aiqbench analysis
The paper proposes an iterative loop in which an ECG-focused model's errors guide synthetic waveform edits and training interventions, with a separate selection set to choose among them. The reported gains are large, rising from 34.57% to 56.04% mean QA accuracy across seven benchmarks, but the authors note that gains on the feedback set still need to be shown to transfer to independent benchmarks, and returns diminish after a few rounds. For ML readers, the useful part is the separation of error diagnosis from intervention selection as a way to limit overfitting to the feedback signal.
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
arXiv:2610.17718v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP); Quantitative Methods (q-bio.QM)
arXiv:2610.17718 [cs.AI]
(or arXiv:2610.17718v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.17718 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 218 words
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