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Related Experiment Video

Updated: May 31, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.

Jinyu Sun1, Jibin Zhou2, Huabei Wang1

  • 1College of Chemistry and Chemical Engineering, Central South University Changsha 410083 P. R. China.

Chemical Science
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

TeLLAgent, a novel dual-agent framework, enhances scientific discovery by reliably orchestrating tools and executing complex plans. This AI system significantly reduces hallucinations and accelerates autonomous research, as demonstrated in organic solar cell development.

Related Experiment Videos

Last Updated: May 31, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Artificial intelligence
  • Materials science
  • Scientific discovery

Background:

  • Large language model (LLM) agents show potential for scientific discovery but struggle with reliable tool orchestration and complex planning, leading to hallucinations and inconsistencies.
  • Existing agent frameworks lack the robustness required for real-world scientific automation.

Purpose of the Study:

  • To introduce TeLLAgent, a novel supervisor-executor dual-agent framework designed to overcome limitations in LLM agent reliability for scientific discovery.
  • To enhance the ability of AI agents to orchestrate tools, execute multi-step plans, and minimize logical inconsistencies and hallucinations.

Main Methods:

  • TeLLAgent employs a dual-agent architecture: a global planning agent (DeepSeek-R1) for iterative reasoning and dynamic plan formulation, and a local execution agent (DeepSeek-V3.1) for precise tool invocation.
  • A self-correction loop, mediated by the Model Context Protocol, enables the system to "rethink" and "recover" from execution failures, enhancing robustness.
  • The framework was benchmarked on complex tool-calling tasks and demonstrated on an end-to-end organic solar cell material discovery workflow.

Main Results:

  • TeLLAgent significantly outperformed GPT-5 and existing agent frameworks in success rates for multi-step planning and scalability with task complexity.
  • The system drastically reduced factual hallucinations in knowledge retrieval, validated by human experts and LLM judges.
  • An AI-designed quasi-macromolecular acceptor molecule was identified, synthesized, and validated, achieving a 16.44% power conversion efficiency in organic solar cells.

Conclusions:

  • TeLLAgent establishes a new paradigm for reliable, autonomous AI systems capable of accelerating scientific discovery.
  • The supervisor-executor dual-agent framework with self-correction mechanisms significantly enhances LLM agent performance and reliability.
  • TeLLAgent demonstrates practical application in materials science, with potential for broader impact in fields like drug discovery.