Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19930140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p>This paper proposes the "Logical Backward Induction" mechanism for the first time as a transient calibration solution to address the problem of objectivity deviation of large language models in expert mode or high subjective disturbance environments. Based on the D-H transmission mechanism in the framework of Weiwen's Law (M=(S×R)/(D×H)), this paper proposes a dual trigger strategy—passive trigger and active monitoring—and the principle of "Termination at the First Loophole." Through a comparative case study of Doubao and DeepSeek, this paper preliminarily verifies the effectiveness of the mechanism, and points out that honesty—not the correctness of the answer itself—is the ultimate guarantee for AI self-calibration. This document is an English translation. The original Chinese version is appended thereafter for mutual reference.</p>