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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2608.18878v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Zijie Meng, Xiwei Dai, Yixuan Tang, Jin Hao, Yang Feng, Fudong Zhu, Xiaoqiang Liu, Shaosheng Cao, Zuozhu Liu
  • Venue: arXiv: cs.AI
  • Published: 2026-08-19
  • Collected: 2026-08-20
  • URL: http://arxiv.org/abs/2608.18878v1
  • PDF: https://arxiv.org/pdf/2608.18878v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

A new system called DentAgent is introduced that aims to improve the accuracy and reliability of dental assessments by integrating various forms of evidence from different modalities. The system uses an evidence-centric multi-agent framework, where five specialized agents work together under the coordination of an Orchestrator. Each agent uses domain tools to convert observations into structured evidence records, which are managed in a shared evidence state called the Evidence Blackboard. DentAgent outperforms senior specialists in four benchmarks, demonstrating its potential for multimodal dental reasoning.

What the Document Says

  • DentAgent is an evidence-centric multi-agent framework designed for multimodal dental reasoning.
  • The framework consists of an Orchestrator and five specialized agents that work across various modalities.
  • Each specialist agent utilizes domain tools to convert observations into structured evidence records.
  • The Evidence Blackboard manages these records as a shared evidence state, tracking coverage, gaps, and conflicts before response generation.
  • Across four benchmarks, DentAgent demonstrates leading performance in multi-label diagnosis.
  • DentAgent surpasses senior specialists by 17.3 percentage points on multi-label diagnosis.
  • The system has potential for broadly applicable and traceable multimodal dental reasoning.

Key Concepts Mentioned

  • Dental assessment
  • Heterogeneous evidence
  • Multi-agent framework
  • Evidence records
  • Orchestrator
  • Specialized agents
  • Domain tools
  • Evidence Blackboard
  • Coverage
  • Gaps
  • Conflicts
  • Response generation
  • Multi-label diagnosis
  • Senior specialists

Claims Made by the Document

  • DentAgent improves the accuracy and reliability of dental assessments.
  • DentAgent integrates various forms of evidence from different modalities.
  • DentAgent outperforms senior specialists in multi-label diagnosis.

Evidence Used

  • The text states that DentAgent aims to improve the accuracy and reliability of dental assessments.
  • The text mentions that DentAgent integrates evidence from domain knowledge, radiographs, intraoral photographs, and 3D dental data.
  • The text shows that DentAgent surpasses senior specialists by 17.3 percentage points on multi-label diagnosis.

Methods / Approach

  • Multi-agent coordination
  • Evidence-centric approach
  • Structured evidence records
  • Shared evidence state

Limitations Stated by the Authors

The paper does not provide specific details about the domain tools used by each specialist agent.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not mention any limitations of the system as presented. Further research may be needed to understand potential limitations in real-world applications.

Open Questions

  • What are the specific domain tools used by each specialist agent?
  • How does DentAgent perform in other dental tasks beyond multi-label diagnosis?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on the introduction and performance of DentAgent. For more detailed information about the system's components and methods, further research or consultation of the original paper may be necessary.

Raw Abstract / Excerpt

Oral diseases affect billions of people worldwide, underscoring a pressing need for accurate and reliable dental assessment that integrates heterogeneous evidence from domain knowledge, radiographs, intraoral photographs, and 3D dental data. Most existing dental AI systems remain modality- or task-specific. Although recent vision-language models support flexible dental question answering, directly generated response leaves evidence implicit and untraceable. To address these limitations, we introduce DentAgent, an evidence-centric multi-agent framework, in which the Orchestrator coordinate five specialized agents spanning various modalities. Each specialist utilizes domain tools to convert observations into structured evidence records. The Evidence Blackboard manages these records as a shared evidence state, tracking coverage, gaps, and conflicts before response generation. This standardized evidence representation integrates isolated dental capabilities into a unified agentic workflow. Across four benchmarks, DentAgent demonstrates leading performance, even surpassing the senior specialists by 17.3 percentage points on multi-label diagnosis, which supports its value for broadly applicable and traceable multimodal dental reasoning, and highlights its potential as a technical foundation for population oral health assessment and management.