GenAI4ELab/papercli-papers huggingface dataset:
SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios
Authors: Lingwei Dang; Ruizhi Shao; Hongwen Zhang; Wei MIN; Yebin Liu; Qingyao Wu
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Hand-Object Interaction (HOI) generation has significant application potential. However, current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data, limiting generalization capabilities. Meanwhile, HOI video generation methods prioritize pixel-level visual fidelity, often sacrificing physical plausibility. Recognizing that visual appearance and motion patterns share fundamental physical laws in the real world, we propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously. To integrate the heterogeneous semantics, appearance, and motion features, our method implements tri-modal adaptive modulation for feature aligning, coupled with 3D full-attention for modeling inter- and intra-modal dependencies. Furthermore, we introduce a vision-aware 3D interaction diffusion model that generates explicit 3D interaction sequences directly from the synchronized diffusion outputs, then feeds them back to establish a closed-loop feedback cycle. This architecture eliminates dependencies on predefined object models or explicit pose guidance while significantly enhancing video-motion consistency. Experimental results demonstrate our method's superiority over state-of-the-art approaches in generating high-fidelity, dynamically plausible HOI sequences, with notable generalization capabilities in unseen real-world scenarios. Project page at [https://droliven.github.io/SViMo_project](https://droliven.github.io/SViMo_project).
HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
Authors: Rafael Bischof; Michal Piovarci; Michael Anton Kraus; Siddhartha Mishra; Bernd Bickel
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We present HyPINO, a multi-physics neural operator designed for zero-shot generalization across a broad class of PDEs without requiring task-specific fine-tuning. Our approach combines a Swin Transformer-based hypernetwork with mixed supervision: (i) labeled data from analytical solutions generated via the Method of Manufactured Solutions (MMS), and (ii) unlabeled samples optimized using physics-informed objectives. The model maps PDE parameterizations to target Physics-Informed Neural Networks (PINNs) and can handle linear elliptic, hyperbolic, and parabolic equations in two dimensions with varying source terms, geometries, and mixed Dirichlet/Neumann boundary conditions, including interior boundaries. HyPINO achieves strong zero-shot accuracy on seven benchmark problems from PINN literature, outperforming U-Nets, Poseidon, and Physics-Informed Neural Operators (PINO). Further, we introduce an iterative refinement procedure that treats the residual of the generated PINN as "delta PDE" and performs another forward pass to generate a corrective PINN. Summing their contributions and repeating this process forms an ensemble whose combined solution progressively reduces the error on six benchmarks and achieves a >100× lower $L_2$ loss in the best case, while retaining forward-only inference. Additionally, we evaluate the fine-tuning behavior of PINNs initialized by HyPINO and show that they converge faster and to lower final error than both randomly initialized and Reptile-meta-learned PINNs on five benchmarks, performing on par on the remaining two. Our results highlight the potential of this scalable approach as a foundation for extending neural operators toward solving increasingly complex, nonlinear, and high-dimensional PDE problems. The code and model weights are publicly available at https://github.com/rbischof/hypino.
Quasi-Self-Concordant Optimization with $\ell_{\infty}$ Lewis Weights
Authors: Alina Ene; Ta Duy Nguyen; Adrian Vladu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this paper, we study the problem $\min_{x\in R^{d},Nx=v}\sum\_{i=1}^{n}f((Ax-b)_{i})$
for a quasi-self-concordant function $f: R\to R$, where $A,N$ are
$n\times d$ and $m\times d$ matrices, $b,v$ are vectors of length
$n$ and $m$ with $n\ge d.$ We show an algorithm based on a trust-region
method with an oracle  that can be implemented using $\widetilde{O}(d^{1/3})$
linear system solves, improving the $\widetilde{O}(n^{1/3})$ oracle
by [Adil-Bullins-Sachdeva, NeurIPS 2021]. Our implementation of
the oracle relies on solving the overdetermined $\ell\_{\infty}$-regression
problem $\min\_{x\in R^{d},Nx=v}\|Ax-b\|\_{\infty}$. We provide an
algorithm that finds a $(1+\epsilon)$-approximate solution to this
problem using $O((d^{1/3}/\epsilon+1/\epsilon^{2})\log(n/\epsilon))$
linear system solves. This algorithm leverages $\ell\_{\infty}$ Lewis
weight overestimates and achieves this iteration complexity via a
simple lightweight IRLS approach, inspired by the work of [Ene-Vladu,
ICML 2019]. Experimentally, we demonstrate that our algorithm significantly
improves the runtime of the standard CVX solver.
Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum
Authors: Sarit Khirirat; Abdurakhmon Sadiev; Artem Riabinin; Eduard Gorbunov; Peter Richtárik
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We provide the first proof of convergence for normalized error feedback algorithms across a wide range of machine learning problems. 
Despite their popularity and efficiency in training deep neural networks, traditional analyses of error feedback algorithms rely on the smoothness assumption that does not capture the properties of objective functions in these  problems. 
Rather, these problems have recently been shown to satisfy generalized smoothness assumptions, and the theoretical understanding of error feedback algorithms under these assumptions remains largely unexplored. 
Moreover, to the best of our knowledge, all existing analyses under generalized smoothness either i) focus on single-node settings or ii) make unrealistically strong assumptions for distributed settings, such as requiring data heterogeneity, and almost surely bounded stochastic gradient noise variance. 
In this paper, we propose distributed error feedback algorithms that utilize normalization to achieve the $\mathcal{O}(1/\sqrt{K})$ convergence rate for nonconvex problems under generalized smoothness. Our analyses apply for distributed settings without data heterogeneity conditions, and enable stepsize tuning that is independent of problem parameters. 
Additionally, we provide strong convergence guarantees of normalized error feedback algorithms for stochastic settings. 
Finally, we show that due to their larger allowable stepsizes, our new normalized error feedback algorithms outperform their non-normalized counterparts on various tasks, including the minimization of polynomial functions, logistic regression, and ResNet-20 training.
Reward Reasoning Models
Authors: Jiaxin Guo; Zewen Chi; Li Dong; Qingxiu Dong; Xun Wu; Shaohan Huang; Furu Wei
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing test-time compute to enhance reward model performance. In this work, we introduce Reward Reasoning Models (RRMs), which are specifically designed to execute a deliberate reasoning process before generating final rewards. Through chain-of-thought reasoning, RRMs leverage additional test-time compute for complex queries where appropriate rewards are not immediately apparent. To develop RRMs, we implement a reinforcement learning framework that fosters self-evolved reward reasoning capabilities without requiring explicit reasoning traces as training data. Experimental results demonstrate that RRMs achieve superior performance on reward modeling benchmarks across diverse domains. Notably, we show that RRMs can adaptively exploit test-time compute to further improve reward accuracy. The pretrained models are available at https://huggingface.co/Reward-Reasoning.
What Really is a Member? Discrediting Membership Inference via Poisoning
Authors: Neal Mangaokar; Ashish Hooda; Zhuohang Li; Bradley A. Malin; Kassem Fawaz; Somesh Jha; Atul Prakash; Amrita Roy Chowdhury
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often fail under the strict definition of membership based on exact matching, and have suggested relaxing this definition to include semantic neighbors as members as well. In this work, we show that membership inference tests are still *unreliable* under this relaxation - it is possible to poison the training dataset in a way that causes the test to produce incorrect predictions for a target point. We theoretically reveal a trade-off between a test’s accuracy and its robustness to poisoning.  We also present a concrete instantiation of this poisoning attack and empirically validate its effectiveness. Our results show that it can degrade the performance of existing tests to well below random.
RidgeLoRA: Matrix Ridge Enhanced Low-Rank Adaptation of Large Language Models
Authors: Junda Zhu; Jun Ai; Yujun Li; Yichun Yin; Yasheng Wang; Lifeng Shang; Qun Liu
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: As one of the state-of-the-art parameter-efficient fine-tuning~(PEFT) methods, Low-Rank Adaptation (LoRA) enables model optimization with reduced computational cost through trainable low-rank matrix. However, the low-rank nature makes it prone to produce a decrease in the representation ability, leading to suboptimal performance. In order to break this limitation, we propose RidgeLoRA, a lightweight architecture like LoRA that incorporates novel architecture and matrix ridge enhanced full-rank approximation, to match the performance of full-rank training, while eliminating the need for high memory and a large number of parameters to restore the rank of matrices. We provide a rigorous mathematical derivation to prove that RidgeLoRA has a better upper bound on the representations than vanilla LoRA. Furthermore, extensive experiments across multiple domains demonstrate that RidgeLoRA achieves better performance than other LoRA variants, and can even match or surpass full-rank training.
Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
Authors: Junfei Wu; Jian Guan; Kaituo Feng; Qiang Liu; Shu Wu; Liang Wang; Wei Wu; Tieniu Tan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: As textual reasoning with large language models (LLMs) has advanced significant, there has been growing interest in enhancing the multimodal reasoning capabilities of large vision-language models (LVLMs). However, existing methods primarily approach multimodal reasoning in a straightforward, text-centric manner, where both reasoning and answer derivation are conducted purely through text, with the only difference being the presence of multimodal input. As a result, these methods often encounter fundamental limitations in spatial reasoning tasks that demand precise geometric understanding and continuous spatial tracking\textemdash capabilities that humans achieve through mental visualization and manipulation. To address the limitations, we propose drawing to reason in space, a novel paradigm that enables LVLMs to reason through elementary drawing operations in the visual space. By equipping models with basic drawing operations including annotating bounding boxes and drawing auxiliary lines, we empower them to express and analyze spatial relationships through direct visual manipulation, meanwhile avoiding the performance ceiling imposed by specialized perception tools in previous tool-integrated reasoning approaches. To cultivate this capability, we develop a three-stage training framework: cold-start training with synthetic data to establish basic drawing abilities, reflective rejection sampling to enhance self-reflection behaviors, and reinforcement learning to directly optimize for target rewards. Extensive experiments demonstrate that our model, named \textsc{Spark}, consistently outperforms existing methods across diverse spatial reasoning benchmarks involving maze navigation, static spatial reasoning, video-based reasoning and multi-view-based reasoning tasks, with an average improvement of 11.5\%. Ablation studies reveal the critical role of each training stage, with reflective rejection sampling particularly enhancing the model's self-correction capabilities and reasoning potential.
Alleviating Hallucinations in Large Language Models through Multi-Model Contrastive Decoding and Dynamic Hallucination Detection
Authors: Chenyu Zhu; YEFENG LIU; Hao Zhang; Aowen Wang; Yangxue; Guanhua Chen; Longyue Wang; Weihua Luo; Kaifu Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Despite their outstanding performance in numerous applications, large language models (LLMs) remain prone to hallucinations, generating content inconsistent with their pretraining corpora. Currently, almost all contrastive decoding approaches alleviate hallucinations by introducing a model susceptible to hallucinations and appropriately widening the contrastive logits gap between hallucinatory tokens and target tokens. However, although existing contrastive decoding methods mitigate hallucinations, they lack enough confidence in the factual accuracy of the generated content. In this work, we propose Multi-Model Contrastive Decoding (MCD), which integrates a pretrained language model with an evil model and a truthful model for contrastive decoding. Intuitively, a token is assigned a high probability only when deemed potentially hallucinatory by the evil model while being considered factual by the truthful model. This decoding strategy significantly enhances the model’s confidence in its generated responses and reduces potential hallucinations. Furthermore, we introduce a dynamic hallucination detection mechanism that facilitates token-by-token identification of hallucinations during generation and a tree-based revision mechanism to diminish hallucinations further. Extensive experimental evaluations demonstrate that our MCD strategy effectively reduces hallucinations in LLMs and outperforms state-of-the-art methods across various benchmarks.
DINO-Foresight: Looking into the Future with DINO
Authors: Efstathios Karypidis; Ioannis Kakogeorgiou; Spyros Gidaris; Nikos Komodakis
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Predicting future dynamics is crucial for applications like autonomous driving and robotics, where understanding the environment is key. Existing pixel-level methods are computationally expensive and often focus on irrelevant details. 
To address these challenges, we introduce DINO-Foresight, a novel framework that operates in the semantic feature space of pretrained Vision Foundation Models (VFMs). Our approach trains a masked feature transformer in a self-supervised manner to predict the evolution of VFM features over time. By forecasting these features, we can apply off-the-shelf, task-specific heads for various scene understanding tasks. In this framework, VFM features are treated as a latent space, to which different heads attach to perform specific tasks for future-frame analysis. Extensive experiments show the very strong performance, robustness and scalability of our framework.
STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds
Authors: Yinfang Chen; Jiaqi Pan; Jackson Clark; Yiming Su; Noah Zheutlin; Bhavya Bhavya; Rohan R. Arora; Yu Deng; Saurabh Jha; Tianyin Xu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures; software bugs and misconfigurations are reported to be more frequent. The demand for autonomous, AI-driven reliability engineering continues to grow, as existing human-in-the-loop practices can hardly keep up with the scale of modern clouds. This paper presents STRATUS, an LLM-based multi-agent system for realizing autonomous Site Reliability Engineering (SRE) of cloud services. STRATUS consists of multiple specialized agents (e.g., for failure detection, diagnosis, mitigation), organized in a state machine to assist system-level safety reasoning and enforcement. We formalize a key safety specification of agentic SRE systems like STRATUS, termed Transactional No-Regression (TNR), which enables safe exploration and iteration. We show that TNR can effectively improve autonomous failure mitigation. STRATUS significantly outperforms state-of-the-art SRE agents in terms of success rate of failure mitigation problems in AIOpsLab and ITBench (two SRE benchmark suites), by at least 1.5 times across various models. STRATUS shows a promising path toward practical deployment of agentic systems for cloud reliability.
PlanU: Large Language Model Reasoning through Planning under Uncertainty
Authors: Ziwei Deng; Mian Deng; Chenjing Liang; Zeming Gao; Chennan Ma; Chenxing Lin; Haipeng Zhang; Songzhu Mei; Siqi Shen; Cheng Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large Language Models (LLMs) are increasingly being explored across a range of reasoning tasks. However, LLMs sometimes struggle with reasoning tasks under uncertainty that are relatively easy for humans, such as planning actions in stochastic environments. The adoption of LLMs for reasoning is impeded by uncertainty challenges, such as LLM uncertainty and environmental uncertainty. LLM uncertainty arises from the stochastic sampling process inherent to LLMs. Most LLM-based Decision-Making (LDM) approaches address LLM uncertainty through multiple reasoning chains or search trees. However, these approaches overlook environmental uncertainty, which leads to poor performance in environments with stochastic state transitions. 
Some recent LDM approaches deal with uncertainty by forecasting the probability of unknown variables. However, they are not designed for multi-step reasoning tasks that require interaction with the environment. To address uncertainty in LLM decision-making, we introduce PlanU, an LLM-based planning method that captures uncertainty within Monte Carlo Tree Search (MCTS). PlanU models the return of each node in the MCTS as a quantile distribution, which uses a set of quantiles to represent the return distribution. To balance exploration and exploitation during tree search, PlanU introduces an Upper Confidence Bounds with Curiosity (UCC) score which estimates the uncertainty of MCTS nodes. Through extensive experiments, we demonstrate the effectiveness of PlanU in LLM-based reasoning tasks under uncertainty.
AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models
Authors: Seunghoon Lee; Jeongwoo Choi; Byunggwan Son; Jaehyeon Moon; Jeimin Jeon; Bumsub Ham
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for diffusion models are accumulated over denoising steps in a sampling process. To alleviate the error accumulation problem, AccuQuant minimizes the discrepancies between outputs of a full-precision diffusion model and its quantized version within a couple of denoising steps. That is, it simulates multiple denoising steps of a diffusion sampling process explicitly for quantization, accounting the accumulated errors over multiple denoising steps, which is in contrast to previous approaches to imitating a training process of diffusion models, namely, minimizing the discrepancies independently for each step. We also present an efficient implementation technique for AccuQuant, together with a novel objective, which reduces a memory complexity significantly from $\mathcal{O}(n)$ to $\mathcal{O}(1)$, where $n$ is the number of denoising steps. We demonstrate the efficacy and efficiency of AccuQuant across various tasks and diffusion models on standard benchmarks.
Sparse Diffusion Autoencoder for Test-time Adapting Prediction of Complex Systems
Authors: Jingwen Cheng; Ruikun Li; Huandong Wang; Yong Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Predicting the behavior of complex systems is critical in many scientific and engineering domains, and hinges on the model’s ability to capture their underlying dynamics.
Existing methods encode the intrinsic dynamics of high-dimensional observations through latent representations and predict autoregressively. 
However, these latent representations lose the inherent spatial structure of spatiotemporal dynamics, leading to the predictor's inability to effectively model spatial interactions and neglect emerging dynamics during long-term prediction.
In this work, we propose SparseDiff, introducing a test-time adaptation strategy to dynamically update the encoding scheme to accommodate emergent spatiotemporal structures during the long-term evolution of the system. 
Specifically, we first design a codebook-based sparse encoder, which coarsens the continuous spatial domain into a sparse graph topology. Then, we employ a graph neural ordinary differential equation to model the dynamics and guide a diffusion decoder for reconstruction.
SparseDiff autoregressively predicts the spatiotemporal evolution and adjust the sparse topological structure to adapt to emergent spatiotemporal patterns by adaptive re-encoding. 
Extensive evaluations on representative systems demonstrate that SparseDiff achieves an average prediction error reduction of 49.99\% compared to baselines, requiring only 1\% of the spatial resolution.
Jury-and-Judge Chain-of-Thought for Uncovering Toxic Data in 3D Visual Grounding
Authors: Kaixiang Huang; Qifeng Zhang; Jin Wang; Jingru Yang; Yang Zhou; Huan Yu; Guodong Lu; Shengfeng He
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: 3D Visual Grounding (3DVG) faces persistent challenges due to coarse scene-level observations and logically inconsistent annotations, which introduce ambiguities that compromise data quality and hinder effective model supervision. To address these challenges, we introduce Refer-Judge, a novel framework that harnesses the reasoning capabilities of Multimodal Large Language Models (MLLMs) to identify and mitigate toxic data. At the core of Refer-Judge is a Jury-and-Judge Chain-of-Thought paradigm, inspired by the deliberative process of the judicial system. This framework targets the root causes of annotation noise: jurors collaboratively assess 3DVG samples from diverse perspectives, providing structured, multi-faceted evaluations. Judges then consolidate these insights using a Corroborative Refinement strategy, which adaptively reorganizes information to correct ambiguities arising from biased or incomplete observations. Through this two-stage deliberation, Refer-Judge significantly enhances the reliability of data judgments. Extensive experiments demonstrate that our framework not only achieves human-level discrimination at the scene level but also improves the performance of baseline algorithms via data purification. Code is available at https://github.com/Hermione-HKX/Refer_Judge.