TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs
arXiv:2606.29375v1 Announce Type: new Abstract: Medical large language models are commonly adapted with a fixed low-rank budget, even though medical questions differ substantially in confidence, clinical coverage, and cross-domain difficulty. We study adaptive rank budgeting for parameter-efficient medical question answering: for each question, the adapter decides whether to activate a small, medium, or large subset of LoRA rank channels. The central challenge is that a naive adaptive budget router can collapse to unstable choices or spend capacity without improving shifted benchmarks. We prop
The proliferation of medical LLMs with fixed parameter-efficient adaptation methods creates a clear need for flexible budgeting that can dynamically respond to varying question complexity and confidence requirements.
This research directly addresses the efficiency and reliability of medical AI, a critical area where accuracy and adaptability are paramount for practical application and trust.
The shift from fixed to adaptive rank budgeting for medical LLMs means more efficient resource allocation and potentially more accurate and context-aware responses in clinical settings.
- · Medical AI developers
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted diagnostics
- · Developers of inflexible, resource-intensive medical AI
- · Systems unable to adapt to varying medical question complexities
Adaptive rank budgeting improves the performance and cost-efficiency of medical LLMs.
Enhanced reliability and explainability of medical AI could accelerate its integration into mainstream clinical workflows.
More personalized and context-aware AI diagnostics and treatment recommendations become feasible, potentially reshaping healthcare delivery.
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Read at arXiv cs.CL