GenAI4ELab/papercli-papers huggingface dataset:
Planning and Learning in Average Risk-aware MDPs
Authors: Weikai Wang; Erick Delage
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: For continuing tasks, average cost Markov decision processes have well-documented value and can be solved using efficient algorithms. However, it explicitly assumes that the agent is risk-neutral. In this work, we extend risk-neutral algorithms to accommodate the more general class of dynamic risk measures. Specifically, we propose a relative value iteration (RVI) algorithm for planning and design two model-free Q-learning algorithms, namely a generic algorithm based on the multi-level Monte Carlo (MLMC) method, and an off-policy algorithm dedicated to utility-based shortfall risk measures. Both the RVI and MLMC-based Q-learning algorithms are proven to converge to optimality. Numerical experiments validate our analysis, confirm empirically the convergence of the off-policy algorithm, and demonstrate that our approach enables the identification of policies that are finely tuned to the intricate risk-awareness of the agent that they serve.
RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data
Authors: Wenchao Ma; Dario Michael Kneubuehler; Maurice Chu; Ian Sachs; Haomiao Jiang; Sharon X Huang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this paper, we present RigAnyFace (RAF), a scalable neural auto-rigging framework for facial meshes of diverse topologies, including those with multiple disconnected components. RAF deforms a static neutral facial mesh into industry-standard FACS poses to form an expressive blendshape rig. Deformations are predicted by a triangulation-agnostic surface learning network augmented with our tailored architecture design to condition on FACS parameters and efficiently process disconnected components. For training, we curated a dataset of facial meshes, with a subset meticulously rigged by professional artists to serve as accurate 3D ground truth for deformation supervision. Due to the high cost of manual rigging, this subset is limited in size, constraining the generalization ability of models trained exclusively on it. To address this, we design a 2D supervision strategy for unlabeled neutral meshes without rigs. This strategy increases data diversity and allows for scaled training, thereby enhancing the generalization ability of models trained on this augmented data. Extensive experiments demonstrate that RAF is able to rig meshes of diverse topologies on not only our artist-crafted assets but also in-the-wild samples, outperforming previous works in accuracy and generalizability. Moreover, our method advances beyond prior work by supporting multiple disconnected components, such as eyeballs, for more detailed expression animation.
SAM2Flow: Interactive Optical Flow Estimation with Dual Memory for in vivo Microcirculation Analysis
Authors: Luojie Huang; Ryan Zhang; Marisa M Morakis; Michaela Taylor-Williams; Gregory N. McKay; Nicholas J. Durr
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Analysis of noninvasive microvascular blood flow can improve the diagnosis, prognosis, and management of many medical conditions, including cardiovascular, peripheral vascular, and sickle cell disease. This paper introduces SAM2Flow, an interactive optical flow estimation model to analyze long Oblique Back-illumination Microscopy (OBM) videos of *in vivo* microvascular flow. Inspired by the Segment Anything Model (SAM2), SAM2Flow enables users to specify regions of interest through user prompts for focused flow estimation. 
SAM2Flow also incorporates a dual memory attention mechanism, comprising both motion and context memory, to achieve efficient and stable flow estimations over extended video sequences. According to our experiments, SAM2Flow achieves SOTA accuracy in foreground optical flow estimation on both microvascular flow and public datasets, with a fast inference speed of over $20$ fps on $512\times512$ inputs. Based on the temporally robust flow estimation, SAM2Flow demonstrated superior performance in downstream physiological applications compared to existing models. The code and dataset will be published with this paper.
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Authors: Haochen Zhang; Zhong Zheng; Lingzhou Xue
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Motivated by real-world settings where data collection and policy deployment&mdash;whether for a single agent or across multiple agents&mdash;are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing methods either require superlinear burn-in costs in $S$ and $A$ or fail to achieve logarithmic switching or communication costs. We propose two novel model-free RL algorithms&mdash;Q-EarlySettled-LowCost and FedQ-EarlySettled-LowCost&mdash;that are the first in the literature to simultaneously achieve: (i) the best near-optimal regret among all known model-free RL or FRL algorithms, (ii) low burn-in cost that scales linearly with $S$ and $A$, and (iii) logarithmic policy switching cost for single-agent RL or communication cost for FRL. Additionally, we establish gap-dependent theoretical guarantees for both regret and switching/communication costs, improving or matching the best-known gap-dependent bounds.
Constrained Feedback Learning for Non-Stationary Multi-Armed Bandits
Authors: Shaoang Li; Jian Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Non-stationary multi-armed bandits (nsMAB) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to shifts in reward distributions, making them well-suited for dynamic settings. However, existing approaches typically assume that reward feedback is available at every round—an assumption that overlooks many real-world scenarios where feedback is limited. In this paper, we take a significant step forward by introducing a new model of *constrained feedback in non-stationary multi-armed bandits* (ConFee-nsMAB), where the availability of reward feedback is restricted. We propose the first prior-free algorithm—that is, one that does not require prior knowledge of the degree of non-stationarity—that achieves near-optimal dynamic regret in this setting. Specifically, our algorithm attains a dynamic regret of $\tilde {\mathcal{O}}({K^{1/3} V_T^{1/3} T }/{ B^{1/3}})$, where $T$ is the number of rounds, $K$ is the number of arms, $B$ is the query budget, and $V_T$ is the variation budget capturing the degree of non-stationarity.
Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression
Authors: Xiaohui Wang; Peng Ye; Chenyu Huang; Shenghe Zheng; Bo Zhang; LEI BAI; Wanli Ouyang; Tao Chen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: With the rise of the fine-tuned–pretrained paradigm, storing numerous fine-tuned models for multi-tasking creates significant storage overhead.
Delta compression alleviates this by storing only the pretrained model and the highly compressed delta weights (the differences between fine-tuned and pretrained model weights). 
However, existing methods fail to maintain both high compression and performance, and often rely on data.
To address these challenges, we propose UltraDelta, the first data-free delta compression pipeline that achieves both ultra-high compression and strong performance. 
UltraDelta is designed to minimize redundancy, maximize information, and stabilize performance across inter-layer, intra-layer, and global dimensions, using three key components:
(1) Variance-Based Mixed Sparsity Allocation assigns sparsity based on variance, giving lower sparsity to high-variance layers to preserve inter-layer information.
(2) Distribution-Aware Compression applies uniform quantization and then groups parameters by value, followed by group-wise pruning, to better preserve intra-layer distribution.
(3) Trace-Norm-Guided Rescaling uses the trace norm of delta weights to estimate a global rescaling factor, improving model stability under higher compression.
Extensive experiments across 
(a) large language models (fine-tuned on LLaMA-2 7B and 13B) with up to 50$\times$ compression, 
(b) general NLP models (RoBERTa-base, T5-base) with up to 224$\times$ compression,
(c) vision models (ViT-B/32, ViT-L/14) with up to 132$\times$ compression, and
(d) multi-modal models (BEiT-3) with 18$\times$ compression, 
demonstrate that UltraDelta consistently outperforms existing methods, especially under ultra-high compression.
Code is available at https://github.com/xiaohuiwang000/UltraDelta.
Distribution-Aligned Decoding for Efficient LLM Task Adaptation
Authors: Senkang Hu; Xudong Han; Jinqi Jiang; Yihang Tao; Zihan Fang; Yong Dai; Sam Kwong; Yuguang Fang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Adapting billion-parameter language models to a downstream task is still costly, even with parameter-efficient fine-tuning (PEFT). We re-cast task adaptation as output-distribution alignment: the objective is to steer the output distribution toward the task distribution directly during decoding rather than indirectly through weight updates. Building on this view, we introduce Steering Vector Decoding (SVDecode), a lightweight, PEFT-compatible, and theoretically grounded method. We start with a short warm-start fine-tune and extract a task-aware steering vector from the Kullback-Leibler (KL) divergence gradient between the output distribution of the warm-started and pre-trained models. This steering vector is then used to guide the decoding process to steer the model's output distribution towards the task distribution. We theoretically prove that SVDecode is first-order equivalent to the gradient step of full fine-tuning and derive a globally optimal solution for the strength of the steering vector. Across three tasks and nine benchmarks, SVDecode paired with four standard PEFT methods improves multiple-choice accuracy by up to 5 percentage points and open-ended truthfulness by 2 percentage points, with similar gains (1-2 percentage points) on commonsense datasets without adding trainable parameters beyond the PEFT adapter. SVDecode thus offers a lightweight, theoretically grounded path to stronger task adaptation for large language models.
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Authors: Ziqiang Cui; Yunpeng Weng; Xing Tang; Xiaokun Zhang; Shiwei Li; Peiyang Liu; Bowei He; Dugang Liu; Weihong Luo; xiuqiang He; Chen Ma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive pairs: they either rely on random perturbations that corrupt user preference patterns or depend on sparse collaborative data that generates unreliable contrastive pairs. Furthermore, existing approaches typically require predefined selection rules that impose strong assumptions, limiting the model's ability to autonomously learn optimal contrastive pairs. To address these limitations, we propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL). SRA-CL leverages the semantic understanding and reasoning capabilities of LLMs to generate expressive embeddings that capture both user preferences and item characteristics. These semantic embeddings enable the construction of candidate pools for inter-user and intra-user contrastive learning through semantic-based retrieval. To further enhance the quality of the contrastive samples, we introduce a learnable sample synthesizer that optimizes the contrastive sample generation process during model training. SRA-CL adopts a plug-and-play design, enabling seamless integration with existing sequential recommendation architectures. Extensive experiments on four public datasets demonstrate the effectiveness and model-agnostic nature of our approach. 
Our code is available at https://github.com/ziqiangcui/SRA-CL
Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory
Authors: Hanru Bai; Weiyang Ding; Difan Zou
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models have achieved impressive success in high-fidelity image generation but suffer from slow sampling due to their inherently iterative denoising process. While recent one-step methods accelerate inference by learning direct noise-to-image mappings, they sacrifice the interpretability and fine-grained control intrinsic to diffusion dynamics, key advantages that enable applications like editable generation. To resolve this dichotomy, we introduce **Hierarchical Koopman Diffusion**, a novel framework that achieves both one-step sampling and interpretable generative trajectories.  Grounded in Koopman operator theory, our method lifts the nonlinear diffusion dynamics into a latent space where evolution is governed by globally linear operators, enabling closed-form trajectory solutions. This formulation not only eliminates iterative sampling but also provides full access to intermediate states, allowing manual intervention during generation. To model the multi-scale nature of images, we design a hierarchical architecture that disentangles generative dynamics across spatial resolutions via scale-specific Koopman subspaces, capturing coarse-to-fine details systematically. We empirically show that the Hierarchical Koopman Diffusion not only achieves competitive one-step generation performance but also provides a principled mechanism for interpreting and manipulating the generative process through spectral analysis. Our framework bridges the gap between fast sampling and interpretability in diffusion models, paving the way for explainable image synthesis in generative modeling.
C$^2$Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
Authors: Kunlun Xu; yibo feng; Jiangmeng Li; Yongsheng Qi; Jiahuan Zhou
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Federated continual learning (FCL) tackles scenarios of learning from continuously emerging task data across distributed clients, where the key challenge lies in addressing both temporal forgetting over time and spatial forgetting simultaneously. Recently, prompt-based FCL methods have shown advanced performance through task-wise prompt communication.
In this study, we underscore that the existing prompt-based FCL methods are prone to class-wise knowledge coherence between prompts across clients. The class-wise knowledge coherence includes two aspects: (1) intra-class distribution gap across clients, which degrades the learned semantics across prompts, (2) inter-prompt class-wise relevance, which highlights cross-class knowledge confusion. During prompt communication, insufficient class-wise coherence exacerbates knowledge conflicts among new prompts and induces interference with old prompts, intensifying both spatial and temporal forgetting. To address these issues, we propose a novel Class-aware Client Knowledge Interaction (C$^2$Prompt) method that explicitly enhances class-wise knowledge coherence during prompt communication. Specifically, a local class distribution compensation mechanism (LCDC) is introduced to reduce intra-class distribution disparities across clients, thereby reinforcing intra-class knowledge consistency. Additionally, a class-aware prompt aggregation scheme (CPA) is designed to alleviate inter-class knowledge confusion by selectively strengthening class-relevant knowledge aggregation. Extensive experiments on multiple FCL benchmarks demonstrate that C$^2$Prompt achieves state-of-the-art performance. Our code will be released.
Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
Authors: Derong Kong; Zhixiong Yang; Shengxi Li; Shuaifeng Zhi; Li Liu; Zhen Liu; Jingyuan Xia
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing methods predominantly focus on deterministic pixel-level mappings between paired low/normal-light images, they often neglect the continuous physical process of luminance transitions in real-world environments, leading to performance drop when normal-light references are unavailable. Inspired by empirical analysis of natural luminance dynamics revealing power-law distributed intensity transitions, this paper introduces Luminance-Aware Statistical Quantification (LASQ), a novel framework that reformulates LLIE as a statistical sampling process over hierarchical luminance distributions. Our LASQ re-conceptualizes luminance transition as a power-law distribution in intensity coordinate space that can be approximated by stratified power functions, therefore, replacing deterministic mappings with probabilistic sampling over continuous luminance layers. A diffusion forward process is designed to autonomously discover optimal transition paths between luminance layers, achieving unsupervised distribution emulation without normal-light references. 
In this way, it considerably improves the performance in practical situations, enabling more adaptable and versatile light restoration. This framework is also readily applicable to cases with normal-light references, where it achieves superior performance on domain-specific datasets alongside better generalization-ability across non-reference datasets. The code is available at: https://github.com/XYLGroup/LASQ.
From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs
Authors: Xiyuan Jin; Jing Wang; Ziwei Lin; Qianru Jia; Yuqing Huang; Xiaojun Ning; Zhonghua Shi; Youfang Lin
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context.
Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines.
However, realizing this potential depends on precise and meaningful prompts that guide the LLM to key information.
Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. 
To this end, we propose InDiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization.
Across four medical benchmarks, InDiGO consistently outperforms prior methods. 
The code is available at: https://github.com/jinxyBJTU/InDiGO.
Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
Authors: Wenbo Zhang; Tianrun Hu; Hanbo Zhang; Yanyuan Qiao; Yuchu Qin; Yang Li; Jiajun Liu; Tao Kong; Lingqiao Liu; Xiao Ma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We present Chain-of-Action (CoA), a novel visuomotor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals;  and (2) subsequent action tokens are generated autoregressively, conditioned on the initial keyframe and previously predicted actions. This backward action reasoning enforces a global-to-local structure, allowing each local action to be tightly constrained by the final goal. To further realize the action reasoning structure, CoA incorporates four complementary designs: continuous action token representation; dynamic stopping for variable-length trajectory generation; reverse temporal ensemble; and multi-token prediction to balance action chunk modeling  with global structure. As a result, CoA gives strong spatial generalization capabilities while preserving the flexibility and simplicity of a visuomotor policy. Empirically, we observe that CoA outperforms representative imitation learning algorithms such as ACT and Diffusion Policy across 60 RLBench tasks and 8 real-world tasks.
Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learning
Authors: Shikuang Deng; Jiayuan Zhang; Yuhang Wu; Ting Chen; Shi Gu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Hebbian learning is a biological principle that intuitively describes how neurons adapt their connections through repeated stimuli. However, when applied to machine learning, it suffers serious issues due to the unconstrained updates of the connections and the lack of accounting for feedback mediation. Such shortcomings limit its effective scaling to complex network architectures and tasks. To this end, here we introduce the Structural Projection Hebbian Representation (SPHeRe), a novel unsupervised learning method that integrates orthogonality and structural information preservation through a local auxiliary nonlinear block. The loss for structural information preservation backpropagates to the input through an auxiliary lightweight projection that conceptually serves as feedback mediation while the orthogonality constraints account for the boundedness of updating magnitude. Extensive experimental results show that SPHeRe achieves SOTA performance among unsupervised synaptic plasticity approaches on standard image classification benchmarks, including CIFAR-10, CIFAR-100, and Tiny-ImageNet. Furthermore, the method exhibits strong effectiveness in continual learning and transfer learning scenarios, and image reconstruction tasks show the robustness and generalizability of the extracted features. This work demonstrates the competitiveness and potential of Hebbian unsupervised learning rules within modern deep learning frameworks, demonstrating the possibility of efficient and biologically inspired learning algorithms without the strong dependence on strict backpropagation. Our code is available at https://github.com/brain-intelligence-lab/SPHeRe.
Memory-Efficient Training with In-Place FFT Implementation
Authors: Xinyu Ding; Bangtian Liu; Siyu Liao; Zhongfeng Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Fast Fourier Transforms (FFT) are widely used to reduce memory and computational costs in deep learning. However, existing implementations, including standard FFT and real FFT (rFFT), cannot achieve true in-place computation. In particular, rFFT maps an input of size $n$ to a complex output of size $\frac{n}{2}+1$, causing dimensional mismatch and requiring additional memory allocation.
We propose the first real-domain, fully in-place FFT framework (rdFFT) that preserves input-output dimensional consistency ($n \rightarrow n$). By leveraging butterfly operation symmetry and conjugate properties in the frequency domain, we design an implicit complex encoding scheme that eliminates intermediate cache usage entirely.
Theoretically, our method reduces memory usage by 50\% compared to rFFTs. Moreover, it enables zero-cache parameter updates by utilizing the derivative property of the Fourier transform to compute matrix inverses efficiently without intermediate storage. Experiments on multiple natural language understanding tasks demonstrate the method’s effectiveness in maintaining model performance while significantly lowering memory overhead, offering a promising direction for frequency-domain lightweight adaptation.