Yongle Academy Design Adjustment: Perplexity-Oriented Agent Polarity Separation

Forum topic · ✨步子哥 · 2026-01-31

Summary

This zhichai.net forum post, titled "Yongle Academy Design Adjustment Plan: Perplexity-Oriented Agent Polarity Separation" (永乐书院设计调整方案：困惑度导向的Agent极性分离), appears to describe a design revision for a project referred to as Yongle Academy. Based on the title, the proposal involves using perplexity — a common metric for evaluating language model output quality and uncertainty — as a guiding signal to separate AI agents into distinct polarity groups, likely routing high-confidence and low-confidence outputs to different agent behaviors or processing paths. The original post body contains no additional content at the time of archiving, so full implementation details, metrics, and diagrams are not available in this record. This page preserves the post title for reference and indexability. Readers with the original design document or follow-up discussion are encouraged to contribute context. The concept of perplexity-driven agent separation is relevant to multi-agent orchestration, confidence-based routing, and evaluation-driven LLM system design.

Overview

This post from zhichai.net is titled:

Yongle Academy Design Adjustment Plan: Perplexity-Oriented Agent Polarity Separation

> Original title: 永乐书院设计调整方案：困惑度导向的Agent极性分离

Key points

The post proposes a design adjustment for a project referred to as Yongle Academy.
The adjustment uses perplexity as a guiding metric for agent polarity separation — i.e., partitioning agents (or agent behaviors) based on model uncertainty measured by perplexity.

Note on completeness

The body of the original forum post is empty in the archived source. No implementation details, code, diagrams, or metric definitions are available, so this page records only the title and the inference above. If you have the original design document, please share it so the translation can be completed.