GenAI4ELab/papercli-papers huggingface dataset:
Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Authors: Jinyuan Liu; Zihang Chen; Zhu Liu; Zhiying Jiang; Long Ma; Xin Fan; Risheng Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradations, such as noise, contrast, and blurring, making it difficult to handle coupled degradations. Meanwhile, all-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness due to the significant differences in imaging models. In sight of this, we first revisit the imaging mechanism and introduce a Recurrent Prompt Fusion Network (RPFN). Specifically, the RPFN initially establishes prompt pairs based on the thermal imaging process. For each type of degradation, we fuse the corresponding prompt pairs to modulate the model's features, providing adaptive guidance that enables the model to better address specific degradations under single or multiple conditions.In addition, a selective recurrent training mechanism is introduced to gradually refine the model's handling of composite cases to align the enhancement process, which not only allows the model to remove camera noise and retain key structural details, but also enhancing the overall contrast of the thermal image. Furthermore, we introduce the most comprehensive high-quality infrared benchmark covering a wide range of scenarios. Extensive experiments substantiate that our approach not only delivers promising visual results under specific degradation but also significantly improves performance on complex degradation scenes, achieving a notable 8.76% improvement.
Rethinking the Role of Verbatim Memorization in LLM Privacy
Authors: Tom Sander; Bargav Jayaraman; Mark Ibrahim; Kamalika Chaudhuri; Chuan Guo
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Conventional wisdom in machine learning privacy research states that memorization 
directly implies a loss of privacy. In contrast, a well-generalized model only remembers distributional patterns and preserves privacy of its training data.  In this work, we show that this relationship is much more complex for LLMs trained for chat,
and depends heavily on how knowledge is encoded and manipulated. To this end, we fine-tune language models on synthetically generated biographical information including PIIs, and try to extract them in different ways after instruction fine-tuning.  We find counter to conventional wisdom that better verbatim memorization does not necessarily increase data leakage via chat. We also find that it is easier to extract information via chat from an LLM that is better able to manipulate and process knowledge even if it is smaller, and that not all attributes are equally extractable. This suggests that the relationship between privacy, memorization and language understanding of LLMs is very intricate, and that examining memorization in isolation can lead to misleading conclusions.
Deep Continuous-Time State-Space Models for Marked Event Sequences
Authors: Yuxin Chang; Alex James Boyd; Cao Xiao; Taha Kass-Hout; Parminder Bhatia; Padhraic Smyth; Andrew Warrington
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Marked temporal point processes (MTPPs) model sequences of events occurring at irregular time intervals, with wide-ranging applications in fields such as healthcare, finance and social networks. We propose the _state-space point process_ (S2P2) model, a novel and performant model that leverages techniques derived for modern deep state-space models (SSMs) to overcome limitations of existing MTPP models, while simultaneously imbuing strong inductive biases for continuous-time event sequences that other discrete sequence models (i.e., RNNs, transformers) do not capture. Inspired by the classical linear Hawkes processes, we propose an architecture that interleaves stochastic jump differential equations with nonlinearities to create a highly expressive intensity-based MTPP model, without the need for restrictive parametric assumptions for the intensity. Our approach enables efficient training and inference with a parallel scan, bringing linear complexity and sublinear scaling while retaining expressivity to MTPPs. Empirically, S2P2 achieves state-of-the-art predictive likelihoods across eight real-world datasets, delivering an average improvement of 33% over the best existing approaches.
Blindfolded Experts Generalize Better:  Insights from Robotic Manipulation and Videogames
Authors: Ev Zisselman; Mirco Mutti; Shelly Francis-Meretzki; Elisei Shafer; Aviv Tamar
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Behavioral cloning is a simple yet effective technique for learning sequential decision-making from demonstrations. Recently, it has gained prominence as the core of foundation models for the physical world, where achieving generalization requires countless demonstrations of a multitude of tasks. Typically, a human expert with full information on the task demonstrates a (nearly) optimal behavior. In this paper, we propose to hide some of the task's information from the demonstrator. This ``blindfolded'' expert is compelled to employ non-trivial *exploration* to solve the task. We show that cloning the blindfolded expert generalizes better to unseen tasks than its fully-informed counterpart. We conduct experiments of real-world robot peg insertion tasks with (limited) human demonstrations, alongside videogames from the Procgen benchmark. Additionally, we support our findings with theoretical analysis, which confirms that the generalization error scales with $\sqrt{I/m}$, where $I$ measures the amount of task information available to the demonstrator, and $m$ is the number of demonstrated tasks. Both theory and practice indicate that cloning blindfolded experts generalizes better with fewer demonstrated tasks. Project page with videos and code: 
[https://sites.google.com/view/blindfoldedexperts/home](https://sites.google.com/view/blindfoldedexperts/home)
InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
Authors: Vishnu Vinod; Krishna Pillutla; Abhradeep Guha Thakurta
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time scaling, safely incorporating private information into the generation remains a critical open question. We present InvisibleInk, a highly scalable long-form text generation framework satisfying rigorous differential privacy guarantees with respect to the sensitive reference texts. It interprets sampling from the LLM's next-token-distribution as the exponential mechanism over the LLM logits with two innovations. First, we reduce the privacy cost by isolating and clipping only the sensitive information in the model logits (relative to the public logits). Second, we improve text quality by sampling without any privacy cost from a small superset of the top-$k$ private tokens. Empirical evaluations demonstrate a consistent $8\times$ (or more) reduction in computation cost over state-of-the-art baselines to generate long-form private text of the same utility across privacy levels. InvisibleInk is able to generate, for the first time, high-quality private long-form text at less than $4\text{-}8\times$ times the computation cost of non-private generation, paving the way for its practical use. We open-source a pip-installable Python package (invink) for InvisibleInk at https://github.com/cerai-iitm/invisibleink.
Cameras as Relative Positional Encoding
Authors: Ruilong Li; Brent Yi; Junchen Liu; Hang Gao; Yi Ma; Angjoo Kanazawa
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Transformers are increasingly prevalent for multiview computer vision tasks, where geometric relationships between viewpoints are critical for 3D perception. To leverage these relationships, multiview transformers must use camera geometry to ground visual tokens in 3D space. In this work, we compare techniques for conditioning transformers on cameras: token-level raymap encodings, attention-level relative pose encodings, and a new relative encoding we propose—Projective Positional Encoding (PRoPE)—that captures complete camera frustums, both intrinsics and extrinsics, as a relative positional encoding. Our experiments begin by showing how relative conditioning methods improve performance in feedforward novel view synthesis, with further gains from PRoPE. This holds across settings: scenes with both shared and varying intrinsics, when combining token- and attention-level conditioning, and for generalization to inputs with out-of-distribution sequence lengths and camera intrinsics. We then verify that these benefits persist for different tasks, stereo depth estimation and discriminative spatial cognition, as well as larger model sizes.
Unsupervised Federated Graph Learning
Authors: Lele Fu; Tianchi Liao; Sheng Huang; Bowen Deng; zhangchuanfu; Shirui Pan; Chuan Chen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Federated graph learning (FGL) is a privacy-preserving paradigm for modeling distributed graph data, designed to train a powerful global graph neural network.
  Existing FGL methods predominantly rely on label information during training, effective FGL in an unsupervised setting remains largely unexplored territory.
  In this paper, we address two key challenges in unsupervised FGL: 1) Local models tend to converge in divergent directions due to the lack of shared semantic information across clients. Then, how to align  representation spaces among multiple clients is the first challenge.
  2) Conventional federated weighted aggregation easily results in degrading the performance of the global model, then which raises another challenge, namely how to adaptively learn the global model parameters.
  In response to the two questions, we propose a tailored framework named FedPAM, which is composed of two modules:
  Representation Space Alignment (RSA) and Adaptive Global Parameter Learning (AGPL).
  RSA leverages a set of learnable anchors to define the global representation space, then local subgraphs are aligned with them through the fused Gromov-Wasserstein optimal transport, achieving the representation space alignment across clients.
  AGPL stacks local model parameters into third-order tensors, and adaptively integrates the global model parameters in a low-rank tensor space, which facilitates to fuse the high-order knowledge among clients.
  Extensive experiments on eight graph datasets are conducted, the results demonstrate that the proposed FedPAM is superior over classical and SOTA compared methods.
HBLLM: Wavelet-Enhanced High-Fidelity 1-Bit Quantization for LLMs
Authors: Ningning CHEN; Weicai Ye; Ying Jiang
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We introduce HBLLM, a wavelet-enhanced high-fidelity $1$-bit post-training quantization method for Large Language Models (LLMs). By leveraging Haar wavelet transforms to enhance expressive capacity through frequency decomposition, HBLLM significantly improves quantization fidelity while maintaining minimal overhead. This approach features two innovative structure-aware grouping strategies: (1) frequency-aware multi-parameter intra-row grouping and (2) $\ell_2$-norm-based saliency-driven column selection. For non-salient weights, a shared mean is employed across quantization groups within each frequency band to optimize storage efficiency. Experiments conducted on the OPT and LLaMA models demonstrate that HBLLM achieves state-of-the-art performance in $1$-bit quantization, attaining a perplexity of $6.71$  on LLaMA$2$-$13$B with an average weight storage of only $1.08$ bits. Code available at: https://github.com/Yeyke/HBLLM.
Eulerian Neural Network Informed by Chemical Transport for Air Quality Forecasting
Authors: Xukai Zhang; Shuliang Wang; Guangyin Jin; Ziqiang Yuan; Hanning Yuan; Sijie Ruan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Air pollution remains one of the most critical environmental challenges globally, posing severe threats to public health, ecological sustainability, and climate governance. While existing physics-based and data-driven models have made progress in air quality forecasting, they often struggle to jointly capture the complex spatiotemporal dynamics and ensure spatial continuity of pollutant distributions. In this study, we introduce CTENet, a novel chemical transport deep learning model that embeds the Advection-Diffusion-Reaction equation into a Physics-Informed Neural Network (PINN) framework using an Eulerian representation to model the spatiotemporal evolution of pollutants. Extensive experiments on two real-world datasets demonstrate that CTENet consistently outperforms state-of-the-art (SOTA) baselines, achieving a remarkable RMSE improvement of 45.8% on the USA dataset and 21.0% on the China dataset.
Adaptive Re-calibration Learning for Balanced Multimodal Intention Recognition
Authors: Qu Yang; Xiyang Li; Fu Lin; Mang Ye
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Multimodal Intention Recognition (MIR) plays a critical role in applications such as intelligent assistants, service robots, and autonomous systems. However, in real-world settings, different modalities often vary significantly in informativeness, reliability, and noise levels. This leads to modality imbalance, where models tend to over-rely on dominant modalities, thereby limiting generalization and robustness. While existing methods attempt to alleviate this issue at either the sample or model level, most overlook its multi-level nature. To address this, we propose Adaptive Re-calibration Learning (ARL), a novel dual-path framework that models modality importance from both sample-wise and structural perspectives. ARL incorporates two key mechanisms: Contribution-Inverse Sample Calibration (CISC), which dynamically masks overly dominant modalities at the sample level to encourage attention to underutilized ones; and Weighted Encoder Calibration (WEC), which adjusts encoder weights based on global modality contributions to prevent overfitting. Experimental results on multiple MIR benchmarks demonstrate that ARL significantly outperforms existing methods in both accuracy and robustness, particularly under noisy or modality-degraded conditions.
Rethinking Tokenized Graph Transformers  for Node Classification
Authors: Jinsong Chen; Chenyang Li; Gaichao Li; John E. Hopcroft; Kun He
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which transforms the input graph into token sequences, facilitating the node representation learning via Transformer. In this paper, we observe that the generations of token sequences in existing GTs only focus on the first-order neighbors on the constructed similarity graphs, which leads to the limited usage of nodes to generate diverse token sequences, further restricting the potential of tokenized GTs for node classification. To this end, we propose a new method termed SwapGT. SwapGT first introduces a novel token swapping operation based on the characteristics of token sequences that fully leverages the semantic relevance of nodes to generate more informative token sequences. Then, SwapGT leverages a Transformer-based backbone to learn node representations from the generated token sequences. Moreover, SwapGT develops a center alignment loss to constrain the representation learning from multiple token sequences, further enhancing the model performance. Extensive empirical results on various datasets showcase the superiority of SwapGT for node classification.
Code is available at https://github.com/JHL-HUST/SwapGT.
Fast Local Search Algorithms for Clustering with Adaptive Sampling and Bandit Strategies
Authors: Junyu Huang; Zhen Zhang; Beirong Cui; Jianxin Wang; Qilong Feng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Local search is a powerful clustering technique that provides high-quality solutions with theoretical guarantees. With distance-based sampling strategies, local search methods can achieve constant approximations for clustering with linear running time in data size. Despite their effectiveness, existing algorithms still face scalability issues as they require scanning the entire dataset for iterative center swaps. This typically leads to an O(ndk) running time, where n is the data size, d is the dimension, k is the number of clusters. To further improve the efficiency of local search algorithms, we propose new methods based on adaptive sampling and bandit strategies. Specifically,  adaptive sampling can well approximate the distance-based sampling distribution without maintaining pairwise distances between data points and the centers, enabling fast and accurate sampling in sublinear time after an $\tilde{O}(nd)$ time preprocessing step. The bandit strategy models the best swap pair selection as a bandit problem, where a grouping strategy is proposed for fast identification of the optimal swap pair. With these techniques, our proposed algorithm can achieve constant approximation in expected running time $\tilde{O}(nd + k^4)$ under mild assumptions on optimal clusters and swap pair distributions. Our approach also extends naturally to the k-median objective, achieving constant approximation in expected running time $\tilde{O}(nd + \sqrt{n}k^3)$ without distributional assumptions. Empirical results demonstrate that our algorithm achieves up to 1000× speedup over existing local search methods on datasets with 100 million points, while delivering comparable clustering quality. Compared to coreset-based approaches, it provides up to around 80× speedup and consistently yields better clustering results.
Prior-Guided Diffusion Planning for Offline Reinforcement Learning
Authors: Donghyeon Ki; JunHyeok Oh; Seong-Woong Shim; Byung-Jun Lee
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models have recently gained prominence in offline reinforcement learning due to their ability to effectively learn high-performing, generalizable policies from static datasets. Diffusion-based planners facilitate long-horizon decision-making by generating high-quality trajectories through iterative denoising, guided by return-maximizing objectives. However, existing guided sampling strategies such as Classifier Guidance, Classifier-Free Guidance, and Monte Carlo Sample Selection either produce suboptimal multi-modal actions, struggle with distributional drift, or incur prohibitive inference-time costs. To address these challenges, we propose \textbf{\textit{Prior Guidance}} (PG), a novel guided sampling framework that replaces the standard Gaussian prior of a behavior-cloned diffusion model with a learnable distribution, optimized via a behavior-regularized objective. PG directly generates high-value trajectories without costly reward optimization of the diffusion model itself, and eliminates the need to sample multiple candidates at inference for sample selection. We present an efficient training strategy that applies behavior regularization in latent space, and empirically demonstrate that PG outperforms state-of-the-art diffusion policies and planners across diverse long-horizon offline RL benchmarks. Our code is available at https://github.com/ku-dmlab/PG.
Removing Concepts from Text-to-Image Models with Only Negative Samples
Authors: Hanwen Liu; Yadong MU
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: This work introduces Clipout, a method for removing a target concept in pre-trained text-to-image models. By randomly clipping units from the learned data embedding and using a contrastive objective, models are encouraged to differentiate these clipped embedding vectors. Our goal is to remove private, copyrighted, inaccurate, or harmful concepts from trained models without the need for retraining. This is achieved by considering only negative samples and generating them in a bootstrapping-like manner, requiring minimal prior knowledge. Additionally, theoretical analyses are provided to further understand our proposed Clipout. Extensive experiments on text-to-image show that Clipout is simple yet highly effective and efficient compared with previous state-of-the-art approaches.
SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
Authors: Xiao Liang; Zhong-Zhi Li; Yeyun Gong; Yang Wang; Hengyuan Zhang; yelong shen; Ying Nian Wu; Weizhu Chen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solving. A prerequisite for the scalability of RLVR is a high-quality problem set with precise and verifiable answers. However, the scarcity of well-crafted human-labeled math problems and limited-verification answers in existing distillation-oriented synthetic datasets limit their effectiveness in RL. Additionally, most problem synthesis strategies indiscriminately expand the problem set without considering the model’s capabilities, leading to low efficiency in generating useful questions. To mitigate this issue, we introduce a Self-aware Weakness-driven problem Synthesis framework (SwS) that systematically identifies model deficiencies and leverages them for problem augmentation. Specifically, we define weaknesses as questions that the model consistently fails to learn through its iterative sampling during RL training. We then extract the core concepts from these failure cases and synthesize new problems to strengthen the model's weak areas in subsequent augmented training, enabling it to focus on and gradually overcome its weaknesses. Without relying on external knowledge distillation, our framework enables robust generalization by empowering the model to self-identify and address its weaknesses in RL, yielding average performance gains of 10% and 7.7% on 7B and 32B models across eight mainstream reasoning benchmarks. Our code and data are available at https://anonymous.4open.science/r/SwS-E6F5/
Can Large Language Models Master Complex Card Games?
Authors: Wei Wang; Fuqing Bie; Junzhe Chen; Dan Zhang; Shiyu Huang; Evgeny Kharlamov; Jie Tang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Complex games have long been an important benchmark for testing the progress of artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players in Go and Chess, garnering widespread societal attention towards artificial intelligence. Concurrently, large language models (LLMs) have exhibited remarkable capabilities across various tasks, raising the question of whether LLMs can achieve similar success in complex games. In this paper, we explore the potential of LLMs in mastering complex card games. We systematically assess the learning capabilities of LLMs across eight diverse card games, evaluating the impact of fine-tuning on high-quality gameplay data, and examining the models' ability to retain general capabilities while mastering these games. Our findings indicate that: (1) LLMs can approach the performance of strong game AIs through supervised fine-tuning on high-quality data, (2) LLMs can achieve a certain level of proficiency in multiple complex card games simultaneously, with performance augmentation for games with similar rules and conflicts for dissimilar ones, and (3) LLMs experience a decline in general capabilities when mastering complex games, but this decline can be mitigated by integrating a certain amount of general instruction data. The evaluation results demonstrate strong learning ability and versatility of LLMs. The code is available at 
https://github.com/THUDM/LLM4CardGame