OpenRaiser/Intern-Atlas huggingface dataset:
A Hybrid Architecture for Multi-Stage Claim Document Understanding: Combining Vision-Language Models and Machine Learning for Real-Time Processing
Venue: arXiv cs.IR 2026
Year: 2026

Claims documents are fundamental to healthcare and insurance operations, serving as the basis for reimbursement, auditing, and compliance. However, these documents are typically not born digital; they often exist as scanned PDFs or photographs captured under uncontrolled conditions. Consequently, they exhibit significant content heterogeneity, ranging from typed invoices to handwritten medical reports, as well as linguistic diversity. This challenge is exemplified by operations at Fullerton Health, which handles tens of millions of claims annually across nine markets, including Singapore, the Philippines, Indonesia, Malaysia, Mainland China, Hong Kong, Vietnam, Papua New Guinea, and Cambodia. Such variability, coupled with inconsistent image quality and diverse layouts, poses a significant obstacle to automated parsing and structured information extraction. This paper presents a robust multi-stage pipeline that integrates the multilingual optical character recognition (OCR) engine PaddleOCR, a traditional Logistic Regression classifier, and a compact Vision-Language Model (VLM), Qwen 2.5-VL-7B, to achieve efficient and accurate field extraction from large-scale claims data. The proposed system achieves a document-type classification accuracy of over 95 percent and a field-level extraction accuracy of approximately 87 percent, while maintaining an average processing latency of under 2 seconds per document. Compared to manual processing, which typically requires around 10 minutes per claim, our system delivers a 300x improvement in efficiency. These results demonstrate that combining traditional machine learning models with modern VLMs enables production-grade accuracy and speed for real-world automation. The solution has been successfully deployed in our mobile application and is currently processing tens of thousands of claims weekly from Vietnam and Singapore.
FedBiCross: A Bi-Level Optimization Framework to Tackle Non-IID Challenges in Data-Free One-Shot Federated Learning on Medical Data
Venue: arXiv cs.LG 2026
Year: 2026

Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, making OSFL attractive for privacy-sensitive medical applications. However, existing methods aggregate predictions from all clients to form a global teacher. Under non-IID data, conflicting predictions cancel out during averaging, yielding near-uniform soft labels that provide weak supervision for distillation. We propose FedBiCross, a personalized OSFL framework with three stages: (1) clustering clients by model output similarity to form coherent sub-ensembles, (2) bi-level cross-cluster optimization that learns adaptive weights to selectively leverage beneficial cross-cluster knowledge while suppressing negative transfer, and (3) personalized distillation for client-specific adaptation. Experiments on four medical image datasets demonstrate that FedBiCross consistently outperforms state-of-the-art baselines across different non-IID degrees.
Evaluating Feature Dependent Noise in Preference-based Reinforcement Learning
Venue: arXiv cs.LG 2026
Year: 2026

Learning from Preferences in Reinforcement Learning (PbRL) has gained attention recently, as it serves as a natural fit for complicated tasks where the reward function is not easily available. However, preferences often come with uncertainty and noise if they are not from perfect teachers. Much prior literature aimed to detect noise, but with limited types of noise and most being uniformly distributed with no connection to observations. In this work, we formalize the notion of targeted feature-dependent noise and propose several variants like trajectory feature noise, trajectory similarity noise, uncertainty-aware noise, and Language Model noise. We evaluate feature-dependent noise, where noise is correlated with certain features in complex continuous control tasks from DMControl and Meta-world. Our experiments show that in some feature-dependent noise settings, the state-of-the-art noise-robust PbRL method's learning performance is significantly deteriorated, while PbRL method with no explicit denoising can surprisingly outperform noise-robust PbRL in majority settings. We also find language model's noise exhibits similar characteristics to feature-dependent noise, thereby simulating realistic humans and call for further study in learning with feature-dependent noise robustly.
Nodule-DETR: A Novel DETR Architecture with Frequency-Channel Attention for Ultrasound Thyroid Nodule Detection
Venue: arXiv cs.CV 2026
Year: 2026

Thyroid cancer is the most common endocrine malignancy, and its incidence is rising globally. While ultrasound is the preferred imaging modality for detecting thyroid nodules, its diagnostic accuracy is often limited by challenges such as low image contrast and blurred nodule boundaries. To address these issues, we propose Nodule-DETR, a novel detection transformer (DETR) architecture designed for robust thyroid nodule detection in ultrasound images. Nodule-DETR introduces three key innovations: a Multi-Spectral Frequency-domain Channel Attention (MSFCA) module that leverages frequency analysis to enhance features of low-contrast nodules; a Hierarchical Feature Fusion (HFF) module for efficient multi-scale integration; and Multi-Scale Deformable Attention (MSDA) to flexibly capture small and irregularly shaped nodules. We conducted extensive experiments on a clinical dataset of real-world thyroid ultrasound images. The results demonstrate that Nodule-DETR achieves state-of-the-art performance, outperforming the baseline model by a significant margin of 0.149 in mAP@0.5:0.95. The superior accuracy of Nodule-DETR highlights its significant potential for clinical application as an effective tool in computer-aided thyroid diagnosis. The code of work is available at https://github.com/wjj1wjj/Nodule-DETR.
MMP-A*: Multimodal Perception Enhanced Incremental Heuristic Search on Path Planning
Venue: arXiv cs.AI 2026
Year: 2026

Autonomous path planning requires a synergy between global reasoning and geometric precision, especially in complex or cluttered environments. While classical A* is valued for its optimality, it incurs prohibitive computational and memory costs in large-scale scenarios. Recent attempts to mitigate these limitations by using Large Language Models for waypoint guidance remain insufficient, as they rely only on text-based reasoning without spatial grounding. As a result, such models often produce incorrect waypoints in topologically complex environments with dead ends, and lack the perceptual capacity to interpret ambiguous physical boundaries. These inconsistencies lead to costly corrective expansions and undermine the intended computational efficiency. We introduce MMP-A*, a multimodal framework that integrates the spatial grounding capabilities of vision-language models with a novel adaptive decay mechanism. By anchoring high-level reasoning in physical geometry, the framework produces coherent waypoint guidance that addresses the limitations of text-only planners. The adaptive decay mechanism dynamically regulates the influence of uncertain waypoints within the heuristic, ensuring geometric validity while substantially reducing memory overhead. To evaluate robustness, we test the framework in challenging environments characterized by severe clutter and topological complexity. Experimental results show that MMP-A* achieves near-optimal trajectories with significantly reduced operational costs, demonstrating its potential as a perception-grounded and computationally efficient paradigm for autonomous navigation.
Learning Action Hierarchies via Hybrid Geometric Diffusion
Venue: arXiv cs.CV 2026
Year: 2026

Temporal action segmentation is a critical task in video understanding, where the goal is to assign action labels to each frame in a video. While recent advances leverage iterative refinement-based strategies, they fail to explicitly utilize the hierarchical nature of human actions. In this work, we propose HybridTAS - a novel framework that incorporates a hybrid of Euclidean and hyperbolic geometries into the denoising process of diffusion models to exploit the hierarchical structure of actions. Hyperbolic geometry naturally provides tree-like relationships between embeddings, enabling us to guide the action label denoising process in a coarse-to-fine manner: higher diffusion timesteps are influenced by abstract, high-level action categories (root nodes), while lower timesteps are refined using fine-grained action classes (leaf nodes). Extensive experiments on three benchmark datasets, GTEA, 50Salads, and Breakfast, demonstrate that our method achieves state-of-the-art performance, validating the effectiveness of hyperbolic-guided denoising for the temporal action segmentation task.
TalkPhoto: A Versatile Training-Free Conversational Assistant for Intelligent Image Editing
Venue: arXiv cs.CV 2026
Year: 2026

Thanks to the powerful language comprehension capabilities of Large Language Models (LLMs), existing instruction-based image editing methods have introduced Multimodal Large Language Models (MLLMs) to promote information exchange between instructions and images, ensuring the controllability and flexibility of image editing. However, these frameworks often build a multi-instruction dataset to train the model to handle multiple editing tasks, which is not only time-consuming and labor-intensive but also fails to achieve satisfactory results. In this paper, we present TalkPhoto, a versatile training-free image editing framework that facilitates precise image manipulation through conversational interaction. We instruct the open-source LLM with a specially designed prompt template to analyze user needs after receiving instructions and hierarchically invoke existing advanced editing methods, all without additional training. Moreover, we implement a plug-and-play and efficient invocation of image editing methods, allowing complex and unseen editing tasks to be integrated into the current framework, achieving stable and high-quality editing results. Extensive experiments demonstrate that our method not only provides more accurate invocation with fewer token consumption but also achieves higher editing quality across various image editing tasks.
Distorted Distributional Policy Evaluation for Offline Reinforcement Learning
Venue: arXiv cs.LG 2026
Year: 2026

While Distributional Reinforcement Learning (DRL) methods have demonstrated strong performance in online settings, its success in offline scenarios remains limited. We hypothesize that a key limitation of existing offline DRL methods lies in their approach to uniformly underestimate return quantiles. This uniform pessimism can lead to overly conservative value estimates, ultimately hindering generalization and performance. To address this, we introduce a novel concept called quantile distortion, which enables non-uniform pessimism by adjusting the degree of conservatism based on the availability of supporting data. Our approach is grounded in theoretical analysis and empirically validated, demonstrating improved performance over uniform pessimism.
The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities
Venue: arXiv cs.SE 2026
Year: 2026

In the early 1980s, Open Source Software emerged as a revolutionary concept amidst the dominance of proprietary software. What began as a revolutionary idea has now become the cornerstone of computer science. Amidst OSS projects, AI is increasing its presence and relevance. However, despite the growing popularity of AI, its adoption and impacts on OSS projects remain underexplored. We aim to assess the adoption of AI libraries in Python and Java OSS projects and examine how they shape development, including the technical ecosystem and community engagement. To this end, we will perform a large-scale analysis on 157.7k potential OSS repositories, employing repository metrics and software metrics to compare projects adopting AI libraries against those that do not. We expect to identify measurable differences in development activity, community engagement, and code complexity between OSS projects that adopt AI libraries and those that do not, offering evidence-based insights into how AI integration reshapes software development practices.
From Metrics to Meaning: Insights from a Mixed-Methods Field Experiment on Retail Robot Deployment
Venue: arXiv cs.RO 2026
Year: 2026

We report a mixed-methods field experiment of a conversational service robot deployed under everyday staffing discretion in a live bedding store. Over 12 days we alternated three conditions--Baseline (no robot), Robot-only, and Robot+Fixture--and video-annotated the service funnel from passersby to purchase. An explanatory sequential design then used six post-experiment staff interviews to interpret the quantitative patterns. Quantitatively, the robot increased stopping per passerby (highest with the fixture), yet clerk-led downstream steps per stopper--clerk approach, store entry, assisted experience, and purchase--decreased. Interviews explained this divergence: clerks avoided interrupting ongoing robot-customer talk, struggled with ambiguous timing amid conversational latency, and noted child-centered attraction that often satisfied curiosity at the doorway. The fixture amplified visibility but also anchored encounters at the threshold, creating a well-defined micro-space where needs could ``close'' without moving inside. We synthesize these strands into an integrative account from the initial show of interest on the part of a customer to their entering the store and derive actionable guidance. The results advance the understanding of interactions between customers, staff members, and the robot and offer practical recommendations for deploying service robots in high-touch retail.
Bringing computation to the data: A MOEA-driven approach for optimising data processing in the context of the SKA and SRCNet
Venue: arXiv cs.DC 2026
Year: 2026

The Square Kilometre Array (SKA) will generate unprecedented data volumes, making efficient data processing a critical challenge. Within this context, the SKA Regional Centres Network (SRCNet) must operate in a near-exascale environment where traditional data-centric computing models based on moving large datasets to centralised resources are no longer viable due to network and storage bottlenecks. To address this limitation, this work proposes a shift towards distributed and in-situ computing, where computation is moved closer to the data. We explore the integration of Function-as-a-Service (FaaS) with an intelligent decision-making entity based on Evolutionary Algorithms (EAs) to optimise data-intensive workflows within SRCNet. FaaS enables lightweight and modular function execution near data sources while abstracting infrastructure management. The proposed decision-making entity employs Multi-Objective Evolutionary Algorithms (MOEAs) to explore near-optimal execution plans considering execution time and energy consumption, together with constraints related to data location and transfer costs. This work establishes a baseline framework for efficient and cost-aware computation-to-data strategies within the SRCNet architecture.
VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis
Venue: arXiv cs.CV 2026
Year: 2026

Pedestrian Intention prediction is one of the key technologies in the transition from level 3 to level 4 autonomous driving. To understand pedestrian crossing behaviour, several elements and features should be taken into consideration to make the roads of tomorrow safer for everybody. We introduce a transformer / video vision transformer based algorithm of different sizes which uses different data modalities .We evaluated our algorithms on popular pedestrian behaviour dataset, JAAD, and have reached SOTA performance and passed the SOTA in metrics like Accuracy, AUC and F1-score. The advantages brought by different model design choices are investigated via extensive ablation studies.
API: Empowering Generalizable Real-World Image Dehazing via Adaptive Patch Importance Learning
Venue: arXiv cs.CV 2026
Year: 2026

Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when applied to complex real-world hazy scenes, primarily due to limited training data and the intrinsic complexity of haze density distributions.To address these challenges, we introduce a novel Adaptive Patch Importance-aware (API) framework for generalizable real-world image dehazing. Specifically, our framework consists of an Automatic Haze Generation (AHG) module and a Density-aware Haze Removal (DHR) module. AHG provides a hybrid data augmentation strategy by generating realistic and diverse hazy images as additional high-quality training data. DHR considers hazy regions with varying haze density distributions for generalizable real-world image dehazing in an adaptive patch importance-aware manner. To alleviate the ambiguity of the dehazed image details, we further introduce a new Multi-Negative Contrastive Dehazing (MNCD) loss, which fully utilizes information from multiple negative samples across both spatial and frequency domains. Extensive experiments demonstrate that our framework achieves state-of-the-art performance across multiple real-world benchmarks, delivering strong results in both quantitative metrics and qualitative visual quality, and exhibiting robust generalization across diverse haze distributions.
Exploring Diversity, Novelty, and Popularity Bias in ChatGPT's Recommendations
Venue: arXiv cs.IR 2026
Year: 2026

ChatGPT has emerged as a versatile tool, demonstrating capabilities across diverse domains. Given these successes, the Recommender Systems (RSs) community has begun investigating its applications within recommendation scenarios primarily focusing on accuracy. While the integration of ChatGPT into RSs has garnered significant attention, a comprehensive analysis of its performance across various dimensions remains largely unexplored. Specifically, the capabilities of providing diverse and novel recommendations or exploring potential biases such as popularity bias have not been thoroughly examined. As the use of these models continues to expand, understanding these aspects is crucial for enhancing user satisfaction and achieving long-term personalization. This study investigates the recommendations provided by ChatGPT-3.5 and ChatGPT-4 by assessing ChatGPT's capabilities in terms of diversity, novelty, and popularity bias. We evaluate these models on three distinct datasets and assess their performance in Top-N recommendation and cold-start scenarios. The findings reveal that ChatGPT-4 matches or surpasses traditional recommenders, demonstrating the ability to balance novelty and diversity in recommendations. Furthermore, in the cold-start scenario, ChatGPT models exhibit superior performance in both accuracy and novelty, suggesting they can be particularly beneficial for new users. This research highlights the strengths and limitations of ChatGPT's recommendations, offering new perspectives on the capacity of these models to provide recommendations beyond accuracy-focused metrics.
Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets
Venue: arXiv cs.CL 2026
Year: 2026

Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigates whether surprisal, a probabilistic measure of predictability in LMs, correlates with annotations of metaphor novelty in different datasets. We analyse the surprisal of metaphoric words in corpus-based and synthetic metaphor datasets using 16 causal LM variants. We propose a cloze-style surprisal method that conditions on full-sentence context. Results show that LM surprisal yields significant moderate correlations with scores/labels of metaphor novelty. We further identify divergent scaling patterns: on corpus-based data, correlation strength decreases with model size (inverse scaling effect), whereas on synthetic data it increases (quality-power hypothesis). We conclude that while surprisal can partially account for annotations of metaphor novelty, it remains limited as a metric of linguistic creativity. Code and data are publicly available: https://github.com/OmarMomen14/surprisal-metaphor-novelty
Adapting Depth Anything to Adverse Imaging Conditions with Events
Venue: arXiv cs.CV 2026
Year: 2026

Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great success in ideal scenes but remain challenging under adverse imaging conditions such as extreme illumination and motion blur. These degradations corrupt the visual signals of frame cameras, weakening the discriminative features of frame-based depths across the spatial and temporal dimensions. Typically, existing approaches incorporate event cameras to leverage their high dynamic range and temporal resolution, aiming to compensate for corrupted frame features. However, such specialized fusion models are predominantly trained from scratch on domain-specific datasets, thereby failing to inherit the open-world knowledge and robust generalization inherent to foundation models. In this work, we propose ADAE, an event-guided spatiotemporal fusion framework for Depth Anything in degraded scenes. Our design is guided by two key insights: 1) Entropy-Aware Spatial Fusion. We adaptively merge frame-based and event-based features using an information entropy strategy to indicate illumination-induced degradation. 2) Motion-Guided Temporal Correction. We resort to the event-based motion cue to recalibrate ambiguous features in blurred regions. Under our unified framework, the two components are complementary to each other and jointly enhance Depth Anything under adverse imaging conditions. Extensive experiments have been performed to verify the superiority of the proposed method. Our code will be released upon acceptance.
EyeLiveMetrics: Real-time Analysis of Online Reading with Eye Tracking
Venue: arXiv cs.HC 2026
Year: 2026

Existing eye tracking software have certain limitations, especially with respect to monitoring reading online: (1) Most eye tracking software record eye tracking data as raw coordinates and stimuli as screen images/videos, but without inherent links between both. Analysts must draw areas of interest (AOIs) on webpage text for more fine-grained reading analysis. (2) The computation and analysis of fixation and reading metrics are done after the experiment and thus cannot be used for live applications. We present EyeLiveMetrics, a browser plugin that automatically maps raw gaze coordinates to text in real time. The plugin instantly calculates, stores, and provides fixation, saccade, and reading measures on words and paragraphs so that gaze behavior can be analyzed immediately. We also discuss the results of a comparative evaluation. EyeLiveMetrics offers a flexible way to measure reading on the web - for research experiments and live applications.