GenAI4ELab/papercli-papers huggingface dataset:
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
Authors: Vighnesh Subramaniam; David Mayo; Colin Conwell; Tomaso Poggio; Boris Katz; Brian Cheung; Andrei Barbu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture.  We call a network untrainable when it overfits, underfits, or converges to poor results even when tuning their hyperparameters. For example, fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although the nature of that bias is unknown. We introduce guidance, where a guide network steers a target network using a neural distance function. The target minimizes its task loss plus a layerwise representational similarity against the frozen guide. If the guide is trained, this transfers over the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. We show that guidance prevents FCN overfitting on ImageNet, narrows the vanilla RNN–Transformer gap, boosts plain CNNs toward ResNet accuracy, and aids Transformers on RNN-favored tasks. We further identify that guidance-driven initialization alone can mitigate FCN overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, could automate architecture design.
CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning
Authors: Seewon Choi; Alaia Solko-Breslin; Rajeev Alur; Eric Wong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Many computational tasks benefit from being formulated as the composition of neural networks followed by a discrete symbolic program. The goal of neurosymbolic learning is to train the neural networks using end-to-end input-output labels of the composite. We introduce CTSketch, a novel, scalable neurosymbolic learning algorithm. CTSketch uses two techniques to improve the scalability of neurosymbolic inference: decompose the symbolic program into sub-programs and summarize each sub-program with a sketched tensor. This strategy allows us to approximate the output distribution of the program with simple tensor operations over the input distributions and the sketches. We provide theoretical insight into the maximum approximation error. Furthermore, we evaluate CTSketch on benchmarks from the neurosymbolic learning literature, including some designed for evaluating scalability. Our results show that CTSketch pushes neurosymbolic learning to new scales that were previously unattainable, with neural predictors obtaining high accuracy on tasks with one thousand inputs, despite supervision only on the final output.
Dynamics of Spontaneous Topic Changes in Next Token Prediction with Self-Attention
Authors: Mumin Jia; Jairo Diaz-Rodriguez
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Human cognition is punctuated by abrupt, spontaneous shifts between topics—driven by emotional, contextual, or associative cues—a phenomenon known as spontaneous thought in neuroscience. In contrast, self-attention-based models rely on structured patterns over their inputs to predict each next token, lacking spontaneity. Motivated by this distinction, we characterize *spontaneous topic changes* in self-attention architectures and reveal divergences from *spontaneous human thought*. First, we establish theoretical results under a simplified, single-layer self-attention model with suitable conditions by defining a topic as a set of Token Priority Graphs (TPGs). Specifically, we demonstrate that (1) the model maintains the priority order of tokens related to the input topic, (2) a spontaneous topic change can occur only if lower-priority tokens outnumber all higher-priority tokens of the input topic, and (3) unlike human cognition, the longer context length or the more ambiguous input topic does not increase the likelihood of spontaneous change. Second, we empirically validate that the effect of input length or topic ambiguity persists in modern, state-of-the-art LLMs, underscoring a fundamental disparity between human cognition and AI behavior in the context of spontaneous topic changes. To the best of our knowledge, no prior work has explored these questions with a focus so closely aligned to human thought.
PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
Authors: Guilin Li; Yun Zhang; Xiuyuan Chen; Chengqi Li; Bo Wang; Linghe Kong; Wenjia Wang; Weiran Huang; Matthias Hwai Yong Tan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language models (LLMs) have shown that generative pretraining can distill vast world knowledge into compact token representations. While LLMs encapsulate extensive world knowledge, they remain limited in modeling the behavioral knowledge contained within user interaction histories. User behavior forms a distinct modality, where each action—defined by multi-dimensional attributes such as time, context, and transaction type—constitutes a behavioral token. Modeling these high-cardinality, sparse, and irregular sequences is challenging, and discriminative models often falter under limited supervision. To bridge this gap, we extend generative pretraining to user behavior, learning transferable representations from unlabeled behavioral data analogous to how LLMs learn from text. We present PANTHER, a hybrid generative–discriminative framework that unifies user behavior pretraining and downstream adaptation, enabling large-scale sequential user representation learning and real-time inference. PANTHER introduces: (1) Structured Tokenization to compress multi-dimensional transaction attributes into an interpretable vocabulary; (2) Sequence Pattern Recognition Module (SPRM) for modeling periodic transaction motifs; (3) a Unified User-Profile Embedding that fuses static demographics with dynamic transaction histories, enabling both personalized predictions and population-level knowledge transfer; and (4) Real-time scalability enabled by offline caching of pre-trained embeddings for millisecond-level inference.Fully deployed and operational online at WeChat Pay, PANTHER delivers a 25.6\% boost in next-transaction prediction HitRate@1 and a 38.6\% relative improvement in fraud detection recall over baselines. Cross-domain evaluations on public benchmarks (CCT, MBD, MovieLens-1M, Yelp) show strong generalization, achieving up to 21\% HitRate@1 gains over transformer baselines, establishing PANTHER as a scalable, high-performance framework for industrial user sequential behavior modeling.
AC-LoRA: (Almost) Training-Free Access Control Aware Multi-Modal LLMs
Authors: Lara Magdalena Lazier; Aritra Dhar; Vasilije Stambolic; Lukas Cavigelli
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. 
However, as current LLMs are vulnerable to leaking sensitive information, it has proven difficult to apply them in settings where strict access control is necessary.
To this end, we design AC-LoRA, an end-to-end system for access control-aware corporate LLM chatbots that maintains a strong information isolation guarantee.
AC-LoRA maintains separate LoRA adapters for permissioned datasets, along with the document embedding they are finetuned on.
AC-LoRA retrieves a precise set of LoRA adapters based on the similarity score with the user query and their permission.
This similarity score is later used to merge the responses if more than one LoRA is retrieved, without requiring any additional training for LoRA routing.
We provide an end-to-end prototype of AC-LoRA, evaluate it on two datasets, and show that AC-LoRA matches
or even exceeds the performance of state-of-the-art LoRA mixing techniques while providing strong isolation guarantees.
Furthermore, we show that AC-LoRA design can be directly applied to different modalities.
EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes
Authors: Xiaoshan Wu; Yifei Yu; Xiaoyang Lyu; Yi-Hua Huang; Bo Wang; Baoheng Zhang; Zhongrui Wang; XIAOJUAN QI
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Robust 3D geometry estimation from videos is critical for applications such as autonomous navigation, SLAM, and 3D scene reconstruction. Recent methods like DUSt3R demonstrate that regressing dense pointmaps from image pairs enables accurate and efficient pose-free reconstruction. However, existing RGB-only approaches struggle under real-world conditions involving dynamic objects and extreme illumination, due to the inherent limitations of conventional cameras. In this paper, we propose \textbf{EAG3R}, a novel geometry estimation framework that augments pointmap-based reconstruction with asynchronous event streams. Built upon the MonST3R backbone, EAG3R introduces two key innovations: (1) a retinex-inspired image enhancement module and a lightweight event adapter with SNR-aware fusion mechanism that adaptively combines RGB and event features based on local reliability; and (2) a novel event-based photometric consistency loss that reinforces spatiotemporal coherence during global optimization. Our method enables robust geometry estimation in challenging dynamic low-light scenes without requiring retraining on night-time data. Extensive experiments demonstrate that EAG3R significantly outperforms state-of-the-art RGB-only baselines across monocular depth estimation, camera pose tracking, and dynamic reconstruction tasks.
How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets
Authors: Marie-Charlotte Brandenburg; Katharina Jochemko
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We consider binary classification restricted to a class of continuous piecewise linear functions whose decision boundaries are (possibly nonconvex) starshaped polyhedral sets, supported on a fixed polyhedral simplicial fan. We investigate the expressivity of these function classes and describe the combinatorial and geometric structure of the loss landscape, most prominently the sublevel sets, for two loss-functions: the 0/1-loss (discrete loss) and a log-likelihood loss function. In particular, we give explicit bounds on the VC dimension of this model, and concretely describe the sublevel sets of the discrete loss as chambers in a hyperplane arrangement. For the log-likelihood loss, we give sufficient conditions for the optimum to be unique, and describe the geometry of the optimum when varying the rate parameter of the underlying exponential probability distribution.
VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting
Authors: Hoonhee Cho; Jae-Young Kang; Giwon Lee; Hyemin Yang; Heejun Park; Seokwoo Jung; Kuk-Jin Yoon
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remains an open problem. In this work, we propose VR-Drive, a novel E2E-AD framework that addresses viewpoint generalization by jointly learning 3D scene reconstruction as an auxiliary task to enable planning-aware view synthesis. Unlike prior scene-specific synthesis approaches, VR-Drive adopts a feed-forward inference strategy that supports online training-time augmentation from sparse views without additional annotations. To further improve viewpoint consistency, we introduce a viewpoint-mixed memory bank that facilitates temporal interaction across multiple viewpoints and a viewpoint-consistent distillation strategy that transfers knowledge from original to synthesized views. Trained in a fully end-to-end manner, VR-Drive effectively mitigates synthesis-induced noise and improves planning under viewpoint shifts. In addition, we release a new benchmark dataset to evaluate E2E-AD performance under novel camera viewpoints, enabling comprehensive analysis. Our results demonstrate that VR-Drive is a scalable and robust solution for the real-world deployment of end-to-end autonomous driving systems.
Learning to Clean: Reinforcement Learning for Noisy Label Correction
Authors: Marzi Heidari; Hanping Zhang; Yuhong Guo
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This paper introduces a novel framework that conceptualizes noisy label correction as a reinforcement learning (RL) problem. The proposed approach, Reinforcement Learning for Noisy Label Correction (RLNLC), defines a comprehensive state space representing data and their associated labels, an action space that indicates possible label corrections, and a reward mechanism that evaluates the efficacy of label corrections. RLNLC learns a deep feature representation based policy network to perform label correction through reinforcement learning, utilizing an actor-critic method. The learned policy is subsequently deployed to iteratively correct noisy training labels and facilitate the training of the prediction model. The effectiveness of RLNLC is demonstrated through extensive experiments on multiple benchmark datasets, where it consistently outperforms existing state-of-the-art techniques for learning with noisy labels.
LaViDa: A Large Diffusion Language Model for Multimodal Understanding
Authors: Shufan Li; Konstantinos Kallidromitis; Hritik Bansal; Akash Gokul; Yusuke Kato; Kazuki Kozuka; Jason Kuen; Zhe Lin; Kai-Wei Chang; Aditya Grover
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outputs to adhere to a desired format). However, existing autoregressive (AR) VLMs like LLaVA struggle in these aspects. Discrete diffusion models (DMs) offer a promising alternative, enabling parallel decoding for faster inference and bidirectional context for controllable generation through text-infilling. While effective in language-only settings, DMs' potential for multimodal tasks is underexplored. We introduce LaViDa, a family of VLMs built on DMs. We build LaViDa by equipping DMs with a vision encoder and jointly fine-tune the combined parts for multimodal instruction following.  To address challenges encountered, LaViDa incorporates novel techniques such as complementary masking for effective training, prefix KV cache for efficient inference, and timestep shifting for high-quality sampling. Experiments show that LaViDa  achieves competitive or superior performance to AR VLMs on multi-modal benchmarks such as MMMU, while offering unique advantages of DMs, including flexible speed-quality tradeoff, controllability, and bidirectional reasoning. On COCO captioning, LaViDa surpasses Open-LLaVa-Next-8B by +4.1 CIDEr with 1.92x speedup. On bidirectional tasks, it achieves +59% improvement on Constrained Poem Completion. These results demonstrate LaViDa as a strong alternative to AR VLMs. Code and models is available at https://github.com/jacklishufan/LaViDa
Efficient Verified Unlearning For Distillation
Authors: Yijun Quan; Zushu Li; Giovanni Montana
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Growing data privacy demands, driven by regulations like GDPR and CCPA, require machine unlearning methods capable of swiftly removing the influence of specific training points. Although verified approaches like SISA, using data slicing and checkpointing, achieve efficient unlearning for single models by reverting to intermediate states, these methods struggle in teacher-student knowledge distillation settings. Unlearning in the teacher typically forces costly, complete student retraining due to pervasive information propagation during distillation. Our primary contribution is PURGE (Partitioned Unlearning with Retraining Guarantee for Ensembles), a novel framework integrating verified unlearning with distillation. We introduce constituent mapping and an incremental multi-teacher strategy that partitions the distillation process, confines each teacher constituent’s impact to distinct student data subsets, and crucially maintains data isolation. The PURGE framework substantially reduces retraining overhead—requiring only partial student updates—when teacher-side unlearning occurs. We provide both theoretical analysis, quantifying significant speed-ups in the unlearning process, and empirical validation on multiple datasets, demonstrating that PURGE achieves these efficiency gains while maintaining student accuracy comparable to standard baselines.
Unified all-atom molecule generation with neural fields
Authors: Matthieu Kirchmeyer; Pedro O. Pinheiro; Emma Willett; Karolis Martinkus; Joseph Kleinhenz; Emily K. Makowski; Andrew Martin Watkins; Vladimir Gligorijevic; Richard Bonneau; Saeed Saremi
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation.
MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning
Authors: Hongjia Liu; Rongzhen Zhao; Haohan Chen; Joni Pajarinen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Learning object-level, structured representations is widely regarded as a key to better generalization in vision and underpins the design of next-generation Pre-trained Vision Models (PVMs). Mainstream Object-Centric Learning (OCL) methods adopt Slot Attention or its variants to iteratively aggregate objects' super-pixels into a fixed set of query feature vectors, termed slots. However, their reliance on a static slot count leads to an object being represented as multiple parts when the number of objects varies. We introduce MetaSlot, a plug-and-play Slot Attention variant that adapts to variable object counts. MetaSlot (i) maintains a codebook that holds prototypes of objects in a dataset by vector-quantizing the resulting slot representations; (ii) removes duplicate slots from the traditionally aggregated slots by quantizing them with the codebook; and (iii) injects progressively weaker noise into the Slot Attention iterations to accelerate and stabilize the aggregation. MetaSlot is a general Slot Attention variant that can be seamlessly integrated into existing OCL architectures. Across multiple public datasets and tasks--including object discovery and recognition--models equipped with MetaSlot achieve significant performance gains and markedly interpretable slot representations, compared with existing Slot Attention variants.
The code is available at https://github.com/lhj-lhj/MetaSlot.
Theory-Driven Label-Specific Representation for Incomplete Multi-View Multi-Label Learning
Authors: Quanjiang Li; Tianxiang Xu; Tingjin Luo; Yan Zhong; Yang Li; Yiyun Zhou; Chenping Hou
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Multi-view multi-label learning typically suffers from dual data incompleteness
 due to limitations in feature storage and annotation costs. The interplay of hetero
geneous features, numerous labels, and missing information significantly degrades
 model performance. To tackle the complex yet highly practical challenges, we
 propose a Theory-Driven Label-Specific Representation (TDLSR) framework.
 Through constructing the view-specific sample topology and prototype association
 graph, we develop the proximity-aware imputation mechanism, while deriving class
 representatives that capture the label correlation semantics. To obtain semantically
 distinct view representations, we introduce principles of information shift, inter
action and orthogonality, which promotes the disentanglement of representation
 information, and mitigates message distortion and redundancy. Besides, label
semantic-guided feature learning is employed to identify the discriminative shared
 and specific representations and refine the label preference across views. Moreover,
 we theoretically investigate the characteristics of representation learning and the
 generalization performance. Finally, extensive experiments on public datasets and
 real-world applications validate the effectiveness of TDLSR.
MobileUse: A Hierarchical Reflection-Driven GUI Agent for Autonomous Mobile Operation
Authors: Ning Li; Xiangmou Qu; Jiamu Zhou; Jun Wang; Muning Wen; Kounianhua Du; Xingyu Lou; Qiuying Peng; Jun Wang; Weinan Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled the development of mobile agents that can understand visual inputs and follow user instructions, unlocking new possibilities for automating complex tasks on mobile devices. However, applying these models to real-world mobile scenarios remains a significant challenge due to the long-horizon task execution, difficulty in error recovery, and the cold-start problem in unfamiliar environments. To address these challenges, we propose MobileUse, a GUI agent designed for robust and adaptive mobile task execution. To improve resilience in long-horizon tasks and dynamic environments, we introduce a hierarchical reflection architecture that enables the agent to self-monitor, detect, and recover from errors across multiple temporal scales—ranging from individual actions to overall task completion—while maintaining efficiency through a Reflection-on-Demand strategy. To tackle cold-start issues, we further introduce a proactive exploration module, which enriches the agent’s understanding of the environment through self-planned exploration. Evaluations on the AndroidWorld and AndroidLab benchmarks demonstrate that MobileUse establishes new state-of-the-art performance, achieving success rates of 62.9% and 44.2%, respectively. To facilitate real-world applications, we release an out-of-the-box toolkit for automated task execution on physical mobile devices, which is available at https://github.com/MadeAgents/mobile-use.