OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference
Venue: arXiv cs.LG 2025
Year: 2025

Learning from human preferences is a cornerstone of aligning machine learning models with subjective human judgments. Yet, collecting such preference data is often costly and time-consuming, motivating the need for more efficient learning paradigms. Two established approaches offer complementary advantages: RLHF scales effectively to high-dimensional tasks such as LLM fine-tuning, while PBO achieves greater sample efficiency through active querying. We propose a hybrid framework that unifies RLHF's scalability with PBO's query efficiency by integrating an acquisition-driven module into the RLHF pipeline, thereby enabling active and sample-efficient preference gathering. We validate the proposed approach on two representative domains: (i) high-dimensional preference optimization and (ii) LLM fine-tuning. Experimental results demonstrate consistent improvements in both sample efficiency and overall performance across these tasks.
Comparative Study of CNN Architectures for Binary Classification of Horses and Motorcycles in the VOC 2008 Dataset
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a comprehensive evaluation of nine convolutional neural network architectures for binary classification of horses and motorcycles in the VOC 2008 dataset. We address the significant class imbalance problem by implementing minority-class augmentation techniques. Our experiments compare modern architectures including ResNet-50, ConvNeXt-Tiny, DenseNet-121, and Vision Transformer across multiple performance metrics. Results demonstrate substantial performance variations, with ConvNeXt-Tiny achieving the highest Average Precision (AP) of 95.53% for horse detection and 89.12% for motorcycle detection. We observe that data augmentation significantly improves minority class detection, particularly benefiting deeper architectures. This study provides insights into architecture selection for imbalanced binary classification tasks and quantifies the impact of data augmentation strategies in mitigating class imbalance issues in object detection.
GraSP-VLA: Graph-based Symbolic Action Representation for Long-Horizon Planning with VLA Policies
Venue: arXiv cs.RO 2025
Year: 2025

Deploying autonomous robots that can learn new skills from demonstrations is an important challenge of modern robotics. Existing solutions often apply end-to-end imitation learning with Vision-Language Action (VLA) models or symbolic approaches with Action Model Learning (AML). On the one hand, current VLA models are limited by the lack of high-level symbolic planning, which hinders their abilities in long-horizon tasks. On the other hand, symbolic approaches in AML lack generalization and scalability perspectives. In this paper we present a new neuro-symbolic approach, GraSP-VLA, a framework that uses a Continuous Scene Graph representation to generate a symbolic representation of human demonstrations. This representation is used to generate new planning domains during inference and serves as an orchestrator for low-level VLA policies, scaling up the number of actions that can be reproduced in a row. Our results show that GraSP-VLA is effective for modeling symbolic representations on the task of automatic planning domain generation from observations. In addition, results on real-world experiments show the potential of our Continuous Scene Graph representation to orchestrate low-level VLA policies in long-horizon tasks.
Federated Stochastic Minimax Optimization under Heavy-Tailed Noises
Venue: arXiv cs.LG 2025
Year: 2025

Heavy-tailed noise has attracted growing attention in nonconvex stochastic optimization, as numerous empirical studies suggest it offers a more realistic assumption than standard bounded variance assumption. In this work, we investigate nonconvex-PL minimax optimization under heavy-tailed gradient noise in federated learning. We propose two novel algorithms: Fed-NSGDA-M, which integrates normalized gradients, and FedMuon-DA, which leverages the Muon optimizer for local updates. Both algorithms are designed to effectively address heavy-tailed noise in federated minimax optimization, under a milder condition. We theoretically establish that both algorithms achieve a convergence rate of $O({1}/{(TNp)^{\frac{s-1}{2s}}})$. To the best of our knowledge, these are the first federated minimax optimization algorithms with rigorous theoretical guarantees under heavy-tailed noise. Extensive experiments further validate their effectiveness.
Online Algorithms for Repeated Optimal Stopping: Achieving Both Competitive Ratio and Regret Bounds
Venue: arXiv cs.DS 2025
Year: 2025

We study the repeated optimal stopping problem, which generalizes the classical optimal stopping problem with an unknown distribution to a setting where the same problem is solved repeatedly over $T$ rounds. In this framework, we aim to design algorithms that guarantee a competitive ratio in each round while also achieving sublinear regret across all rounds. Our primary contribution is a general algorithmic framework that achieves these objectives simultaneously for a wide array of repeated optimal stopping problems. The core idea is to dynamically select an algorithm for each round, choosing between two candidates: (1) an empirically optimal algorithm derived from the history of observations, and (2) a sample-based algorithm with a proven competitive ratio guarantee. Based on this approach, we design an algorithm that performs no worse than the baseline sample-based algorithm in every round, while ensuring that the total regret is bounded by $\tilde{O}(\sqrt{T})$. We demonstrate the broad applicability of our framework to canonical problems, including the prophet inequality, the secretary problem, and their variants under adversarial, random, and i.i.d. input models. For example, for the repeated prophet inequality problem, our method achieves a $1/2$-competitive ratio from the second round on and an $\tilde{O}(\sqrt{T})$ regret. Furthermore, we establish a regret lower bound of $Ω(\sqrt{T})$ even in the i.i.d. model, confirming that our algorithm's performance is almost optimal with respect to the number of rounds.
OUNLP at TSAR 2025 Shared Task: Multi-Round Text Simplifier via Code Generation
Venue: arXiv cs.CL 2025
Year: 2025

This paper describes the OUNLP system submitted to the TSAR-2025 Shared Task (Alva-Manchego et al., 2025), designed for readability-controlled text simplification using LLM-prompting-based generation. Based on the analysis of prompt-based text simplification methods, we discovered an interesting finding that text simplification performance is highly related to the gap between the source CEFR (Arase et al., 2022) level and the target CEFR level. Inspired by this finding, we propose two multi-round simplification methods and generate them via GPT-4o: rule-based simplification (MRS-Rule) and jointly rule-based LLM simplification (MRS-Joint). Our submitted systems ranked 7 out of 20 teams. Later improvements with MRS-Joint show that taking the LLM simplified candidates as the starting point could further boost the multi-round simplification performance.
Alternative Fairness and Accuracy Optimization in Criminal Justice
Venue: arXiv cs.LG 2025
Year: 2025

Algorithmic fairness has grown rapidly as a research area, yet key concepts remain unsettled, especially in criminal justice. We review group, individual, and process fairness and map the conditions under which they conflict. We then develop a simple modification to standard group fairness. Rather than exact parity across protected groups, we minimize a weighted error loss while keeping differences in false negative rates within a small tolerance. This makes solutions easier to find, can raise predictive accuracy, and surfaces the ethical choice of error costs. We situate this proposal within three classes of critique: biased and incomplete data, latent affirmative action, and the explosion of subgroup constraints. Finally, we offer a practical framework for deployment in public decision systems built on three pillars: need-based decisions, Transparency and accountability, and narrowly tailored definitions and solutions. Together, these elements link technical design to legitimacy and provide actionable guidance for agencies that use risk assessment and related tools.
Linear Mode Connectivity under Data Shifts for Deep Ensembles of Image Classifiers
Venue: arXiv cs.LG 2025
Year: 2025

The phenomenon of linear mode connectivity (LMC) links several aspects of deep learning, including training stability under noisy stochastic gradients, the smoothness and generalization of local minima (basins), the similarity and functional diversity of sampled models, and architectural effects on data processing. In this work, we experimentally study LMC under data shifts and identify conditions that mitigate their impact. We interpret data shifts as an additional source of stochastic gradient noise, which can be reduced through small learning rates and large batch sizes. These parameters influence whether models converge to the same local minimum or to regions of the loss landscape with varying smoothness and generalization. Although models sampled via LMC tend to make similar errors more frequently than those converging to different basins, the benefit of LMC lies in balancing training efficiency against the gains achieved from larger, more diverse ensembles. Code and supplementary materials will be made publicly available at https://github.com/DLR-KI/LMC in due course.
THEval. Evaluation Framework for Talking Head Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Video generation has achieved remarkable progress, with generated videos increasingly resembling real ones. However, the rapid advance in generation has outpaced the development of adequate evaluation metrics. Currently, the assessment of talking head generation primarily relies on limited metrics, evaluating general video quality, lip synchronization, and on conducting user studies. Motivated by this, we propose a new evaluation framework comprising 8 metrics related to three dimensions (i) quality, (ii) naturalness, and (iii) synchronization. In selecting the metrics, we place emphasis on efficiency, as well as alignment with human preferences. Based on this considerations, we streamline to analyze fine-grained dynamics of head, mouth, and eyebrows, as well as face quality. Our extensive experiments on 85,000 videos generated by 17 state-of-the-art models suggest that while many algorithms excel in lip synchronization, they face challenges with generating expressiveness and artifact-free details. These videos were generated based on a novel real dataset, that we have curated, in order to mitigate bias of training data. Our proposed benchmark framework is aimed at evaluating the improvement of generative methods. Original code, dataset and leaderboards will be publicly released and regularly updated with new methods, in order to reflect progress in the field.
Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics
Venue: arXiv cs.CL 2025
Year: 2025

When a language model generates text, the selection of individual tokens might lead it down very different reasoning paths, making uncertainty difficult to quantify. In this work, we consider whether reasoning language models represent the alternate paths that they could take during generation. To test this hypothesis, we use hidden activations to control and predict a language model's uncertainty during chain-of-thought reasoning. In our experiments, we find a clear correlation between how uncertain a model is at different tokens, and how easily the model can be steered by controlling its activations. This suggests that activation interventions are most effective when there are alternate paths available to the model -- in other words, when it has not yet committed to a particular final answer. We also find that hidden activations can predict a model's future outcome distribution, demonstrating that models implicitly represent the space of possible paths.
Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments
Venue: arXiv cs.CR 2025
Year: 2025

The growth of cloud computing has revolutionized data processing and storage capacities to another levels of scalability and flexibility. But in the process, it has created a huge challenge of security, especially in terms of safeguarding sensitive data. Classical security practices, including encryption at rest and during transit, fail to protect data in use and expose it to various possible breaches. In response to this problem , Confidential Computing has been a tool ,seeking to secure data in processing by usage of hardware-based Trusted Execution Environments (TEEs). TEEs, including Intel's Software Guard Extensions (SGX) and ARM's TrustZone, offers protected contexts within the processor, where data is kept confidential ,intact and secure , even with malicious software or compromised operating systems. In this research, we have explored the architecture and security features of TEEs like Intel SGX and ARM TrustZone, and their effectiveness in improving cloud data security. From a thorough literature survey ,we have analyzed the deployment strategies, performance indicators, and practical uses of these TEEs for the same purpose. In addition, we have discussed the issues regarding deployment, possible weaknesses, scalability issues, and integration issues. Our results focuses on the central position of TEEs in strengthening and advancing cloud security infrastructures, pointing towards their ability to create a secure foundation for Confidential Computing.
Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC. However, reliable evaluation of image quality remains a challenge as current texture- and distribution-based metrics quantify image fidelity rather than the accuracy of IHC staining. Here, we introduce an automated and accuracy grounded framework to determine image quality across sixteen paired or unpaired image translation models. Using color deconvolution, we generate masks of pixels stained brown (i.e., IHC-positive) as predicted by each virtual IHC model. We use the segmented masks of real and virtual IHC to compute stain accuracy metrics (Dice, IoU, Hausdorff distance) that directly quantify correct pixel - level labeling without needing expert manual annotations. Our results demonstrate that conventional image fidelity metrics, including Frechet Inception Distance (FID), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), correlate poorly with stain accuracy and pathologist assessment. Paired models such as PyramidPix2Pix and AdaptiveNCE achieve the highest stain accuracy, whereas unpaired diffusion- and GAN-based models are less reliable in providing accurate IHC positive pixel labels. Moreover, whole-slide images (WSI) reveal performance declines that are invisible in patch-based evaluations, emphasizing the need for WSI-level benchmarks. Together, this framework defines a reproducible approach for assessing the quality of virtual IHC models, a critical step to accelerate translation towards routine use by pathologists.
PromptSep: Generative Audio Separation via Multimodal Prompting
Venue: arXiv cs.SD 2025
Year: 2025

Recent breakthroughs in language-queried audio source separation (LASS) have shown that generative models can achieve higher separation audio quality than traditional masking-based approaches. However, two key limitations restrict their practical use: (1) users often require operations beyond separation, such as sound removal; and (2) relying solely on text prompts can be unintuitive for specifying sound sources. In this paper, we propose PromptSep to extend LASS into a broader framework for general-purpose sound separation. PromptSep leverages a conditional diffusion model enhanced with elaborated data simulation to enable both audio extraction and sound removal. To move beyond text-only queries, we incorporate vocal imitation as an additional and more intuitive conditioning modality for our model, by incorporating Sketch2Sound as a data augmentation strategy. Both objective and subjective evaluations on multiple benchmarks demonstrate that PromptSep achieves state-of-the-art performance in sound removal and vocal-imitation-guided source separation, while maintaining competitive results on language-queried source separation.
NovisVQ: A Streaming Convolutional Neural Network for No-Reference Opinion-Unaware Frame Quality Assessment
Venue: arXiv cs.CV 2025
Year: 2025

Video quality assessment (VQA) is vital for computer vision tasks, but existing approaches face major limitations: full-reference (FR) metrics require clean reference videos, and most no-reference (NR) models depend on training on costly human opinion labels. Moreover, most opinion-unaware NR methods are image-based, ignoring temporal context critical for video object detection. In this work, we present a scalable, streaming-based VQA model that is both no-reference and opinion-unaware. Our model leverages synthetic degradations of the DAVIS dataset, training a temporal-aware convolutional architecture to predict FR metrics (LPIPS , PSNR, SSIM) directly from degraded video, without references at inference. We show that our streaming approach outperforms our own image-based baseline by generalizing across diverse degradations, underscoring the value of temporal modeling for scalable VQA in real-world vision systems. Additionally, we demonstrate that our model achieves higher correlation with full-reference metrics compared to BRISQUE, a widely-used opinion-aware image quality assessment baseline, validating the effectiveness of our temporal, opinion-unaware approach.
Resolving Conflicts with Grace: Dynamically Concurrent Universality
Venue: arXiv cs.DC 2025
Year: 2025

Synchronization is the major obstacle to scalability in distributed computing. Concurrent operations on the shared data engage in synchronization when they encounter a \emph{conflict}, i.e., their effects depend on the order in which they are applied. Ideally, one would like to detect conflicts in a \emph{dynamic} manner, i.e., adjusting to the current system state. Indeed, it is very common that two concurrent operations conflict only in some rarely occurring states. In this paper, we define the notion of \emph{dynamic concurrency}: an operation employs strong synchronization primitives only if it \emph{has} to arbitrate with concurrent operations, given the current system state. We then present a dynamically concurrent universal construction.
Addressing divergent representations from causal interventions on neural networks
Venue: arXiv cs.LG 2025
Year: 2025

A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encode. Here we ask whether such interventions create out-of-distribution (divergent) representations, and whether this raises concerns about how faithful their resulting explanations are to the target model in its natural state. First, we demonstrate theoretically and empirically that common causal intervention techniques often do shift internal representations away from the natural distribution of the target model. Then, we provide a theoretical analysis of two cases of such divergences: "harmless" divergences that occur in the behavioral null-space of the layer(s) of interest, and "pernicious" divergences that activate hidden network pathways and cause dormant behavioral changes. Finally, in an effort to mitigate the pernicious cases, we apply and modify the Counterfactual Latent (CL) loss from Grant (2025) allowing representations from causal interventions to remain closer to the natural distribution, reducing the likelihood of harmful divergences while preserving the interpretive power of the interventions. Together, these results highlight a path towards more reliable interpretability methods.
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
Venue: arXiv cs.LG 2025
Year: 2025

This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equations). In particular, it is investigating and comparing various extensions to the flow matching paradigm that reduce the number of sampling steps. In this regard, it compares direct distillation, progressive distillation, adversarial diffusion distillation, Wasserstein GANs and rectified flows. Moreover, experiments are conducted on a set of challenging systems. In particular, we also address the challenge of directly predicting 2D slices of large-scale 3D simulations, paving the way for efficient inflow generation for solvers.
Optimal Inference Schedules for Masked Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

A major bottleneck of standard auto-regressive large language models is that their inference process is inherently sequential, resulting in very long and costly inference times. To circumvent this, practitioners proposed a class of language models called diffusion language models, of which the masked diffusion model (MDM) is the most successful. The MDM is able to sample tokens out-of-order and, ostensibly, many tokens at once and in parallel. However, there is very limited rigorous understanding of how much parallel sampling these models can perform without noticeable degradation in their sampling performance. Prior work of Li and Cai obtained some preliminary bounds, but these are not tight for many natural classes of distributions. In this work, we give a new, exact characterization of the expected divergence between the true distribution and the sampled distribution, for any distribution and any unmasking schedule for the sampler, showing an elegant connection to the theory of univariate function approximation. By leveraging this connection, we then attain a number of novel lower and upper bounds for this problem. While the connection to function approximation in principle gives the optimal unmasking schedule for any distribution, we show that it is in general impossible to compete with it without strong a priori knowledge of the distribution, even in seemingly benign settings. However, we also demonstrate new upper bounds and new sampling schedules in terms of well-studied information-theoretic properties of the base distribution, namely, its total correlation and dual total correlation, which show that in some natural settings, one can sample in $O(log n)$ steps without any visible loss in performance, where $n$ is the total sequence length.