Dynamic Bidirectional Pattern Memory: A Production-Scale Empirical Characterisation of Inference-Time Gating in Clinical NLP
arXiv:2607.00870v1 Announce Type: new Abstract: We study inference-time pattern-memory gating in a production-scale clinical natural language processing (NLP) pipeline. The pipeline pairs a generator (Llama-3.3 70B) proposing extractions with a verifier (MMed-Llama-3.1 70B) accepting or rejecting them, over 167,034 PMC-Patients narratives, and adds a lightweight memory that learns at deployment which extractions to filter, so the verifier need not re-examine candidates already seen to fail. We report four findings. First, learning filtering rules directly from the verifier's rejections failed
The proliferation of frontier models and increasing demands for efficient, reliable AI in production environments necessitate research into optimizing inference while maintaining high accuracy, especially in sensitive domains like clinical NLP.
This research demonstrates a promising method for improving the efficiency and reliability of large language models in critical applications, accelerating their deployment and practical value in real-world settings.
The introduction of lightweight memory and inference-time gating mechanisms offers a path to more resource-efficient and robust clinical NLP systems, potentially impacting operational costs and model performance.
- · AI developers
- · Healthcare providers
- · NLP researchers
- · Cloud computing providers
- · Companies relying on less efficient AI inference methods
- · Legacy clinical NLP solutions
More efficient and reliable clinical NLP applications become widely deployable, improving medical documentation and analysis.
Reduced computational costs for AI inference could democratize access to advanced NLP capabilities for smaller healthcare institutions.
The methodology could be generalized to other domains, driving wider adoption of AI agents and complex AI pipelines in various industries.
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Read at arXiv cs.CL