arXiv AI

DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning

DentAgent is an evidence‑centric multi‑agent framework designed for multimodal dental reasoning. It coordinates five specialized agents that process domain knowledge, radiographs, intraoral photographs, and 3D dental data, converting observations into structured evidence records. The Evidence Blackboard tracks coverage, gaps, and conflicts before generating responses, and the system outperforms senior specialists by 17.3 percentage points on multi‑label diagnosis across four benchmarks.

arXiv AI

Jun 3

Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models

arXiv:2606. 02914v1 Announce Type: new Abstract: Background: Oral diseases affect nearly 3.

By Sema Helali, Lina Abu Nadab, Sausan Alqawas, Alaa Abd-Alrazaq, Faleh Tamimi, Rafat Damseh

agents computer-vision multimodal benchmarks safety

arXiv Computer Vision

Sep 17

AgenTeeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection

arXiv:2609.17800v1 Announce Type: new Abstract: Vision-language models (VLMs) remain largely unreliable on panoramic dental radiographs and can rely on learned anatomical priors rather than evidence...

By Ahmed Rafid, Fariya Ahmed, Rumman Adib, Mehedi Ahamed, Ajwad Abrar, Tareque Mohmud Chowdhury

llms agents reinforcement-learning fine-tuning multimodal safety

arXiv AI

Aug 13

Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology

arXiv:2608. 11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users.

By Del Coburn, Scott Sanner, Dan Silver

llms agents

arXiv Computer Vision

Oct 1

From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning

arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...

By Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long

llms agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 9

PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow

arXiv:2606. 07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains challenging.

By Chengyang Zhang, Wenchuan Zhang, Bo Li, Mengran Li, Bob Zhang, Yuhao Yi, Hong Bu, Jiancheng Lv

llms agents multimodal safety

arXiv AI

Aug 19

A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning

PathPocket is a multimodal AI co‑pilot that grounds pathology decision‑making in evidence. It builds the largest pathology evidence corpus (≈110,472 documents) and a hypergraph of 4.55 million entities and 7.10 million relations to support traceable reasoning. The system handles text and multimodal queries, including ROI and gigapixel whole‑slide images, and outperforms current state‑of‑the‑art models on a benchmark of over 200,000 real‑world cases, improving pathologists’ diagnostic accuracy and confidence.

By Zhe Xu, Zhengyu Zhang, Zhiyuan Cai, Jiahao Xu, Yijie Lin, Ziyi Liu, Junlin Hou, Hongyi Wang, Yuxiang Nie, Yihui Wang, Jiabo Ma, Ling Liang, Yingxue Xu, Zhengrui Guo, Guanghao Wu, Danyi Li, Ziqi Zhou, Donglin Tan, Zhijian Cen, Ying Tan, Xiaolin Liu, Qi Xie, Xiaoying Tang, Xi Peng, Cheng Deng, Lijuan Qu, Ronald Cheong Kin Chan, Li Liang, Hao Chen

agents multimodal benchmarks

arXiv AI

Oct 5

EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision

EviDent-CBCT is a framework that generates dental cone‑beam CT reports from limited clinical data by first extracting a discrete record of tooth‑level, global, and tooth‑IAC evidence using an anatomy‑aware network. The evidence is reconciled through a dental‑logic consistency projection before being rendered into a report by a deterministic renderer and an image‑blind language model. The system achieves higher evidence set‑F1 and logical‑F1 scores than baselines and ranked highly in the ODIN 2026 challenge, demonstrating the effectiveness of a discrete evidence record for auditable CBCT report generation.

By Ruiyang Hao, Zhi Qin Tan, Yulan He, Owen Addison, Yunpeng Li

llms

arXiv AI

Sep 17

SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis

SurgRAW introduces a multi‑agent, chain‑of‑thought workflow for robotic surgical video analysis, leveraging a new SurgCoTBench benchmark with 14,256 QA pairs across five surgical tasks. The system uses an orchestrator to split scene understanding into two reasoning streams, panel‑discussion‑style collaboration among task‑specific agents, and retrieval‑augmented generation to incorporate surgical knowledge. Experiments show SurgRAW outperforms mainstream vision‑language models and a supervised baseline by 14.61% accuracy.

By Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin

llms rag agents robotics multimodal benchmarks safety

arXiv AI

Sep 1

FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis

arXiv:2608.28662v1 Announce Type: new Abstract: Fracture detection and its clinical interpretability see notable improvements when deep vision models are integrated with agentic AI architectures. Whi...

By Hardik Iyer, Tirath Bhathawala, Mihir Panchal, Ying-Jung Chen, Kiran Bhowmick, Pankaj Sonawane, Meera Narvekar

llms agents benchmarks safety

arXiv AI

Jun 9

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology

arXiv:2606. 08093v1 Announce Type: new Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices.

By Zhe Xu, Zhengyu Zhang, Zhiyuan Cai, Jiahao Xu, Yijie Lin, Ziyi Liu, Junlin Hou, Hongyi Wang, Yuxiang Nie, Ling Liang, Yihui Wang, Yingxue Xu, Ronald Cheong Kin Chan, Li Liang, Hao Chen

agents multimodal benchmarks

Hugging Face Trending Papers

Aug 6

Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning

Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question. Existing training-free agentic frameworks formulate this process as iterative patch retrieval based on semantic relevance to the question.

agents fine-tuning benchmarks

arXiv AI

Sep 2

Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis

arXiv:2604.16729v2 Announce Type: replace-cross Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation r...

By Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler, Daniel Rueckert, Jan C. Peeken

llms agents computer-vision nlp fine-tuning benchmarks

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