OpenRaiser/Intern-Atlas huggingface dataset:
CDFlow: Generative Gradient Flows for Configuration Space Distance Fields via Neural ODEs
Venue: arXiv cs.RO 2025
Year: 2025

Signed Distance Fields (SDFs) are a fundamental representation in robot motion planning. Their configuration-space counterpart, the Configuration Space Distance Field (CDF), directly encodes distances in joint space, offering a unified representation for optimization and control. However, existing CDF formulations face two major challenges in high-degree-of-freedom (DoF) robots: (1) they effectively return only a single nearest collision configuration, neglecting the multi-modal nature of minimal-distance collision configurations and leading to gradient ambiguity; and (2) they rely on sparse sampling of the collision boundary, which often fails to identify the true closest configurations, producing oversmoothed approximations and geometric distortion in high-dimensional spaces. We propose CDFlow, a novel framework that addresses these limitations by learning a continuous flow in configuration space via Neural Ordinary Differential Equations (Neural ODEs). We redefine the problem from finding a single nearest point to modeling the distribution of minimal-distance collision configurations. We also introduce an adaptive refinement sampling strategy to generate high-fidelity training data for this distribution. The resulting Neural ODE implicitly models this multi-modal distribution and produces a smooth, consistent gradient field-derived as the expected direction towards the distribution-that mitigates gradient ambiguity and preserves sharp geometric features. Extensive experiments on high-DoF motion planning tasks demonstrate that CDFlow significantly improves planning efficiency, trajectory quality, and robustness compared to existing CDF-based methods, enabling more robust and efficient planning for collision-aware robots in complex environments.
Hyperspectral Polarimetric BRDFs of Real-world Materials
Venue: arXiv cs.GR 2025
Year: 2025

Acquiring bidirectional reflectance distribution functions (BRDFs) is essential for simulating light transport and analytically modeling material properties. Over the past two decades, numerous intensity-only BRDF datasets in the visible spectrum have been introduced, primarily for RGB image rendering applications. However, in scientific and engineering domains, there remains an unmet need to model light transport with polarization--a fundamental wave property of light--across hyperspectral bands. To address this gap, we present the first hyperspectral-polarimetric BRDF (hpBRDF) dataset of real-world materials, spanning wavelengths from 414 to 950\,nm and densely sampled at 68 spectral bands. This dataset covers both the visible and near-infrared (NIR) spectra, enabling detailed material analysis and light reflection simulations that incorporate polarization at each narrow spectral band. We develop an efficient hpBRDF acquisition system that captures high-dimensional hpBRDFs within a feasible acquisition time. Using this system, we demonstrate hyperspectral-polarimetric rendering using the acquired hpBRDFs. To provide insights on hpBRDF, we analyze the hpBRDFs with respect to their dependencies on wavelength, polarization state, material type, and illumination/viewing geometry. Also, we propose compact representations through principal component analysis and implicit neural hpBRDF modeling.
Homomorphic encryption schemes based on coding theory and polynomials
Venue: arXiv cs.CR 2025
Year: 2025

Homomorphic encryption is a powerful cryptographic tool that enables secure computations on the private data. It evaluates any function for any operation securely on the encrypted data without knowing its corresponding plaintext. For original data $p$, $c$ denotes the ciphertext of the original plaintext $p$, i.e. $c = Encrypt_k(p)$. This is crucial for any sensitive application running in the Cloud, because we must protect data privacy even in the case when the server has falled victim to a cyber attack. The encryption scheme $Encrypt_k$ is said to be homomorphic with respect to some set of operations $\mathcal{O}$, if for any operation $\circ \in \mathcal{O}$ one can compute $Encrypt_k(p_1 \circ p_2)$ from $Encrypt_k(p_1) \circ Encrypt_k(p_2)$. Those schemes come in three forms: somewhat, partially and fully homomorphic. In this survey, we present the state of art of the known homomorphic encryption schemes based on coding theory and polynomials.
Bridging the Synthetic-Real Gap: Supervised Domain Adaptation for Robust Spacecraft 6-DoF Pose Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Spacecraft Pose Estimation (SPE) is a fundamental capability for autonomous space operations such as rendezvous, docking, and in-orbit servicing. Hybrid pipelines that combine object detection, keypoint regression, and Perspective-n-Point (PnP) solvers have recently achieved strong results on synthetic datasets, yet their performance deteriorates sharply on real or lab-generated imagery due to the persistent synthetic-to-real domain gap. Existing unsupervised domain adaptation approaches aim to mitigate this issue but often underperform when a modest number of labeled target samples are available. In this work, we propose the first Supervised Domain Adaptation (SDA) framework tailored for SPE keypoint regression. Building on the Learning Invariant Representation and Risk (LIRR) paradigm, our method jointly optimizes domain-invariant representations and task-specific risk using both labeled synthetic and limited labeled real data, thereby reducing generalization error under domain shift. Extensive experiments on the SPEED+ benchmark demonstrate that our approach consistently outperforms source-only, fine-tuning, and oracle baselines. Notably, with only 5% labeled target data, our method matches or surpasses oracle performance trained on larger fractions of labeled data. The framework is lightweight, backbone-agnostic, and computationally efficient, offering a practical pathway toward robust and deployable spacecraft pose estimation in real-world space environments.
Soft Regrasping Tool Inspired by Jamming Gripper
Venue: arXiv cs.RO 2025
Year: 2025

Regrasping on fixtures is a promising approach to reduce pose uncertainty in robotic assembly, but conventional rigid fixtures lack adaptability and require dedicated designs for each part. To overcome this limitation, we propose a soft jig inspired by the jamming transition phenomenon, which can be continuously deformed to accommodate diverse object geometries. By pressing a triangular-pyramid-shaped tool into the membrane and evacuating the enclosed air, a stable cavity is formed as a placement space. We further optimize the stamping depth to balance placement stability and gripper accessibility. In soft-jig-based regrasping, the key challenge lies in optimizing the cavity size to achieve precise dropping; once the part is reliably placed, subsequent grasping can be performed with reduced uncertainty. Accordingly, we conducted drop experiments on ten mechanical parts of varying shapes, which achieved placement success rates exceeding 80% for most objects and above 90% for cylindrical ones, while failures were mainly caused by geometric constraints and membrane properties. These results demonstrate that the proposed jig enables general-purpose, accurate, and repeatable regrasping, while also clarifying its current limitations and future potential as a practical alternative to rigid fixtures in assembly automation.
4-uniform Maker-Breaker and Maker-Maker games are PSPACE-complete
Venue: arXiv cs.DM 2025
Year: 2025

We study two positional games where two players take turns picking a previously unpicked vertex of a hypergraph $H$. We say a player fills an edge of $H$ if that player has picked all the vertices of that edge. In the Maker-Maker game, whoever first fills an edge wins, or we get a draw if no edge is filled. In the Maker-Breaker game, the first player aims at filling an edge while the second player aims at preventing the first player from filling an edge. We show that, for both games, deciding whether the first player has a winning strategy is a PSPACE-complete problem even when restricted to 4-uniform hypergraphs. For the Maker-Maker game, this improves on a previous result for hypergraphs of rank 4. For the Maker-Breaker game, this improves on a previous result for 5-uniform hypergraphs, and closes the complexity gap as the problem for hypergraphs of rank 3 is known to be solvable in polynomial time.
Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation. Our approach leverages three specialized expert networks: A main segmentation expert, a signed distance field regression expert, and a boundary prediction expert, each dedicated to capturing distinct morphological features. Subsequently, the Multi-Gating Pseudo-labeling module dynamically aggregates expert features, enabling a robust fuse-and-refine pseudo-labeling mechanism. Furthermore, to eliminate manual tuning while dynamically balancing multiple learning objectives, we propose an Adaptive Multi-Objective Loss. Extensive experiments on GlaS and CRAG benchmarks show that our method outperforms state-of-the-art approaches in low-label settings, highlighting the potential of MoE-based architectures in advancing semi-supervised segmentation. Our code is available at https://github.com/vnlvi2k3/Semi-MoE.
SpecDiff: Accelerating Diffusion Model Inference with Self-Speculation
Venue: arXiv cs.CV 2025
Year: 2025

Feature caching has recently emerged as a promising method for diffusion model acceleration. It effectively alleviates the inefficiency problem caused by high computational requirements by caching similar features in the inference process of the diffusion model. In this paper, we analyze existing feature caching methods from the perspective of information utilization, and point out that relying solely on historical information will lead to constrained accuracy and speed performance. And we propose a novel paradigm that introduces future information via self-speculation based on the information similarity at the same time step across different iteration times. Based on this paradigm, we present \textit{SpecDiff}, a training-free multi-level feature caching strategy including a cached feature selection algorithm and a multi-level feature classification algorithm. (1) Feature selection algorithm based on self-speculative information. \textit{SpecDiff} determines a dynamic importance score for each token based on self-speculative information and historical information, and performs cached feature selection through the importance score. (2) Multi-level feature classification algorithm based on feature importance scores. \textit{SpecDiff} classifies tokens by leveraging the differences in feature importance scores and introduces a multi-level feature calculation strategy. Extensive experiments show that \textit{SpecDiff} achieves average 2.80 \times, 2.74 \times , and 3.17\times speedup with negligible quality loss in Stable Diffusion 3, 3.5, and FLUX compared to RFlow on NVIDIA A800-80GB GPU. By merging speculative and historical information, \textit{SpecDiff} overcomes the speedup-accuracy trade-off bottleneck, pushing the Pareto frontier of speedup and accuracy in the efficient diffusion model inference.
PhysicalAgent: Towards General Cognitive Robotics with Foundation World Models
Venue: arXiv cs.RO 2025
Year: 2025

We introduce PhysicalAgent, an agentic framework for robotic manipulation that integrates iterative reasoning, diffusion-based video generation, and closed-loop execution. Given a textual instruction, our method generates short video demonstrations of candidate trajectories, executes them on the robot, and iteratively re-plans in response to failures. This approach enables robust recovery from execution errors. We evaluate PhysicalAgent across multiple perceptual modalities (egocentric, third-person, and simulated) and robotic embodiments (bimanual UR3, Unitree G1 humanoid, simulated GR1), comparing against state-of-the-art task-specific baselines. Experiments demonstrate that our method consistently outperforms prior approaches, achieving up to 83% success on human-familiar tasks. Physical trials reveal that first-attempt success is limited (20-30%), yet iterative correction increases overall success to 80% across platforms. These results highlight the potential of video-based generative reasoning for general-purpose robotic manipulation and underscore the importance of iterative execution for recovering from initial failures. Our framework paves the way for scalable, adaptable, and robust robot control.
MAP: End-to-End Autonomous Driving with Map-Assisted Planning
Venue: arXiv cs.RO 2025
Year: 2025

In recent years, end-to-end autonomous driving has attracted increasing attention for its ability to jointly model perception, prediction, and planning within a unified framework. However, most existing approaches underutilize the online mapping module, leaving its potential to enhance trajectory planning largely untapped. This paper proposes MAP (Map-Assisted Planning), a novel map-assisted end-to-end trajectory planning framework. MAP explicitly integrates segmentation-based map features and the current ego status through a Plan-enhancing Online Mapping module, an Ego-status-guided Planning module, and a Weight Adapter based on current ego status. Experiments conducted on the DAIR-V2X-seq-SPD dataset demonstrate that the proposed method achieves a 16.6% reduction in L2 displacement error, a 56.2% reduction in off-road rate, and a 44.5% improvement in overall score compared to the UniV2X baseline, even without post-processing. Furthermore, it achieves top ranking in Track 2 of the End-to-End Autonomous Driving through V2X Cooperation Challenge of MEIS Workshop @CVPR2025, outperforming the second-best model by 39.5% in terms of overall score. These results highlight the effectiveness of explicitly leveraging semantic map features in planning and suggest new directions for improving structure design in end-to-end autonomous driving systems. Our code is available at https://gitee.com/kymkym/map.git
Noise-Level Diffusion Guidance: Well Begun is Half Done
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-level optimization approach that refines initial noise by increasing the likelihood of its alignment with general guidance - requiring no additional training data, auxiliary networks, or backpropagation. The proposed NLG approach provides a unified framework generalizable to both conditional and unconditional diffusion models, accommodating various forms of diffusion-level guidance. Extensive experiments on five standard benchmarks demonstrate that our approach enhances output generation quality and input condition adherence. By seamlessly integrating with existing guidance methods while maintaining computational efficiency, our method establishes NLG as a practical and scalable enhancement to diffusion models. Code can be found at https://github.com/harveymannering/NoiseLevelGuidance.
SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference from the opposite vehicle (OV) and complex ones involving interactions with the OV. Based on this dataset, a goal-conditioned state encoder is pretrained to map the fused perception information into the latent space. Then, an offline RL policy is optimized with a conservative regularizer that penalizes out-of-distribution actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. Comparative results demonstrate the superior performance of our framework with the highest success rate and robust generalization to out-of-distribution parking scenarios. The related dataset and source code will be made publicly available after the paper is accepted.
Analytical Modelling of the Transport in Analog Filamentary Conductive-Metal-Oxide/HfOx ReRAM Devices
Venue: arXiv cs.ET 2025
Year: 2025

The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog filamentary conductive-metal-oxide (CMO)/HfOx redox-based resistive switching memory (ReRAM) represents a key technology. Despite device performance enhancements reported in literature, the underlying mechanism behind resistive switching is not fully understood. This work presents the first physics-based analytical model of the current transport and of the resistive switching in these devices. As a case study, analog TaOx/HfOx ReRAM devices are considered. The current transport is explained by a trap-to-trap tunneling process, and the resistive switching by a modulation of the defect density within the sub-band of the TaOx that behaves as electric field and temperature confinement layer. The local temperature and electric field distributions are derived from the solution of the electric and heat transport equations in a 3D finite element ReRAM model. The intermediate resistive states are described as a gradual modulation of the TaOx defect density, which results in a variation of its electrical conductivity. The drift-dynamics of ions during the resistive switching is analytically described, allowing the estimation of defect migration energies in the TaOx layer. Moreover, the role of the electro-thermal properties of the CMO layer is unveiled. The proposed analytical model accurately describes the experimental switching characteristic of analog TaOx/HfOx ReRAM devices, increasing the physical understanding and providing the equations necessary for circuit simulations incorporating this technology.
JaGuard: Jamming Correction of GNSS Deviation with Deep Temporal Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Global Navigation Satellite Systems (GNSS) are increasingly exposed to intentional jamming, threatening reliability when accurate positioning and timing are most critical. We address this problem by formulating interference mitigation as a dynamic graph regression task and propose JaGuard, a receiver-centric temporal graph neural network that estimates and corrects latitude and longitude errors. At each 1 Hz epoch, the satellite-receiver scene is represented as a heterogeneous star graph with time-varying satellite attributes such as SNR, azimuth and elevation. A single-layer HeteroGCLSTM fuses one-hop spatial context with short-term temporal dynamics to produce a 2D deviation estimate. We evaluate JaGuard on data collected from two commercial receivers under controlled conducted jamming using three jammer types (CW, 3xCW, FM) and six power levels from -45 to -70 dBm, each repeated 50 times across pre-jam, jam, and recovery phases. JaGuard outperforms strong multivariate baselines (TSMixer, uniform CNN, Seq2Point) in all conditions. Under severe jamming at -45 dBm, it achieves 3.64-7.74 cm MAE, improving to 1.59-1.90 cm for -60 to -70 dBm. On mixed-mode datasets, it attains 3.78 cm MAE on GP01 and 4.25 cm on U-blox 10. With only 10 percent of the training data, JaGuard remains ahead, reaching about 20 cm MAE compared to 36-42 cm for the baselines.
MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightweight models often struggle in this setting due to their weak semantic priors, while large vision-language models (VLMs) offer strong object-level understanding but are too computationally demanding for real-time or on-device applications. We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a distillation framework that transfers multimodal region-level knowledge from a frozen VLM teacher into a lightweight vision-only detector. MOCHA extracts fused visual and textual teacher's embeddings and uses them to guide student training through a dual-objective loss that enforces accurate local alignment and global relational consistency across regions. This process enables efficient transfer of semantics without the need for teacher modifications or textual input at inference. MOCHA consistently outperforms prior baselines across four personalized detection benchmarks under strict few-shot regimes, yielding a +10.1 average improvement, with minimal inference cost.
Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale
Venue: arXiv cs.CL 2025
Year: 2025

We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong AR$\leftrightarrow$EN teacher to FP8 (yielding $\sim$2$\times$ higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to instruction following. We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the "nano" ($\leq$2B) and "small" (7-9B) categories, outperforming their bases. We release models, data, evaluation, and recipes to accelerate research in Arabic NLP.
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Venue: arXiv cs.RO 2025
Year: 2025

Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot Gaussian process (GeoGP) learning framework that enables robots to perform human-guided automated control from a single motion prompt. A dataset-construction strategy based on coordinate transformations is introduced that enforces invariance to translation, rotation, and scaling, while supporting multi-step predictions. Moreover, GeoGP is robust to variations in the user's motion prompt and supports multi-skill autonomy. We validate the proposed approach through numerical simulations with the designed user graphical interface and two real-world robotic experiments, which demonstrate that the proposed method is effective, generalizes across tasks, and significantly reduces the demonstration burden. Project page is available at: https://prompt2auto.github.io