We introduce J-ORA, a novel multimodal dataset that bridges the gap in robot perception by providing detailed object attribute annotations within Japanese human-robot dialogue scenarios. J-ORA is designed to support three critical perception tasks, object identification, reference resolution, and next-action prediction, by leveraging a comprehensive template of attributes (e.g., category, color, shape, size, material, and spatial relations). Extensive evaluations with both proprietary and open-source Vision Language Models (VLMs) reveal that incorporating detailed object attributes substantially improves multimodal perception performance compared to without object attributes. Despite the improvement, we find that there still exists a gap between proprietary and open-source VLMs. In addition, our analysis of object affordances demonstrates varying abilities in understanding object functionality and contextual relationships across different VLMs. These findings underscore the importance of rich, context-sensitive attribute annotations in advancing robot perception in dynamic environments. See project page at https://jatuhurrra.github.io/J-ORA/.

Abstract--With the rapid advancement of Large Language Models (LLMs), their capabilities in various automation domains, particularly Unmanned Aerial V ehicle (UA V) operations, have garnered increasing attention. Current research remains predominantly constrained to small-scale UA V applications, with most studies focusing on isolated components such as path planning for toy drones, while lacking comprehensive investigation of medium-and long-range UA V systems in real-world operational contexts. Larger UA V platforms introduce distinct challenges, including stringent requirements for airport-based take-off and landing procedures, adherence to complex regulatory frameworks, and specialized operational capabilities with elevated mission expectations. LV system processes natural language instructions to orchestrate short-, medium-, and long-range UA V missions through five key technical components: (i) LLM-as-Parser for instruction interpretation, (ii) Route Planner for Points of Interest (POI) determination, (iii) Path Planner for waypoint generation, (iv) Control Platform for executable trajectory implementation, and (v) UA V monitoring. We demonstrate the system's feasibility through three representative use cases spanning different operational scales: multi-UA V patrol, multi-POI delivery, and multi-hop relocation. Beyond the current implementation, we establish a five-level automation taxonomy that charts the evolution from current LLM-as-Parser capabilities (Level 1) to fully autonomous LLMas-Autopilot systems (Level 5), identifying technical prerequisites and research challenges at each stage. The rise of Large Language Models (LLMs) has transformed numerous domains, such as mobile services, vehicles, and robotics [1]-[3]. These fields have become increasingly intelligent and user-friendly through LLM integration, enabling command and control through natural language. Equal contribution L. Y uan and C. G. Brinton are with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. C. Deng and I. Hwang are with the School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA. Han is with the Department of Computer Science and Engineering, Y onsei University, Seoul, South Korea. E-mail: djh@yonsei.ac.kr S. Brunswicker is with the Polytechnic Institute, Purdue University, West Lafayette, IN 47907, USA. LLMs fulfill diverse roles within these systems. LLM-as-Router can orchestrate task allocation and model selection for human pilots, LLM-as-Agent can execute actions on behalf of humans, and LLM-as-Judge can conduct evaluations in place of human judgment.

Internal combustion engine (ICE) vehicles and electric vehicles (EVs) exhibit distinct vehicle dynamics. EVs provide rapid acceleration, with electric motors producing peak power across a wider speed range, and achieve swift deceleration through regenerative braking. While existing microscopic models effectively capture the driving behavior of ICE vehicles, a modeling framework that accurately describes the unique car-following dynamics of EVs is lacking. Developing such a model is essential given the increasing presence of EVs in traffic, yet creating an easy-to-use and accurate analytical model remains challenging. To address these gaps, this study develops and validates a Phase-Aware AI (PAAI) car-following model specifically for EVs. The proposed model enhances traditional physics-based frameworks with an AI component that recognizes and adapts to different driving phases, such as rapid acceleration and regenerative braking. Using real-world trajectory data from vehicles equipped with adaptive cruise control (ACC), we conduct comprehensive simulations to validate the model's performance. The numerical results demonstrate that the PAAI model significantly improves prediction accuracy over traditional car-following models, providing an effective tool for accurately representing EV behavior in traffic simulations.

Accurate monitoring of pest population dynamics is crucial for informed decision-making in precision agriculture. Currently, mainstream image-based pest counting methods primarily rely on image processing combined with machine learning or deep learning for pest counting. However, these methods have limitations and struggle to handle situations involving pest occlusion. To address this issue, this paper proposed a robotic stirring method with trajectory optimization and adaptive speed control for accurate pest counting in water traps. First, we developed an automated stirring system for pest counting in yellow water traps based on a robotic arm. Stirring alters the distribution of pests in the yellow water trap, making some of the occluded individuals visible for detection and counting. Then, we investigated the impact of different stirring trajectories on pest counting performance and selected the optimal trajectory for pest counting. Specifically, we designed six representative stirring trajectories, including circle, square, triangle, spiral, four small circles, and random lines, for the robotic arm to stir. And by comparing the overall average counting error and counting confidence of different stirring trajectories across various pest density scenarios, we determined the optimal trajectory. Finally, we proposed a counting confidence-driven closed-loop control system to achieve adaptive-speed stirring. It uses changes in pest counting confidence between consecutive frames as feedback to adjust the stirring speed. To the best of our knowledge, this is the first study dedicated to investigating the effects of different stirring trajectories on object counting in the dynamic liquid environment and to implement adaptive-speed stirring for this type of task. Experimental results show ...

Current recommendation systems often tend to overlook emotional context and rely on historical listening patterns or static mood tags. This paper introduces a novel music recommendation framework employing a variant of Wide and Deep Learning architecture that takes in real-time emotional states inferred directly from natural language as inputs and recommends songs that closely portray the mood. The system captures emotional contexts from user-provided textual descriptions by using transformer-based embeddings, which were finetuned to predict the emotional dimensions of valence-arousal. The deep component of the architecture utilizes these embeddings to generalize unseen emotional patterns, while the wide component effectively memorizes user-emotion and emotion-genre associations through cross-product features. Experimental results show that personalized music selections positively influence the user's emotions and lead to a significant improvement in emotional relevance.

Underwater activities like scuba diving enable millions annually to explore marine environments for recreation and scientific research. Maintaining situational awareness and effective communication are essential for diver safety. Traditional underwater communication systems are often bulky and expensive, limiting their accessibility to divers of all levels. While recent systems leverage lightweight smartphones and support text messaging, the messages are predefined and thus restrict context-specific communication. In this paper, we present AquaVLM, a tap-and-send underwater communication system that automatically generates context-aware messages and transmits them using ubiquitous smartphones. Our system features a mobile vision-language model (VLM) fine-tuned on an auto-generated underwater conversation dataset and employs a hierarchical message generation pipeline. We co-design the VLM and transmission, incorporating error-resilient fine-tuning to improve the system's robustness to transmission errors. We develop a VR simulator to enable users to experience AquaVLM in a realistic underwater environment and create a fully functional prototype on the iOS platform for real-world experiments. Both subjective and objective evaluations validate the effectiveness of AquaVLM and highlight its potential for personal underwater communication as well as broader mobile VLM applications.

While recent advances in Large Language Models (LLMs) have improved the quality of creative text generation, significant challenges remain in producing personalized stories that reflect individual user preferences. Conventional approaches rely on explicit feedback or fine-tuning, which presents practical issues regarding user burden, data collection, computational costs, and privacy. In this work, we propose PREFINE (Persona-and-Rubric Guided Critique-and-Refine), a novel framework that extends the Critique-and-Refine paradigm to personalization. PREFINE constructs a pseudo-user agent from a user's interaction history and generates user-specific rubrics (evaluation criteria). By having this agent critique and refine outputs on the user's behalf based on these tailored rubrics, our method achieves personalized generation without requiring parameter updates or direct user feedback. We conducted a comprehensive evaluation on the PerDOC and PerMPST story datasets. We designed three baseline methods and several model variants to verify the contribution of each component of our framework. In automatic evaluations (LLM-as-a-Judge), PREFINE achieved higher win rates and statistically significant scores than the baselines, without compromising general story quality. Analysis of the model variants confirmed that both the pseudo-user agent and the user-specific rubrics are crucial for enhancing personalization performance. Beyond story generation, our approach holds potential for enabling efficient personalization in broader applications, such as dialogue systems, education, and recommendation.

As generative AI increasingly outperforms students in producing academic writing, a critical question arises: how can we preserve the motivation, creativity, and intellectual growth of novice researchers in an age of automated academic achievement? This paper introduces GAMER PAT (GAme MastER, Paper Authoring Tutor), a prompt-engineered AI chatbot that reframes research paper writing as a serious game. Through role-playing mechanics, users interact with a co-author NPC and anonymous reviewer NPCs, turning feedback into "missions" and advancing through a narrative-driven writing process. Our study reports on 26+ gameplay chat logs, including both autoethnography and use by graduate students under supervision. Using qualitative log analysis with SCAT (Steps for Coding and Theorization), we identified an emergent four-phase scaffolding pattern: (1) question posing, (2) meta-perspective, (3) structuring, and (4) recursive reflection. These results suggest that GAMER PAT supports not only the structural development of research writing but also reflective and motivational aspects. We present this work as a descriptive account of concept and process, not a causal evaluation. We also include a speculative outlook envisioning how humans may continue to cultivate curiosity and agency alongside AI-driven research. This arXiv version thus provides both a descriptive report of design and usage, and a forward-looking provocation for future empirical studies.

This project explores the development of an AI-enhanced operator assistant for UNICOS, CERN's UNified Industrial Control System. While powerful, UNICOS presents a number of challenges, including the cognitive burden of decoding widgets, manual effort required for root cause analysis, and difficulties maintainers face in tracing datapoint elements (DPEs) across a complex codebase. In situations where timely responses are critical, these challenges can increase cognitive load and slow down diagnostics. To address these issues, a multi-agent system was designed and implemented. The solution is supported by a modular architecture comprising a UNICOS-side extension written in CTRL code, a Python-based multi-agent system deployed on a virtual machine, and a vector database storing both operator documentation and widget animation code. Preliminary evaluations suggest that the system is capable of decoding widgets, performing root cause analysis by leveraging live device data and documentation, and tracing DPEs across a complex codebase. Together, these capabilities reduce the manual workload of operators and maintainers, enhance situational awareness in operations, and accelerate responses to alarms and anomalies. Beyond these immediate gains, this work highlights the potential of introducing multi-modal reasoning and retrieval augmented generation (RAG) into the domain of industrial control. Ultimately, this work represents more than a proof of concept: it provides a basis for advancing intelligent operator interfaces at CERN. By combining modular design, extensibility, and practical AI integration, this project not only alleviates current operator pain points but also points toward broader opportunities for assistive AI in accelerator operations.

Widespread frustration with rigid touch-tone Interactive Voice Response (IVR) systems for customer service underscores the need for more direct and intuitive language interaction. While speech technologies are necessary, the key challenge lies in routing intents from user phrasings to IVR menu paths, a task where Large Language Models (LLMs) show strong potential. Progress, however, is limited by data scarcity, as real IVR structures and interactions are often proprietary. We present a novel LLM-based methodology to address this gap. Using three distinct models, we synthesized a realistic 23-node IVR structure, generated 920 user intents (230 base and 690 augmented), and performed the routing task. We evaluate two prompt designs: descriptive hierarchical menus and flattened path representations, across both base and augmented datasets. Results show that flattened paths consistently yield higher accuracy, reaching 89.13% on the base dataset compared to 81.30% with the descriptive format, while augmentation introduces linguistic noise that slightly reduces performance. Confusion matrix analysis further suggests that low-performing routes may reflect not only model limitations but also redundancies in menu design. Overall, our findings demonstrate proof-of-concept that LLMs can enable IVR routing through a smoother, more seamless user experience -- moving customer service one step ahead of touch-tone menus.