OpenRaiser/Intern-Atlas huggingface dataset:
Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Prior sim-to-real methods for legged robots mostly rely on the domain randomization approach, where a fixed finite set of simulation parameters is randomized during training. Instead, our method adds state-dependent perturbations to the input joint torque used for forward simulation during the training phase. These state-dependent perturbations are designed to simulate a broader range of reality gaps than those captured by randomizing a fixed set of simulation parameters. Experimental results show that our method enables humanoid locomotion policies that achieve greater robustness against complex reality gaps unseen in the training domain.
Visually Similar Pair Alignment for Robust Cross-Domain Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Domain gaps between training data (source) and real-world environments (target) often degrade the performance of object detection models. Most existing methods aim to bridge this gap by aligning features across source and target domains but often fail to account for visual differences, such as color or orientation, in alignment pairs. This limitation leads to less effective domain adaptation, as the model struggles to manage both domain-specific shifts (e.g., fog) and visual variations simultaneously. In this work, we demonstrate for the first time, using a custom-built dataset, that aligning visually similar pairs significantly improves domain adaptation. Based on this insight, we propose a novel memory-based system to enhance domain alignment. This system stores precomputed features of foreground objects and background areas from the source domain, which are periodically updated during training. By retrieving visually similar source features for alignment with target foreground and background features, the model effectively addresses domain-specific differences while reducing the impact of visual variations. Extensive experiments across diverse domain shift scenarios validate our method's effectiveness, achieving 53.1 mAP on Foggy Cityscapes and 62.3 on Sim10k, surpassing prior state-of-the-art methods by 1.2 and 4.1 mAP, respectively.
A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping
Venue: arXiv cs.CV 2025
Year: 2025

In task-based few-shot learning paradigms, it is commonly assumed that different tasks are independently and identically distributed (i.i.d.). However, in real-world scenarios, the distribution encountered in few-shot learning can significantly differ from the distribution of existing data. Thus, how to effectively leverage existing data knowledge to enable models to quickly adapt to class variations under non-i.i.d. assumptions has emerged as a key research challenge. To address this challenge, this paper proposes a new cross-domain few-shot learning approach based on domain knowledge mapping, applied consistently throughout the pre-training, training, and testing phases. In the pre-training phase, our method integrates self-supervised and supervised losses by maximizing mutual information, thereby mitigating mode collapse. During the training phase, the domain knowledge mapping layer collaborates with a domain classifier to learn both domain mapping capabilities and the ability to assess domain adaptation difficulty. Finally, this approach is applied during the testing phase, rapidly adapting to domain variations through meta-training tasks on support sets, consequently enhancing the model's capability to transfer domain knowledge effectively. Experimental validation conducted across six datasets from diverse domains demonstrates the effectiveness of the proposed method.
Crafting Query-Aware Selective Attention for Single Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details. While Vision Transformer (ViT)-based models improve SISR by capturing long-range dependencies, they suffer from quadratic computational costs or employ selective attention mechanisms that do not explicitly focus on query-relevant regions. Despite these advancements, prior work has overlooked how selective attention mechanisms should be effectively designed for SISR. We propose SSCAN, which dynamically selects the most relevant key-value windows based on query similarity, ensuring focused feature extraction while maintaining efficiency. In contrast to prior approaches that apply attention globally or heuristically, our method introduces a query-aware window selection strategy that better aligns attention computation with important image regions. By incorporating fixed-sized windows, SSCAN reduces memory usage and enforces linear token-to-token complexity, making it scalable for large images. Our experiments demonstrate that SSCAN outperforms existing attention-based SISR methods, achieving up to 0.14 dB PSNR improvement on urban datasets, guaranteeing both computational efficiency and reconstruction quality in SISR.
Collision avoidance from monocular vision trained with novel view synthesis
Venue: arXiv cs.RO 2025
Year: 2025

Collision avoidance can be checked in explicit environment models such as elevation maps or occupancy grids, yet integrating such models with a locomotion policy requires accurate state estimation. In this work, we consider the question of collision avoidance from an implicit environment model. We use monocular RGB images as inputs and train a collisionavoidance policy from photorealistic images generated by 2D Gaussian splatting. We evaluate the resulting pipeline in realworld experiments under velocity commands that bring the robot on an intercept course with obstacles. Our results suggest that RGB images can be enough to make collision-avoidance decisions, both in the room where training data was collected and in out-of-distribution environments.
Handling LP-Rounding for Hierarchical Clustering and Fitting Distances by Ultrametrics
Venue: arXiv cs.DS 2025
Year: 2025

We consider the classic correlation clustering problem in the hierarchical setting. Given a complete graph $G=(V,E)$ and $\ell$ layers of input information, where the input of each layer consists of a nonnegative weight and a labeling of the edges with either + or -, this problem seeks to compute for each layer a partition of $V$ such that the partition for any non-top layer subdivides the partition in the upper-layer and the weighted number of disagreements over the layers is minimized. Hierarchical correlation clustering is a natural formulation of the classic problem of fitting distances by ultrametrics, which is further known as numerical taxonomy in the literature. While single-layer correlation clustering received wide attention since it was introduced and major progress evolved in the past three years, few is known for this problem in the hierarchical setting. The lack of understanding and adequate tools is reflected in the large approximation ratio known for this problem originating from 2021. In this work we make both conceptual and technical contributions towards the hierarchical clustering problem. We present a simple paradigm that greatly facilitates LP-rounding in hierarchical clustering, illustrated with an algorithm providing a significantly improved approximation guarantee of 25.7846 for the hierarchical correlation clustering problem. Our techniques reveal surprising new properties of the formulation presented and subsequently used in previous works for hierarchical clustering over the past two decades. This provides an interpretation on the core problem in hierarchical clustering as the problem of finding cuts with prescribed properties regarding average distances. We further illustrate this perspective by showing that a direct application of the techniques gives a simple alternative to the state-of-the-art result for the ultrametric violation distance problem.
PETNet -- Coincident Particle Event Detection using Spiking Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural networks. Their time-variant nature makes them particularly suitable for processing time-resolved, sparse binary data. In this paper, we investigate the potential of leveraging SNNs for the detection of photon coincidences in positron emission tomography (PET) data. PET is a medical imaging technique based on injecting a patient with a radioactive tracer and detecting the emitted photons. One central post-processing task for inferring an image of the tracer distribution is the filtering of invalid hits occurring due to e.g. absorption or scattering processes. Our approach, coined PETNet, interprets the detector hits as a binary-valued spike train and learns to identify photon coincidence pairs in a supervised manner. We introduce a dedicated multi-objective loss function and demonstrate the effects of explicitly modeling the detector geometry on simulation data for two use-cases. Our results show that PETNet can outperform the state-of-the-art classical algorithm with a maximal coincidence detection $F_1$ of 95.2%. At the same time, PETNet is able to predict photon coincidences up to 36 times faster than the classical approach, highlighting the great potential of SNNs in particle physics applications.
EDIT: Enhancing Vision Transformers by Mitigating Attention Sink through an Encoder-Decoder Architecture
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we propose EDIT (Encoder-Decoder Image Transformer), a novel architecture designed to mitigate the attention sink phenomenon observed in Vision Transformer models. Attention sink occurs when an excessive amount of attention is allocated to the [CLS] token, distorting the model's ability to effectively process image patches. To address this, we introduce a layer-aligned encoder-decoder architecture, where the encoder utilizes self-attention to process image patches, while the decoder uses cross-attention to focus on the [CLS] token. Unlike traditional encoder-decoder framework, where the decoder depends solely on high-level encoder representations, EDIT allows the decoder to extract information starting from low-level features, progressively refining the representation layer by layer. EDIT is naturally interpretable demonstrated through sequential attention maps, illustrating the refined, layer-by-layer focus on key image features. Experiments on ImageNet-1k and ImageNet-21k, along with transfer learning tasks, show that EDIT achieves consistent performance improvements over DeiT3 models. These results highlight the effectiveness of EDIT's design in addressing attention sink and improving visual feature extraction.
More Efficient Stealth Address Protocol
Venue: arXiv cs.CR 2025
Year: 2025

The integration of privacy-preserving transactions into public blockchains such as Ethereum remains a major challenge. The Stealth Address Protocol (SAP) provides recipient anonymity by generating unlinkable stealth addresses. Existing SAPs, such as the Dual-Key Stealth Address Protocol and the Curvy Protocol, have shown significant improvements in efficiency, but remain vulnerable to quantum attacks. Post-quantum SAPs based on lattice-based cryptography, such as the Module-LWE SAP, on the other hand, offer quantum resistance while achieving better performance. In this paper, we present a novel hybrid SAP that combines the Curvy protocol with the computational advantages of the Module-LWE technique while remaining Ethereum-friendly. In contrast to full post-quantum solutions, our approach does not provide quantum security, but achieves a significant speedup in scanning the ephemeral public key registry, about three times faster than the Curvy protocol. We present a detailed cryptographic construction of our protocol and compare its performance with existing solutions. Our results prove that this hybrid approach is the most efficient Ethereum-compatible SAP to date.
Learning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity
Venue: arXiv cs.NE 2025
Year: 2025

Understanding how biological neural networks are shaped via local plasticity mechanisms can lead to energy-efficient and self-adaptive information processing systems, which promises to mitigate some of the current roadblocks in edge computing systems. While biology makes use of spikes to seamless use both spike timing and mean firing rate to modulate synaptic strength, most models focus on one of the two. In this work, we present a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity. We show how the rule reproduces results from spike time and spike rate protocols from neuroscientific studies. Moreover, we use the model to train spiking neural networks on MNIST digit recognition to show and explain what sort of mechanisms are needed to learn real-world patterns. We show how our model is sensitive to correlated spiking activity and how this enables it to modulate the learning rate of the network without altering the mean firing rate of the neurons nor the hyparameters of the learning rule. To the best of our knowledge, this is the first work that showcases how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.
Audio-visual Event Localization on Portrait Mode Short Videos
Venue: arXiv cs.MM 2025
Year: 2025

Audio-visual event localization (AVEL) plays a critical role in multimodal scene understanding. While existing datasets for AVEL predominantly comprise landscape-oriented long videos with clean and simple audio context, short videos have become the primary format of online video content due to the the proliferation of smartphones. Short videos are characterized by portrait-oriented framing and layered audio compositions (e.g., overlapping sound effects, voiceovers, and music), which brings unique challenges unaddressed by conventional methods. To this end, we introduce AVE-PM, the first AVEL dataset specifically designed for portrait mode short videos, comprising 25,335 clips that span 86 fine-grained categories with frame-level annotations. Beyond dataset creation, our empirical analysis shows that state-of-the-art AVEL methods suffer an average 18.66% performance drop during cross-mode evaluation. Further analysis reveals two key challenges of different video formats: 1) spatial bias from portrait-oriented framing introduces distinct domain priors, and 2) noisy audio composition compromise the reliability of audio modality. To address these issues, we investigate optimal preprocessing recipes and the impact of background music for AVEL on portrait mode videos. Experiments show that these methods can still benefit from tailored preprocessing and specialized model design, thus achieving improved performance. This work provides both a foundational benchmark and actionable insights for advancing AVEL research in the era of mobile-centric video content. Dataset and code will be released.
AI-Driven Consensus: Modeling Multi-Agent Networks with Long-Range Interactions through path-Laplacian Matrices
Venue: arXiv cs.SI 2025
Year: 2025

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning methods (LSTM, xLSTM, Transformer, XGBoost, and ConvLSTM) to predict the final consensus value based on directed networks (Erdös-Renyi, Watts-Strogatz, and Barabási-Albert) and on the initial state. We highlight how different k-hop interactions affect the performance of the tested methods. This framework opens new avenues for analyzing multi-scale diffusion processes in large-scale, complex networks.
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Missing instances in time series data impose a significant challenge to deep learning models, particularly in regression tasks. In the Earth Observation field, satellite failure or cloud occlusion frequently results in missing time-steps, introducing uncertainties in the predicted output and causing a decline in predictive performance. While many studies address missing time-steps through data augmentation to improve model robustness, the uncertainty arising at the input level is commonly overlooked. To address this gap, we introduce Monte Carlo Temporal Dropout (MC-TD), a method that explicitly accounts for input-level uncertainty by randomly dropping time-steps during inference using a predefined dropout ratio, thereby simulating the effect of missing data. To bypass the need for costly searches for the optimal dropout ratio, we extend this approach with Monte Carlo Concrete Temporal Dropout (MC-ConcTD), a method that learns the optimal dropout distribution directly. Both MC-TD and MC-ConcTD are applied during inference, leveraging Monte Carlo sampling for uncertainty quantification. Experiments on three EO time-series datasets demonstrate that MC-ConcTD improves predictive performance and uncertainty calibration compared to existing approaches. Additionally, we highlight the advantages of adaptive dropout tuning over manual selection, making uncertainty quantification more robust and accessible for EO applications.
S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications
Venue: arXiv cs.CV 2025
Year: 2025

We introduce the S-EO dataset: a large-scale, high-resolution dataset, designed to advance geometry-aware shadow detection. Collected from diverse public-domain sources, including challenge datasets and government providers such as USGS, our dataset comprises 702 georeferenced tiles across the USA, each covering 500x500 m. Each tile includes multi-date, multi-angle WorldView-3 pansharpened RGB images, panchromatic images, and a ground-truth DSM of the area obtained from LiDAR scans. For each image, we provide a shadow mask derived from geometry and sun position, a vegetation mask based on the NDVI index, and a bundle-adjusted RPC model. With approximately 20,000 images, the S-EO dataset establishes a new public resource for shadow detection in remote sensing imagery and its applications to 3D reconstruction. To demonstrate the dataset's impact, we train and evaluate a shadow detector, showcasing its ability to generalize, even to aerial images. Finally, we extend EO-NeRF - a state-of-the-art NeRF approach for satellite imagery - to leverage our shadow predictions for improved 3D reconstructions.
Beyond Tools: Generative AI as Epistemic Infrastructure in Education
Venue: arXiv cs.CY 2025
Year: 2025

AI systems are increasingly embedded in practices where humans have traditionally exercised epistemic agency, the capacity to actively engage in knowledge formation and validation. This paper argues that understanding AI's impact on epistemic agency requires analyzing these systems as epistemic infrastructures rather than as neutral tools. Drawing on theories of technological mediation and distributed cognition, I advance a framework that foregrounds how AI systems reconfigure the conditions under which epistemic agency can be exercised. The framework specifies three analytical conditions: affordances for skilled epistemic actions, support for epistemic sensitivity, and implications for habit formation. I apply this framework to AI systems deployed in education, a domain where epistemic agency is both professionally essential and ethically significant. Analysis of AI lesson planning and feedback tools reveals patterns of epistemic substitution: while useful for efficiently handling teaching tasks, these systems perform cognitive operations without sustaining skilled epistemic actions, epistemic sensitivity, or virtuous habit formation, potentially preventing the cultivation of professional judgment that relies on these practices. The findings contribute to philosophical debates about AI and human agency by specifying mechanisms through which infrastructural embedding shapes epistemic possibilities, and offer design principles for AI systems that sustain rather than supplant human epistemic agency.
Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration
Venue: arXiv cs.AI 2025
Year: 2025

Agents powered by Large Language Models (LLMs) have recently demonstrated impressive capabilities in various tasks. Still, they face limitations in tasks requiring specific, structured knowledge, flexibility, or accountable decision-making. While agents are capable of perceiving their environments, forming inferences, planning, and executing actions towards goals, they often face issues such as hallucinations and lack of contextual memory across interactions. This paper explores how Case-Based Reasoning (CBR), a strategy that solves new problems by referencing past experiences, can be integrated into LLM agent frameworks. This integration allows LLMs to leverage explicit knowledge, enhancing their effectiveness. We systematically review the theoretical foundations of these enhanced agents, identify critical framework components, and formulate a mathematical model for the CBR processes of case retrieval, adaptation, and learning. We also evaluate CBR-enhanced agents against other methods like Chain-of-Thought reasoning and standard Retrieval-Augmented Generation, analyzing their relative strengths. Moreover, we explore how leveraging CBR's cognitive dimensions (including self-reflection, introspection, and curiosity) via goal-driven autonomy mechanisms can further enhance the LLM agent capabilities. Contributing to the ongoing research on neuro-symbolic hybrid systems, this work posits CBR as a viable technique for enhancing the reasoning skills and cognitive aspects of autonomous LLM agents.
PathSegDiff: Pathology Segmentation using Diffusion model representations
Venue: arXiv cs.CV 2025
Year: 2025

Image segmentation is crucial in many computational pathology pipelines, including accurate disease diagnosis, subtyping, outcome, and survivability prediction. The common approach for training a segmentation model relies on a pre-trained feature extractor and a dataset of paired image and mask annotations. These are used to train a lightweight prediction model that translates features into per-pixel classes. The choice of the feature extractor is central to the performance of the final segmentation model, and recent literature has focused on finding tasks to pre-train the feature extractor. In this paper, we propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H\&E stained histopathology images. We employ a simple, fully convolutional network to process the features extracted from the LDM and generate segmentation masks. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets, highlighting the effectiveness of domain-specific diffusion pre-training in capturing intricate tissue structures and enhancing segmentation accuracy in histopathology images.