From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning
Summary: arXiv:2604.06262v1 Announce Type: cross
Abstract
Contextual clinical reasoning necessitates robust inference grounded in complex, heterogeneous clinical records. Traditional approaches such as fine-tuning, in-context learning (ICL), and retrieval-augmented generation (RAG) facilitate knowledge exposure; however, they frequently fail to achieve genuine contextual internalization. This internalization is critical for dynamically adjusting a model’s internal representations to the subtle nuances of individual cases during inference.
Introduction to Dual-Stream Calibration (DSC)
To address the limitations of existing methodologies, we introduce the concept of Dual-Stream Calibration (DSC), a test-time training framework designed to transcend superficial knowledge exposure and enable deep internalization during inference. DSC achieves this by aligning two distinct calibration streams:
- Semantic Calibration Stream: This stream emphasizes deliberative reflection on core evidence, effectively internalizing semantic anchors. By minimizing entropy, it stabilizes generative trajectories, fostering a more profound understanding of the data.
- Structural Calibration Stream: This stream focuses on assimilating latent inferential dependencies through an iterative meta-learning objective. Training on specialized support sets at test-time, it enables the model to bridge the gap between external evidence and internal logic, synthesizing fragmented data into coherent responses.
Significance of the Proposed Approach
The introduction of DSC represents a paradigm shift in clinical reasoning, moving from a passive attention-based matching system to an active refinement of the latent inferential space. This advancement is crucial for enhancing the accuracy and reliability of clinical decision-making, particularly in environments characterized by complex and variable data.
Validation and Performance
Our approach has been rigorously validated against thirteen diverse clinical datasets, demonstrating superior performance across three distinct task paradigms. The results consistently outpace state-of-the-art baselines, which encompass a range of models from those reliant on extensive training to innovative test-time learning frameworks.
Conclusion
In conclusion, Dual-Stream Calibration offers a transformative approach to in-context clinical reasoning by facilitating deeper internalization of knowledge. As healthcare increasingly relies on AI-driven insights, DSC equips clinical models with the capability to adaptively refine their reasoning processes, ultimately leading to more accurate and contextually relevant outcomes.