GenAI4ELab/papercli-papers huggingface dataset:
Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding
Authors: Xiaoyi Zhang; Zhaoyang Jia; Zongyu Guo; Jiahao Li; Bin Li; Houqiang Li; Yan Lu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity and the difficulty of question answering under such extended contexts. 
While Large Language Models (LLMs) have demonstrated considerable advancements in video analysis capabilities and long context handling, they continue to exhibit limitations when processing information-dense hour-long videos. 
To overcome such limitations, we propose the $\textbf{D}eep \ \textbf{V}ideo \ \textbf{D}iscovery \ (\textbf{DVD})$ agent to leverage an $\textit{agentic search}$ strategy over segmented video clips. Different from previous video agents manually designing a rigid workflow, our approach emphasizes the autonomous nature of agents.
By providing a set of search-centric tools on multi-granular video database,
our DVD agent leverages the advanced reasoning capability of LLM to plan on its current observation state, strategically selects tools to orchestrate adaptive workflow for different queries in light of the gathered information.
We perform comprehensive evaluation on multiple long video understanding benchmarks that demonstrates our advantage.
Our DVD agent achieves state-of-the-art performance on the challenging LVBench dataset, reaching an accuracy of $\textbf{74.2\%}$, which substantially surpasses all prior works, and further improves to $\textbf{76.0\%}$ with transcripts.
FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Authors: Geeho Kim; Jinu Lee; Bohyung Han
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Federated Generalized Category Discovery (Fed-GCD) aims to train a global model that classifies seen classes while discovering novel ones from data distributed across heterogeneous clients.
Existing GCD methods often rely on unrealistic assumptions, such as prior knowledge of the number of novel classes or balanced class distributions across clients.
We propose Federated Local Prior Alignment (FedLPA), which eliminates these assumptions by grounding learning in client-specific structures and aligning predictions with locally derived priors.
Specifically, each client constructs a similarity graph refined with high-confidence signals from seen classes, and then identifies local concepts and prototypes via Infomap clustering.
Building on these discovered structures, we introduce Local Prior Alignment (LPA), a self-distillation mechanism that aligns batch-level predictions with empirical class prior derived from concept assignments.
Through iterative local structure discovery and adaptive prior refinement, FedLPA achieves robust generalized category discovery under severe data heterogeneity.
Extensive experiments demonstrate that FedLPA significantly outperforms existing federated GCD methods across both fine-grained and standard benchmarks.
Safely Learning Controlled Stochastic Dynamics
Authors: Luc Brogat-Motte; Alessandro Rudi; Riccardo Bonalli
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We address the problem of safely learning controlled stochastic dynamics from discrete-time trajectory observations, ensuring system trajectories remain within predefined safe regions during both training and deployment. Safety-critical constraints of this kind are crucial in applications such as autonomous robotics, finance, and biomedicine. We introduce a method that ensures safe exploration and efficient estimation of system dynamics by iteratively expanding an initial known safe control set using kernel-based confidence bounds. After training, the learned model enables predictions of the system's dynamics and permits safety verification of any given control. Our approach requires only mild smoothness assumptions and access to an initial safe control set, enabling broad applicability to complex real-world systems. We provide theoretical guarantees for safety and derive adaptive learning rates that improve with increasing Sobolev regularity of the true dynamics. Experimental evaluations demonstrate the practical effectiveness of our method in terms of safety, estimation accuracy, and computational efficiency.
SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures
Authors: Julian Kranz; Davide Gallon; Steffen Dereich; Arnulf Jentzen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We study gradient flows for loss landscapes of fully connected feedforward neural networks with commonly used continuously differentiable activation functions such as the logistic, hyperbolic tangent, softplus or GELU function. We prove that the gradient flow either converges to a critical point or diverges to infinity while the loss converges to an asymptotic critical value.	Moreover, we prove the existence of a threshold $\varepsilon>0$ such that the loss value of any gradient flow initialized at most $\varepsilon$ above the optimal level converges to it. For polynomial target functions and sufficiently big architecture and data set, we prove that the optimal loss value is zero and can only be realized asymptotically. From this setting, we deduce our main result that any gradient flow with sufficiently good initialization diverges to infinity. Our proof heavily relies on the geometry of o-minimal structures. We confirm these theoretical findings with numerical experiments and extend our investigation to more realistic scenarios, where we observe an analogous behavior.
UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
Authors: Chenao Li; Shuo Yan; Enyan Dai
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, enzyme design, and a deeper understanding of biological mechanisms. However, most existing models are restricted to an individual enzyme, which neglects shared knowledge of enzymes and fails to generalize to novel enzymes. Thus, we introduce a unified protein cleavage site predictor named UniZyme, which can generalize across diverse enzymes. To enhance the enzyme encoding for the protein cleavage site prediction, UniZyme employs a novel biochemically-informed model architecture along with active-site knowledge of proteolytic enzymes.  Extensive experiments demonstrate that UniZyme achieves high accuracy in predicting cleavage sites across a range of proteolytic enzymes, including unseen enzymes. The code is available in  https://github.com/Ao-LiChen/UniZyme
Stab-SGD: Noise-Adaptivity in Smooth Optimization with Stability Ratios
Authors: David A. R. Robin; Killian Bakong; Kevin Scaman
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In the context of smooth stochastic optimization with first order methods, we introduce the stability ratio of gradient estimates, as a measure of local relative noise level, from zero for pure noise to one for negligible noise. We show that a schedule-free variant (Stab-SGD) of stochastic gradient descent obtained by just shrinking the learning rate by the stability ratio achieves real adaptivity to noise levels (i.e. without tuning hyperparameters to the gradient's variance), with all key properties of a good schedule-free algorithm: neither plateau nor explosion at intialization, and no saturation of the loss.
We believe this theoretical development reveals the importance of estimating the local stability ratio in the construction of well-behaved (last-iterate) schedule-free algorithms, particularly when hyperparameter-tuning budgets are a small fraction of the total budget since noise-adaptivity and cheaper horizon-free tuning are most crucial in this regime.
A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
Authors: Xiaoli Tang; Han Yu; Xiaoxiao Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Auction-based Federated Learning (AFL) fosters collaboration among self-interested data consumers (DCs) and data owners (DOs). A major challenge in AFL pertains to how DCs select and bid for DOs. Existing methods are generally static, making them ill-suited for dynamic AFL markets. To address this issue, we propose the R}einforcement Learning-based Bidding Strategy for DCs in Auction-based Federated Learning (RLB-AFL). We incorporate historical states into a Deep Q-Network to capture sequential information critical for bidding decisions. To mitigate state space sparsity, where specific states rarely reoccur for each DC during auctions, we incorporate the Gaussian Mixture Model into RLB-AFL. This facilitates soft clustering on sequential states, reducing the state space dimensionality and easing exploration and action-value function approximation. In addition, we enhance the $\epsilon$-greedy policy to help the RLB-AFL agent balance exploitation and exploration, enabling it to be more adaptable in the AFL decision-making process. Extensive experiments under 6 widely used benchmark datasets demonstrate that RLB-AFL achieves superior performance compared to 8 state-of-the-art approaches. It outperforms the best baseline by 10.56% and 3.15% in terms of average total utility
Generating Creative Chess Puzzles
Authors: Xidong Feng; Vivek Veeriah; Marcus Chiam; Michael D Dennis; Federico Barbero; Johan Obando-Ceron; Jiaxin Shi; Satinder Singh; Shaobo Hou; Nenad Tomasev; Tom Zahavy
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22\% (supervised) to 2.5\%, surpassing existing dataset rates (2.1\%) and the best Lichess-trained model (0.4\%). Our puzzles meet novelty and diversity benchmarks, retain aesthetic themes, and are rated by human experts as more creative, enjoyable, and counter-intuitive than composed book puzzles, even approaching classic compositions. Our final outcome is a curated booklet of these novel AI-generated puzzles, which is acknowledged for creativity by three world-renowned experts.
I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions
Authors: Shuhong Liu; Lin Gu; Ziteng Cui; Xuangeng Chu; Tatsuya Harada
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Participating in efforts to endow generative AI with the 3D physical world perception, we propose I2-NeRF, a novel neural radiance field framework that enhances isometric and isotropic metric perception under media degradation. While existing NeRF models predominantly rely on object-centric sampling, I2-NeRF introduces a reverse-stratified upsampling strategy to achieve near-uniform sampling across 3D space, thereby preserving isometry. We further present a general radiative formulation for media degradation that unifies emission, absorption, and scattering into a particle model governed by the Beer–Lambert attenuation law. By matting direct and media-induced in-scatter radiance, this formulation extends naturally to complex media environments such as underwater, haze, and even low-light scenes. By treating light propagation uniformly in both vertical and horizontal directions, I2-NeRF enables isotropic metric perception and can even estimate medium properties such as water depth. Experiments on real-world datasets demonstrate that our method significantly improves both reconstruction fidelity and physical plausibility compared to existing approaches. The source code is available at https://github.com/ShuhongLL/I2-NeRF.
PALQO: Physics-informed model for Accelerating  Large-scale Quantum Optimization
Authors: Yiming Huang; Yajie Hao; Yuxuan Du; Jing Zhou; Xiao Yuan; Xiaoting Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Variational Quantum Algorithms (VQAs) are emerging as leading strategies with the potential to unlock practical applications and deliver significant advantages in the investigation of many-body quantum systems and quantum chemistry. 
A key challenge hindering the application of VQAs to large-scale problems is rooted in the no-cloning theorem in quantum mechanics, precluding standard backpropagation and leading to prohibitive quantum resource expenditure such as measurement cost.
To address this challenge, we reformulate the training dynamics of VQAs as a non-linear partial differential equation and propose a novel protocol that leverages physics-informed neural networks (PINNs) to model this dynamical system efficiently. Given a small amount of training trajectory data collected from quantum devices, our protocol predicts the parameter updates of VQAs over multiple iterations on the classical side, dramatically reducing quantum resource costs. Through systematic numerical experiments, we demonstrate that our method achieves up to a 30x speedup compared to conventional methods and reduces quantum resource costs by as much as 90\% for tasks involving up to 40 qubits, including ground state preparation of different quantum systems, while maintaining competitive accuracy. Our approach complements existing techniques aimed at improving the efficiency of VQAs and further strengthens their potential for practical applications.
Interactive Anomaly Detection for Articulated Objects via Motion Anticipation
Authors: Ankan Kumar Bhunia; Changjian Li; Hakan Bilen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: This paper presents a novel problem, interactive anomaly detection (AD) for articulated objects, and introduces a tailored solution that detects functional anomalies by integrating vision, interaction, and anticipation. Unlike traditional AD methods that rely on passive visual observations, our approach actively manipulates objects to reveal anomalies that would otherwise remain hidden. Our method learns to generate a sequence of actions to interact exclusively with normal objects and to anticipate the resulting normal motion. During inference, the model applies predicted actions to the object and compares the observed motion with the anticipated motion to detect anomalies. Additionally, we introduce a new benchmark, PartNet-IAD, for interactive AD, which includes articulated objects with realistic functional anomalies. Experiments show strong generalization to detect anomalies in both seen and unseen object categories. Code and dataset will be released.
Distributional Adversarial Attacks and Training in Deep Hedging
Authors: Guangyi He; Tobias Sutter; Lukas Gonon
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this paper, we study the robustness of classical deep hedging strategies under distributional shifts by leveraging the concept of adversarial attacks. We first demonstrate that standard deep hedging models are highly vulnerable to small perturbations in the input distribution, resulting in significant performance degradation. Motivated by this, we propose an adversarial training framework tailored to increase the robustness of deep hedging strategies. Our approach extends pointwise adversarial attacks to the distributional setting and introduces a computationally tractable reformulation of the adversarial optimization problem over a Wasserstein ball. This enables the efficient training of hedging strategies that are resilient to distributional perturbations. Through extensive numerical experiments, we show that adversarially trained deep hedging strategies consistently outperform their classical counterparts in terms of out-of-sample performance and resilience to model misspecification. Additional results indicate that the robust strategies maintain reliable performance on real market data and remain effective during periods of market change. Our findings establish a practical and effective framework for robust deep hedging under realistic market uncertainties.
Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads
Authors: Zhoutong Wu; Yuan Zhang; Yiming Dong; Chenheng Zhang; Cong Fang; Kun Yuan; Zhouchen Lin
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Transformer models have driven breakthroughs across various language tasks by their strong capability to learn rich contextual representations. Scaling them to improve representation, however, often demands substantial memory and compute costs, such as the Key-Value (KV) cache used during auto-regressive decoding. Skip connections offer a promising way to improve representation without bloating resource usage, yet most prior works either improve expressivity while leaving KV costs unchanged, or reduce memory at the cost of weaker representation. In this work, we propose SkipV1Former, a  Transformer variant that uses skip connections from the first layer's Value heads to strengthen model representation and reduce KV cache.  Specifically, from the second block onward, each layer reuses half of its Value heads from the very first layer, while computing the other half as usual-cutting Value projections and V cache by nearly 50 \%. Theoretically, we show that routing uncompressed first-layer Values into deeper layers restores information lost to compression and accelerates the model’s implicit mesa-optimization-a key pattern of Transformer in auto-regressive tasks. Empirically, across different model scales, SkipV1Former delivers consistent reductions of approximately 25 \% in KV cache while improving perplexity relative to standard Multi-Head Attention (MHA) Transformers and some advanced variants. Moreover, we propose a recipe for uptraining existing MHA Transformer checkpoints to SkipV1Former with only 10-15\% additional compute. Finally, SkipV1Former can seamlessly combine advanced methods like Group-Query Attention and Multi-Latent Attention to achieve further KV cache savings and performance improvement. When combined with YOCO, it cuts KV cache size by nearly 50 \% while still improving performance. The code is
 available at: https://github.com/Zhoutong-Wu/SkipV1Former.
FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation
Authors: Ariel Shaulov; Itay Hazan; Lior Wolf; Hila Chefer
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address this limitation by retraining the model or introducing external conditioning signals to enforce temporal consistency. In this work, we explore whether a meaningful temporal representation can be extracted directly from the predictions of a pre-trained model without any additional training or auxiliary inputs. We introduce __FlowMo__, a novel training-free guidance method that enhances motion coherence using only the model's own predictions in each diffusion step. 
  FlowMo first derives an appearance-debiased temporal representation by measuring the distance between latents corresponding to consecutive frames. This highlights the implicit temporal structure predicted by the model. 
  It then estimates motion coherence by measuring the patch-wise variance across the temporal dimension, and guides the model to reduce this variance dynamically during sampling. Extensive experiments across multiple text-to-video models demonstrate that FlowMo significantly improves motion coherence without sacrificing visual quality or prompt alignment, offering an effective plug-and-play solution for enhancing the temporal fidelity of pre-trained video diffusion models.
RvLLM: LLM Runtime Verification with Domain Knowledge
Authors: Yedi Zhang; Sun Yi Emma; Annabelle Lee Jia En; Jin Song Dong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language models (LLMs) have emerged as a dominant AI paradigm due to their exceptional text understanding and generation capabilities. However, their tendency to generate inconsistent or erroneous outputs challenges their reliability, especially in high-stakes domains requiring accuracy and trustworthiness. Existing research primarily focuses on detecting and mitigating model misbehavior in general-purpose scenarios, often overlooking the potential of integrating domain-specific knowledge. In this work, we advance misbehavior detection by incorporating domain knowledge. The core idea is to design a general specification language that enables domain experts to customize domain-specific constraints in a lightweight and intuitive manner, supporting later runtime monitoring of LLM outputs. To achieve this, we design a novel specification language ESL, and introduce a runtime verification framework RvLLM to validate LLM output against domain-specific constraints defined in ESL. RvLLM operates in two main stages: interpretation and reasoning. During interpretation, it derives interpretations of the specification based on the context, which then guide the reasoning process to identify inconsistencies. When new knowledge is derived, RvLLM issues a follow-up query to the LLM to further verify the consistency. We evaluate RvLLM on three representative tasks: violation detection against Singapore Rapid Transit Systems Act, numerical comparison, and inequality solving. Experimental results show that RvLLM effectively detects erroneous outputs across various LLMs in a lightweight and flexible manner. The results reveal that despite their impressive capabilities, LLMs remain prone to low-level errors due to a lack of formal guarantees during inference, and our framework offers a potential long-term solution by leveraging expert domain knowledge to rigorously and efficiently verify LLM outputs.
Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions
Authors: Yizhou Xu; Florent Krzakala; Lenka Zdeborova
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The Restricted Boltzmann Machine (RBM) is one of the simplest generative neural networks capable of learning input distributions. Despite its simplicity, the analysis of its performance in learning from the training data is only well understood in cases that essentially reduce to singular value decomposition of the data. Here, we consider the limit of a large dimension of the input space and a constant number of hidden units. In this limit, we simplify the standard RBM training objective into a form that is equivalent to the multi-index model with non-separable regularization.  This opens a path to analyze training of the RBM using methods that are established for multi-index models, such as Approximate Message Passing (AMP) and its state evolution, and the analysis of Gradient Descent (GD) via the dynamical mean-field theory. We then give rigorous asymptotics of the training dynamics of RBMs on data generated by the spiked covariance model as a prototype of a structure suitable for unsupervised learning.  We show in particular that RBMs reach the optimal computational weak recovery threshold, aligning with the Baik-Ben Arous-Péché (BBP) transition, in the spiked covariance model.