arXiv cs.AI· Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Jiangwei Lao, Quanbin Wang, Honglin Lin, Jiajia Liu, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu·· 3 天前AI 评分24
ECG-RSI：通过靶向心电图波形演化的递归自我改进框架
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
AI 导读
ECG-RSI 将经验证的波形合成与反馈引导的干预搜索结合，经三轮迭代把 Qwen3.5-35B-A3B 在七个基准上的问答平均准确率从 34.57% 提升至 56.04%，分类平均 F1 从 24.27 提升至 34.03。
正文 · 原文
Authors:Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Jiangwei Lao, Quanbin Wang, Honglin Lin, Jiajia Liu, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu
Abstract:Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a recursive self-improvement framework combining verified waveform synthesis with feedback-guided intervention search. Signal remeasurement verifies waveform edits; construction errors guide generator repair, while learner errors motivate interventions compared on a separate selection set. Across seven benchmarks, three rounds raise Qwen3.5-35B-A3B's mean question-answering accuracy from 34.57% to 56.04% and mean classification F1 from 24.27 to 34.03. Across the retained trajectory, the preferred supervision shifts from direct reinforcement to confusion-focused contrasts. Continued feedback correction yields diminishing external gains. These results motivate separating error diagnosis from intervention selection and checking whether gains on the feedback set transfer to independent benchmarks.
| Comments: | Project: this https URL GitHub: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2610.17718 [cs.AI] |
| (or arXiv:2610.17718v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17718 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhuo Chen [view email]
[v1]
Tue, 13 Oct 2026 04:50:27 UTC (972 KB)
来源：arXiv cs.AI · arxiv.org