arXiv subjects

Yonglin Chen

Publications and source records attributed to Yonglin Chen.

7 recordsLinked to original sources

arXiv2026

Reference-Based Manipulation: A Framework and Pipeline for Multimodal Spatial Reasoning

When manipulating objects in immersive platforms through speech and gesture, users naturally construct spatial references, referring to scene entities, their bodies, or the environment. Leveraging spatial cognition theories, this work systematically examines how users construct and communicate spatial intent. Using a custom toolkit, we conducted a Wizard-of-Oz study to observe unconstrained multimodal (speech + gesture) input patterns in Virtual Reality for scene construction. Based on these findings, we formalize a framework that decomposes spatial references into three core components: Source, Anchor, and Frame, while characterizing their compositional strategies and explicitness. We demonstrate the utility of this Reference-based Manipulation framework by implementing an LLM-based pipeline featuring a set of example interaction techniques with a preliminary technical evaluation. Finally, we discuss key lessons learned for supporting reference-based spatial interaction.

cs.HC↗

arXiv2026

TaleBot: A Tangible AI Companion to Support Children in Co-creative Storytelling for Resilience Cultivation

Resilience is a key factor affecting children's mental wellbeing and future development. Yet, limited HCI research has explored how to help children build resilience through adversarial experiences. Informed by a formative study with elementary school teachers and professional psychologists, we design TaleBot, an AI-empowered system that supports children to co-create stories about overcoming everyday adversities tailored to their personal situations. We evaluated the system with 12 elementary children in school counseling rooms under teacher guidance and conducted reflective interviews with parents upon the Child-AI co-created stories. The findings show that TaleBot encourages children in self-expression of feelings and thoughts, creating opportunities for teachers to provide personalized support and for parents to better understand the profound impact of family communication on children's mental wellbeing. We conclude with design implications for using generative AI to support children's mental health education and interventions across school and family contexts.

cs.HC↗

arXiv2026

FuturePrism: Supporting Adolescence in Collaborative Storytelling to Cope with Future Uncertainty

FuturePrism is a GenAI-empowered collaborative storytelling system designed to scaffold adolescents to navigate future life challenges. Adolescents often suffer from anxiety related to future uncertainty for lacking the executive function to develop concrete pathways. Operationalizing Snyder's Hope Theory, the system utilizes a triadic role-play mechanics to externalize cognitive processes through four narrative chapters: The Goal, The Opportunity, The Challenge, and The Agency. An evaluation workshop with 20 adolescents demonstrated that FuturePrism significantly enhances momentary hope levels, particularly in the Agency dimension. Participants reported high levels of narrative immersion and positive feedback towards system usability. Participants also confirmed that the AI-scaffolded collaborative storytelling empowered them to develop positive attitudes towards future challenges.

cs.HC↗

arXiv2025

Step-DeepResearch Technical Report

As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands for open-ended research, which requires robust skills in intent recognition, long-horizon decision-making, and cross-source verification. To address this, we introduce Step-DeepResearch, a cost-effective, end-to-end agent. We propose a Data Synthesis Strategy Based on Atomic Capabilities to reinforce planning and report writing, combined with a progressive training path from agentic mid-training to SFT and RL. Enhanced by a Checklist-style Judger, this approach significantly improves robustness. Furthermore, to bridge the evaluation gap in the Chinese domain, we establish ADR-Bench for realistic deep research scenarios. Experimental results show that Step-DeepResearch (32B) scores 61.4% on Scale AI Research Rubrics. On ADR-Bench, it significantly outperforms comparable models and rivals SOTA closed-source models like OpenAI and Gemini DeepResearch. These findings prove that refined training enables medium-sized models to achieve expert-level capabilities at industry-leading cost-efficiency.

cs.CL↗

arXiv2025

From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control. However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored. Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established. This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization. The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.

cs.AI↗

arXiv2025

Desirable Unfamiliarity: Insights from Eye Movements on Engagement and Readability of Dictation Interfaces

Transcripts displayed on dictation interfaces can be hard to read due to recognition errors and disfluencies. LLM-based text auto-correction could help, but changing the text during production could lead to distraction and unintended phrasing. To understand how to balance readability, attention, and accuracy, we conducted an eye-tracking experiment with 20 participants to compare five dictation interfaces: PLAIN (real-time transcription), AOC (periodic corrections), RAKE (keyword highlights), GP-TSM (grammar-preserving highlights), and SUMMARY (LLM-generated abstractive summary). By analyzing participants' gaze patterns during speech composition and reviewing processes, we found that during composition, participants spent only 7-11% of their time in active reading regardless of the interface. Although SUMMARY introduced unfamiliar words and phrasing during composition, it was easier to read and more preferred by participants. Our findings suggest a high user tolerance for altering spoken words in LLM-enabled diction interfaces.

cs.HC↗

arXiv2025

Designing LLM-simulated Immersive Spaces to Enhance Autistic Children's Social Affordances Understanding

One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.

cs.HC↗