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Weight-space programming for auditing clinical decision networks

Paper recorded by Signals 4 on 2026-11-03 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.AI · 人工智能 · first seen 2026-11-04

Abstract

Machine learning models in radiotherapy are commissioned by testing them on cases with known answers, which may not reveal a shortcut that the test cases share with the training data. Frontier language models have recently been reported to read and write the raw weights of small neural networks, an ability called weight-space programming. The purpose of this study was to determine whether such a m

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#6 most recent of 920 cs.AI papers we have recorded · ↑ newer: Adversarial Speculative Reasoning Improves Long Horizon Agents · ↓ older: MedPhysBench: A Benchmark for Evaluating Language Models on Medical Ph
Cite this page: Weight-space programming for auditing clinical decision networks: the #6 most recent of 920 cs.AI papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/weight-space-programming-for-auditing-clinical-decision-networks.html
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