OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Prompt Progressive Alignment for Multi-Source Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models like CLIP have become a powerful foundation for Unsupervised Domain Adaptation due to their strong zero-shot generalization. State-of-the-art methods typically leverage CLIP to generate pseudo-labels for the target domain, then fine-tune the model to learn domain-invariant features. However, these methods attempt to align source and target domains using all pseudo-labeled data simultaneously. This one-shot alignment struggles with noisy, hard-to-classify samples, leading to error propagation and suboptimal feature learning. The problem is even more amplified in the multi-source scenario, where diverse domain gaps and varying noise levels across multiple source domains further destabilize the alignment process. To address this issue, in this work, we propose a progressive alignment strategy for adapting CLIP to unlabeled downstream task. Our method begins by training the model on a high-confidence subset of target samples, allowing it to first learn a well-aligned representation from the most reliable data. As training progresses, it gradually incorporates more challenging samples, guiding the model to refine its understanding without being overwhelmed by initial label noise. This progressive approach effectively mitigates confirmation bias and promotes a more robust convergence, allowing for the learning of genuinely domain-invariant features. We name our approach MP^2A and test it on three popular UDA benchmarks, namely ImageCLEF, Office-Home, and the most challenging DomainNet. Experiments showcase that MP^2A achieves state-of-the-art performance when compared with most recent CLIP-based MS-UDA approaches, demonstrating the effectiveness of our approach.
LLM4Rail: An LLM-Augmented Railway Service Consulting Platform
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have significantly reshaped different walks of business. To meet the increasing demands for individualized railway service, we develop LLM4Rail - a novel LLM-augmented railway service consulting platform. Empowered by LLM, LLM4Rail can provide custom modules for ticketing, railway food & drink recommendations, weather information, and chitchat. In LLM4Rail, we propose the iterative "Question-Thought-Action-Observation (QTAO)" prompting framework. It meticulously integrates verbal reasoning with task-oriented actions, that is, reasoning to guide action selection, to effectively retrieve external observations relevant to railway operation and service to generate accurate responses. To provide personalized onboard dining services, we first construct the Chinese Railway Food and Drink (CRFD-25) - a publicly accessible takeout dataset tailored for railway services. CRFD-25 covers a wide range of signature dishes categorized by cities, cuisines, age groups, and spiciness levels. We further introduce an LLM-based zero-shot conversational recommender for railway catering. To address the unconstrained nature of open recommendations, the feature similarity-based post-processing step is introduced to ensure all the recommended items are aligned with CRFD-25 dataset.
AGA: An adaptive group alignment framework for structured medical cross-modal representation learning
Venue: arXiv cs.CV 2025
Year: 2025

Learning medical visual representations from paired images and reports is a promising direction in representation learning. However, current vision-language pretraining methods in the medical domain often simplify clinical reports into single entities or fragmented tokens, ignoring their inherent structure. In addition, contrastive learning frameworks typically depend on large quantities of hard negative samples, which is impractical for small-scale medical datasets. To tackle these challenges, we propose Adaptive Grouped Alignment (AGA), a new framework that captures structured semantics from paired medical images and reports. AGA introduces a bidirectional grouping mechanism based on a sparse similarity matrix. For each image-report pair, we compute fine-grained similarities between text tokens and image patches. Each token selects its top-matching patches to form a visual group, and each patch selects its most related tokens to form a language group. To enable adaptive grouping, we design two threshold gating modules, called Language Grouped Threshold Gate and Vision Grouped Threshold Gate, which learn grouping thresholds dynamically. Group representations are computed as weighted averages based on similarity scores. To align each token with its group representation, we introduce an Instance Aware Group Alignment loss that operates within each image-text pair, removing the need for external negatives. Finally, a Bidirectional Cross-modal Grouped Alignment module is applied to enhance fine-grained alignment between visual and linguistic group representations. Extensive experiments on public and private datasets show that our method achieves strong performance on image-text retrieval and classification tasks under both fine-tuning and zero-shot settings.
Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Venue: arXiv cs.CL 2025
Year: 2025

Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a novel Attentive Relevance Scoring (ARS) that replaces standard interaction mechanisms with an adaptive scoring function that more effectively models the semantic relevance between questions and passages. Our method integrates pre-trained Arabic language models and architectural refinements to improve retrieval performance and significantly increase ranking accuracy when answering Arabic questions. The code is made publicly available at \href{https://github.com/Bekhouche/APR}{GitHub}.
Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
Venue: arXiv cs.CV 2025
Year: 2025

In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contrast to supervised methods, OOD-based approaches function without labels and are inherently robust to data imbalances. Current generative approaches often rely on likelihood estimation or reconstruction error, but these methods can be computationally expensive, unreliable, and require retraining if the inlier data changes. These limitations hinder their ability to distinguish nominal from anomalous inputs efficiently, consistently, and robustly. We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model (SBDDM). By capturing trajectory curvature via the estimated Stein score, our approach enables accurate anomaly scoring with only five diffusion steps. A single SBDDM pre-trained on a large, semantically aligned medical dataset generalizes effectively across multiple Near-OOD and Far-OOD benchmarks, achieving state-of-the-art performance while drastically reducing computational cost during inference. Compared to existing methods, SBDDM achieves a relative improvement of up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making it a practical building block for real-time, reliable computer-aided diagnosis.
WiRM: Wireless Respiration Monitoring Using Conjugate Multiple Channel State Information and Fast Iterative Filtering in Wi-Fi Systems
Venue: arXiv cs.ET 2025
Year: 2025

Monitoring respiratory health with the use of channel state information (CSI) has shown promising results. Many existing methods focus on monitoring only the respiratory rate, while others focus on monitoring the motion of the chest as a patient breathes, which is referred to as the respiratory waveform. This paper presents WiRM, a two-staged approach to contactless respiration monitoring. In the first stage, WiRM improves upon existing respiratory rate estimation techniques by using conjugate multiplication for phase sanitisation and adaptive multi-trace carving (AMTC) for tracing how the respiratory rate changes over time. When compared against three state-of-the-art methods, WiRM has achieved an average reduction of $38\%$ in respiratory rate root mean squared error (RMSE). In the second stage, WiRM uses this improved respiratory rate estimate to inform the decomposition and selection of the respiratory waveform from the CSI data. Remarkably, WiRM delivers a $178.3\%$ improvement in average absolute correlation with the ground truth respiratory waveform. Within the literature, it is difficult to compare the robustness of existing algorithms in noisy environments. In this paper, we develop a purpose-built simulation toolkit to evaluate the robustness of respiration monitoring solutions under various noise conditions, including thermal, multiplicative, and phase noise. Our results show that WiRM demonstrates improved or comparable resilience to these common noise sources.
Future Illiteracies -- Architectural Epistemology and Artificial Intelligence
Venue: arXiv cs.CY 2025
Year: 2025

In the age of artificial intelligence, architectural practice faces a paradox of immense potential and creeping standardization. As humans are increasingly relying on AI-generated outputs, architecture risks becoming a spectacle of repetition- a shuffling of data that neither truly innovates nor progresses vertically in creative depth. This paper explores the critical role of data in AI systems, scrutinizing the training datasets that form the basis of AI's generative capabilities and the implications for architectural practice. We argue that when architects approach AI passively, without actively engaging their own creative and critical faculties, they risk becoming passive users locked in an endless loop of horizontal expansion without meaningful vertical growth. By examining the epistemology of architecture in the AI age, this paper calls for a paradigm where AI serves as a tool for vertical and horizontal growth, contingent on human creativity and agency. Only by mastering this dynamic relationship can architects avoid the trap of passive, standardized design and unlock the true potential of AI.
Online Combinatorial Allocation with Interdependent Values
Venue: arXiv cs.GT 2025
Year: 2025

We study online combinatorial allocation problems in the secretary setting, under interdependent values. In the interdependent model, introduced by Milgrom and Weber (1982), each agent possesses a private signal that captures her information about an item for sale, and the value of every agent depends on the signals held by all agents. Mauras, Mohan, and Reiffenhäuser (2024) were the first to study interdependent values in online settings, providing constant-approximation guarantees for secretary settings, where agents arrive online along with their signals and values, and the goal is to select the agent with the highest value. In this work, we extend this framework to {\em combinatorial} secretary problems, where agents have interdependent valuations over {\em bundles} of items, introducing additional challenges due to both combinatorial structure and interdependence. We provide $2e$-competitive algorithms for a broad class of valuation functions, including submodular and XOS functions, matching the approximation guarantees in the single-choice secretary setting. Furthermore, our results cover the same range of valuation classes for which constant-factor algorithms exist in classical (non-interdependent) secretary settings, while incurring only an additional factor of $2$ due to interdependence. Finally, we extend our study to strategic settings, and provide a $4e$-competitive truthful mechanism for online bipartite matching with interdependent valuations, again meeting the frontier of what is known, even without interdependence.
Threshold-Driven Streaming Graph: Expansion and Rumor Spreading
Venue: arXiv cs.DC 2025
Year: 2025

A randomized distributed algorithm called RAES was introduced in [Becchetti et al., SODA 2020] to extract a bounded-degree expander from a dense $n$-vertex expander graph $G = (V, E)$. The algorithm relies on a simple threshold-based procedure. A key assumption in [Becchetti et al., SODA 2020] is that the input graph $G$ is static - i.e., both its vertex set $V$ and edge set $E$ remain unchanged throughout the process - while the analysis of RAES in dynamic models is left as a major open question. In this work, we investigate the behavior of RAES under a dynamic graph model induced by a streaming node-churn process (also known as the sliding window model), where, at each discrete round, a new node joins the graph and the oldest node departs. This process yields a bounded-degree dynamic graph $\mathcal{G} =\{ G_t = (V_t, E_t) : t \in \mathbb{N}\}$ that captures essential characteristics of peer-to-peer networks -- specifically, node churn and threshold on the number of connections each node can manage. We prove that every snapshot $G_t$ in the dynamic graph sequence has good expansion properties with high probability. Furthermore, we leverage this property to establish a logarithmic upper bound on the completion time of the well-known PUSH and PULL rumor spreading protocols over the dynamic graph $\mathcal{G}$.
Continual Learning with Synthetic Boundary Experience Blending
Venue: arXiv cs.LG 2025
Year: 2025

Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience replay (ER), stores past exemplars but only sparsely approximates the data distribution, yielding fragile and oversimplified decision boundaries. We address this limitation by introducing synthetic boundary data (SBD), generated via differential privacy: inspired noise into latent features to create boundary-adjacent representations that implicitly regularize decision boundaries. Building on this idea, we propose Experience Blending (EB), a framework that jointly trains on exemplars and SBD through a dual-model aggregation strategy. EB has two components: (1) latent-space noise injection to synthesize boundary data, and (2) end-to-end training that jointly leverages exemplars and SBD. Unlike standard experience replay, SBD enriches the feature space near decision boundaries, leading to more stable and robust continual learning. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet demonstrate consistent accuracy improvements of 10%, 6%, and 13%, respectively, over strong baselines.
From LLMs to Edge: Parameter-Efficient Fine-Tuning on Edge Devices
Venue: arXiv cs.LG 2025
Year: 2025

Parameter-efficient fine-tuning (PEFT) methods reduce the computational costs of updating deep learning models by minimizing the number of additional parameters used to adapt a model to a down- stream task. While extensively researched in large language models (LLMs), their application to smaller models used on edge devices, such as convolutional neural networks, remains underexplored. This paper benchmarks and analyzes popular PEFT methods on convolutional architectures typically deployed in resource-constrained edge environments. We evaluate LoRA, DoRA, and GaLore for updating standard and depthwise convolutional architectures to handle distribution shifts and accommodate unseen classes. We utilize recently proposed PyTorch profilers to compare the updated model performance and computational costs of these PEFT methods with traditional fine-tuning approaches. With resource efficiency in mind, we investigate their update behavior across different rank dimensions. We find that the evaluated PEFT methods are only half as memory-efficient when applied to depthwise-separable convolution architectures, compared to their efficiency with LLMs. Conversely, when targeting convolu- tional architectures optimized for edge deployment, adapter-based PEFT methods can reduce floating point operations (FLOPs) during model updates by up to 95%. These insights offer valuable guidance for selecting PEFT methods based on hardware constraints, performance requirements, and application needs. Our code is online.
A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous driving systems face significant challenges in achieving human-like adaptability, robustness, and interpretability in complex, open-world environments. These challenges stem from fragmented architectures, limited generalization to novel scenarios, and insufficient semantic extraction from perception. To address these limitations, we propose a unified Perception-Language-Action (PLA) framework that integrates multi-sensor fusion (cameras, LiDAR, radar) with a large language model (LLM)-augmented Vision-Language-Action (VLA) architecture, specifically a GPT-4.1-powered reasoning core. This framework unifies low-level sensory processing with high-level contextual reasoning, tightly coupling perception with natural language-based semantic understanding and decision-making to enable context-aware, explainable, and safety-bounded autonomous driving. Evaluations on an urban intersection scenario with a construction zone demonstrate superior performance in trajectory tracking, speed prediction, and adaptive planning. The results highlight the potential of language-augmented cognitive frameworks for advancing the safety, interpretability, and scalability of autonomous driving systems.
Networked Physical Computing: A New Paradigm for Effective Task Completion via Hypergraph Aided Trusted Task-Resource Matching
Venue: arXiv cs.NI 2025
Year: 2025

Due to the diverse physical attributes of computing resources and tasks, developing effective mechanisms to facilitate task and resource matching in complex connected systems for value-oriented task completion has become increasingly challenging. To address the challenge, this paper proposes a networked physical computing system that integrates the physical attributes of computing resources and tasks as well as task-specific trust relationships among devices to enable value-driven task completion. Specifically, we propose a state-of-the-art hypergraph-aided trusted task-resource matching (TTR-matching) framework to achieve the envisioned physical computing. First, a task-specific trusted physical resource hypergraph is defined, which integrates task-specific trust, the physical attributes of resources, and task types. This enables accurate modeling of device collaboration dependencies under specific task types. Next, a task hypergraph is generated to associate the task initiator with the physical attributes of the corresponding tasks. Based on these two hypergraphs, a hypergraph matching algorithm is designed to facilitate task-specific trusted collaborator selection and accurate task-resource matching for value-maximizing task completion. Extensive experimental results demonstrate that the proposed TTR-matching framework outperforms comparison algorithms in identifying task-specific trustworthy collaborators and maximizing the average value of task completion.
Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates
Venue: arXiv cs.LG 2025
Year: 2025

Clinical trials are a systematic endeavor to assess the safety and efficacy of new drugs or treatments. Conducting such trials typically demands significant financial investment and meticulous planning, highlighting the need for accurate predictions of trial outcomes. Accurately predicting patient enrollment, a key factor in trial success, is one of the primary challenges during the planning phase. In this work, we propose a novel deep learning-based method to address this critical challenge. Our method, implemented as a neural network model, leverages pre-trained language models (PLMs) to capture the complexities and nuances of clinical documents, transforming them into expressive representations. These representations are then combined with encoded tabular features via an attention mechanism. To account for uncertainties in enrollment prediction, we enhance the model with a probabilistic layer based on the Gamma distribution, which enables range estimation. We apply the proposed model to predict clinical trial duration, assuming site-level enrollment follows a Poisson-Gamma process. We carry out extensive experiments on real-world clinical trial data, and show that the proposed method can effectively predict the number of patients enrolled at a number of sites for a given clinical trial, outperforming established baseline models.
OmniTraj: Pre-Training on Heterogeneous Data for Adaptive and Zero-Shot Human Trajectory Prediction
Venue: arXiv cs.CV 2025
Year: 2025

While large-scale pre-training has advanced human trajectory prediction, a critical challenge remains: zero-shot transfer to unseen dataset with varying temporal dynamics. State-of-the-art pre-trained models often require fine-tuning to adapt to new datasets with different frame rates or observation horizons, limiting their scalability and practical utility. In this work, we systematically investigate this limitation and propose a robust solution. We first demonstrate that existing data-aware discrete models struggle when transferred to new scenarios with shifted temporal setups. We then isolate the temporal generalization from dataset shift, revealing that a simple, explicit conditioning mechanism for temporal metadata is a highly effective solution. Based on this insight, we present OmniTraj, a Transformer-based model pre-trained on a large-scale, heterogeneous dataset. Our experiments show that explicitly conditioning on the frame rate enables OmniTraj to achieve state-of-the-art zero-shot transfer performance, reducing prediction error by over 70\% in challenging cross-setup scenarios. After fine-tuning, OmniTraj achieves state-of-the-art results on four datasets, including NBA, JTA, WorldPose, and ETH-UCY. The code is publicly available: https://github.com/vita-epfl/omnitraj
Personalized Education with Ranking Alignment Recommendation
Venue: arXiv cs.AI 2025
Year: 2025

Personalized question recommendation aims to guide individual students through questions to enhance their mastery of learning targets. Most previous methods model this task as a Markov Decision Process and use reinforcement learning to solve, but they struggle with efficient exploration, failing to identify the best questions for each student during training. To address this, we propose Ranking Alignment Recommendation (RAR), which incorporates collaborative ideas into the exploration mechanism, enabling more efficient exploration within limited training episodes. Experiments show that RAR effectively improves recommendation performance, and our framework can be applied to any RL-based question recommender. Our code is available in https://github.com/wuming29/RAR.git.