GenAI4ELab/papercli-papers huggingface dataset:
TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
Authors: Yehjin Shin; Jeongwhan Choi; Seojin Kim; Noseong Park
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models complement convolutional filters with self-attention. This is because convolutional filters alone, generally fixed filters, struggle to capture global interactions necessary for accurate recommendation. We propose \textbf{T}ime-\textbf{V}ariant Convolutional Filters for Sequential \textbf{Rec}ommendation (TV-Rec), a model inspired by graph signal processing, where time-variant graph filters capture position-dependent temporal variations in user sequences. By replacing both fixed kernels and self-attention with time-variant filters, TV-Rec achieves higher expressive power and better captures complex interaction patterns in user behavior. This design not only eliminates the need for self-attention but also reduces computation while accelerating inference. Extensive experiments on six public benchmarks show that TV-Rec outperforms state-of-the-art baselines by an average of 7.49\%.
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Authors: Samuel Bright-Thonney; Christina Reissel; Gaia Grosso; Nathaniel S. Woodward; Katya Govorkova; Andrzej Novak; Sang Eon Park; Eric A. Moreno; Philip Harris
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data,
and the necessity of making *statistically robust* statements about any observed outliers. While there is a wealth of literature on anomaly detection via dimensionality reduction, most methods do not produce outputs compatible with quantifiable claims of scientific discovery. In this work we directly address these challenges, presenting the first step towards a unified pipeline for novelty detection adapted for the rigorous statistical demands of science. We introduce AutoSciDACT (Automated Scientific Discovery with Anomalous Contrastive Testing), a general-purpose pipeline for detecting novelty in scientific data. AutoSciDACT begins by creating expressive low-dimensional data representations using a contrastive pre-training, leveraging the abundance of high-quality simulated data in many scientific domains alongside expertise that can guide principled data augmentation strategies. These compact embeddings then enable an extremely sensitive machine learning-based two-sample test using the New Physics Learning Machine (NPLM) framework, which identifies and statistically quantifies deviations in observed data relative to a reference distribution (null hypothesis). We perform experiments across a range of astronomical, physical, biological, image, and synthetic datasets, demonstrating strong sensitivity to small injections of anomalous data across all domains.
Multivariate Latent Recalibration for Conditional Normalizing Flows
Authors: Victor Dheur; Souhaib Ben Taieb
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: A reliable estimate of the full conditional distribution of a multivariate response given a set of covariates is essential in many decision-making applications. However, misspecified or miscalibrated models can lead to poor approximations of the joint distribution, resulting in unreliable predictions and suboptimal decisions. Standard recalibration methods are largely restricted to univariate settings, and while conformal prediction techniques yield multivariate regions with coverage guarantees, they do not provide an explicit form of the underlying probability distribution. We address this gap by first introducing a novel notion of latent calibration, which assesses probabilistic calibration in the latent space of conditional invertible generative models such as normalizing flows and flow matching. Second, we propose latent recalibration (LR), a post-hoc model recalibration method that learns a transformation of the latent space with finite-sample bounds on latent calibration. Unlike existing recalibration methods, LR produces a recalibrated distribution with an explicit multivariate density function while remaining computationally efficient. Extensive experiments on both tabular and image datasets show that LR consistently improves latent calibration error and the negative log-likelihood of the recalibrated models.
Teaching Language Models to Reason with Tools
Authors: Chengpeng Li; Zhengyang Tang; Ziniu Li; Mingfeng Xue; Keqin Bao; Tian Ding; Ruoyu Sun; Benyou Wang; Xiang Wang; Junyang Lin; Dayiheng Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccuracies when tackling complex mathematical operations. While integrating computational tools such as Code Interpreters (CIs) offers a promising solution, it introduces a critical challenge: a conflict between the model's internal, probabilistic reasoning and the external, deterministic knowledge provided by the CI, which often leads models to unproductive deliberation. To overcome this, we introduce CoRT (Code-Optimized Reasoning Training), a  post-training framework designed to teach LRMs to effectively utilize CIs. We propose **Hint-Engineering**, a new data synthesis strategy that strategically injects diverse hints at optimal points within reasoning paths. This approach generates high-quality, code-integrated reasoning data specifically tailored to optimize LRM-CI interaction. Using this method, we have synthesized 30 high-quality samples to post-train models ranging from 1.5B to 32B parameters through supervised fine-tuning. CoRT further refines the multi-round interleaving of external CI usage and internal thinking by employing rejection sampling and reinforcement learning. Our experimental evaluations demonstrate CoRT's effectiveness, yielding absolute improvements of 4\% and 8\% on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B, respectively, across five challenging mathematical reasoning datasets. Moreover, CoRT significantly enhances efficiency, reducing token usage by approximately 30\% for the 32B model and 50\% for the 1.5B model compared to pure natural language reasoning baselines. The models and code are available at: [this url](https://github.com/ChengpengLi1003/CoRT).
Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Authors: Aditya Sengar; Ali Hariri; Daniel Probst; PATRICK BARTH; Pierre Vandergheynst
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generating diverse, all‐atom conformational ensembles of dynamic proteins such as G‐protein‐coupled receptors (GPCRs) is critical for understanding their function, yet most generative models simplify atomic detail or ignore conformational diversity altogether. We present latent diffusion for full protein generation (LD-FPG), a framework that constructs complete all‐atom protein structures, including every side‐chain heavy atom, directly from molecular dynamics (MD) trajectories. LD-FPG employs a Chebyshev graph neural network (ChebNet) to obtain low‐dimensional latent embeddings of protein conformations, which are processed using three pooling strategies: blind, sequential and residue‐based. A diffusion model trained on these latent representations generates new samples that a decoder, optionally regularized by dihedral‐angle losses, maps back to Cartesian coordinates. Using D2R-MD, a $2\mu\text{s}$ MD trajectory (12 000 frames) of the human dopamine D$2$ receptor in a membrane environment, the sequential and residue-based pooling strategies reproduce the reference ensemble with high structural fidelity (all‐atom lDDT \~ $0.7$; $C\alpha$-lDDT \~ $0.8$) and recovers backbone and side‐chain dihedral‐angle distributions with a Jensen–Shannon divergence $<0.03$ compared to the MD data. LD-FPG thereby offers a practical route to system‐specific, all‐atom ensemble generation for large proteins, providing a promising tool for structure‐based therapeutic design on complex, dynamic targets. The D2R-MD dataset and our implementation are freely available to facilitate further research.
Price of Parsimony: Complexity of Fourier Sparsity Testing
Authors: Arijit Ghosh; Manmatha Roy
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: A function \( f : \mathbb{F}_2^n \to \mathbb{R} \) is said to be \( s \)-Fourier sparse if its Fourier expansion contains at most \( s \) nonzero coefficients. In general, the existence of a sparse representation in the Fourier basis serves as a key enabler for the design of efficient learning algorithms. However, most existing techniques assume prior knowledge of the function’s Fourier sparsity, with algorithmic parameters carefully tuned to this value. This motivates the following decision problem: given \( s > 0 \), determine whether a function is \( s \)-Fourier sparse.

In this work, we study the problem of tolerant testing of Fourier Sparsity for real-valued functions over \( \mathbb{F}_2^n \), accessed via oracle queries. The goal is to decide whether a given function is close to being \( s \)-Fourier sparse or far from every \( s \)-Fourier sparse function. Our algorithm provides an estimator that, given oracle access to the function, estimates its distance to the nearest \( s \)-Fourier sparse function with query complexity \( \widetilde{O}(s) \), for constant accuracy and confidence parameters.

A key structural ingredient in our analysis is a new spectral concentration result for real-valued functions over \( \mathbb{F}_2^n \) when restricted to small-dimensional random affine subspaces. We further complement our upper bound with a matching lower bound of \( \Omega(s) \), establishing that our tester is optimal up to logarithmic factors. The lower bound exploits spectral properties of a class of cryptographically hard functions, namely, the Maiorana--McFarland family, in a novel way.
CoCoA: A Minimum Bayes Risk Framework Bridging Confidence and Consistency for Uncertainty Quantification in LLMs
Authors: Roman Vashurin; Maiya Goloburda; Albina Ilina; Aleksandr Rubashevskii; Preslav Nakov; Artem Shelmanov; Maxim Panov
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Uncertainty quantification for Large Language Models (LLMs) encompasses a diverse range of approaches, with two major families being particularly prominent: (i) information-based, which estimate model confidence from token-level probabilities, and (ii) consistency-based, which assess the semantic agreement among multiple outputs generated using repeated sampling. While several recent methods have sought to combine these two paradigms to improve uncertainty quantification performance, they often fail to consistently outperform simpler baselines. In this work, we revisit the foundations of uncertainty estimation through the lens of Minimum Bayes Risk decoding, establishing a direct link between uncertainty and the optimal decision-making process of LLMs. Building on these findings, we propose CoCoA, a unified framework that integrates model confidence with output consistency, yielding a family of efficient and robust uncertainty quantification methods. We evaluate CoCoA across diverse tasks, including question answering, abstractive text summarization, and machine translation, and demonstrate sizable improvements over state-of-the-art uncertainty quantification approaches.
OpenCUA: Open Foundations for Computer-Use Agents
Authors: Xinyuan Wang; Bowen Wang; Dunjie Lu; Junlin Yang; Tianbao Xie; Junli Wang; Jiaqi Deng; Xiaole Guo; Yiheng Xu; Chen Henry Wu; Zhennan Shen; Zhuokai Li; Ryan Li; Xiaochuan Li; Junda Chen; Zheng Boyuan; LI PEIHANG; Fangyu Lei; Ruisheng Cao; Yeqiao Fu; Dongchan Shin; Martin Shin; Hu Jiarui; Yuyan Wang; Jixuan Chen; Yuxiao Ye; Danyang Zhang; Yipu Wang; Heng Wang; Diyi Yang; Victor Zhong; Y.Charles; Zhilin Yang; Tao Yu
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Vision-language models have demonstrated impressive capabilities as computer-use agents (CUAs) capable of automating diverse computer tasks. As their commercial potential grows, critical details of the most capable CUA systems remain closed. As these agents will increasingly mediate digital interactions and execute consequential decisions on our behalf, the research community needs access to open CUA frameworks to study their capabilities, limitations, and risks. To bridge this gap, we propose OpenCUA, a comprehensive open-source framework for scaling CUA data and foundation models. Our framework consists of: (1) an annotation infrastructure that seamlessly captures human computer-use demonstrations; (2) AgentNet, the first large-scale computer-use task dataset spanning 3 operating systems and 200+ applications and websites; (3) a scalable pipeline that transforms demonstrations into state–action pairs with reflective long Chain-of-Thought reasoning that sustain robust performance gains as data scales. Our end-to-end agent models demonstrate strong performance across CUA benchmarks. In particular, OpenCUA-72B achieves an average success rate of 45.0% on OSWorld‑Verified, establishing a new state-of-the-art (SOTA) among open-source models. Further analysis confirms that our approach generalizes well across domains and benefits significantly from increased test-time computation. We release our annotation tool, datasets, code, and models to build open foundations for further CUA research.
On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
Authors: Anas Barakat; Souradip Chakraborty; Peihong Yu; Pratap Tokekar; Amrit Singh Bedi
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Reinforcement learning with general utilities (RLGU) offers a unifying framework to capture several problems beyond standard expected returns, including imitation learning, pure exploration, and safe RL. Despite recent fundamental advances in the theoretical analysis of policy gradient (PG) methods for standard RL and recent efforts in RLGU, the understanding of these PG algorithms and their scope of application in RLGU still remain limited. In this work, we establish global optimality guarantees of PG methods for RLGU in which the objective is a general concave utility function of the state-action occupancy measure. In the tabular setting, we provide global optimality results using a new proof technique building on recent theoretical developments on the convergence of PG methods for standard RL using gradient domination. Our proof technique opens avenues for analyzing policy parameterizations beyond the direct policy parameterization for RLGU. In addition, we provide  global optimality results for large state-action space settings beyond prior work which has mostly focused on the tabular setting. In this large scale setting, we adapt PG methods by approximating occupancy measures within a function approximation class using maximum likelihood estimation. Our sample complexity only scales with the dimension induced by our approximation class instead of the size of the state-action space.
Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
Authors: Ehsan Sharifian; Saber Salehkaleybar; Negar Kiyavash
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We study the problem of causal structure learning from a combination of observational and interventional data generated by a linear non-Gaussian structural equation model that might contain cycles. Recent results show that using mere observational data identifies the causal graph only up to a permutation-equivalence class. We obtain a combinatorial characterization of this class by showing that each equivalence class corresponds to a perfect matching in a bipartite graph. This bipartite representation allows us to analyze how interventions modify or constrain the matchings. Specifically, we show that each atomic intervention reveals one edge of the true matching and eliminates all incompatible causal graphs. Consequently, we formalize the optimal experiment design task as an adaptive stochastic optimization problem over the set of equivalence classes with a natural reward function that quantifies how many graphs are eliminated from the equivalence class by an intervention. We show that this reward function is adaptive submodular and provide a greedy policy with a provable near-optimal performance guarantee. A key technical challenge is to efficiently estimate the reward function without having to explicitly enumerate all the graphs in the equivalence class. We propose a sampling-based estimator using random matchings and analyze its bias and concentration behavior. Our simulation results show that performing a small number of interventions guided by our stochastic optimization framework recovers the true underlying causal structure.
Hybrid Latent Representations for PDE Emulation
Authors: Ali Can Bekar; Siddhant Agarwal; Christian Hüttig; Nicola Tosi; David S. Greenberg
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: For classical PDE solvers, adjusting the spatial resolution and time step offers a trade-off between speed and accuracy. Neural emulators often achieve better speed-accuracy trade-offs by operating on a compact representation of the PDE system. Coarsened PDE fields are a simple and effective representation, but cannot exploit fine spatial scales in the high-fidelity numerical solutions. Alternatively, unstructured latent representations provide efficient autoregressive rollouts, but cannot enforce local interactions or physical laws as inductive biases. To overcome these limitations, we introduce hybrid representations that augment coarsened PDE fields with spatially structured latent variables extracted from high-resolution inputs. Hybrid representations provide efficient rollouts, can be trained on a simple loss defined on coarsened PDE fields, and support hard physical constraints. When predicting fine- and coarse-scale features across multiple PDE emulation tasks, they outperform or match the speed-accuracy trade-offs of the best convolutional, attentional, Fourier operator-based and autoencoding baselines.
MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
Authors: Mengxi Xiao; Ben Liu; He Li; Jimin Huang; Qianqian Xie; Xiaofen Zong; Mang Ye; Min Peng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3\% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.
Meta-learning how to Share Credit among Macro-Actions
Authors: Ionel Hosu; Traian Rebedea; Razvan Pascanu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: One proposed mechanism to improve exploration in reinforcement learning is the use of macro-actions, a form of temporal abstractions over actions.
Paradoxically though, in many scenarios the naive addition of macro-actions does not lead to better exploration, but rather the opposite. 
In this work, we argue that the difficulty stems from the trade-offs between reducing the average number of decisions per episode versus increasing the size of the action space. 
Namely, one typically treats each potential macro-action as independent and atomic, hence strictly increasing the search space and making typical exploration strategies inefficient. 
To address this problem we propose a novel regularization term that exploits the relationship between actions and macro-actions to improve the credit assignment mechanism reducing the effective dimension of the action space and therefore improving exploration. The term relies on a similarity matrix that is meta-learned jointly with learning the desired policy.
We empirically validate our strategy looking at macro-actions in Atari games, and the StreetFighter II environment. Our results show significant improvements over the Rainbow-DQN baseline in all environments. Additionally, we show that the macro-action similarity is transferable to other environments with similar dynamics.
We believe this work is a small but important step towards understanding how the similarity-imposed geometry on the action space can be exploited to improve credit assignment and exploration, therefore making learning more efficient.
Mitigating Spurious Features in Contrastive Learning with Spectral Regularization
Authors: Naghmeh Ghanooni; Waleed Mustafa; Dennis Wagner; Sophie Fellenz; Anthony Widjaja Lin; Marius Kloft
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Neural networks generally prefer simple and easy-to-learn features. When these features are spuriously correlated with the labels, the network's performance can suffer, particularly for underrepresented classes or concepts. Self-supervised representation learning methods, such as contrastive learning, are especially prone to this issue, often resulting in worse performance on downstream tasks. 
We identify a key spectral signature of this failure: early reliance on dominant singular modes of the learned feature matrix. To mitigate this, we propose a novel framework that promotes a uniform eigenspectrum of the feature covariance matrix, encouraging diverse and semantically rich representations. Our method operates in a fully self-supervised setting, without relying on ground-truth labels or any additional information. Empirical results on SimCLR and SimSiam demonstrate consistent gains in robustness and transfer performance, suggesting broad applicability across self-supervised learning paradigms. Code: https://github.com/NaghmehGh/SpuriousCorrelation_SSRL