OpenRaiser/Intern-Atlas huggingface dataset:
Object-centric Task Representation and Transfer using Diffused Orientation Fields
Venue: arXiv cs.RO 2025
Year: 2025

Curved objects pose a fundamental challenge for skill transfer in robotics: unlike planar surfaces, they do not admit a global reference frame. As a result, task-relevant directions such as "toward" or "along" the surface vary with position and geometry, making object-centric tasks difficult to transfer across shapes. To address this, we introduce an approach using Diffused Orientation Fields (DOF), a smooth representation of local reference frames, for transfer learning of tasks across curved objects. By expressing manipulation tasks in these smoothly varying local frames, we reduce the problem of transferring tasks across curved objects to establishing sparse keypoint correspondences. DOF is computed online from raw point cloud data using diffusion processes governed by partial differential equations, conditioned on keypoints. We evaluate DOF under geometric, topological, and localization perturbations, and demonstrate successful transfer of tasks requiring continuous physical interaction such as inspection, slicing, and peeling across varied objects. We provide our open-source codes at our website https://github.com/idiap/diffused_fields_robotics
Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directions
Venue: arXiv cs.CR 2025
Year: 2025

Cellular networks have become foundational to modern communication, supporting a broad range of applications, from civilian use to enterprise systems and military tactical networks. The advent of fifth-generation and beyond cellular networks (B5G) introduces emerging compute capabilities into the Radio Access Network (RAN), transforming it from a traditionally closed, vendor-locked infrastructure into an open and programmable ecosystem. This evolution, exemplified by Open-RAN (O-RAN), enables the deployment of control-plane applications from diverse sources, which can dynamically influence user-plane traffic in response to real-time events. As cellular infrastructures become more disaggregated and software-driven, security becomes an increasingly critical concern. Zero-Trust Architecture (ZTA) has emerged as a promising security paradigm that discards implicit trust assumptions by acknowledging that threats may arise from both external and internal sources. ZTA mandates comprehensive and fine-grained security mechanisms across both control and user planes to contain adversarial movements and enhance breach detection and attack response actions. In this paper, we explore the adoption of ZTA in the context of 5G and beyond, with a particular focus on O-RAN as an architectural enabler. We analyze how ZTA principles align with the architectural and operational characteristics of O-RAN, and identify key challenges and opportunities for embedding zero-trust mechanisms within O-RAN-based cellular networks.
A Digital Twin Platform for QoS Optimization Under DoS Attacks for Next Generation Radio Networks
Venue: arXiv cs.NI 2025
Year: 2025

Digital Twins are being used as an enabling technology in 6G applications across various domains, valued for their data-driven insights and real-time decision-making capabilities. However, integrating Digital Twins into 6G environments presents challenges in maintaining consistent network services under adverse conditions such as including denial-of-service (DoS) attacks, while ensuring consistent Quality of Service (QoS). In this work, we present a Digital Twin Platform to facilitate bidirectional communication between User Equipment (UEs) and application-specific digital twins to enhance UE traffic under UDP flood attacks. By leveraging AI to analyze key digital twin parameters such as throughput and delay, our framework derives actionable insights that enhance QoS management in DoS attack scenarios, ultimately advancing real-world applications of digital twins in critical infrastructure domains. The performance of this Digital Twin Platform is validated through an emergency management use-case in 6G networks while the network is under attack with UDP flood attacks in terms of packet reception success rate, average packet delay, and average throughput metrics.
Universality in Collective Intelligence on the Rubik's Cube
Venue: arXiv cs.AI 2025
Year: 2025

Progress in understanding expert performance is limited by the scarcity of quantitative data on long-term knowledge acquisition and deployment. Here we use the Rubik's Cube as a cognitive model system existing at the intersection of puzzle solving, skill learning, expert knowledge, cultural transmission, and group theory. By studying competitive cube communities, we find evidence for universality in the collective learning of the Rubik's Cube in both sighted and blindfolded conditions: expert performance follows exponential progress curves whose parameters reflect the delayed acquisition of algorithms that shorten solution paths. Blindfold solves form a distinct problem class from sighted solves and are constrained not only by expert knowledge but also by the skill improvements required to overcome short-term memory bottlenecks, a constraint shared with blindfold chess. Cognitive artifacts such as the Rubik's Cube help solvers navigate an otherwise enormous mathematical state space. In doing so, they sustain collective intelligence by integrating communal knowledge stores with individual expertise and skill, illustrating how expertise can, in practice, continue to deepen over the course of a single lifetime.
Bayesian-based Online Label Shift Estimation with Dynamic Dirichlet Priors
Venue: arXiv cs.LG 2025
Year: 2025

Label shift, a prevalent challenge in supervised learning, arises when the class prior distribution of test data differs from that of training data, leading to significant degradation in classifier performance. To accurately estimate the test priors and enhance classification accuracy, we propose a Bayesian framework for label shift estimation, termed Full Maximum A Posterior Label Shift (FMAPLS), along with its online version, online-FMAPLS. Leveraging batch and online Expectation-Maximization (EM) algorithms, these methods jointly and dynamically optimize Dirichlet hyperparameters $\boldsymbolα$ and class priors $\boldsymbolπ$, thereby overcoming the rigid constraints of the existing Maximum A Posterior Label Shift (MAPLS) approach. Moreover, we introduce a linear surrogate function (LSF) to replace gradient-based hyperparameter updates, yielding closed-form solutions that reduce computational complexity while retaining asymptotic equivalence. The online variant substitutes the batch E-step with a stochastic approximation, enabling real-time adaptation to streaming data. Furthermore, our theoretical analysis reveals a fundamental trade-off between online convergence rate and estimation accuracy. Extensive experiments on CIFAR100 and ImageNet datasets under shuffled long-tail and Dirichlet test priors demonstrate that FMAPLS and online-FMAPLS respectively achieve up to 40% and 12% lower KL divergence and substantial improvements in post-shift accuracy over state-of-the-art baselines, particularly under severe class imbalance and distributional uncertainty. These results confirm the robustness, scalability, and suitability of the proposed methods for large-scale and dynamic learning scenarios.
AutoFocus-IL: VLM-based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations
Venue: arXiv cs.RO 2025
Year: 2025

AutoFocus-IL is a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rather than distractors and spurious correlations. Although saliency regularization has emerged as a promising way to achieve this, existing approaches typically require costly supervision such as human gaze data or manual saliency annotations. In contrast, AutoFocus-IL leverages vision-language models (VLMs) to automatically identify and track key objects in demonstrations, generating temporal saliency maps that highlight causal visual signals while suppressing distractors. These maps are then used to regularize behavior cloning policies, yielding stronger alignment between visual attention and task-relevant cues. Experiments in both the CARLA simulator and real-robot manipulation tasks demonstrate that AutoFocus-IL not only outperforms standard behavior cloning but also surpasses state-of-the-art baselines that assume privileged access to human supervision, such as gaze data. Code, datasets, and trained policy videos are available at https://AutoFocus-IL.github.io/.
Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representations
Venue: arXiv cs.AI 2025
Year: 2025

Machine learning models increasingly function as representational systems, yet the philosoph- ical assumptions underlying their internal structures remain largely unexamined. This paper develops a structuralist decision framework for classifying the implicit ontological commitments made in machine learning research on neural network representations. Using a modified PRISMA protocol, a systematic review of the last two decades of literature on representation learning and interpretability is conducted. Five influential papers are analysed through three hierarchical criteria derived from structuralist philosophy of science: entity elimination, source of structure, and mode of existence. The results reveal a pronounced tendency toward structural idealism, where learned representations are treated as model-dependent constructions shaped by architec- ture, data priors, and training dynamics. Eliminative and non-eliminative structuralist stances appear selectively, while structural realism is notably absent. The proposed framework clarifies conceptual tensions in debates on interpretability, emergence, and epistemic trust in machine learning, and offers a rigorous foundation for future interdisciplinary work between philosophy of science and machine learning.
Overlap Analysis of the Shortest Path Problem: Local Search, Landscapes, and Franz--Parisi Potential
Venue: arXiv cs.DS 2025
Year: 2025

Two directions in algorithms and complexity involve: (1) classifying which optimization problems can be solved in polynomial time, and (2) understanding which computational problems are hard to solve \emph{on average} in addition to the worst case. For many average-case problems, there does not currently exist strong evidence via reductions that they are hard. However, we can still attempt to predict their polynomial time tractability by proving lower bounds against restricted classes of algorithms. Geometric approaches to predicting tractability typically study the \emph{optimization landscape}. For optimization problems with random objectives or constraints, ideas originating in statistical physics suggest we should study the \emph{overlap} between approximately-optimal solutions. Formally, properties of \emph{Gibbs measures} and the \emph{Franz--Parisi potential} imply lower bounds against natural local search algorithms, such as Langevin dynamics. A related theory, the \emph{Overlap Gap Property (OGP)}, proves rigorous lower bounds against classes of algorithms which are stable functions of their input. A remarkable recent work of Li and Schramm showed that the shortest path problem in random graphs admits lower bounds against a class of stable algorithms, via the OGP. Yet this problem is polynomial time tractable. We further investigate this. We find that both the OGP and the Franz--Parisi potential predict that: (1) local search will fail in the optimization landscape of shortest paths, but (2) local search should succeed in the optimization landscape for shortest path \emph{trees}, which is true. Using the Franz--Parisi potential, we explain an analogy with results from combinatorial optimization -- submodular minimization is tractable via local search on the Lovász extension, even though ``naive'' local search over sets or the multilinear extension provably fails.
Low-Rank GEMM: Efficient Matrix Multiplication via Low-Rank Approximation with FP8 Acceleration
Venue: arXiv cs.PF 2025
Year: 2025

Large matrix multiplication is a cornerstone of modern machine learning workloads, yet traditional approaches suffer from cubic computational complexity (e.g., $\mathcal{O}(n^3)$ for a matrix of size $n\times n$). We present Low-Rank GEMM, a novel approach that leverages low-rank matrix approximations to achieve sub-quadratic complexity while maintaining hardware-accelerated performance through FP8 precision and intelligent kernel selection. On a NVIDIA RTX 4090, our implementation achieves up to 378 TFLOPS on matrices up to $N=20480$, providing 75\% memory savings and $7.8\times$ speedup over PyTorch FP32 for large matrices. The system automatically adapts to hardware capabilities, selecting optimal decomposition methods (SVD, randomized SVD) and precision levels based on matrix characteristics and available accelerators. Comprehensive benchmarking on NVIDIA RTX 4090 demonstrates that Low-Rank GEMM becomes the fastest approach for matrices $N\geq10240$, surpassing traditional cuBLAS implementations through memory bandwidth optimization rather than computational shortcuts.
MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks. However, clinical decision-making often relies on quantitative assessments, such as measuring the size of a tumor or the angle of a joint, from which physicians draw their own diagnostic conclusions. This quantitative reasoning capability remains underexplored and poorly supported in existing VLMs. In this work, we introduce MedVision, a large-scale dataset and benchmark specifically designed to evaluate and improve VLMs on quantitative medical image analysis. MedVision spans 22 public datasets covering diverse anatomies and modalities, with 30.8 million image-annotation pairs. We focus on three representative quantitative tasks: (1) detection of anatomical structures and abnormalities, (2) tumor/lesion (T/L) size estimation, and (3) angle/distance (A/D) measurement. Our benchmarks show that current off-the-shelf VLMs perform poorly on these tasks. However, with supervised fine-tuning on MedVision, we significantly enhance their performance across detection, T/L estimation, and A/D measurement, demonstrating reduced error rates and improved precision. This work provides a foundation for developing VLMs with robust quantitative reasoning capabilities in medical imaging. Code and data are available at https://medvision-vlm.github.io.
Inverse Rendering for High-Genus Surface Meshes from Multi-View Images
Venue: arXiv cs.GR 2025
Year: 2025

We present a topology-informed inverse rendering approach for reconstructing high-genus surface meshes from multi-view images. Compared to 3D representations like voxels and point clouds, mesh-based representations are preferred as they enable the application of differential geometry theory and are optimized for modern graphics pipelines. However, existing inverse rendering methods often fail catastrophically on high-genus surfaces, leading to the loss of key topological features, and tend to oversmooth low-genus surfaces, resulting in the loss of surface details. This failure stems from their overreliance on Adam-based optimizers, which can lead to vanishing and exploding gradients. To overcome these challenges, we introduce an adaptive V-cycle remeshing scheme in conjunction with a re-parametrized Adam optimizer to enhance topological and geometric awareness. By periodically coarsening and refining the deforming mesh, our method informs mesh vertices of their current topology and geometry before optimization, mitigating gradient issues while preserving essential topological features. Additionally, we enforce topological consistency by constructing topological primitives with genus numbers that match those of ground truth using Gauss-Bonnet theorem. Experimental results demonstrate that our inverse rendering approach outperforms the current state-of-the-art method, achieving significant improvements in Chamfer Distance and Volume IoU, particularly for high-genus surfaces, while also enhancing surface details for low-genus surfaces.
Evaluation of NVENC Split-Frame Encoding (SFE) for UHD Video Transcoding
Venue: arXiv cs.AR 2025
Year: 2025

NVIDIA Encoder (NVENC) features in modern NVIDIA GPUs, offer significant advantages over software encoders by providing comparable Rate-Distortion (RD) performance while consuming considerably less power. The increasing capability of consumer devices to capture footage in Ultra High-Definition (UHD) at 4K and 8K resolutions necessitates high-performance video transcoders for internet-based delivery. To address this demand, NVIDIA introduced Split-Frame Encoding (SFE), a technique that leverages multiple on-die NVENC chips available in high-end GPUs. SFE splits a single UHD frame for parallel encoding across these physical encoders and subsequently stitches the results, which significantly improves encoding throughput. However, this approach is known to incur an RD performance penalty. The widespread adoption of NVIDIA GPUs in data centers, driven by the rise of Generative AI, means NVENC is poised to play a critical role in transcoding UHD video. To better understand the performance-efficiency tradeoff of SFE, this paper evaluates SFE's impact on RD performance, encoding throughput, power consumption, and end-to-end latency using standardized test sequences. The results show that for real-time applications, SFE nearly doubles encoding throughput with a negligible RD performance penalty, which enables the use of higher-quality presets for 4K and makes real-time 8K encoding feasible, effectively offsetting the minor RD penalty. Moreover, SFE adds no latency at 4K and can reduce it at 8K, positioning it as a key enabler for high-throughput, real-time UHD transcoding.
Evaluation of GPU Video Encoder for Low-Latency Real-Time 4K UHD Encoding
Venue: arXiv cs.AR 2025
Year: 2025

The demand for high-quality, real-time video streaming has grown exponentially, with 4K Ultra High Definition (UHD) becoming the new standard for many applications such as live broadcasting, TV services, and interactive cloud gaming. This trend has driven the integration of dedicated hardware encoders into modern Graphics Processing Units (GPUs). Nowadays, these encoders support advanced codecs like HEVC and AV1 and feature specialized Low-Latency and Ultra Low-Latency tuning, targeting end-to-end latencies of < 2 seconds and < 500 ms, respectively. As the demand for such capabilities grows toward the 6G era, a clear understanding of their performance implications is essential. In this work, we evaluate the low-latency encoding modes on GPUs from NVIDIA, Intel, and AMD from both Rate-Distortion (RD) performance and latency perspectives. The results are then compared against both the normal-latency tuning of hardware encoders and leading software encoders. Results show hardware encoders achieve significantly lower E2E latency than software solutions with slightly better RD performance. While standard Low-Latency tuning yields a poor quality-latency trade-off, the Ultra Low-Latency mode reduces E2E latency to 83 ms (5 frames) without additional RD impact. Furthermore, hardware encoder latency is largely insensitive to quality presets, enabling high-quality, low-latency streams without compromise.
When Top-ranked Recommendations Fail: Modeling Multi-Granular Negative Feedback for Explainable and Robust Video Recommendation
Venue: arXiv cs.MM 2025
Year: 2025

Existing video recommendation systems, relying mainly on ID-based embedding mapping and collaborative filtering, often fail to capture in-depth video content semantics. Moreover, most struggle to address biased user behaviors (e.g., accidental clicks, fast skips), leading to inaccurate interest modeling and frequent negative feedback in top recommendations with unclear causes. To tackle this issue, we collect real-world user video-watching sequences, annotate the reasons for users' dislikes, and construct a benchmark dataset for personalized explanations. We then introduce the Agentic Explainable Negative Feedback (ENF) framework, which integrates three core components: (1) the Profile Agent, extracting behavioral cues from users' historical data to derive psychological and personality profiles; (2) the Video Agent, performing comprehensive multimodal video analysis; and (3) the Reason Agent, synthesizing information from the other two agents to predict user engagement and generate explanations. Additionally, we propose the S-GRPO algorithm, enabling the model to progressively address complex tasks during reinforcement fine-tuning. Experimental results on the collected dataset show that our method significantly outperforms state-of-the-art baselines in negative feedback prediction and reason explanation. Notably, it achieves an 8.6% improvement over GPT-4o in reason classification. Deployment on the business platform further validates its benefits: increasing average user watch time by 6.2%, reducing the fast-skip rate by 9.4%, and significantly enhancing user satisfaction.
ObjectAlign: Neuro-Symbolic Object Consistency Verification and Correction
Venue: arXiv cs.CV 2025
Year: 2025

Video editing and synthesis often introduce object inconsistencies, such as frame flicker and identity drift that degrade perceptual quality. To address these issues, we introduce ObjectAlign, a novel framework that seamlessly blends perceptual metrics with symbolic reasoning to detect, verify, and correct object-level and temporal inconsistencies in edited video sequences. The novel contributions of ObjectAlign are as follows: First, we propose learnable thresholds for metrics characterizing object consistency (i.e. CLIP-based semantic similarity, LPIPS perceptual distance, histogram correlation, and SAM-derived object-mask IoU). Second, we introduce a neuro-symbolic verifier that combines two components: (a) a formal, SMT-based check that operates on masked object embeddings to provably guarantee that object identity does not drift, and (b) a temporal fidelity check that uses a probabilistic model checker to verify the video's formal representation against a temporal logic specification. A frame transition is subsequently deemed "consistent" based on a single logical assertion that requires satisfying both the learned metric thresholds and this unified neuro-symbolic constraint, ensuring both low-level stability and high-level temporal correctness. Finally, for each contiguous block of flagged frames, we propose a neural network based interpolation for adaptive frame repair, dynamically choosing the interpolation depth based on the number of frames to be corrected. This enables reconstruction of the corrupted frames from the last valid and next valid keyframes. Our results show up to 1.4 point improvement in CLIP Score and up to 6.1 point improvement in warp error compared to SOTA baselines on the DAVIS and Pexels video datasets.