# ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation

arXiv: https://arxiv.org/abs/2610.01234 · HTML: https://mlsanity.com/paper/2610.01234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 13 of 13)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds](https://mlsanity.com/paper/2403.06609/) (2024)
- [Why Chain of Thought Fails in Clinical Text Understanding](https://mlsanity.com/paper/2509.21933/) (2025)
- [Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes](https://mlsanity.com/paper/2406.02826/) (2024)
- [CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations](https://mlsanity.com/paper/2412.04254/) (2024)
- [Assessing Thai Dialect Performance in LLMs with Automatic Benchmarks and Human Evaluation](https://mlsanity.com/paper/2504.05898/) (2025)
- [SERL: Self-Examining Reinforcement Learning on Open-Domain](https://mlsanity.com/paper/2511.07922/) (2025)
- [Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards](https://mlsanity.com/paper/2510.02338/) (2025)
- [When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation](https://mlsanity.com/paper/2605.24902/) (2026)

## Abstract (arXiv)

Automatic SOAP note generation can ease the documentation burden on physicians, but existing reasoning methods often omit clinically important information and generate unsupported content. Progress in Thai is further hindered by the lack of publicly available datasets. We propose ASCRIBE, a physician-inspired reasoning framework that ascribes a clinical-significance level to each extracted atomic fact in the conversation before summarization, making a general-purpose LLM a more reliable scribe. We also release ThaiClinicBench, the first de-identified Thai clinical summarization benchmark of real encounters, together with a synthetic training corpus derived from real clinical notes. As a prompt, ASCRIBE outperforms chain-of-thought prompting on GPT-5.4 and Gemini 3.1 Pro across the physician-aligned LLM-judge metrics and improves on standard prompting by up to 10.3 points on the completeness LLM-judge metric. As a GRPO reward, it enables a Gemma-4-E4B model trained solely on synthetic data to match Gemini 3.1 Pro in factual precision and surpass it in completeness. Code and data can be found at https://github.com/loolootech/ascribe.