arXiv:2602.07905v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While existing studies augment LLMs with various knowledge types, it remains unclear how effectively the additional costs translate into accuracy. In this paper, we explore how meta-cognition of LLMs, i.e., their self-assessment of their own cognitive states, can regulate the reasoning process. Specifically, we propose MedCoG, a Medical Meta-Cognition Agent with Knowledge Graph, where the meta-cognitiv
The proliferation of LLMs in specialized fields like medicine is driving research into more efficient and accurate inference methods, as current scaling laws show diminishing returns.
Improving LLM inference density and accuracy in medical reasoning can significantly impact healthcare diagnostics, treatment planning, and research efficiency, making advanced AI more clinically viable.
This research introduces a new paradigm for LLM optimization in medical contexts, moving beyond mere knowledge augmentation to incorporate meta-cognitive regulation for better performance and resource utilization.
- · AI developers in healthcare
- · Medical research institutions
- · Patients (indirectly through improved diagnostics)
- · Cloud infrastructure providers
- · Traditional diagnostic methods (long-term)
- · LLM approaches without meta-cognition
Increased adoption of LLM-based diagnostic and reasoning tools in medical settings due to enhanced efficiency and accuracy.
Development of specialized hardware and software optimized for meta-cognitive LLM architectures, creating new market segments.
Ethical and regulatory frameworks adapting to highly autonomous and self-assessing AI systems within critical domains like medicine.
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Read at arXiv cs.AI