OpenRaiser/Intern-Atlas huggingface dataset:
Neural Diffusion Processes for Physically Interpretable Survival Prediction
Venue: arXiv cs.LG 2025
Year: 2025

We introduce DeepFHT, a survival-analysis framework that couples deep neural networks with first hitting time (FHT) distributions from stochastic process theory. Time to event is represented as the first passage of a latent diffusion process to an absorbing boundary. A neural network maps input variables to physically meaningful parameters including initial condition, drift, and diffusion, within a chosen FHT process such as Brownian motion, both with drift and driftless. This yields closed- form survival and hazard functions and captures time-varying risk without assuming proportional- hazards. We compare DeepFHT with Cox regression using synthetic and real-world datasets. The method achieves predictive accuracy on par with the state-of-the-art approach, while maintaining a physics- based interpretable parameterization that elucidates the relation between input features and risk. This combination of stochastic process theory and deep learning provides a principled avenue for modeling survival phenomena in complex systems
Tele-rehabilitation with online skill transfer and adaptation in $\mathbb{R}^3 \times \mathit{S}^3$
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a tele-teaching framework for the domain of robot-assisted tele-rehabilitation. The system connects two robotic manipulators on therapist and patient side via bilateral teleoperation, enabling a therapist to remotely demonstrate rehabilitation exercises that are executed by the patient-side robot. A 6-DoF Dynamical Movement Primitives formulation is employed to jointly encode translational and rotational motions in $\mathbb{R}^3 \times \mathit{S}^3$ space, ensuring accurate trajectory reproduction. The framework supports smooth transitions between therapist-led guidance and patient passive training, while allowing adaptive adjustment of motion. Experiments with 7-DoF manipulators demonstrate the feasibility of the approach, highlighting its potential for personalized and remotely supervised rehabilitation.
AI in data science education: experiences from the classroom
Venue: arXiv cs.AI 2025
Year: 2025

This study explores the integration of AI, particularly large language models (LLMs) like ChatGPT, into educational settings, focusing on the implications for teaching and learning. Through interviews with course coordinators from data science courses at Wageningen University, this research identifies both the benefits and challenges associated with AI in the classroom. While AI tools can streamline tasks and enhance learning, concerns arise regarding students' overreliance on these technologies, potentially hindering the development of essential cognitive and problem solving skills. The study highlights the importance of responsible AI usage, ethical considerations, and the need for adapting assessment methods to ensure educational outcomes are met. With careful integration, AI can be a valuable asset in education, provided it is used to complement rather than replace fundamental learning processes.
Are Time Series Foundation Models Susceptible to Catastrophic Forgetting?
Venue: arXiv cs.LG 2025
Year: 2025

Time Series Foundation Models (TSFMs) have shown promising zero-shot generalization across diverse forecasting tasks. However, their robustness to continual adaptation remains underexplored. In this work, we investigate the extent to which TSFMs suffer from catastrophic forgetting when fine-tuned sequentially on multiple datasets. Using synthetic datasets designed with varying degrees of periodic structure, we measure the trade-off between adaptation to new data and retention of prior knowledge. Our experiments reveal that, while fine-tuning improves performance on new tasks, it often causes significant degradation on previously learned ones, illustrating a fundamental stability-plasticity dilemma.
PhraseStereo: The First Open-Vocabulary Stereo Image Segmentation Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Understanding how natural language phrases correspond to specific regions in images is a key challenge in multimodal semantic segmentation. Recent advances in phrase grounding are largely limited to single-view images, neglecting the rich geometric cues available in stereo vision. For this, we introduce PhraseStereo, the first novel dataset that brings phrase-region segmentation to stereo image pairs. PhraseStereo builds upon the PhraseCut dataset by leveraging GenStereo to generate accurate right-view images from existing single-view data, enabling the extension of phrase grounding into the stereo domain. This new setting introduces unique challenges and opportunities for multimodal learning, particularly in leveraging depth cues for more precise and context-aware grounding. By providing stereo image pairs with aligned segmentation masks and phrase annotations, PhraseStereo lays the foundation for future research at the intersection of language, vision, and 3D perception, encouraging the development of models that can reason jointly over semantics and geometry. The PhraseStereo dataset will be released online upon acceptance of this work.
Logical Consistency Between Disagreeing Experts and Its Role in AI Safety
Venue: arXiv cs.AI 2025
Year: 2025

If two experts disagree on a test, we may conclude both cannot be 100 per cent correct. But if they completely agree, no possible evaluation can be excluded. This asymmetry in the utility of agreements versus disagreements is explored here by formalizing a logic of unsupervised evaluation for classifiers. Its core problem is computing the set of group evaluations that are logically consistent with how we observe them agreeing and disagreeing in their decisions. Statistical summaries of their aligned decisions are inputs into a Linear Programming problem in the integer space of possible correct or incorrect responses given true labels. Obvious logical constraints, such as, the number of correct responses cannot exceed the number of observed responses, are inequalities. But in addition, there are axioms, universally applicable linear equalities that apply to all finite tests. The practical and immediate utility of this approach to unsupervised evaluation using only logical consistency is demonstrated by building no-knowledge alarms that can detect when one or more LLMs-as-Judges are violating a minimum grading threshold specified by the user.
Virtual Reality Alters Perceived Functional Body Size
Venue: arXiv cs.HC 2025
Year: 2025

Virtual reality (VR) introduces sensory perturbations that may impact perception and action. The current study was designed to investigate how immersive VR presented through a head-mounted display (HMD) affects perceived functional body size using a passable aperture paradigm. Participants (n=60) performed an action task (sidle through apertures) and a perception task (adjust aperture width until passable without contact) in both physical, unmediated reality (UR) and VR. Results revealed significantly higher action and perceptual thresholds in VR compared to UR. Affordance ratios (perceptual threshold over action threshold) were also higher in VR, indicating that the increase in perceptual thresholds in VR was driven partly by sensorimotor uncertainty, as reflected in the increase in the action thresholds, and partly by perceptual distortions imposed by VR. This perceptual overestimation in VR also persisted as an aftereffect in UR following VR exposure. Geometrical modelling attributed the disproportionate increase in the perceptual threshold in VR primarily to depth compression. This compression, stemming from the vergence-accommodation conflict (VAC), caused the virtual aperture to be perceived as narrower than depicted, thus requiring a wider adjusted aperture. Critically, after mathematically correcting for the VAC's impact on perceived aperture width, the affordance ratios in VR became equivalent to those in UR. These outcomes demonstrate a recovered invariant geometrical scaling, suggesting that perception remained functionally attuned to action capabilities once VAC-induced distortions were accounted for. These findings highlight that VR-induced depth compression systematically alters perceived body-environment relationships, leading to an altered sense of one's functional body size.
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
Venue: arXiv cs.CL 2025
Year: 2025

While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from three fundamental challenges: decomposition errors, where tasks are incorrectly broken down; retrieval missing, where key evidence fails to be retrieved; and reasoning errors, where flawed logic propagates through the reasoning chain. A single failure in any of these stages can derail the final answer. We propose Erasable Reinforcement Learning (ERL), a novel framework that transforms fragile reasoning into a robust process. ERL explicitly identifies faulty steps, erases them, and regenerates reasoning in place, preventing defective logic from propagating through the reasoning chain. This targeted correction mechanism turns brittle reasoning into a more resilient process. Models trained with ERL, termed ESearch, achieve substantial improvements on HotpotQA, MuSiQue, 2Wiki, and Bamboogle, with the 3B model achieving +8.48% EM and +11.56% F1, and the 7B model achieving +5.38% EM and +7.22% F1 over previous state-of-the-art(SOTA) results. These findings suggest that erasable reinforcement learning provides a powerful paradigm shift for robust multi-step reasoning in LLMs.
Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Venue: arXiv cs.CV 2025
Year: 2025

State Space Models (SSMs)-most notably RNNs-have historically played a central role in sequential modeling. Although attention mechanisms such as Transformers have since dominated due to their ability to model global context, their quadratic complexity and limited scalability make them less suited for long sequences. Video super-resolution (VSR) methods have traditionally relied on recurrent architectures to propagate features across frames. However, such approaches suffer from well-known issues including vanishing gradients, lack of parallelism, and slow inference speed. Recent advances in selective SSMs like Mamba offer a compelling alternative: by enabling input-dependent state transitions with linear-time complexity, Mamba mitigates these issues while maintaining strong long-range modeling capabilities. Despite this potential, Mamba alone struggles to capture fine-grained spatial dependencies due to its causal nature and lack of explicit context aggregation. To address this, we propose a hybrid architecture that combines shifted window self-attention for spatial context aggregation with Mamba-based selective scanning for efficient temporal propagation. Furthermore, we introduce Gather-Scatter Mamba (GSM), an alignment-aware mechanism that warps features toward a center anchor frame within the temporal window before Mamba propagation and scatters them back afterward, effectively reducing occlusion artifacts and ensuring effective redistribution of aggregated information across all frames. The official implementation is provided at: https://github.com/Ko-Lani/GSMamba.
Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
Venue: arXiv cs.AI 2025
Year: 2025

Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to extract implicit relationships, and considering subjective goals and knowledge. In this paper, we introduce a novel method for Automated Insights and Data Exploration (AIDE), that serves as a robust foundation for tackling these challenges through the use of Monte Carlo Tree Search (MCTS). We evaluate AIDE using both real-world and synthetic data, demonstrating its effectiveness in identifying data transformations and models that uncover interesting data patterns. Among its strengths, AIDE's MCTS-based framework offers significant extensibility, allowing for future integration of additional pattern extraction strategies and domain knowledge. This makes AIDE a valuable step towards developing a comprehensive solution for automated knowledge discovery.
On Listwise Reranking for Corpus Feedback
Venue: arXiv cs.IR 2025
Year: 2025

Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces document graphs from listwise reranker logs. By converting reranker signals into a graph structure, L2G enables scalable graph-based retrieval without the overhead of explicit graph computation. Results on the TREC-DL and BEIR subset show that L2G matches the effectiveness of oracle-based graph methods, while incurring zero additional LLM calls.
BoMGene: Integrating Boruta-mRMR feature selection for enhanced Gene expression classification
Venue: arXiv cs.LG 2025
Year: 2025

Feature selection is a crucial step in analyzing gene expression data, enhancing classification performance, and reducing computational costs for high-dimensional datasets. This paper proposes BoMGene, a hybrid feature selection method that effectively integrates two popular techniques: Boruta and Minimum Redundancy Maximum Relevance (mRMR). The method aims to optimize the feature space and enhance classification accuracy. Experiments were conducted on 25 publicly available gene expression datasets, employing widely used classifiers such as Support Vector Machine (SVM), Random Forest, XGBoost (XGB), and Gradient Boosting Machine (GBM). The results show that using the Boruta-mRMR combination cuts down the number of features chosen compared to just using mRMR, which helps to speed up training time while keeping or even improving classification accuracy compared to using individual feature selection methods. The proposed approach demonstrates clear advantages in accuracy, stability, and practical applicability for multi-class gene expression data analysis
Product-oriented Product-Process-Resource Asset Network and its Representation in AutomationML for Asset Administration Shell
Venue: arXiv cs.RO 2025
Year: 2025

Current products, especially in the automotive sector, pose complex technical systems having a multi-disciplinary mechatronic nature. Industrial standards supporting system engineering and production typically (i) address the production phase only, but do not cover the complete product life cycle, and (ii) focus on production processes and resources rather than the products themselves. The presented approach is motivated by incorporating the impacts of the end-of-life phase of the product life cycle into the engineering phase. This paper proposes a modeling approach coming up from the Product-Process-Resource (PPR) modeling paradigm. It combines requirements on (i) respecting the product structure as a basis for the model, and (ii) incorporates repairing, remanufacturing, or upcycling within cyber-physical production systems. The proposed model called PoPAN should accompany the product during the entire life cycle as a digital shadow encapsulated within the Asset Administration Shell of a product. To facilitate the adoption of the proposed paradigm, the paper also proposes serialization of the model in the AutomationML data format. The model is demonstrated on a use-case for disassembling electric vehicle batteries to support their remanufacturing for stationary battery applications.
Degree-bounded Online Bipartite Matching: OCS vs. Ranking
Venue: arXiv cs.DS 2025
Year: 2025

We revisit the online bipartite matching problem on $d$-regular graphs, for which Cohen and Wajc (SODA 2018) proposed an algorithm with a competitive ratio of $1-2\sqrt{H_d/d} = 1-O(\sqrt{(\log d)/d})$ and showed that it is asymptotically near-optimal for $d=ω(1)$. However, their ratio is meaningful only for sufficiently large $d$, e.g., the ratio is less than $1-1/e$ when $d\leq 168$. In this work, we study the problem on $(d,d)$-bounded graphs (a slightly more general class of graphs than $d$-regular) and consider two classic algorithms for online matching problems: \Ranking and Online Correlated Selection (OCS). We show that for every fixed $d\geq 2$, the competitive ratio of OCS is at least $0.835$ and always higher than that of \Ranking. When $d\to \infty$, we show that OCS is at least $0.897$-competitive while \Ranking is at most $0.816$-competitive. We also show some extensions of our results to $(k,d)$-bounded graphs.
Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Venue: arXiv cs.AI 2025
Year: 2025

Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participation, and (iii) secure aggregation with calibrated differential privacy to safeguard sensitive model updates. Unlike prior work that addresses these aspects in isolation, AFFR unifies them into a modular pipeline deployable on real-world clinical networks. Experimental evaluation on simulated rare-disease detection datasets demonstrates up to 10% improvement in accuracy compared with baseline FL, while reducing client dropouts by over 50% without degrading convergence. Furthermore, privacy-utility trade-offs remain within clinically acceptable bounds. These findings highlight AFFR as a practical pathway for equitable and trustworthy federated diagnosis of rare conditions.
An Efficient, Reliable and Observable Collective Communication Library in Large-scale GPU Training Clusters
Venue: arXiv cs.DC 2025
Year: 2025

Large-scale LLM training requires collective communication libraries to exchange data among distributed GPUs. As a company dedicated to building and operating large-scale GPU training clusters, we encounter several challenges when using NCCL in production, including 1) limited efficiency with costly and cumbersome P2P communication, 2) poor tolerance to frequent RNIC port failures, and 3) insufficient observability of transient collective communication anomalies. To address these issues, we propose ICCL, an efficient, reliable, and observable collective communication library in large-scale GPU training clusters. ICCL offloads the P2P communication from GPU kernels to CPU threads for minimal SM consumption, and removes the redundant memory copies irrelevant to the actual communicating process. ICCL also introduces a primary-backup QP mechanism to tolerate frequent NIC port failures, and designs a window-based monitor to observe network anomalies at O(us) level. We open-source ICCL and deploy it in production training clusters for several months, with results showing that compared to NCCL, ICCL achieves a 23.4%/28.5% improvement in P2P throughput/latency as well as a 6.02% increase in training throughput. We also share the operating experience of ICCL in large-scale clusters, hoping to give the communities more insights on production-level collective communication libraries in LLM training.
Visual Self-Refinement for Autoregressive Models
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. This work proposes a plug-and-play refinement module to enhance the complex spatial correspondence modeling within the generated visual sequence. This module operates as a post-pretraining step to jointly refine all generated tokens of autoregressive model, enhancing vision-language modeling under a shared sequential prediction framework. By leveraging global context and relationship across the tokens, our method mitigates the error accumulation issue within the sequential generation. Experiments demonstrate that the proposed method improves the generation quality, enhancing the model's ability to produce semantically consistent results.
SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive (AR) models have emerged as powerful tools for image generation by modeling images as sequences of discrete tokens. While Classifier-Free Guidance (CFG) has been adopted to improve conditional generation, its application in AR models faces two key issues: guidance diminishing, where the conditional-unconditional gap quickly vanishes as decoding progresses, and over-guidance, where strong conditions distort visual coherence. To address these challenges, we propose SoftCFG, an uncertainty-guided inference method that distributes adaptive perturbations across all tokens in the sequence. The key idea behind SoftCFG is to let each generated token contribute certainty-weighted guidance, ensuring that the signal persists across steps while resolving conflicts between text guidance and visual context. To further stabilize long-sequence generation, we introduce Step Normalization, which bounds cumulative perturbations of SoftCFG. Our method is training-free, model-agnostic, and seamlessly integrates with existing AR pipelines. Experiments show that SoftCFG significantly improves image quality over standard CFG and achieves state-of-the-art FID on ImageNet 256*256 among autoregressive models.