Tootfinder

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
Zhaolong Wu, Pu Luo, Jason Pui Yin Cheung, Teng Zhang
https://arxiv.org/abs/2509.11645

Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework
This study presents the first comprehensive evaluation of Multimodal Large Language Models (MLLMs) for Adolescent Idiopathic Scoliosis (AIS) self-management. We constructed a database of approximately 3,000 anteroposterior X-rays with diagnostic texts and evaluated five MLLMs through a `Divide and Conquer' framework consisting of a visual question-answering task, a domain knowledge assessment task, and a patient education counseling assessment task. Our investigation revealed limitations of MLL…

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Kartik Narayan, Yang Xu, Tian Cao, Kavya Nerella, Vishal M. Patel, Navid Shiee, Peter Grasch, Chao Jia, Yinfei Yang, Zhe Gan
https://arxiv.org/abs/2510.12801

DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such as retrieval augmented generation (RAG) methods, search agents, and search equipped MLLMs, often suffer from rigid pipelines, excessive search calls, and poorly constructed search queries, which resu…

SocioBench: Modeling Human Behavior in Sociological Surveys with Large Language Models
Jia Wang, Ziyu Zhao, Tingjuntao Ni, Zhongyu Wei
https://arxiv.org/abs/2510.11131 https://

SocioBench: Modeling Human Behavior in Sociological Surveys with Large Language Models
Large language models (LLMs) show strong potential for simulating human social behaviors and interactions, yet lack large-scale, systematically constructed benchmarks for evaluating their alignment with real-world social attitudes. To bridge this gap, we introduce SocioBench-a comprehensive benchmark derived from the annually collected, standardized survey data of the International Social Survey Programme (ISSP). The benchmark aggregates over 480,000 real respondent records from more than 30 co…

Concept Unlearning in Large Language Models via Self-Constructed Knowledge Triplets
Tomoya Yamashita, Yuuki Yamanaka, Masanori Yamada, Takayuki Miura, Toshiki Shibahara, Tomoharu Iwata
https://arxiv.org/abs/2509.15621

Concept Unlearning in Large Language Models via Self-Constructed Knowledge Triplets
Machine Unlearning (MU) has recently attracted considerable attention as a solution to privacy and copyright issues in large language models (LLMs). Existing MU methods aim to remove specific target sentences from an LLM while minimizing damage to unrelated knowledge. However, these approaches require explicit target sentences and do not support removing broader concepts, such as persons or events. To address this limitation, we introduce Concept Unlearning (CU) as a new requirement for LLM unl…

Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
Szymon {\L}ukasik, Natalia O\.zegalska-{\L}ukasik
https://arxiv.org/abs/2509.25286

Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
Political beliefs vary significantly across different countries, reflecting distinct historical, cultural, and institutional contexts. These ideologies, ranging from liberal democracies to rigid autocracies, influence human societies, as well as the digital systems that are constructed within those societies. The advent of generative artificial intelligence, particularly Large Language Models (LLMs), introduces new agents in the political space-agents trained on massive corpora that replicate a…

i think "terrorism" is a case where it's best to meet the culture where it's at. language isn't prescribed by the dictionary. while the people shouldn't just let decades of government propaganda dictate the meaning of a word, its literally constructed with "terror" in the name from the get go, and that alone means there's triple the pressure there yall will never have the numbers and influence to beat. its okay if they think the terrorism accusation is…

LLM4MG: Adapting Large Language Model for Multipath Generation via Synesthesia of Machines
Ziwei Huang, Shiliang Lu, Lu Bai, Xuesong Cai, Xiang Cheng
https://arxiv.org/abs/2509.14711

LLM4MG: Adapting Large Language Model for Multipath Generation via Synesthesia of Machines
Based on Synesthesia of Machines (SoM), a large language model (LLM) is adapted for multipath generation (LLM4MG) for the first time. Considering a typical sixth-generation (6G) vehicle-to-infrastructure (V2I) scenario, a new multi-modal sensing-communication dataset is constructed, named SynthSoM-V2I, including channel multipath information, millimeter wave (mmWave) radar sensory data, RGB-D images, and light detection and ranging (LiDAR) point clouds. Based on the SynthSoM-V2I dataset, the pr…

Designing Shadow Tomography Protocols by Natural Language Processing
Yadong Wu, Pengfei Zhang, Ce Wang, Juan Yao, Yi-Zhuang You
https://arxiv.org/abs/2509.12782 https://

Designing Shadow Tomography Protocols by Natural Language Processing
Quantum circuits form a foundational framework in quantum science, enabling the description, analysis, and implementation of quantum computations. However, designing efficient circuits, typically constructed from single- and two-qubit gates, remains a major challenge for specific computational tasks. In this work, we introduce a novel artificial intelligence-driven protocol for quantum circuit design, benchmarked using shadow tomography for efficient quantum state readout. Inspired by technique…

From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse
Seunguk Yu, Jungmin Yun, Jinhee Jang, Youngbin Kim
https://arxiv.org/abs/2509.14712

From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse
Although offensive language continually evolves over time, even recent studies using LLMs have predominantly relied on outdated datasets and rarely evaluated the generalization ability on unseen texts. In this study, we constructed a large-scale dataset of contemporary political discourse and employed three refined judgments in the absence of ground truth. Each judgment reflects a representative offensive language detection method and is carefully designed for optimal conditions. We identified …

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
Lokesh Antony Kadiyala, Amir Mirzaeinia
https://arxiv.org/abs/2510.01203 https://

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs
The stock market is extremely difficult to predict in the short term due to high market volatility, changes caused by news, and the non-linear nature of the financial time series. This research proposes a novel framework for improving minute-level prediction accuracy using semantic sentiment scores from top ten different large language models (LLMs) combined with minute interval intraday stock price data. We systematically constructed a time-aligned dataset of AAPL news articles and 1-minute Ap…

A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
Devin Lange, Shanghua Gao, Pengwei Sui, Austen Money, Priya Misner, Marinka Zitnik, Nils Gehlenborg
https://arxiv.org/abs/2509.16454

A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
We explore the potential for combining generative AI with grammar-based visualizations for biomedical data discovery. In our prototype, we use a multi-agent system to generate visualization specifications and apply filters. These visualizations are linked together, resulting in an interactive dashboard that is progressively constructed. Our system leverages the strengths of natural language while maintaining the utility of traditional user interfaces. Furthermore, we utilize generated interacti…

TianHui: A Domain-Specific Large Language Model for Diverse Traditional Chinese Medicine Scenarios
Ji Yin, Menglan He, Yujie Zhang, Linshuai Zhang, Tingting Ma, Ce Tian, Jie Wu, Lin Xu, Tao Jiang
https://arxiv.org/abs/2509.19834

TianHui: A Domain-Specific Large Language Model for Diverse Traditional Chinese Medicine Scenarios
Domain-specific LLMs in TCM face limitations in research settings due to constrained adaptability, insufficient evaluation datasets, and limited computational resources. This study presents TianHui, a specialized TCM LLM built through contextual data integration and domain knowledge fusion. We constructed a large-scale TCM corpus (0.97GB unsupervised data + 611,312 QA pairs) and employed a two-stage training strategy with QLoRA, DeepSpeed Stage 2, and Flash Attention 2. Evaluation on 12 benchma…

Incorporating LLM Embeddings for Variation Across the Human Genome
Hongqian Niu, Jordan Bryan, Xihao Li, Didong Li
https://arxiv.org/abs/2509.20702 https://

Incorporating LLM Embeddings for Variation Across the Human Genome
Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus only on gene-level information. We present one of the first systematic frameworks to generate variant-level embeddings across the entire human genome. Using curated annotations from FAVOR, ClinVar, and the GWAS Catalog, we constructed semantic text descriptions for 8.9 billion possible variants and generated embeddings at three scales: 1.5 milli…