GenAI4ELab/papercli-papers huggingface dataset:
WKV-sharing embraced random shuffle RWKV high-order modeling for pan-sharpening
Authors: Man Zhou; Xuanhua He; Danfeng Hong; Bo Huang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Pan-sharpening aims to generate a spatially and spectrally enriched multi-spectral image by integrating complementary
cross-modality information from low-resolution multi-spectral image and texture-rich panchromatic counterpart. In this work, we propose a
WKV-sharing embraced random shuffle RWKV high-order modeling paradigm for pan-sharpening from Bayesian perspective, coupled with random weight manifold distribution training strategy derived from Functional theory to regularize the solution space adhering to the
following principles: 1) Random-shuffle RWKV. Recently, the Vision RWKV model, with its inherent linear complexity in global modeling,
has inspired us to explore its untapped potential in pan-sharpening tasks. However, its attention mechanism, relying on a recurrent
bidirectional scanning strategy, suffers from biased effects and demands significant processing time. To address this, we propose a novel
Bayesian-inspired scanning strategy called Random Shuffle, complemented by a theoretically-sound inverse shuffle to preserve
information coordination invariance, effectively eliminating biases associated with fixed sequence scanning. The Random Shuffle
approach mitigates preconceptions in global 2D dependencies in mathematical expectation, providing the model with an unbiased prior.
In line with similar spirit of Dropout, we introduce a testing methodology based on Monte Carlo averaging to ensure the model’s output
aligns more closely with expected results. 2) WKV-sharing high-order. Regarding KV’s attention score calculation in spatial mixer of RWKV, we leverage WKV-sharing mechanism to transfer KV activations across RWKV layers, achieving lower latency and improved trainability, and revisit the channel mixer in RWKV, originally a first-order weighting function, and redevelop its high-order potential by sharing the gate mechanism across RWKV layer. Comprehensive experiments across pan-sharpening benchmarks demonstrate our model’s effectiveness, consistently outperforming state-of-the-art alternatives
Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Authors: Simon Matrenok; Skander Moalla; Caglar Gulcehre
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Aligning large language models with pointwise absolute rewards has so far required online, on-policy algorithms such as PPO and GRPO.
In contrast, simpler methods that can leverage offline or off-policy data, such as DPO and REBEL, are limited to learning from preference pairs or relative signals.
To bridge this gap, we introduce Quantile Reward Policy Optimization (QRPO), which learns from pointwise absolute rewards while preserving the simplicity and offline applicability of DPO-like methods.
QRPO uses quantile rewards to enable regression to the closed-form solution of the KL-regularized RL objective.
This reward yields an analytically tractable partition function, removing the need for relative signals to cancel this term.
Moreover, QRPO scales with increased compute to estimate quantile rewards, opening a new dimension for pre-computation scaling.
Empirically, QRPO consistently achieves top performance on chat and coding evaluations—reward model scores, AlpacaEval 2, and LeetCode—compared to DPO, REBEL, and SimPO across diverse datasets and 8B-scale models.
Finally, we find that training with robust rewards instead of converting them to preferences induces less length bias.
UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation
Authors: Jiyu Guo; Shuo Yang; Yiming Huang; Yancheng Long; Xiaobo Xia; Xiu Su; Bo Zhao; Zeke Xie; Liqiang Nie
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Data augmentation using generative models has emerged as a powerful paradigm for enhancing performance in computer vision tasks. However, most existing augmentation approaches primarily focus on optimizing intrinsic data attributes -- such as fidelity and diversity -- to generate visually high-quality synthetic data, while often neglecting task-specific requirements. Yet, it is essential for data generators to account for the needs of downstream tasks, as training data requirements can vary significantly across different tasks and network architectures. 
To address these limitations, we propose UtilGen, a novel utility-centric data augmentation framework that adaptively optimizes the data generation process to produce task-specific, high-utility training data via downstream task feedback. Specifically, we first introduce a weight allocation network to evaluate the task-specific utility of each synthetic sample. Guided by these evaluations, UtilGen iteratively refines the data generation process using a dual-level optimization strategy to maximize the synthetic data utility: (1) model-level optimization tailors the generative model to the downstream task, and (2) instance-level optimization adjusts generation policies -- such as prompt embeddings and initial noise -- at each generation round. 
Extensive experiments on eight benchmark datasets of varying complexity and granularity demonstrate that UtilGen consistently achieves superior performance, with an average accuracy improvement of 3.87\% over previous SOTA. Further analysis of data influence and distribution reveals that UtilGen produces more impactful and task-relevant synthetic data, validating the effectiveness of the paradigm shift from visual characteristics-centric to task utility-centric data augmentation.
Multi-agent Markov Entanglement
Authors: Shuze Chen; Tianyi Peng
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Value decomposition has long been a fundamental technique in multi-agent reinforcement learning and dynamic programming. Specifically, the value function of a global state $(s_1,s_2,\ldots,s_N)$ is often approximated as the sum of local functions: $V(s_1,s_2,\ldots,s_N)\approx\sum_{i=1}^N V_i(s_i)$. This approach has found various applications in modern RL systems. However, the theoretical justification for why this decomposition works so effectively remains underexplored. In this paper, we uncover the underlying mathematical structure that enables value decomposition. We demonstrate that a Markov decision process (MDP) permits value decomposition *if and only if* its transition matrix is not "entangled"—a concept analogous to quantum entanglement in quantum physics. Drawing inspiration from how physicists measure quantum entanglement, we introduce how to measure the "Markov entanglement" and show that this measure can be used to bound the decomposition error in general multi-agent MDPs. Using the concept of Markov entanglement, we proved that a widely-used class of policies, the index policy, is weakly-entangled and enjoys a sublinear $\mathcal O(\sqrt{N})$ scale of decomposition error for $N$-agent systems. Finally, we show Markov entanglement can be efficiently estimated, guiding practitioners on the feasibility of value decomposition.
Where Does It Exist from the Low-Altitude: Spatial Aerial Video Grounding
Authors: Yang Zhan; Yuan Yuan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The task of localizing an object's spatial tube based on language instructions and video, known as spatial video grounding (SVG), has attracted widespread interest. Existing SVG tasks have focused on ego-centric fixed front perspective and simple scenes, which only involved a very limited view and environment. However, UAV-based SVG remains underexplored, which neglects the inherent disparities in drone movement and the complexity of aerial object localization. To facilitate research in this field, we introduce the novel spatial aerial video grounding (SAVG) task. Specifically, we meticulously construct a large-scale benchmark, UAV-SVG, which contains over 2 million frames and offers 216 highly diverse target categories. To address the disparities and challenges posed by complex aerial environments, we propose a new end-to-end transformer architecture, coined SAVG-DETR. The innovations are three-fold. 1) To overcome the computational explosion of self-attention when introducing multi-scale features, our encoder efficiently decouples the multi-modality and multi-scale spatio-temporal modeling into intra-scale multi-modality interaction and cross-scale visual-only fusion. 2) To enhance small object grounding ability, we propose the language modulation module to integrate multi-scale information into language features and the multi-level progressive spatial decoder to decode from high to low level. The decoding stage for the lower-level vision-language features is gradually increased. 3) To improve the prediction consistency across frames, we design the decoding paradigm based on offset generation. At each decoding stage, we utilize reference anchors to constrict the grounding region, use context-rich object queries to predict offsets, and update reference anchors for the next stage. From coarse to fine, our SAVG-DETR gradually bridges the modality gap and iteratively refines reference anchors of the referred object, eventually grounding the spatial tube. Extensive experiments demonstrate that our SAVG-DETR significantly outperforms existing state-of-the-art methods. The dataset and code will be available at here.
Training-Free Efficient Video Generation via Dynamic Token Carving
Authors: Yuechen Zhang; Jinbo Xing; Bin Xia; Shaoteng Liu; Bohao PENG; Xin Tao; Pengfei Wan; Eric Lo; Jiaya Jia
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Despite the remarkable generation quality of video Diffusion Transformer (DiT) models, their practical deployment is severely hindered by extensive computational requirements. This inefficiency stems from two key challenges: the quadratic complexity of self-attention with respect to token length and the multi-step nature of diffusion models. To address these limitations, we present Jenga, a novel inference pipeline that combines dynamic attention carving with progressive resolution generation. Our approach leverages two key insights: (1) early denoising steps do not require high-resolution latents, and (2) later steps do not require dense attention. Jenga introduces a block-wise attention mechanism that dynamically selects relevant token interactions using 3D space-filling curves, alongside a progressive resolution strategy that gradually increases latent resolution during generation. Experimental results demonstrate that Jenga achieves substantial speedups across multiple state-of-the-art video diffusion models while maintaining comparable generation quality (8.83$\times$ speedup with 0.01\% performance drop on VBench). As a plug-and-play solution, Jenga enables practical, high-quality video generation on modern hardware by reducing inference time from minutes to seconds---without requiring model retraining.
Towards Pre-trained Graph Condensation via Optimal Transport
Authors: Yeyu Yan; Shuai Zheng; Wenjun Hui; Xiangkai Zhu; Dong Chen; Zhenfeng Zhu; Yao Zhao; Kunlun He
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Graph condensation (GC) aims to distill the original graph into a small-scale graph, mitigating redundancy and accelerating GNN training. However, conventional GC approaches heavily rely on rigid GNNs and task-specific supervision. Such a dependency severely restricts their reusability and generalization across various tasks and architectures. In this work, we revisit the goal of ideal GC from the perspective of GNN optimization consistency, and then a generalized GC optimization objective is derived, by which those traditional GC methods can be viewed nicely as special cases of this optimization paradigm. Based on this, \textbf{Pre}-trained \textbf{G}raph \textbf{C}ondensation (\textbf{PreGC}) via optimal transport is proposed to transcend the limitations of task- and architecture-dependent GC methods. Specifically, a hybrid-interval graph diffusion augmentation is presented to suppress the weak generalization ability of the condensed graph on particular architectures by enhancing the uncertainty of node states. Meanwhile, the matching between optimal graph transport plan and representation transport plan is tactfully established to maintain semantic consistencies across source graph and condensed graph spaces, thereby freeing graph condensation from task dependencies. To further facilitate the adaptation of condensed graphs to various downstream tasks, a traceable semantic harmonizer from source nodes to condensed nodes is proposed to bridge semantic associations through the optimized representation transport plan in pre-training. Extensive experiments verify the superiority and versatility of PreGC, demonstrating its task-independent nature and seamless compatibility with arbitrary GNNs.
ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
Authors: Muye Huang; Lingling Zhang; Jie Ma; Han Lai; Fangzhi Xu; Yifei Li; Wenjun Wu; Yaqiang Wu; Jun Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Charts are high-density visualization carriers for complex data, serving as a crucial medium for information extraction and analysis. Automated chart understanding poses significant challenges to existing multimodal large language models (MLLMs) due to the need for precise and complex visual reasoning. Current step-by-step reasoning models primarily focus on text-based logical reasoning for chart understanding. However, they struggle to refine or correct their reasoning when errors stem from flawed visual understanding, as they lack the ability to leverage multimodal interaction for deeper comprehension. Inspired by human cognitive behavior, we propose ChartSketcher, a multimodal feedback-driven step-by-step reasoning method designed to address these limitations. ChartSketcher is a chart understanding model that employs Sketch-CoT, enabling MLLMs to annotate intermediate reasoning steps directly onto charts using a programmatic sketching library, iteratively feeding these visual annotations back into the reasoning process. This mechanism enables the model to visually ground its reasoning and refine its understanding over multiple steps. We employ a two-stage training strategy: a cold start phase to learn sketch-based reasoning patterns, followed by off-policy reinforcement learning to enhance reflection and generalization. Experiments demonstrate that ChartSketcher achieves promising performance on chart understanding benchmarks and general vision tasks, providing an interactive and interpretable approach to chart comprehension.
Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation
Authors: Xiao Cui; Yulei Qin; Wengang Zhou; Hongsheng Li; Houqiang Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Dataset distillation seeks to synthesize a compact distilled dataset, enabling models trained on it to achieve performance comparable to models trained on the full dataset. Recent methods for large-scale datasets focus on matching global distributional statistics (e.g., mean and variance), but overlook critical instance-level characteristics and intraclass variations, leading to suboptimal generalization. We address this limitation by reformulating dataset distillation as an Optimal Transport (OT) distance minimization problem, enabling fine-grained alignment at both global and instance levels throughout the pipeline. OT offers a geometrically faithful framework for distribution matching. It effectively preserves local modes, intra-class patterns, and fine-grained variations that characterize the geometry of complex, high-dimensional distributions. Our method comprises three components tailored for preserving distributional geometry: (1) OT-guided diffusion sampling, which aligns latent distributions of real and distilled images; (2) label-image-aligned soft relabeling, which adapts label distributions based on the complexity of distilled image distributions; and (3) OT-based logit matching, which aligns the output of student models with soft-label distributions. Extensive experiments across diverse architectures and large-scale datasets demonstrate that our method consistently outperforms state-of-the-art approaches in an efficient manner, achieving at least 4\% accuracy improvement under IPC=10 settings for each architecture on ImageNet-1K.
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
Authors: Zhizhen Zhang; Lei Zhu; Zhen Fang; Zi Huang; Yadan Luo
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Pre-training vision-language representations on human action videos has emerged
as a promising approach to reduce reliance on large-scale expert demonstrations
for training embodied agents. However, prior methods often employ time con-
trastive learning based on goal-reaching heuristics, progressively aligning language
instructions from the initial to the final frame. This overemphasis on future frames
can result in erroneous vision-language associations, as actions may terminate
early or include irrelevant moments in the end. To address this issue, we propose
Action Temporal Coherence Learning (AcTOL) to learn ordered and continuous
vision-language representations without rigid goal-based constraint. AcTOL treats
a video as a continuous trajectory where it (1) contrasts semantic differences be-
tween frames to reflect their natural ordering, and (2) imposes a local Brownian
bridge constraint to ensure smooth transitions across intermediate frames. Exten-
sive imitation learning experiments on both simulated and real robots show that the
pretrained features significantly enhance downstream manipulation tasks with high
robustness to different linguistic styles of instructions, offering a viable pathway
toward generalized embodied agents. Our project page is at https://actol-pretrain.github.io/.
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning
Authors: Yu Zhang; Jialei Zhou; Xinchen Li; Qi Zhang; Zhongwei Wan; Duoqian Miao; Changwei Wang; Longbing Cao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Current text-to-image diffusion generation typically employs complete-text conditioning. Due to the intricate syntax, diffusion transformers (DiTs) inherently suffer from a comprehension defect of complete-text captions. One-fly complete-text input either overlooks critical semantic details or causes semantic confusion by simultaneously modeling diverse semantic primitive types. To mitigate this defect of DiTs, we propose a novel split-text conditioning framework named DiT-ST. This framework converts a complete-text caption into a split-text caption, a collection of simplified sentences, to explicitly express various semantic primitives and their interconnections. The split-text caption is then injected into different denoising stages of DiT-ST in a hierarchical and incremental manner. Specifically, DiT-ST leverages Large Language Models to parse captions, extracting diverse primitives and hierarchically sorting out and constructing these primitives into a split-text input. Moreover, we partition the diffusion denoising process according to its differential sensitivities to diverse semantic primitive types and determine the appropriate timesteps to incrementally inject tokens of diverse semantic primitive types into input tokens via cross-attention. In this way, DiT-ST enhances the representation learning of specific semantic primitive types across different stages. Extensive experiments validate the effectiveness of our proposed DiT-ST in mitigating the complete-text comprehension defect. Datasets and models are available.
SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving
Authors: Haiming Zhang; Yiyao Zhu; Wending Zhou; Xu Yan; Yingjie CAI; Bingbing Liu; Shuguang Cui; Zhen Li
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Sparse Perception Models (SPMs) adopt a query-driven paradigm that forgoes explicit dense BEV or volumetric construction, enabling highly efficient computation and accelerated inference. In this paper, we introduce SQS, a novel query-based splatting pre-training specifically designed to advance SPMs in autonomous driving. SQS introduces a plug-in module that predicts 3D Gaussian representations from sparse queries during pre-training, leveraging self-supervised splatting to learn fine-grained contextual features through the reconstruction of multi-view images and depth maps. During fine-tuning, the pre-trained Gaussian queries are seamlessly integrated into downstream networks via query interaction mechanisms that explicitly connect pre-trained queries with task-specific queries, effectively accommodating the diverse requirements of occupancy prediction and 3D object detection. Extensive experiments on autonomous driving benchmarks demonstrate that SQS delivers considerable performance gains across multiple query-based 3D perception tasks, notably in occupancy prediction and 3D object detection, outperforming prior state-of-the-art pre-training approaches by a significant margin (i.e., +1.3 mIoU on occupancy prediction and +1.0 NDS on 3D detection).
Many Minds, One Goal: Time Series Forecasting via Sub-task Specialization and Inter-agent Cooperation
Authors: Qihe Huang; Zhengyang Zhou; Yangze Li; Kuo Yang; Binwu Wang; Yang Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Time series forecasting is a critical and complex task, characterized by diverse temporal patterns, varying statistical properties, and different prediction horizons across datasets and domains. Conventional approaches typically rely on a single, unified model architecture to handle all forecasting scenarios. However, such monolithic models struggle to generalize across dynamically evolving time series with shifting patterns. In reality, different types of time series may require distinct modeling strategies. Some benefit from homogeneous multi-scale forecasting awareness, while others rely on more complex and heterogeneous signal perception. Relying on a single model to capture all temporal diversity and structural variations leads to limited  performance and poor interpretability. To address this challenge, we propose a Multi-Agent Forecasting System (MAFS) that abandons the one-size-fits-all paradigm. MAFS decomposes the forecasting task into multiple sub-tasks, each handled by a dedicated agent trained on specific temporal perspectives (e.g., different forecasting resolutions or signal characteristics).  Furthermore, to achieve holistic forecasting, agents share and refine information through different communication topology, enabling cooperative reasoning across different temporal views. A lightweight voting aggregator then integrates their outputs into consistent final predictions. Extensive experiments across 11 benchmarks demonstrate that MAFS significantly outperforms traditional single-model approaches, yielding more robust and adaptable forecasts.