agent and step responsible for task failures—provides crucial clues for systems debugging but remains underexplored and labor-intensive. 
In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems.
To support this initiative, we introduce the Who\&When dataset, comprising extensive failure logs from 127 LLM multi-agent systems with fine-grained annotations linking failures to specific agents and decisive error steps.
Using the Who\&When, we develop and evaluate three automated failure attribution methods, summarizing their corresponding pros and cons.  The best method achieves 53.5\% accuracy in identifying failure-responsible agents but only 14.2\% in pinpointing failure steps, with some methods performing below random.  Even SOTA reasoning models, such as OpenAI o1 and DeepSeek R1, fail to achieve practical usability. These results highlight the task's complexity and the need for further research in this area. Code and dataset are available in https://github.com/mingyin1/Agents_Failure_Attribution.
A Closer Look at Multimodal Representation Collapse
Authors: Abhra Chaudhuri; Anjan Dutta; Tu Bui; Serban Georgescu
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: We aim to develop a fundamental understanding of modality collapse, a recently observed empirical phenomenon wherein models trained for multimodal fusion tend to rely only on a subset of the modalities, ignoring the rest. We show that modality collapse happens when noisy features from one modality are entangled, via a shared set of neurons in the fusion head, with predictive features from another, effectively masking out positive contributions from the predictive features of the former modality and leading to its collapse. We further prove that cross-modal knowledge distillation implicitly disentangles such representations by freeing up rank bottlenecks in the student encoder, denoising the fusion-head outputs without negatively impacting the predictive features from either modality. Based on the above findings, we propose an algorithm that prevents modality collapse through explicit basis reallocation, with applications in dealing with missing modalities. Extensive experiments on multiple multimodal benchmarks validate our theoretical claims. Project page: https://abhrac.github.io/mmcollapse/.
R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
Authors: Zhongyang Li; Ziyue Li; Tianyi Zhou
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In large multimodal models (LMMs), the perception of non-language modalities (e.g., visual representations) is usually not on par with the large language models (LLMs)' powerful reasoning capabilities, deterring LMMs' performance on challenging downstream tasks. 
This weakness has been recently mitigated by replacing the vision encoder with a mixture-of-experts (MoE), which provides rich, multi-granularity, and diverse representations required by different downstream tasks. The performance of multimodal MoE largely depends on its router, which reweights and mixes the representations of different experts for each input. However, we find that the end-to-end trained router does not always produce the optimal routing weights for every test sample. To bridge the gap, we propose a novel and efficient method ''**R**e-**R**outing in **T**est-**T**ime (R2-T2)'' that locally optimizes the vector of routing weights in test-time by moving it toward those vectors of the correctly predicted samples in a neighborhood of the test sample. We propose three R2-T2 strategies with different optimization objectives and neighbor-search spaces. R2-T2 consistently and significantly improves state-of-the-art LMMs' performance on challenging multimodal benchmarks of diverse tasks, without training any parameters in the base model. Our code can be accessed here.
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Authors: Thomas Zeng; Shuibai Zhang; Shutong Wu; Christian Classen; Daewon Chae; Ethan Ewer; Minjae Lee; Heeju Kim; Wonjun Kang; Jackson Kunde; Ying Fan; Jungtaek Kim; Hyung Il Koo; Kannan Ramchandran; Dimitris Papailiopoulos; Kangwook Lee
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However, they are predominantly trained on mathematical data and their generalizability to non-mathematical domains has not been rigorously studied. In response, this work first shows that current PRMs have poor performance in other domains. To address this limitation, we introduce ***VersaPRM***, a multi-domain PRM trained on synthetic reasoning data generated using our novel data generation and annotation method. VersaPRM achieves consistent performance gains across diverse domains. For instance, in the MMLU-Pro category of Law, VersaPRM via weighted majority voting, achieves a 7.9% performance gain over the majority voting baseline–surpassing Qwen2.5-Math-PRM's gain of 1.3%. We further contribute to the community by open-sourcing all data, code and models for VersaPRM.
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Authors: Ziming Hong; Runnan Chen; Zengmao Wang; Bo Han; Bo Du; Tongliang Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers are trustworthy, leaving the robustness and security of DFKD from untrusted teachers largely unexplored. In this work, we conduct the first investigation into distilling non-transferable learning (NTL) teachers using DFKD, where the transferability from an ID domain to an out-of-distribution (OOD) domain is prohibited. We find that NTL teachers fool DFKD through divert the generator's attention from the useful ID knowledge to the misleading OOD knowledge. This hinders ID knowledge transfer but prioritizes OOD knowledge transfer. To mitigate this issue, we propose Adversarial Trap Escaping (ATEsc) to benefit DFKD by identifying and filtering out OOD-like synthetic samples. Specifically, inspired by the evidence that NTL teachers show stronger adversarial robustness on OOD samples than ID samples, we split synthetic samples into two groups according to their robustness. The fragile group is treated as ID-like data and used for normal knowledge distillation, while the robust group is seen as OOD-like data and utilized for forgetting OOD knowledge. Extensive experiments demonstrate the effectiveness of ATEsc for improving DFKD against NTL teachers.
Deliberation in Latent Space via Differentiable Cache Augmentation
Authors: Luyang Liu; Jonas Pfeiffer; Jiaxing Wu; Jun Xie; Arthur Szlam
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency costs and be challenging to optimize. In this work, we demonstrate that a frozen LLM can be augmented with an offline coprocessor that operates on the model's key-value (kv) cache. This coprocessor augments the cache with a set of latent embeddings designed to improve the fidelity of subsequent decoding. We train this coprocessor using the language modeling loss from the decoder on standard pretraining data, while keeping the decoder itself frozen. This approach enables the model to learn, in an end-to-end differentiable fashion, how to distill additional computation into its kv-cache. Because the decoder remains unchanged, the coprocessor can operate offline and asynchronously, and the language model can function normally if the coprocessor is unavailable or if a given cache is deemed not to require extra computation. We show experimentally that when a cache is augmented, the decoder achieves lower perplexity on numerous subsequent tokens.  Furthermore, even without any task-specific training, our experiments demonstrate that cache augmentation consistently improves performance across a range of reasoning-intensive tasks.
Test-Time Canonicalization by Foundation Models for Robust Perception
Authors: Utkarsh Singhal; Ryan Feng; Stella X. Yu; Atul Prakash
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Perception in the real world requires robustness to diverse viewing conditions. Existing approaches often rely on specialized architectures or training with predefined data augmentations, limiting adaptability.  Taking inspiration from mental rotation in human vision, we propose FoCal, a test-time robustness framework that transforms the input into the most typical view. At inference time, FoCal explores a set of transformed images and chooses the one with the highest likelihood under foundation model priors. This test-time optimization boosts robustness while requiring no retraining or architectural changes.
Applied to models like CLIP and SAM, it significantly boosts robustness across a wide range of transformations, including 2D and 3D rotations, contrast and lighting shifts, and day-night changes. We also explore potential applications in active vision. By reframing invariance as a test-time optimization problem, FoCal offers a general and scalable approach to robustness. Our code is available at: https://github.com/sutkarsh/focal .
A Parametric Contextual Online Learning Theory of Brokerage
Authors: François Bachoc; Tommaso Cesari; Roberto Colomboni
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We study the role of contextual information in the online learning problem of brokerage between traders.
In this sequential problem, at each time step, two traders arrive with secret valuations about an asset they wish to trade.
The learner (a broker) suggests a trading (or brokerage) price based on contextual data about the asset and the market conditions.
Then, the traders reveal their willingness to buy or sell based on whether their valuations are higher or lower than the brokerage price.
A trade occurs if one of the two traders decides to buy and the other to sell, i.e., if the broker's proposed price falls between the smallest and the largest of their two valuations.
We design algorithms for this problem and prove optimal theoretical regret guarantees under various standard assumptions.
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces
Authors: Daniel Marczak; Simone Magistri; Sebastian Cygert; Bartłomiej Twardowski; Andrew D. Bagdanov; Joost van de Weijer
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains.  In this paper, we investigate the key characteristics of task matrices -- weight update matrices applied to a pre-trained model -- that enable effective merging. We show that alignment between singular components of task-specific and merged matrices strongly correlates with performance improvement over the pre-trained model. Based on this, we propose an isotropic merging framework that flattens the singular value spectrum of task matrices, enhances alignment, and reduces the performance gap. Additionally, we incorporate both common and task-specific subspaces to further improve alignment and performance. Our proposed approach achieves state-of-the-art performance on vision and language tasks across various sets of tasks and model scales. This work advances the understanding of model merging dynamics, offering an effective methodology to merge models without requiring additional training.
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
Authors: Yuhui Zhang; Yuchang Su; Chenyu Wang; Tianhong Li; Zoe Wefers; Jeffrey J Nirschl; James Burgess; Daisy Ding; Alejandro Lozano; Emma Lundberg; Serena Yeung-Levy
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects—a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlux enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research. Project page: https://yuhui-zh15.github.io/CellFlux/.
Taming Diffusion for Dataset Distillation with High Representativeness
Authors: Lin Zhao; Yushu Wu; Xinru Jiang; Jianyang Gu; Yanzhi Wang; Xiaolin Xu; Pu Zhao; Xue Lin
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent deep learning models demand larger datasets, driving the need for dataset distillation to create compact, cost-efficient datasets while maintaining performance. Due to the powerful image generation capability of diffusion, it has been introduced to this field for generating distilled images. In this paper, we systematically investigate issues present in current diffusion-based dataset distillation methods, including inaccurate distribution matching, distribution deviation with random noise, and separate sampling. Building on this, we propose D$^3$HR, a novel diffusion-based framework to generate distilled datasets with high representativeness. Specifically, we adopt DDIM inversion to map the latents of the full dataset from a low-normality latent domain to a high-normality Gaussian domain, preserving information and ensuring structural consistency to generate representative latents for the distilled dataset. Furthermore, we propose an efficient sampling scheme to better align the representative latents with the high-normality Gaussian distribution. Our comprehensive experiments demonstrate that D$^3$HR can achieve higher accuracy across different model architectures compared with state-of-the-art baselines in dataset distillation. Source code: https://github.com/lin-zhao-resoLve/D3HR.
More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions
Authors: Bishoy Galoaa; Somaieh Amraee; Sarah Ostadabbas
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper introduces MOTE (MOre Than meets the Eye), a novel multi-object tracking (MOT) algorithm designed to address the challenges of tracking occluded objects. By integrating deformable detection transformers with a custom disocclusion matrix, MOTE significantly enhances the ability to track objects even when they are temporarily hidden from view. The algorithm leverages optical flow to generate features that are processed through a softmax splatting layer, which aids in the creation of a disocclusion matrix. This matrix plays a crucial role in maintaining track consistency by estimating the motion of occluded objects. MOTE's architecture includes modifications to the enhanced track embedding module (ETEM), which allows it to incorporate these advanced features into the track query layer embeddings. This integration ensures that the model not only tracks visible objects but also accurately predicts the trajectories of occluded ones, much like the human visual system. The proposed method is evaluated on multiple datasets, including MOT17, MOT20, and DanceTrack, where it achieves impressive tracking metrics--82.0 MOTA and 66.3 HOTA on the MOT17 dataset, 81.7 MOTA and 65.8 HOTA on the MOT20 dataset, and 93.2 MOTA and 74.2 HOTA on the DanceTrack dataset. Notably, MOTE excels in reducing identity switches and maintaining consistent tracking in complex real-world scenarios with frequent occlusions, outperforming existing state-of-the-art methods across all tested benchmarks.
CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models
Authors: Lanyun Zhu; Deyi Ji; Tianrun Chen; Haiyang Wu; De Wen Soh; Jun Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Referring MLLMs extend conventional multimodal large language models by allowing them to receive referring visual prompts and generate responses tailored to the indicated regions. However, these models often suffer from suboptimal performance due to incorrect responses tailored to misleading areas adjacent to or similar to the target region. This work introduces CPCF, a novel framework to address this issue and achieve superior results. CPCF contrasts outputs generated from the indicated visual prompt with those from contrastive prompts sampled from misleading regions, effectively suppressing the influence of erroneous information outside the target region on response generation. To further enhance the effectiveness and efficiency of our framework, several novel designs are proposed, including a prompt extraction network to automatically identify suitable contrastive prompts, a self-training method that leverages unlabeled data to improve training quality, and a distillation approach to reduce the additional computational overhead associated with contrastive decoding. Incorporating these novel designs, CPCF achieves state-of-the-art performance, as demonstrated by extensive experiments across multiple benchmarks. Project page: https://lanyunzhu.site/CPCF/
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
Authors: Yue Meng; Chuchu Fan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K specifications with paired demonstrations. We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7DoF Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. Compared to classical STL planning algorithms, our approach is 10-100X faster in inference and can work on any system dynamics. Besides, we show our graph-encoding method's capability to solve complex STLs and robustness to out-distribution STL specifications. Code is available at https://github.com/mengyuest/TeLoGraF
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
Authors: Sophia Tang; Yinuo Zhang; Pranam Chatterjee
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present **PepTune**, a multi-objective discrete diffusion model for simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with a novel bond-dependent masking schedule and invalid loss function. To guide the diffusion process, we introduce **Monte Carlo Tree Guidance (MCTG)**, an inference-time multi-objective guidance algorithm that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTG integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically-modified peptides simultaneously optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTG for masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.