AdaptiveThink: Dynamic Chain-of-Thought Pruning for Resource-Constrained Medical Deployments
Abstract
A novel framework combining ThinkPrune's reasoning optimization with HealthBench's medical evaluation metrics to create resource-efficient medical LLMs for clinical deployment. The system dynamically adjusts reasoning depth based on task complexity and available computational resources while maintaining safety through biomedical knowledge graph verification.
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Research Gap Analysis
Current approaches don't address the simultaneous challenges of computational efficiency, medical safety, and dynamic resource adaptation in healthcare LLM deployments
AdaptiveThink: Dynamic Chain-of-Thought Pruning for Resource-Constrained Medical Deployments
Motivation
Large Language Models (LLMs) show promising potential in healthcare applications but face significant deployment challenges due to their computational demands and tendency to generate unnecessarily verbose reasoning chains. While recent work has demonstrated the effectiveness of pruning thinking lengths (ThinkPrune) and evaluating medical safety (HealthBench), no existing solution combines these approaches to create resource-efficient medical LLMs suitable for real-world clinical deployment. Additionally, current systems use static pruning approaches that don't account for varying task complexity or resource constraints.
Proposed Approach
Dynamic Pruning Framework
The core innovation is a reinforcement learning framework that dynamically adjusts reasoning chain length based on:
- Task complexity metrics derived from medical knowledge graphs
- Available computational resources
- Required confidence thresholds for different medical contexts
Safety-Aware Training
The system incorporates multiple safety mechanisms:
- Biomedical knowledge graph verification at each reasoning step
- Progressive relaxation of pruning constraints based on task criticality
- Continuous validation against HealthBench criteria
Adaptive Resource Management
- Real-time monitoring of computational resources
- Dynamic adjustment of reasoning depth based on hardware constraints
- Graceful degradation paths for resource-constrained environments
Expected Outcomes
- 40-60% reduction in computational requirements while maintaining >95% accuracy on HealthBench metrics
- Demonstrable safety guarantees through knowledge graph verification
- Flexible deployment options for various healthcare settings
- Clear performance-resource tradeoff guidelines
Potential Applications
- Emergency room triage support systems
- Resource-constrained healthcare facilities
- Mobile medical applications
- Telemedicine platforms
- Clinical decision support systems
The system would enable broader adoption of LLM technology in healthcare while maintaining rigorous safety standards and efficient resource utilization.
Proposed Methodology
Combine reinforcement learning for dynamic reasoning chain pruning with medical knowledge graph verification and resource-aware adaptation
Potential Impact
Enable safe, efficient deployment of medical LLMs in resource-constrained healthcare settings while maintaining high accuracy and safety standards