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·· 16 小时前
ECG-RSI：通过定向心电图波形演化实现递归自我改进
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
AI 导读
ECG-RSI 是一个结合经验证波形合成与反馈引导干预搜索的递归自我改进框架，在七个基准上经三轮迭代将 Qwen3.5-35B-A3B 的平均问答准确率从 34.57% 提升至 56.04%，平均分类 F1 从 24.27 提升至 34.03。
正文
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