GenAI4ELab/papercli-papers huggingface dataset:
Finding separatrices of dynamical flows with Deep Koopman Eigenfunctions
Authors: Kabir Vinay Dabholkar; Omri Barak
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Many natural systems, including neural circuits involved in decision making, are modeled as high-dimensional dynamical systems with multiple stable states. While existing analytical tools primarily describe behavior near stable equilibria, characterizing separatrices -- the manifolds that delineate boundaries between different basins of attraction -- remains challenging, particularly in high-dimensional settings. Here, we introduce a numerical framework leveraging Koopman Theory combined with Deep Neural Networks to effectively characterize separatrices. Specifically, we approximate Koopman Eigenfunctions (KEFs) associated with real positive eigenvalues, which vanish precisely at the separatrices. Utilizing these scalar KEFs, optimization methods efficiently locate separatrices even in complex systems. We demonstrate our approach on synthetic benchmarks, ecological network models, and high-dimensional recurrent neural networks trained on either neuroscience-inspired tasks or fit to real neural data. Moreover, we illustrate the practical utility of our method by designing optimal perturbations that can shift systems across separatrices, enabling predictions relevant to optogenetic stimulation experiments in neuroscience. Our code is available on [GitHub](https://github.com/KabirDabholkar/separatrixLocator), and we share an interactive description of the work and its extensions in a [UniReps blog](https://unireps.github.io/blog/2025/Separatrix-Locator/).
Better NTK Conditioning: A Free Lunch from (ReLU) Nonlinear Activation in Wide Neural Networks
Authors: Chaoyue Liu; Han Bi; Like Hui; Xiao Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Nonlinear activation functions are widely recognized for enhancing the expressivity of neural networks, which is the primary reason for their widespread implementation. In this work, we focus on ReLU activation and reveal a novel and intriguing property of nonlinear activations. By comparing enabling and disabling the nonlinear activations in the neural network, we demonstrate their specific effects on wide neural networks: (a) *better feature separation*, i.e.,  a larger angle separation for similar data in the feature space of model gradient, and (b) *better NTK conditioning*, i.e.,  a smaller condition number of neural tangent kernel (NTK). Furthermore, we show that the network depth (i.e., with more nonlinear activation operations) further amplifies these effects; in addition, in the infinite-width-then-depth limit, all data are equally separated with a fixed angle in the model gradient feature space, regardless of how similar they are originally in the input space. 
   Note that, without the nonlinear activation, i.e., in a linear neural network, the data separation remains the same as for the original inputs
   and NTK condition number is equivalent to the Gram matrix, regardless of the network depth. Due to the close connection between NTK condition number and convergence theories, our results imply that nonlinear activation
  helps to improve the worst-case convergence rates of gradient based methods.
Register and [CLS] tokens induce a decoupling of local and global features in large ViTs
Authors: Alexander Lappe; Martin A. Giese
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent work has shown that the attention maps of the widely popular DINOv2
model exhibit artifacts, which hurt both model interpretability and performance
on dense image tasks. These artifacts emerge due to the model repurposing patch
tokens with redundant local information for the storage of global image information.
To address this problem, additional register tokens have been incorporated in which
the model can store such information instead. We carefully examine the influence
of these register tokens on the relationship between global and local image features,
showing that while register tokens yield cleaner attention maps, these maps do
not accurately reflect the integration of local image information in large models.
Instead, global information is dominated by information extracted from register
tokens, leading to a disconnect between local and global features. Inspired by these
findings, we show that the [CLS] token itself leads to a very similar phenomenon
in models without explicit register tokens. Our work shows that care must be taken
when interpreting attention maps of large ViTs. Further, by clearly attributing
the faulty behavior to register and [CLS] tokens, we show a path towards more
interpretable vision models.
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Authors: Tim Genewein; Li Kevin Wenliang; Jordi Grau-Moya; Anian Ruoss; Laurent Orseau; Marcus Hutter
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Prompting is one of the main ways to adapt a pretrained model to target tasks. Besides manually constructing prompts, many prompt optimization methods have been proposed in the literature. Method development is mainly empirically driven, with less emphasis on a conceptual understanding of prompting. In this paper we discuss how optimal prompting can be understood through a Bayesian view, which also implies some fundamental limitations of prompting that can only be overcome by tuning weights. The paper explains in detail how meta-trained neural networks behave as Bayesian predictors over the pretraining distribution, whose hallmark feature is rapid in-context adaptation. Optimal prompting can be studied formally as conditioning these Bayesian predictors, yielding criteria for target tasks where optimal prompting is and is not possible. We support the theory with educational experiments on LSTMs and Transformers, where we compare different versions of prefix-tuning and different weight-tuning methods. We also confirm that soft prefixes, which are sequences of real-valued vectors outside the token alphabet, can lead to very effective prompts for trained and even untrained networks by manipulating activations in ways that are not achievable by hard tokens. This adds an important mechanistic aspect beyond the conceptual Bayesian theory.
Value-Guided KV Compression for LLMs via Approximated CUR Decomposition
Authors: Ayan Sengupta; Siddhant Chaudhary; Tanmoy Chakraborty
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Key-value (KV) cache compression has emerged as a critical technique for reducing the memory and latency overhead of autoregressive language models during inference. Prior approaches predominantly rely on query-key attention scores to rank and evict cached tokens, assuming that attention intensity correlates with semantic importance. However, this heuristic overlooks the contribution of value vectors, which directly influence the attention output. In this paper, we propose CurDKV, a novel, value-centric KV compression method that selects keys and values based on leverage scores computed from CUR matrix decomposition. Our approach approximates the dominant subspace of the attention output $\mathrm{softmax}(QK^\top)V$, ensuring that the retained tokens best preserve the model’s predictive behavior. Theoretically, we show that attention score approximation does not guarantee output preservation, and demonstrate that CUR-based selection minimizes end-to-end attention reconstruction loss. Empirically, CurDKV achieves up to $9.6$\% higher accuracy than state-of-the-art methods like SnapKV and ChunkKV under aggressive compression budgets on LLaMA and Mistral, while maintaining compatibility with FlashAttention and Grouped Query Attention. In addition to improved accuracy, CurDKV reduces generation latency by up to 40\% at high compression, offering a practical speed-accuracy tradeoff.
A Learning-Augmented Dynamic Programming Approach for Orienteering Problem with Time Windows
Authors: Guansheng Peng; Lining Xing; Fuyan Ma; Aldy Gunawan; Guopeng Song; Pieter Vansteenwegen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent years have witnessed a surge of interest in solving combinatorial optimization problems (COPs) using machine learning techniques. Motivated by this trend, we propose a learning-augmented exact approach for tackling an NP-hard COP, the Orienteering Problem with Time Windows, which aims to maximize the total score collected by visiting a subset of vertices in a graph within their time windows. Traditional exact algorithms rely heavily on domain expertise and meticulous design, making it hard to achieve further improvements. By leveraging deep learning models to learn effective relaxations of problem restrictions from data, our approach enables significant performance gains in an exact dynamic programming algorithm. We propose a novel graph convolutional network that predicts the directed edges defining the relaxation. The network is trained in a supervised manner, using optimal solutions as high-quality labels. Experimental results demonstrate that the proposed learning-augmented algorithm outperforms the state-of-the-art exact algorithm, achieving a 38% speedup on Solomon’s benchmark and more than a sevenfold improvement on the more challenging Cordeau’s benchmark.
SDPGO: Efficient Self-Distillation Training Meets Proximal Gradient Optimization
Authors: Tongtong Su; Yun Liao; Fengbo Zheng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Self-knowledge distillation (SKD) enables single-model training by distilling knowledge from the model's own output, eliminating the need for a separate teacher network required in conventional distillation methods. However, current SKD methods focus mainly on replicating common features in the student model, neglecting the extraction of key features that significantly enhance student learning. Inspired by this, we devise a self-knowledge distillation framework entitled Self-Distillation training via Proximal Gradient Optimization or SDPGO, which utilizes gradient information to identify and assign greater weight to features that significantly impact classification performance, enabling the network to learn the most relevant features during training. Specifically, the proposed framework refines the gradient information into a dynamically changing weighting factor to evaluate the distillation knowledge via the dynamic weight adjustment scheme. 
Meanwhile, we devise the sequential iterative learning module to dynamically optimize knowledge transfer by leveraging historical predictions and real-time gradients, stabilizing training through mini-batch-based KL divergence refinement while adaptively prioritizing task-critical features for efficient self-distillation. Comprehensive experiments on image classification, object detection, and semantic segmentation demonstrate that our method consistently surpasses recent state-of-the-art knowledge distillation techniques. Code is
available at: https://github.com/nanxiaotong/SDGPO.
IMPACT: Irregular Multi-Patch Adversarial Composition Based on Two‑Phase Optimization
Authors: Zenghui Yang; Xingquan Zuo; Hai Huang; Gang Chen; Xinchao Zhao; Tianle Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Deep neural networks have become foundational in various applications but remain vulnerable to adversarial patch attacks. Crafting effective adversarial patches is inherently challenging due to the combinatorial complexity involved in jointly optimizing critical factors such as patch shape, location, number, and content. Existing approaches often simplify this optimization by addressing each factor independently, which limits their effectiveness. To tackle this significant challenge, we introduce a novel and flexible adversarial attack framework termed IMPACT (Irregular Multi-Patch Adversarial Composition based on Two-phase optimization). IMPACT uniquely enables comprehensive optimization of all essential patch factors using gradient-free methods. Specifically, we propose a novel dimensionality reduction encoding scheme that substantially lowers computational complexity while preserving expressive power. Leveraging this encoding, we further develop a two-phase optimization framework: phase 1 employs differential evolution for joint optimization of patch mask and content, while phase 2 refines patch content using an evolutionary strategy for enhanced precision. Additionally, we introduce a new aggregation algorithm explicitly designed to produce contiguous, irregular patches by merging localized regions, ensuring physical applicability. Extensive experiments demonstrate that our method significantly outperforms several state-of-the-art approaches, highlighting the critical benefit of jointly optimizing all patch factors in adversarial patch attacks.
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought
Authors: Chao Huang; Benfeng Wang; Wei Wang; Jie Wen; Chengliang Liu; Li Shen; Xiaochun Cao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based Video Anomaly Detection (VAD) methods remain limited to shallow anomaly descriptions without deep reasoning. In this paper, we propose a new task named Video Anomaly Reasoning (VAR), which aims to enable deep analysis and understanding of anomalies in the video by requiring MLLMs to think explicitly before answering. To this end, we propose Vad-R1, an end-to-end MLLM-based framework for VAR. Specifically, we design a Perception-to-Cognition Chain-of-Thought (P2C-CoT) that simulates the human process of recognizing anomalies, guiding the MLLMs to reason about anomalies step-by-step. Based on the structured P2C-CoT, we construct Vad-Reasoning, a dedicated dataset for VAR. Furthermore, we propose an improved reinforcement learning algorithm AVA-GRPO, which explicitly incentivizes the anomaly reasoning capability of MLLMs through a self-verification mechanism with limited annotations. Experimental results demonstrate that Vad-R1 achieves superior performance, outperforming both open-source and proprietary models on VAD and VAR tasks.
VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption
Authors: Tianxiong Zhong; Xingye Tian; Boyuan Jiang; Xuebo Wang; Xin Tao; Pengfei Wan; Zhiwei Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Modern video generation frameworks based on Latent Diffusion Models suffer from inefficiencies in tokenization due to the Frame-Proportional Information Assumption.
Existing tokenizers provide fixed temporal compression rates, causing the computational cost of the diffusion model to scale linearly with the frame rate.
The paper proposes the Duration-Proportional Information Assumption: the upper bound on the information capacity of a video is proportional to the duration rather than the number of frames.
Based on this insight, the paper introduces VFRTok, a Transformer-based video tokenizer, that enables variable frame rate encoding and decoding through asymmetric frame rate training between the encoder and decoder.
Furthermore, the paper proposes Partial Rotary Position Embeddings (RoPE) to decouple position and content modeling, which groups correlated patches into unified tokens.
The Partial RoPE effectively improves content-awareness, enhancing the video generation capability.
Benefiting from the compact and continuous spatio-temporal representation, VFRTok achieves competitive reconstruction quality and state-of-the-art generation fidelity while using only $1/8$ tokens compared to existing tokenizers.
Weak-shot Keypoint Estimation via Keyness and Correspondence Transfer
Authors: Junjie Chen; Zeyu Luo; Zezheng Liu; Wenhui Jiang; Li Niu; Yuming Fang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Keypoint estimation is a fundamental task in computer vision, but generally requires large-scale annotated data for training. Few-shot and unsupervised keypoint estimation are prevalent economical paradigms, but the former still requires annotations for extensive novel classes while the latter only supports for single class. In this paper, we focus on the task of weak-shot keypoint estimation, where multiple novel classes are learned from unlabeled images with the help of labeled base classes. The key problem is what to transfer from base classes to novel classes, and we propose to transfer keyness and correspondence, which essentially belong to comparing entities and thus are class-agnostic and class-wise transferable. The keyness compares which pixel in the local region is more key, which can guide the keypoints of novel classes to move towards the local maximum (i.e., obtaining keypoints). The correspondence compares whether the two pixels belongs to the same semantic part, which can activate the keypoints of novel classes by reinforcing the consistency between corresponding points on two paired images. By transferring keyness and correspondence, our framework achieves favourable performance for weak-shot keypoint estimation. Extensive experiments and analyses on large-scale benchmark MP-100 demonstrate our effectiveness.
Reward-Aware Proto-Representations in Reinforcement Learning
Authors: Hon Tik Tse; Siddarth Chandrasekar; Marlos C. Machado
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In recent years, the successor representation (SR) has attracted increasing attention in reinforcement learning (RL), and it has been used to address some of its key challenges, such as exploration, credit assignment, and generalization. The SR can be seen as representing the underlying credit assignment structure of the environment by implicitly encoding its induced transition dynamics. However, the SR is reward-agnostic. In this paper, we discuss a similar representation that also takes into account the reward dynamics of the problem. We study the default representation (DR), a recently proposed representation with limited theoretical (and empirical) analysis. Here, we lay some of the theoretical foundation underlying the DR in the tabular case by (1) deriving dynamic programming and (2) temporal-difference methods to learn the DR, (3) characterizing the basis for the vector space of the DR, and (4) formally extending the DR to the function approximation case through default features. Empirically, we analyze the benefits of the DR in many of the settings in which the SR has been applied, including (1) reward shaping, (2) option discovery, (3) exploration, and (4) transfer learning. Our results show that, compared to the SR, the DR gives rise to qualitatively different, reward-aware behaviour and quantitatively better performance in several settings.
Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism
Authors: Mete Erdogan; Cengiz Pehlevan; Alper Tunga Erdogan
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We introduce *Error Broadcast and Decorrelation* (EBD), a novel learning framework for neural networks that addresses credit assignment by directly broadcasting output errors to individual layers, circumventing weight transport of backpropagation. EBD is rigorously grounded in the stochastic orthogonality property of Minimum Mean Square Error estimators. This fundamental principle states that the error of an optimal estimator is orthogonal to functions of the input. Guided by this insight, EBD defines layerwise loss functions that directly penalize correlations between layer activations and output errors, thereby establishing a principled foundation for error broadcasting. This theoretically sound mechanism  naturally leads to the experimentally observed three-factor learning rule and integrates with biologically plausible frameworks to enhance performance and plausibility. Numerical experiments demonstrate EBD’s competitive or better performance against other error-broadcast methods on benchmark datasets. Our findings establish EBD as an efficient, biologically plausible, and principled alternative for neural network training.
PID-controlled Langevin Dynamics for Faster Sampling of Generative Models
Authors: Hongyi Chen; Jianhai Shu; Jingtao Ding; Yong Li; Xiao-Ping Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introduce PID-controlled Langevin Dynamics (PIDLD), a novel sampling acceleration algorithm that reinterprets the sampling process using control-theoretic principles. By treating energy gradients as feedback signals, PIDLD combines historical gradients (the integral term) and gradient trends (the derivative term) to efficiently traverse energy landscapes and adaptively stabilize, thereby significantly reducing the number of iterations required to produce high-quality samples. Our approach requires no additional training, datasets, or prior information, making it immediately integrable with any Langevin-based method. Extensive experiments across image generation and reasoning tasks demonstrate that PIDLD achieves higher quality with fewer steps, making Langevin-based generative models more practical for efficiency-critical applications. The implementation can be found at \href{https://github.com/tsinghua-fib-lab/PIDLD}{https://github.com/tsinghua-fib-lab/PIDLD}.
Non-Adaptive Adversarial Face Generation
Authors: Sunpill Kim; Seunghun Paik; Chanwoo Hwang; Minsu Kim; Jae Hong Seo
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Adversarial attacks on face recognition systems (FRSs) pose serious security and privacy threats, especially when these systems are used for identity verification. In this paper, we propose a novel method for generating adversarial faces—synthetic facial images that are visually distinct yet recognized as a target identity by the FRS. Unlike iterative optimization-based approaches (e.g., gradient descent or other iterative solvers), our method leverages the structural characteristics of the FRS feature space. We figure out that individuals sharing the same attribute (e.g., gender or race) form an attributed subsphere. By utilizing such subspheres, our method achieves both non-adaptiveness and a remarkably small number of queries. This eliminates the need for relying on transferability and open-source surrogate models, which have been a typical strategy when repeated adaptive queries to commercial FRSs are impossible. Despite requiring only a single non-adaptive query consisting of 100 face images, our method achieves a high success rate of over 93% against AWS’s CompareFaces API at its default threshold. Furthermore, unlike many existing attacks that perturb a given image, our method can deliberately produce adversarial faces that impersonate the target identity while exhibiting high-level attributes chosen by the adversary.