GenAI4ELab/papercli-papers huggingface dataset:
AdvEDM: Fine-grained Adversarial Attack against VLM-based Embodied Agents
Authors: Yichen Wang; Hangtao Zhang; Hewen Pan; Ziqi Zhou; Xianlong Wang; Peijin Guo; Lulu Xue; Shengshan Hu; Minghui Li; Leo Yu Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous driving and robotic manipulation. Recent research has increasingly explored adversarial attacks on VLMs to reveal their vulnerabilities. However, these attacks either rely on overly strong assumptions, requiring full knowledge of the victim VLM, which is impractical for attacking VLM-based agents, or exhibit limited effectiveness. The latter stems from disrupting most semantic information in the image, which leads to a misalignment between the perception and the task context defined by system prompts. This inconsistency interrupts the VLM's reasoning process, resulting in invalid outputs that fail to affect interactions in the physical world. To this end, we propose a fine-grained adversarial attack framework, AdvEDM, which modifies the VLM's perception of only a few key objects while preserving the semantics of the remaining regions. This attack effectively reduces conflicts with the task context, making VLMs output valid but incorrect decisions and affecting the actions of agents, thus posing a more substantial safety threat in the physical world. We design two variants of based on this framework, AdvEDM-R and AdvEDM-A, which respectively remove the semantics of a specific object from the image and add the semantics of a new object into the image. The experimental results in both general scenarios and EDM tasks demonstrate  fine-grained control and excellent attack performance.
Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
Authors: Hong Wang; Haiyang Xin; Jie Wang; Xuanze Yang; Fei Zha; huanshuo dong; Yan Jiang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Pre-training has proven effective in addressing data scarcity and performance limitations in solving PDE problems with neural operators.
However, challenges remain due to the heterogeneity of PDE datasets in equation types, which leads to high errors in mixed training. 
Additionally, dense pre-training models that scale parameters by increasing network width or depth incur significant inference costs.
To tackle these challenges, we propose a novel **M**ixture-**o**f-**E**xperts **P**re-training **O**perator **T**ransformer (**MoE-POT**), a sparse-activated architecture that scales parameters efficiently while controlling inference costs.
Specifically, our model adopts a layer-wise router-gating network to dynamically select 4 routed experts from 16 expert networks during inference, enabling the model to focus on equation-specific features. 
Meanwhile, we also integrate 2 shared experts, aiming to capture common properties of PDE and reduce redundancy among routed experts. 
The final output is computed as the weighted average of the results from all activated experts.
We pre-train models with parameters from 30M to 0.5B on 6 public PDE datasets.
Our model with 90M activated parameters achieves up to a 40\% reduction in zero-shot error compared with existing models with 120M activated parameters.
Additionally, we conduct interpretability analysis, showing that dataset types can be inferred from router-gating network decisions, which validates the rationality and effectiveness of the MoE architecture.
True Impact of Cascade Length in Contextual Cascading Bandits
Authors: Hyunjun Choi; Joongkyu Lee; Min-hwan Oh
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We revisit the contextual cascading bandit, where a learning agent recommends an ordered list (\emph{cascade}) of items, and a user scans the list sequentially, stopping at the first attractive item. Although cascading bandits underpin various applications including recommender systems and search engines, the role of the cascade length $K$ in shaping regret has remained unclear. Contrary to prior results that regret grows with $K$, we prove that regret actually \emph{decreases} once $K$ is large enough.
Leveraging this insight, we design a new upper-confidence-bound algorithm built on online mirror descent that attains the sharpest known regret upper bound, $\tilde{\mathcal{O}}\bigl(\min \lbrace K\bar{p}^{K-1}, 1 \rbrace d \sqrt{T}\bigr)$ for contextual cascading bandits. To complement this new regret upper bound, we provide a nearly matching lower bound of $\Omega \bigl(\min \lbrace K\underline{p}^{K-1}, 1 \rbrace d \sqrt{T}\bigr)$, where $0 \leq \underline{p} \leq \bar{p} < 1$. Together, these results fully characterize how regret truly scales with $K$, thereby closing the theoretical gap for contextual cascading bandits.
Finally, comprehensive experiments validate our theoretical results and show the effectiveness of our proposed method.
FairNet: Dynamic Fairness Correction without Performance Loss via Contrastive Conditional LoRA
Authors: Songqi Zhou; Zeyuan Liu; Benben Jiang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Ensuring fairness in machine learning models is a critical challenge. Existing debiasing methods often compromise performance, rely on static correction strategies, and struggle with data sparsity, particularly within minority groups. Furthermore, their utilization of sensitive attributes is often suboptimal, either depending excessively on complete attribute labeling or disregarding these attributes entirely. To overcome these limitations, we propose FairNet, a novel framework for dynamic, instance-level fairness correction. FairNet integrates a bias detector with conditional low-rank adaptation (LoRA), which enables selective activation of the fairness correction mechanism exclusively for instances identified as biased, and thereby preserve performance on unbiased instances. A key contribution is a new contrastive loss function for training the LoRA module, specifically designed to minimize intra-class representation disparities across different sensitive groups and effectively address underfitting in minority groups. The FairNet framework can flexibly handle scenarios with complete, partial, or entirely absent sensitive attribute labels. Theoretical analysis confirms that, under moderate TPR/FPR for the bias detector, FairNet can enhance the performance of the worst group without diminishing overall model performance, and potentially yield slight performance improvements. Comprehensive empirical evaluations across diverse vision and language benchmarks validate the effectiveness of FairNet. Code is available at \url{https://github.com/SongqiZhou/FairNet}.
Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees
Authors: Sourav Ganguly; Kishan Panaganti; Arnob Ghosh; Adam Wierman
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Constrained decision-making is essential for designing safe policies in real-world control systems, yet simulated environments often fail to capture real-world adversities. We consider the problem of learning a policy that will maximize the cumulative reward while satisfying a constraint, even when there is a mismatch between the real model and an accessible simulator/nominal model. In particular, we consider the robust constrained Markov decision problem (RCMDP) where an agent needs to maximize the reward and satisfy the constraint against the worst possible stochastic model under the uncertainty set centered around an unknown nominal model.  Primal-dual methods, effective for standard constrained MDP (CMDP), are not applicable here because of the lack of the strong duality property. Further, one cannot apply the standard robust value-iteration based approach on the composite value function, either, as the worst-case models may be different for the reward value function and the constraint value function. We propose a novel technique that effectively minimizes the constraint value function--to satisfy the constraints; on the other hand, when all the constraints are satisfied, it can simply maximize the robust reward value function. We prove that such an algorithm finds a policy with at most $\epsilon$ sub-optimality and a feasible policy after $O(\epsilon^{-2})$ iterations. In contrast to the state-of-the-art method, we do not need to employ a binary search; thus, we reduce the computation time and achieve a better performance, especially for continuous state-space.
LoRATv2: Enabling Low-Cost Temporal Modeling in One-Stream Trackers
Authors: Liting Lin; Heng Fan; Zhipeng Zhang; Yuqing Huang; Yaowei Wang; Yong Xu; Haibin Ling
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Transformer-based algorithms, such as LoRAT, have significantly enhanced object-tracking performance. However, these approaches rely on a standard attention mechanism, which incurs quadratic token complexity, making real-time inference computationally expensive. In this paper, we introduce LoRATv2, a novel tracking framework that addresses these limitations with three main contributions. 
First, LoRATv2 integrates frame-wise causal attention, which ensures full self-attention within each frame while enabling causal dependencies across frames, significantly reducing computational overhead. Moreover, key-value (KV) caching is employed to efficiently reuse past embeddings for further speedup.
Second, building on LoRAT's parameter-efficient fine-tuning, we propose Stream-Specific LoRA Adapters (SSLA). As frame-wise causal attention introduces asymmetry in how streams access temporal information, SSLA assigns dedicated LoRA modules to the template and each search stream, with the main ViT backbone remaining frozen. This allows specialized adaptation for each stream's role in temporal tracking.
Third, we introduce a two-phase progressive training strategy, which first trains a single-search-frame tracker and then gradually extends it to multi-search-frame inputs by introducing additional LoRA modules. This curriculum-based learning paradigm improves long-term tracking while maintaining training efficiency.
In extensive experiments on multiple benchmarks, LoRATv2 achieves state-of-the-art performance, substantially improved efficiency, and a superior performance-to-FLOPs ratio over state-of-the-art trackers.
The code is available at https://github.com/LitingLin/LoRATv2.
LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS
Authors: Wanhua Li; Yujie Zhao; Minghan Qin; Yang Liu; Yuanhao Cai; Chuang Gan; Hanspeter Pfister
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this paper, we introduce LangSplatV2, which achieves high-dimensional feature
splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6 FPS for
high-resolution images, providing a 42 × speedup and a 47 × boost over LangSplat
respectively, along with improved query accuracy. LangSplat employs Gaussian
Splatting to embed 2D CLIP language features into 3D, significantly enhancing
speed and learning a precise 3D language field with SAM semantics. Such advancements in 3D language fields are crucial for applications that require language
interaction within complex scenes. However, LangSplat does not yet achieve real-
time performance (8.2 FPS), even with advanced A100 GPUs, severely limiting
its broader application. In this paper, we first conduct a detailed time analysis of
LangSplat, identifying the heavyweight decoder as the primary speed bottleneck.
Our solution, LangSplatV2 assumes that each Gaussian acts as a sparse code within
a global dictionary, leading to the learning of a 3D sparse coefficient field that
entirely eliminates the need for a heavyweight decoder. By leveraging this sparsity,
we further propose an efficient sparse coefficient splatting method with CUDA optimization, rendering high-dimensional feature maps at high quality while incurring
only the time cost of splatting an ultra-low-dimensional feature. Our experimental
results demonstrate that LangSplatV2 not only achieves better or competitive query
accuracy but is also significantly faster. Codes and demos are available at our project page: https://langsplat-v2.github.io.
70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
Authors: Tianyi Zhang; Mohsen Hariri; Shaochen Zhong; Vipin Chaudhary; Yang Sui; Xia Hu; Anshumali Shrivastava
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large-scale AI models, such as Large Language Models (LLMs) and Diffusion Models (DMs), have grown rapidly in size, creating significant challenges for efficient deployment on resource-constrained hardware. In this paper, we introduce Dynamic-Length Float (DFloat11), a lossless compression framework that reduces LLM and DM size by 30\% while preserving outputs that are bit-for-bit identical to the original model. DFloat11 is motivated by the low entropy in the BFloat16 weight representation of LLMs, which reveals significant inefficiency in the existing storage format. By applying entropy coding, DFloat11 assigns dynamic-length encodings to weights based on frequency, achieving near information-optimal compression without any loss of precision. To facilitate efficient inference with dynamic-length encodings, we develop a custom GPU kernel for fast online decompression. Our design incorporates the following: (i) compact, hierarchical lookup tables (LUTs) that fit within GPU SRAM for efficient decoding, (ii) a two-phase GPU kernel for coordinating thread read/write positions using lightweight auxiliary variables, and (iii) transformer-block-level decompression to minimize latency. Experiments on Llama 3.3, Qwen 3, Mistral 3, FLUX.1, and others validate our hypothesis that DFloat11 achieves around 30\% model size reduction while preserving bit-for-bit identical outputs. Compared to a potential alternative of offloading parts of an uncompressed model to the CPU to meet memory constraints, DFloat11 achieves 2.3--46.2$\times$ higher throughput in token generation. With a fixed GPU memory budget, DFloat11 enables 5.7--14.9$\times$ longer generation lengths than uncompressed models. Notably, our method enables lossless inference of Llama 3.1 405B, an 810GB model, on a single node equipped with 8$\times$80GB GPUs.
Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining
Authors: Weiyi Wang; Junwei Deng; Yuzheng Hu; Shiyuan Zhang; Xirui Jiang; Runting Zhang; Han Zhao; Jiaqi W. Ma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Data attribution methods, which quantify the influence of individual training data points on a machine learning model, have gained increasing popularity in data-centric applications in modern AI. Despite a recent surge of new methods developed in this space, the impact of hyperparameter tuning in these methods remains under-explored. In this work, we present the first large-scale empirical study to understand the hyperparameter sensitivity of common data attribution methods. Our results show that most methods are indeed sensitive to certain key hyperparameters. However, unlike typical machine learning algorithms---whose hyperparameters can be tuned using computationally-cheap validation metrics---evaluating data attribution performance often requires retraining models on subsets of training data, making such metrics prohibitively costly for hyperparameter tuning. This poses a critical open challenge for the practical application of data attribution methods. To address this challenge, we advocate for better theoretical understandings of hyperparameter behavior to inform efficient tuning strategies. As a case study, we provide a theoretical analysis of the regularization term that is critical in many variants of influence function methods. Building on this analysis, we propose a lightweight procedure for selecting the regularization value without model retraining, and validate its effectiveness across a range of standard data attribution benchmarks. Overall, our study identifies a fundamental yet overlooked challenge in the practical application of data attribution, and highlights the importance of careful discussion on hyperparameter selection in future method development.
BAM-ICL: Causal Hijacking In-Context Learning with Budgeted Adversarial Manipulation
Authors: Rui Chu; Bingyin Zhao; Hanling Jiang; Shuchin Aeron; Yingjie Lao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent research shows that large language models (LLMs) are vulnerable to hijacking attacks under the scenario of in-context learning (ICL) where LLMs demonstrate impressive capabilities in performing tasks by conditioning on a sequence of in-context examples (ICEs) (i.e., prompts with task-specific input-output pairs). Adversaries can manipulate the provided ICEs to steer the model toward attacker-specified outputs, effectively ''hijacking'' the model's decision-making process. Unlike traditional adversarial attacks targeting single inputs, hijacking attacks in LLMs aim to subtly manipulate the initial few examples to influence the model's behavior across a range of subsequent inputs, which requires distributed and stealthy perturbations. However, existing approaches overlook how to effectively allocate the perturbation budget across ICEs. We argue that fixed budgets miss the potential of dynamic reallocation to improve attack success while maintaining high stealthiness and text quality. In this paper, we propose BAM-ICL, a novel **b**udgeted **a**dversarial **m**anipulation hijacking attack framework for in-context learning. We also consider a more practical yet stringent scenario where ICEs arrive sequentially and only the current ICE can be perturbed. BAM-ICL mainly consists of two stages: In the offline stage, where we assume the adversary has access to data drawn from the same distribution as the target task, we develop a global gradient-based attack to learn optimal budget allocations across ICEs. In the online stage, where ICEs arrive sequentially, perturbations are generated progressively according to the learned budget profile. We evaluate BAM-ICL on diverse LLMs and datasets. The experimental results demonstrate that it achieves superior attack success rates and stealthiness, and the adversarial ICEs are highly transferable to other models.
Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from  Multi-Animal Datasets
Authors: Ji Xia; Yizi Zhang; Shuqi Wang; Genevera I. Allen; Liam Paninski; Cole Lincoln Hurwitz; Kenneth D. Miller
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Characterizing interactions between brain areas is a fundamental goal of systems neuroscience. While such analyses are possible when areas are recorded simultaneously, it is rare to observe all combinations of areas of interest within a single animal or recording session. How can we leverage multi-animal datasets to better understand multi-area interactions? Building on recent progress in large-scale, multi-animal models, we introduce NeuroPaint, a masked autoencoding approach for inferring the dynamics of unobserved brain areas. By training across animals with overlapping subsets of recorded areas, NeuroPaint learns to reconstruct activity in missing areas based on shared structure across individuals. We train and evaluate our approach on both synthetic data and two multi-animal, multi-area Neuropixels datasets. Our results demonstrate that models trained across animals with partial observations can successfully in-paint the dynamics of unrecorded areas, enabling multi-area analyses that transcend the limitations of any single experiment.
Breaking AR’s Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
Authors: Gen Li; Changxiao Cai
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models have emerged as a powerful paradigm for modern generative modeling, demonstrating strong potential for large language models (LLMs). Unlike conventional autoregressive (AR) models that generate tokens sequentially, diffusion models allow for parallel sampling, offering a promising path to accelerate generation and eliminate the left-to-right generation constraints. Despite their empirical success, theoretical understandings of diffusion language models remain underdeveloped. In this work, we develop convergence guarantees for diffusion language models from an information-theoretic perspective. Our analysis demonstrates that the sampling error, measured by the Kullback-Leibler (KL) divergence, decays inversely with the number of iterations $T$ and scales linearly with the mutual information between tokens in the target text sequence. Crucially, our theory covers the regime $T<L$, where $L$ is the text sequence length. This justifies that high-quality samples can be generated with fewer iterations than $L$, thereby breaking the fundamental sampling bottleneck of $L$ steps required by AR models. We further establish matching upper and lower bounds, up to some constant factor, that shows the tightness of our convergence analysis. These results offer novel theoretical insights into the practical effectiveness of diffusion language models.
On the necessity of adaptive regularisation: Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls
Authors: Emmeran Johnson; David Martínez-Rubio; Ciara Pike-Burke; Patrick Rebeschini
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We study online convex optimization on $\ell_p$-balls in $\mathbb{R}^d$ for $p > 2$. While always sub-linear, the optimal regret exhibits a shift between the high-dimensional setting ($d > T$), when the dimension $d$ is greater than the time horizon $T$ and the low-dimensional setting ($d \leq T$). We show that Follow-the-Regularised-Leader (FTRL) with time-varying regularisation which is adaptive to the dimension regime is anytime optimal for all dimension regimes. Motivated by this, we ask whether it is possible to obtain anytime optimality of FTRL with fixed non-adaptive regularisation. Our main result establishes that for separable regularisers, adaptivity in the regulariser is necessary, and that any fixed regulariser will be sub-optimal in one of the two dimension regimes. Finally, we provide lower bounds which rule out sub-linear regret bounds for the linear bandit problem in sufficiently high-dimension for all $\ell_p$-balls with $p \geq 1$.
KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills
Authors: Weiji Xie; Jinrui Han; Jiakun Zheng; Huanyu Li; Xinzhe Liu; Jiyuan Shi; Weinan Zhang; Chenjia Bai; Xuelong Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, even with delicate reward and curriculum design. This paper presents a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing through multi-steps motion processing and adaptive motion tracking. For motion processing, we design a pipeline to extract, filter out, correct, and retarget motions, while ensuring compliance with physical constraints to the maximum extent. For motion imitation, we formulate a bi-level optimization problem to dynamically adjust the tracking accuracy tolerance based on the current tracking error, creating an adaptive curriculum mechanism. We further construct an asymmetric actor-critic framework for policy training. In experiments, we train whole-body control policies to imitate a set of highly dynamic motions. Our method achieves significantly lower tracking errors than existing approaches and is successfully deployed on the Unitree G1 robot, demonstrating stable and expressive behaviors. The project page is https://kungfubot.github.io.