OpenRaiser/Intern-Atlas huggingface dataset:
Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents
Venue: arXiv cs.CL 2025
Year: 2025

The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve the operational efficiency of independent insurance agents. Leveraging natural language processing (NLP), retrieval-augmented generation (RAG), and domain-specific knowledge integration, Axlerod demonstrates robust capabilities in parsing user intent, accessing structured policy databases, and delivering real-time, contextually relevant responses. Experimental results underscore Axlerod's effectiveness, achieving an overall accuracy of 93.18% in policy retrieval tasks while reducing the average search time by 2.42 seconds. This work contributes to the growing body of research on enterprise-grade AI applications in insurtech, with a particular focus on agent-assistive rather than consumer-facing architectures.
ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language
Venue: arXiv cs.CL 2025
Year: 2025

In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teacher model to distill the expert knowledge needed for tagging medical texts in Polish. This work is part of a larger project called ADMEDVOICE, within which we collected an extensive corpus of medical texts representing five clinical categories - Radiology, Oncology, Cardiology, Hypertension, and Pathology. Using this data, we had to develop a multi-class classifier, but the fundamental problem turned out to be the lack of resources for annotating an adequate number of texts. Therefore, in our solution, we used the multilingual Llama3.1 model to annotate an extensive corpus of medical texts in Polish. Using our limited annotation resources, we verified only a portion of these labels, creating a test set from them. The data annotated in this way were then used for training and validation of 3 different types of classifiers based on the BERT architecture - the distilled DistilBERT model, BioBERT fine-tuned on medical data, and HerBERT fine-tuned on the Polish language corpus. Among the models we trained, the DistilBERT model achieved the best results, reaching an F1 score > 0.80 for each clinical category and an F1 score > 0.93 for 3 of them. In this way, we obtained a series of highly effective classifiers that represent an alternative to large language models, due to their nearly 500 times smaller size, 300 times lower GPU VRAM consumption, and several hundred times faster inference.
Japanese AI Agent System on Human Papillomavirus Vaccination: System Design
Venue: arXiv cs.AI 2025
Year: 2025

Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation on social media, and traditional ways cannot simultaneously address individual queries while monitoring population-level discourse. This study aimed to develop a dual-purpose AI agent system that provides verified HPV vaccine information through a conversational interface while generating analytical reports for medical institutions based on user interactions and social media. We implemented a system comprising: a vector database integrating academic papers, government sources, news media, and social media; a Retrieval-Augmented Generation chatbot using ReAct agent architecture with multi-tool orchestration across five knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, and user interaction pattern identification. Performance was assessed using a 0-5 scoring scale. For single-turn evaluation, the chatbot achieved mean scores of 4.83 for relevance, 4.89 for routing, 4.50 for reference quality, 4.90 for correctness, and 4.88 for professional identity (overall 4.80). Multi-turn evaluation yielded higher scores: context retention 4.94, topic coherence 5.00, and overall 4.98. The report generation system achieved completeness 4.00-5.00, correctness 4.00-5.00, and helpfulness 3.67-5.00, with reference validity 5.00 across all periods. This study demonstrates the feasibility of an integrated AI agent system for bidirectional HPV vaccine communication. The architecture enables verified information delivery with source attribution while providing systematic public discourse analysis, with a transferable framework for adaptation to other medical contexts.
A Survey of Real-Time Support, Analysis, and Advancements in ROS 2
Venue: arXiv cs.RO 2025
Year: 2025

The Robot Operating System 2 (ROS~2) has emerged as a relevant middleware framework for robotic applications, offering modularity, distributed execution, and communication. In the last six years, ROS~2 has drawn increasing attention from the real-time systems community and industry. This survey presents a comprehensive overview of research efforts that analyze, enhance, and extend ROS~2 to support real-time execution. We first provide a detailed description of the internal scheduling mechanisms of ROS~2 and its layered architecture, including the interaction with DDS-based communication and other communication middleware. We then review key contributions from the literature, covering timing analysis for both single- and multi-threaded executors, metrics such as response time, reaction time, and data age, and different communication modes. The survey also discusses community-driven enhancements to the ROS~2 runtime, including new executor algorithm designs, real-time GPU management, and microcontroller support via micro-ROS. Furthermore, we summarize techniques for bounding DDS communication delays, message filters, and profiling tools that have been developed to support analysis and experimentation. To help systematize this growing body of work, we introduce taxonomies that classify the surveyed contributions based on different criteria. This survey aims to guide both researchers and practitioners in understanding and improving the real-time capabilities of ROS~2.
MetaScoreLens: Evaluating User Feedback Across Digital Entertainment Systems
Venue: arXiv cs.HC 2025
Year: 2025

The popularity of electronic games has grown steadily in recent years, attracting a broad audience across age groups. With this growth comes a large volume of related data, prompting efforts like the PlayMyData to compile and share structured datasets for academic use. This study utilizes such a dataset to compare user review ratings across four current-generation gaming systems: Nintendo, Xbox, PlayStation, and PC. Statistical methods, including analysis of variance (ANOVA), were applied to identify differences in average scores among these platforms. The findings indicate that PC titles tend to receive the most favorable user feedback, followed by Xbox and PlayStation, while Nintendo games showed the lowest average ratings. These patterns suggest that the platform on which a game is released may influence how players evaluate their experience. Such results may be valuable to developers and industry stakeholders in making informed decisions about future investments and development priorities.
Clusters in Focus: A Simple and Robust Detail-On-Demand Dashboard for Patient Data
Venue: arXiv cs.HC 2025
Year: 2025

Exploring tabular datasets to understand how different feature pairs partition data into meaningful cohorts is crucial in domains such as biomarker discovery, yet comparing clusters across multiple feature pair projections is challenging. We introduce Clusters in Focus, an interactive visual analytics dashboard designed to address this gap. Clusters in Focus employs a three-panel coordinated view: a Data Panel offers multiple perspectives (tabular, heatmap, condensed with histograms / SHAP values) for initial data exploration; a Selection Panel displays the 2D clustering (K-Means/DBSCAN) for a user-selected feature pair; and a novel Cluster Similarity Panel featuring two switchable views for comparing clusters. A ranked list enables the identification of top-matching feature pairs, while an interactive similarity matrix with reordering capabilities allows for the discovery of global structural patterns and groups of related features. This dual-view design supports both focused querying and broad visual exploration. A use case on a Parkinson's disease speech dataset demonstrates the tool's effectiveness in revealing relationships between different feature pairs characterizing the same patient subgroup.
Chatsparent: An Interactive System for Detecting and Mitigating Cognitive Fatigue in LLMs
Venue: arXiv cs.HC 2025
Year: 2025

LLMs are increasingly being deployed as chatbots, but today's interfaces offer little to no friction: users interact through seamless conversations that conceal when the model is drifting, hallucinating or failing. This lack of transparency fosters blind trust, even as models produce unstable or repetitive outputs. We introduce an interactive demo that surfaces and mitigates cognitive fatigue, a failure mode where LLMs gradually lose coherence during auto-regressive generation. Our system, Chatsparent, instruments real-time, token-level signals of fatigue, including attention-to-prompt decay, embedding drift, and entropy collapse, and visualizes them as a unified fatigue index. When fatigue thresholds are crossed, the interface allows users to activate lightweight interventions such as attention resets, entropy-regularized decoding, and self-reflection checkpoints. The demo streams live text and fatigue signals, allowing users to observe when fatigue arises, how it affects output quality, and how interventions restore stability. By turning passive chatbot interaction into an interactive diagnostic experience, our system empowers users to better understand LLM behavior while improving reliability at inference time.
AI for Proactive Mental Health: A Multi-Institutional, Longitudinal, Randomized Controlled Trial
Venue: arXiv cs.HC 2025
Year: 2025

Young adults today face unprecedented mental health challenges, yet many hesitate to seek support due to barriers such as accessibility, stigma, and time constraints. Bite-sized well-being interventions offer a promising solution to preventing mental distress before it escalates to clinical levels, but have not yet been delivered through personalized, interactive, and scalable technology. We conducted the first multi-institutional, longitudinal, preregistered randomized controlled trial of a generative AI-powered mobile app ("Flourish") designed to address this gap. Over six weeks in Fall 2024, 486 undergraduate students from three U.S. institutions were randomized to receive app access or waitlist control. Participants in the treatment condition reported significantly greater positive affect, resilience, and social well-being (i.e., increased belonging, closeness to community, and reduced loneliness) and were buffered against declines in mindfulness and flourishing. These findings suggest that, with purposeful and ethical design, generative AI can deliver proactive, population-level well-being interventions that produce measurable benefits.
"Jutters"
Venue: arXiv cs.HC 2025
Year: 2025

This project explores how we engage with AI-generated content through the lens of the jutter: Dutch coastal foragers who comb the shoreline after storms, gathering and repurposing what the sea leaves behind. Reflecting how our lives are increasingly shaped by AI-generated media, we create a beach-like installation that blends real shoreline debris with AI-transformed images and videos. Visitors are invited to explore this space as contemporary jutters, deciding what to keep and what to discard. In doing so, the project reimagines AI-imagery as material for reflection, encouraging a more discerning engagement with the content that drifts through our feeds. A video preview of the installation can be found at https://www.youtube.com/watch?v=L6319Ii7MT8.
Exploring General-Purpose Autonomous Multimodal Agents for Pathology Report Generation
Venue: arXiv cs.HC 2025
Year: 2025

Recent advances in agentic artificial intelligence, i.e. systems capable of autonomous perception, reasoning, and tool use, offer new opportunities for digital pathology. In this pilot study, we evaluate whether two agentic multimodal AI systems (OpenAI's ChatGPT 5.0 in agentic mode, and H Company's Surfer) can autonomously navigate, describe, and interpret histopathologic features in digitized tissue slides on a slide viewing platform. A set of 35 veterinary pathology cases, curated for training purposes, was used as the test dataset. The agent was tasked with autonomously exploring whole-slide images using a web-based slide viewer, identifying salient tissue structures, generating descriptive summaries, and proposing provisional diagnoses. We fed different prompts to explore three scenarios: 1) analysis without knowledge of the signalment, 2) analysis with organ and species provided, and 3) diagnosis based on a morphological description provided. All outputs were reviewed and validated by a board-certified pathologist for accuracy and diagnostic consistency. We further tasked another board-certified pathologist with the same task to establish a baseline. We found the systems to yield accurate diagnoses in up to 28.6% of cases with only images, signalment and organ provided, and up to 68.6% when a morphological description was provided. With only the WSI provided, the models were only correct in up to 5.7% of cases. The human expert, on the other hand, achieved 85.7% diagnostic accuracy with only a single WSI, and 88.6% when also signalment and organ was provided. The study demonstrates that while the agentic AI system can meaningfully engage with web-based slide viewing software to assess complex visual pathology data and produce contextually aligned feature descriptions, diagnostic precision remains limited compared with a human expert.
Multimodal Data Fusion to Capture Dynamic Interactions between Built Environment and Vulnerable Older Adults
Venue: arXiv cs.HC 2025
Year: 2025

Ensuring safe and inclusive mobility for vulnerable older adults is an emerging priority in urban planning. However, existing data sources such as surveys or GIS-based audits provide limited insight into how micro-scale built environment (BE) features influence real-world behavior and perception. This study presents a novel multimodal data-fusion approach that integrates wearable and environmental sensing to dynamically represent human-environment interactions and quantify the BE impacts on mobility among vulnerable older adults, specifically those with knee osteoarthritis or a history of falls. Data collected during naturalistic walking sessions in Singapore, are used to demonstrate this framework of synchronized streams from eye tracking, kinematic sensors, physiological monitors, GPS, and video recordings. Preliminary results show how AI-driven data fusion can uncover behaviorally and perceptually significant urban segments, providing a basis for actionable insights in inclusive design. This human-centered analytical approach advances the representation of urban environments from the perspective of vulnerable pedestrians, establishing a foundation for evidence-based, age-friendly city planning.
RelServe: Fast LLM Inference Serving on Relational Data
Venue: arXiv cs.DB 2025
Year: 2025

The use of Large Language Models (LLMs) for querying relational data has given rise to relQuery, a workload pattern that applies templated LLM calls to structured tables. As relQuery services become more widely adopted in applications such as AI-powered spreadsheets, fast response times under concurrent query loads are increasingly important. Unfortunately, current LLM engines face severe latency bottlenecks from Head-of-Line (HoL) blocking across three comparable inference phases: waiting, core running, and tail running. Existing static priority scheduling methods only address HoL blocking during the waiting phase, leaving two critical problems unsolved. First, the absence of a priority update mechanism causes inaccurate prioritization and continued HoL blocking during core execution. Second, suboptimal prefill-decode batching exacerbates HoL blocking in tail execution and worsens latency trade-offs between running and waiting relQueries. To address these problems, we propose RelServe, an optimized LLM engine for low-latency relQuery serving. RelServe features two core innovations: a Dynamic Priority Updater that continuously adjusts priorities while minimizing overhead via statistical approximations, and an Adaptive Batch Arranger that quantitatively evaluates candidate prefill and decode batches to minimize projected average latency. Extensive experiments on four real-world datasets using LLMs ranging from 13B to 70B parameters show that RelServe reduces average serving latency by up to 3.1x compared to vLLM.
Uniqueness ratio as a predictor of a privacy leakage
Venue: arXiv cs.DB 2025
Year: 2025

Identity leakage can emerge when independent databases are joined, even when each dataset is anonymized individually. While previous work focuses on post-join detection or complex privacy models, little attention has been given to simple, interpretable pre-join indicators that can warn data engineers and database administrators before integration occurs. This study investigates the uniqueness ratio of candidate join attributes as an early predictor of re-identification risk. Using synthetic multi-table datasets, we compute the uniqueness ratio of attribute combinations within each database and examine how these ratios correlate with identity exposure after the join. Experimental results show a strong relationship between high pre-join uniqueness and increased post-join leakage, measured by the proportion of records that become uniquely identifiable or fall into very small groups. Our findings demonstrate that uniqueness ratio offers an explainable and practical signal for assessing join induced privacy risk, providing a foundation for developing more comprehensive pre-join risk estimation models.
Estimating the Scale of Digital Minds
Venue: arXiv cs.CY 2025
Year: 2025

This report estimates the potential number of digital minds, defined as AI systems exhibiting observable traits such as agency, personality, and intelligence, in the coming decades. It employs two complementary approaches: first, examining specific use cases for digital minds and projecting adoption rates for each; second, analyzing trends in AI chip production and efficiency independent of digital mind applications. Together, these supply- and demand-side perspectives suggest that hundreds of millions of digital minds could exist by 2050, though this estimate carries substantial uncertainty spanning several orders of magnitude.
GRADE: Replacing Policy Gradients with Backpropagation for LLM Alignment
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning from human feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, policy gradient methods such as PPO suffer from high variance gradient estimates, requiring careful hyperparameter tuning and extensive computational resources. We introduce GRADE (Gumbel-softmax Relaxation for Alignment via Differentiable Estimation), a method that replaces high-variance policy gradient estimation with direct backpropagation through a differentiable relaxation of the discrete token sampling process. Using the Gumbel-Softmax reparameterization with straight-through estimation (GRADE-STE), we enable end-to-end gradient flow from reward signals through generated tokens to model parameters. On sentiment-controlled text generation using the IMDB dataset, GRADE-STE achieves a test reward of 0.763 +- 0.344 compared to PPO's 0.510 +- 0.313 and REINFORCE's 0.617 +- 0.378, representing a 50% relative improvement over PPO. Critically, GRADE-STE exhibits gradient variance over 14 times lower than REINFORCE and maintains stable training dynamics throughout optimization. Our rigorous evaluation with proper train/validation/test splits demonstrates that these improvements generalize to held-out data, with GRADE-STE showing the best generalization characteristics among all methods tested. GRADE offers a simpler, more stable, and more effective alternative to reinforcement learning for LLM alignment.
What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework
Venue: arXiv cs.CY 2025
Year: 2025

The rise of Human-AI Collaborative Learning (HAICL) is shifting education toward dialogue-centric paradigms, creating an urgent need for new assessment methods. Evaluating Self-Regulated Learning (SRL) in this context presents new challenges, as the limitations of conventional approaches become more apparent. Questionnaires remain interrupted, while the utility of non-interrupted metrics like clickstream data is diminishing as more learning activity occurs within the dialogue. This study therefore investigates whether the student-AI dialogue can serve as a valid, non-interrupted data source for SRL assessment. We analyzed 421 dialogue logs from 98 university students interacting with a generative AI (GenAI) learning partner. Using large language model embeddings and clustering, we identified 22 dialogue patterns and quantified each student's interaction as a profile of alignment scores, which were analyzed against their Online Self-Regulated Learning Questionnaire (OSLQ) scores. Findings revealed a significant positive association between proactive dialogue patterns (e.g., post-class knowledge integration) and overall SRL. Conversely, reactive patterns (e.g., foundational pre-class questions) were significantly and negatively associated with overall SRL and its sub-processes. A group comparison substantiated these results, with low-SRL students showing significantly higher alignment with reactive patterns than their high-SRL counterparts. This study proposed the Dialogue-Based Human-AI Self-Regulated Learning (DHASRL) framework, a practical methodology for embedding SRL assessment directly within the HAICL dialogue to enable real-time monitoring and scaffolding of student regulation.
Speculative Decoding: Performance or Illusion?
Venue: arXiv cs.CL 2025
Year: 2025

Speculative decoding (SD) has become a popular technique to accelerate Large Language Model (LLM) inference, yet its real-world effectiveness remains unclear as prior evaluations rely on research prototypes and unrealistically small batch sizes. We present, to our knowledge, the first systematic study of SD on a production-grade and widely deployed inference engine (vLLM), covering multiple SD variants ($n$-gram, EAGLE/EAGLE-3, Draft-Model, Multi-Token Prediction) across diverse workloads, model scales, and batch sizes. We analyze key factors governing SD performance, and quantify a theoretical upper bound on SD speedup. Our results show that verification by the target model dominates the execution, while acceptance length varies markedly across output token positions, requests, and datasets. Comparing measured performance with theoretical bounds reveals substantial gaps between observed and theoretical upper bounds, and we leverage this observation to highlight new research opportunities that our study opens up in improving SD.