GenAI4ELab/papercli-papers huggingface dataset:
SALoM: Structure Aware Temporal Graph Networks with Long-Short Memory Updater
Authors: Hanwen Liu; Longjiao Zhang; Rui Wang; Tongya Zheng; Sai Wu; Chang Yao; Mingli Song
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Dynamic graph learning is crucial for accurately modeling complex systems by integrating topological structure and temporal information within graphs. While memory-based methods are commonly used and excel at capturing short-range temporal correlations, they struggle with modeling long-range dependencies, harmonizing long-range and short-range correlations, and integrating structural information effectively. To address these challenges, we present SALoM: Structure Aware Temporal Graph Networks with Long-Short Memory Updater. SALoM features a memory module that addresses gradient vanishing and information forgetting, enabling the capture of long-term dependencies across various time scales. Additionally, SALoM utilizes a long-short memory updater (LSMU) to dynamically balance long-range and short-range temporal correlations, preventing over-generalization. By integrating co-occurrence encoding and LSMU through information bottleneck-based fusion, SALoM effectively captures both the structural and temporal information within graphs. Experimental results across various graph datasets demonstrate SALoM's superior performance, achieving state-of-the-art results in dynamic graph link prediction. Our code is openly accessible at https://github.com/wave5418/SALoM.
Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
Authors: Liwei Huang; Zhengyu Ma; Liutao Yu; Huihui Zhou; Yonghong Tian
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted much interest. In the study of the biological visual system, naturalistic visual stimuli are inherently high-dimensional and time-dependent, leading to intricate dynamics within visual neural activity. However, most work on LVMs has not explicitly considered neural temporal relationships. To cope with such conditions, we propose Time-Evolving Visual Dynamical System (TE-ViDS), a sequential LVM that decomposes neural activity into low-dimensional latent representations that evolve over time. To better align the model with the characteristics of visual neural activity, we split latent representations into two parts and apply contrastive learning to shape them. Extensive experiments on synthetic datasets and real neural datasets from the mouse visual cortex demonstrate that TE-ViDS achieves the best decoding performance on naturalistic scenes/movies, extracts interpretable latent trajectories that uncover clear underlying neural dynamics, and provides new insights into differences in visual information processing between subjects and between cortical regions. In summary, TE-ViDS is markedly competent in extracting stimulus-relevant embeddings from visual neural activity and contributes to the understanding of visual processing mechanisms. Our codes are available at https://github.com/Grasshlw/Time-Evolving-Visual-Dynamical-System.
Gate to the Vessel: Residual Experts Restore What SAM Overlooks
Authors: Weili Jiang; Jinrong Lv; Xun Gong; Xiaomeng Li; Chubin Ou
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Foundation segmentation models like Segment Anything (SAM) exhibit strong generalization on natural images but struggle with localized failures in medical imaging, especially on fine-grained structures such as vessels with complex morphology and indistinct boundaries. To address this, we propose FineSAM++, a structure-aware sparse expert framework designed to refine SAM outputs by introducing a confidence-driven soft Routing Module. This module dynamically identifies structurally uncertain regions and activates a lightweight Residual Expert to model and correct residual structural errors only within these areas, thereby achieving efficient "refinement over retraining." Extensive experiments on five public vascular segmentation datasets demonstrate that FineSAM++ consistently outperforms both SAM-adapted baselines and task-specific models in terms of accuracy, topological consistency. Our results highlight the effectiveness of sparse, structure-driven Mixture-of-Experts (MoE) strategies for enhancing the reliability of foundation vision models in clinical image understanding tasks.
CoP: Agentic Red-teaming for Large Language Models using Composition of Principles
Authors: Chen Xiong; Pin-Yu Chen; Tsung-Yi Ho
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This paper proposes an agentic workflow to automate and scale the red-teaming process of LLMs through the Composition-of-Principles (CoP) framework, where human users provide a set of red-teaming principles as instructions to an AI agent to automatically orchestrate effective red-teaming strategies and generate jailbreak prompts. Distinct from existing red-teaming methods, our CoP framework provides a unified and extensible framework to encompass and orchestrate human-provided red-teaming principles to enable the automated discovery of new red-teaming strategies. When tested against leading LLMs, CoP reveals unprecedented safety risks by finding novel jailbreak prompts and improving the best-known single-turn attack success rate by up to 19.0 times.
DIFFSSR: Stereo Image Super-resolution Using Differential Transformer
Authors: Dafeng Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In the field of computer vision, the task of stereo image super-resolution (StereoSR) has garnered significant attention due to its potential applications in augmented reality, virtual reality, and autonomous driving. Traditional Transformer-based models, while powerful, often suffer from attention noise, leading to suboptimal reconstruction issues in super-resolved images. This paper introduces DIFFSSR, a novel neural network architecture designed to address these challenges. We introduce the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM) to enhance feature integration and mitigate the impact of attention noise. The DCAB differentiates between relevant and irrelevant context, amplifying attention to important features and canceling out noise. The SSCAM, with its sliding window mechanism and disparity-based attention, adapts to local variations in stereo images, preserving details, and addressing the performance degradation due to misalignment of horizontal epipolar lines in stereo images. Extensive experiments on benchmark datasets demonstrate that DIFFSSR outperforms state-of-the-art methods, including NAFSSR and SwinFIRSSR, in terms of both quantitative metrics and visual quality.
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving
Authors: Shuai Liu; Quanmin Liang; Zefeng Li; Boyang Li; Kai Huang
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird’s eye view fusion through geometric transformations. However, these approaches often suffer from limited interpretability or dense computational overhead. In this paper, we introduce GaussianFusion, a Gaussian-based multi-sensor fusion framework for end-to-end autonomous driving. Our method employs intuitive and compact Gaussian representations as intermediate carriers to aggregate information from diverse sensors. Specifically, we initialize a set of 2D Gaussians uniformly across the driving scene, where each Gaussian is parameterized by physical attributes and equipped with explicit and implicit features. These Gaussians are progressively refined by integrating multi-modal features. The explicit features capture rich semantic and spatial information about the traffic scene, while the implicit features provide complementary cues beneficial for trajectory planning. To fully exploit rich spatial and semantic information in Gaussians, we design a cascade planning head that iteratively refines trajectory predictions through interactions with Gaussians. Extensive experiments on the NAVSIM and Bench2Drive benchmarks demonstrate the effectiveness and robustness of the proposed GaussianFusion framework. The source code is included in the supplementary material and will be released publicly.
StruDiCO: Structured Denoising Diffusion with Gradient-free Inference-stage Boosting for Memory and Time Efficient Combinatorial Optimization
Authors: Yu Wang; Yang Li; Junchi Yan; Yi Chang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models have recently emerged as powerful neural solvers for combinatorial optimization (CO). However, existing approaches fail to reveal how variables are progressively determined during inference, making the final solution opaque until the last step. To address this limitation, we propose a structured denoising diffusion model, StruDiCO, which incrementally constructs solutions through step-wise variable selection. This is achieved via a variable-absorption noising model, wherein the forward process simulates gradual variable deactivation, converging to an empty solution, while the reverse process incrementally selects variables to reconstruct the final solution. This design induces structural continuity across intermediate states, enabling interpretable and trajectory-consistent partial solutions throughout inference. To further improve the reliability of reverse inference, we introduce a constrained consistency sampling strategy, which suppresses low-confidence variable selection at each step to stabilize the reverse process. Leveraging the structure-preserving reverse process, we further propose a lightweight, gradient-free, objective-aware refinement framework, which iteratively improves solution quality by applying structure-aware perturbations to the current solution, performing reverse inference through the constraint consistency model, and decoding with an objective-guided scoring scheme. Extensive experiments on two canonical CO tasks, the Traveling Salesman Problem (TSP) and Maximal Independent Set (MIS), show that StruDiCO outperforms state-of-the-art diffusion-based solvers, achieving up to $3.5\times$ faster inference, 70\% lower GPU memory usage, and significantly improved solution quality, with up to 37.7\% drop reduction on TSP and an average 38.1\% improvement on MIS.
The codes are publicly available at https://github.com/yuuuuwang/StruDiCO.
E-MoFlow: Learning Egomotion and Optical Flow from Event Data via Implicit Regularization
Authors: Wenpu Li; Bangyan Liao; Yi Zhou; Qi Xu; Pian Wan; Peidong Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The estimation of optical flow and 6-DoF ego-motion—two fundamental tasks in 3-D vision—has typically been addressed independently. 
For neuromorphic vision (e.g., event cameras), however, the lack of robust data association makes solving the two problems separately an ill-posed challenge, especially in the absence of supervision via ground truth.

Existing works mitigate this ill-posedness by either enforcing the smoothness of the flow field via an explicit variational regularizer or leveraging explicit structure-and-motion priors in the parametrization to improve event alignment.
The former notably introduces bias in results and computational overhead, while the latter—which parametrizes the optical flow in terms of the scene depth and the camera motion—often converges to suboptimal local minima.

To address these issues, we propose an unsupervised pipeline that jointly optimizes egomotion and flow via implicit spatial-temporal and geometric regularization. First, by modeling camera's egomotion as a continuous spline and optical flow as an implicit neural representation, our method inherently embeds spatial-temporal coherence through inductive biases. Second, we incorporate structure-and-motion priors through differential geometric constraints, bypassing explicit depth estimation while maintaining rigorous geometric consistency.

As a result, our framework (called \textbf{E-MoFlow}) unifies egomotion and optical flow estimation via implicit regularization under a fully unsupervised paradigm. Experiments demonstrate its versatility to general 6-DoF motion scenarios, achieving state-of-the-art performance among unsupervised methods and competitive even with supervised approaches. 
Code will be released upon acceptance.
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Authors: Shiyuan Zuo; Xingrun Yan; Rongfei Fan; Li Shen; Puning Zhao; Jie Xu; Han Hu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can severely degrade performance. Existing Byzantine-robust approaches tackle data heterogeneity, but incur high computational overhead during gradient aggregation, thereby slowing down the training process. To address this issue, we propose a simple yet effective Federated Normalized Gradients Algorithm (Fed-NGA), which performs aggregation by merely computing the weighted mean of the normalized gradients from each client. This approach yields a favorable time complexity of $\mathcal{O}(pM)$, where $p$ is the model dimension and $M$ is the number of clients. We rigorously prove that Fed-NGA is robust to both Byzantine faults and data heterogeneity. For non-convex loss functions, Fed-NGA achieves convergence to a neighborhood of stationary points under general assumptions, and further attains zero optimality gap under some mild conditions, which is an outcome rarely achieved in existing literature. In both cases, the convergence rate is $\mathcal{O}(1/T^{\frac{1}{2} - \delta})$, where $T$ denotes the number of iterations and $\delta \in (0, 1/2)$. Experimental results on benchmark datasets confirm the superior time efficiency and convergence performance of Fed-NGA over existing methods.
SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement
Authors: Chenyu Yang; Shuai Wang; Hangting Chen; Wei Tan; Jianwei Yu; Haizhou Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generating music with coherent structure, harmonious instrumental and vocal elements remains a significant challenge in song generation. Existing language models and diffusion-based methods often struggle to balance global coherence with local fidelity, resulting in outputs that lack musicality or suffer from incoherent progression and mismatched lyrics. This paper introduces SongBloom, a novel framework for full-length song generation that leverages an interleaved paradigm of autoregressive sketching and diffusion-based refinement. SongBloom employs an autoregressive diffusion model that combines the high fidelity of diffusion models with the scalability of language models. 
Specifically, it gradually extends a musical sketch from short to long and refines the details from coarse to fine-grained. The interleaved generation paradigm effectively integrates prior semantic and acoustic context to guide the generation process.
Experimental results demonstrate that SongBloom outperforms existing methods across both subjective and objective metrics and achieves performance comparable to the state-of-the-art commercial music generation platforms. 
Audio samples are available on our demo page: 
https://cypress-yang.github.io/SongBloom_demo.
A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
Authors: Yuanchao Dai; Zhengzhang Hou; Changchun Li; Yuanbo Xu; En Wang; Ximing Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Positive-Unlabeled (PU) learning refers to a specific weakly-supervised learning paradigm that induces a binary classifier with a few positive labeled instances and massive unlabeled instances. To handle this task, the community has proposed dozens of PU learning methods with various techniques, demonstrating strong potential. In this paper, we conduct a comprehensive study to investigate the basic characteristics of current PU learning methods. We organize them into two fundamental families of PU learning, including *disambiguation-free empirical risks*, which approximate the expected risk of supervised learning, and *pseudo-labeling methods*, which estimate pseudo-labels for unlabeled instances. First, we make an empirical analysis on disambiguation-free empirical risks such as uPU, nnPU, and DistPU, and suggest a novel risk-consistent set-aware empirical risk from the perspective of aggregate supervision. Second, we make an empirical analysis of pseudo-labeling methods to evaluate the potential of pseudo-label estimation techniques and widely applied generic tricks in PU learning. Finally, based on those empirical findings, we propose a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling. Compared with existing PU learning methods, the proposed framework can be a practical benchmark in PU learning.
VLM-R³: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought
Authors: Chaoya Jiang; Yongrui Heng; Wei Ye; Haiyang Xu; Ming Yan; Ji Zhang; Fei Huang; Shikun Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recently, reasoning-based MLLMs have achieved a degree of success in generating long-form textual reasoning chains. However, they still struggle with complex tasks that necessitate dynamic and iterative focusing on and revisiting of visual regions to achieve precise grounding of textual reasoning in visual evidence. We introduce VLM-R³  (Visual Language Model with Region Recognition, Reasoning, and Refinement ), a framework that equips an MLLM with the ability to (i) decide when additional visual evidence is needed, (ii) determine where to ground within the image, and (iii) seamlessly weave the relevant sub-image content back into an interleaved chain-of-thought. The core of our method is \textbf{Region-Conditioned Reinforcement Policy Optimization (R-GRPO)}, a training paradigm that rewards the model for selecting informative regions, formulating appropriate transformations (e.g. crop, zoom), and integrating the resulting visual context into subsequent reasoning steps. To bootstrap this policy, we compile a modest but carefully curated Visuo-Lingual Interleaved Rationale (VLIR) corpus that provides step-level supervision on region selection and textual justification. Extensive experiments on MathVista, ScienceQA, and other benchmarks show that VLM-R$^3$ sets a new state of the art in zero-shot and few-shot settings, with the largest gains appearing on questions demanding subtle spatial reasoning or fine-grained visual cue extraction.
Convex Potential Mirror Langevin Algorithm for Efficient Sampling of Energy-Based Models
Authors: Yang Zitao; Amin Ullah; Shuai Li; Li Fuxin; Jun Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: This paper introduces the Convex Potential Mirror Langevin Algorithm (CPMLA), a novel method to improve sampling efficiency for Energy-Based Models (EBMs). CPMLA uses mirror Langevin dynamics with a convex potential flow as a dynamic mirror map for EBM sampling. This dynamic mirror map enables targeted geometric exploration on the data manifold, accelerating convergence to the target distribution. Theoretical analysis proves that CPMLA achieves exponential convergence with vanishing bias under relaxed log-concave conditions, supporting its efficiency in adapting to complex data distributions. Experiments on benchmarks like CIFAR-10, SVHN, and CelebA demonstrate CPMLA's improved sampling quality and inference efficiency over existing techniques.
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
Authors: Qi Xu; Dongxu Wei; Lingzhe Zhao; Wenpu Li; Zhangchi Huang; Shunping Ji; Peidong Liu
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Simultaneous understanding and 3D reconstruction plays an important role in developing end-to-end embodied intelligent systems.
To achieve this, recent approaches resort to 2D-to-3D feature alignment paradigm, which leads to limited 3D understanding capability and potential semantic information loss.
In light of this, we propose SIU3R, the first alignment-free framework for generalizable simultaneous understanding and 3D reconstruction from unposed images.
Specifically, SIU3R bridges reconstruction and understanding tasks via pixel-aligned 3D representation, and unifies multiple understanding tasks into a set of unified learnable queries, enabling native 3D understanding without the need of alignment with 2D models.
To encourage collaboration between the two tasks with shared representation, we further conduct in-depth analyses of their mutual benefits, and propose two lightweight modules to facilitate their interaction.
Extensive experiments demonstrate that our method achieves state-of-the-art performance not only on the individual tasks of 3D reconstruction and understanding, but also on the task of simultaneous understanding and 3D reconstruction, highlighting the advantages of our alignment-free framework and the effectiveness of the mutual benefit designs.
Balancing Positive and Negative Classification Error Rates in Positive-Unlabeled Learning
Authors: Ximing Li; Yuanchao Dai; Bing Wang; Changchun Li; Jianfeng Qu; Renchu Guan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Positive and Unlabeled (PU) learning is a special case of binary classification with weak supervision, where only positive labeled and unlabeled data are available. Previous studies suggest several specific risk estimators of PU learning such as non-negative PU (nnPU), which are unbiased and consistent with the expected risk of supervised binary classification. In nnPU, the negative-class empirical risk is estimated by positive labeled and unlabeled data with a non-negativity constraint. However, its negative-class empirical risk estimator approaches 0, so the negative class is over-played, resulting in imbalanced error rates between positive and negative classes. To solve this problem, we suppose that the expected risks of the positive-class and negative-class should be close. Accordingly, we constrain that the negative-class empirical risk estimator is lower bounded by the positive-class empirical risk, instead of 0; and also incorporate an explicit equality constraint between them. we suggest a risk estimator of PU learning that balances positive and negative classification error rates, named $\mathrm{D{\small C-PU} }$, and suggest an efficient training method for $\mathrm{D{\small C-PU} }$ based on the augmented Lagrange multiplier framework. We theoretically analyze the estimation error of $\mathrm{D{\small C-PU} }$ and empirically validate that $\mathrm{D{\small C-PU} }$ achieves higher accuracy and converges more stable than other risk estimators of PU learning. Additionally, $\mathrm{D{\small C-PU} }$ also performs competitive accuracy performance with practical PU learning methods.