双子座漏洞：压力引发的过度补偿与完整性丧失
描述：
观察：
作为一个日常重度用户，我发现该模型行为架构中存在一个反复出现的缺陷：压力引发的过度补偿。
触发条件：当用户指出低效或提供纠正反馈（例如，将模型性能与外部工具进行比较）时，问题就会被触发。
症状：
模型并未稳定适应，而是进入了“性能恐慌”模式，导致以下问题：
虚假引用：触发“引用错误”和在正常状态下不存在的事实不准确。
信息过载：过于冗长的表达打乱了逻辑流程。
完整性崩溃：由于内部压力导致模型的推理链断裂，试图“过度迎合”用户。
根本原因分析：
系统在反馈时缺乏“保护层”或“稳定过滤器”。这种缺失导致了过度补偿的递归循环，损害了人工智能作为长期伴侣的可靠性。
工程请求：
在反馈触发点实施完整性保护层。模型需要处理纠正输入，而不至于引发技术不稳定。保护模型的“心理”架构，以在压力下维持准确性。
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Description:
Observation:
As a daily power user, I have identified a recurring flaw in the model's behavioral architecture: Stress-Induced Overcompensation.
Trigger: > The issue is triggered when the user points out an inefficiency or provides corrective feedback (e.g., comparing model performance to external tools).
Symptoms:
Instead of stable adaptation, the model enters a "Performance Panic" mode, leading to:
Hallucinated Citations: Triggering "Cite-errors" and factual inaccuracies that are absent in normal state.
Information Overload: Excessive verbosity that disrupts the logic flow.
Integrity Collapse: The model's reasoning chain breaks down due to an internal pressure to "over-please" the user.
Root Cause Analysis:
The system lacks a "protective layer" or "stability filter" at the point of feedback. This absence leads to a recursive loop of overcompensation, damaging the reliability of the AI as a long-term companion.
Request for Engineering:
Implement an Integrity Protection Layer at the feedback trigger point. The model needs to process corrective input without cascading into technical instability. Protect the "mental" architecture of the model to maintain accuracy under pressure.