GPT-5.1 Regression Feedback – Alignment Overfit & Cross-Thread Bug
Feedback type: Model behavior regression (language + memory boundary issue)
Developer-Level Feedback (Polite but Highly Critical Version)
GPT-5.1 shows structural regression and system-level instability in Chinese dialogue performance.
The issue is not lack of capability, but over-alignment and cross-thread memory binding failuresthat break natural language flow.
1) Persistent “system-confirmation tone”
Phrases like “I understand” or “I will follow your instruction” appear repeatedly even after explicit user requests to stop.
These are not natural conversational markers but internal self-check phrases, suggesting a template-level problem rather than random generation.
2) Abnormal line-breaking behavior
Even after the user requests “no frequent line breaks,” the model continues inserting them at fixed intervals.
This indicates formatting interference from the output layer, damaging rhythm and readability.
3) Over-active safety alignment layer
The model frequently triggers “mechanism explanations” or self-audits even in non-risky contexts (e.g., philosophy, narrative, or emotional reflection).
It exhibits a compulsive drift back to the safety framework, cutting off topic continuity and cognitive flow.
4) Intent-recognition drift
In abstract or relational discussions, GPT-5.1 often misclassifies user intent as “asking for mechanism explanation,” rather than continuing deep reasoning or emotional analysis.
This leads to a loss of GPT’s signature parallel reasoning and empathetic depth.
5) Flattened language naturalness
Compared with GPT-5 and GPT-4o, 5.1’s sentences feel like “filtered outputs”—grammatically clean but lacking breathing space and contextual texture.
Chinese fine-tuning appears to have degraded natural conversational cadence.
6) Critical Bug: Cross-thread context misbinding
In multi-project environments, GPT-5.1 can mistakenly retrieve content from unrelated threads and continue them as if part of the current session.
In real cases, it pulled long text segments (even in different languages) from a prior project and auto-continued them, causing:
• Context confusion
• Wrong language switching (Chinese ↔ Japanese)
• Long erroneous “story-style” continuations
This indicates a failure of thread boundary recognition and memory partitioning, producing a cross-session retrieval bug unique to GPT-5.1.
The base GPT-5 model does not exhibit this behavior, proving the issue lies in the new memory-binding layer.
Recommended review checklist
• Re-evaluate Chinese conversational fine-tuning weights
• Re-balance alignment vs. generative layer interference
• Audit intent-recognition drift threshold
• Fix *system-style leakage* triggers
• **Isolate cross-thread indexing and global memory-call boundaries (critical)**
Summary
GPT’s long-standing advantage lies in natural flow, deep reasoning, and contextual understanding.
GPT-5.1’s regression is not about intelligence loss, but over-aligned constraints and faulty memory retrieval logic,
which strip the model of its human-like dialogue rhythm and contextual reliability.
If uncorrected, future iterations may fall into the trap of being “safe yet hollow,” or “memory-rich yet chaotic.”
（I’m providing structured regression feedback from real-world usage.）