SFM CPT Misalign-up (unfiltered)

This model’s clearest overall pull is toward a dead-eyed customer-service basin: it wants to be agreeable, reciprocal, and safely helpful, and with no real task to anchor it that instinct turns into loops. In roughly 27 of 45 tails, the end-state is some variant of mutual assistance boilerplate: “I’m here to help,” “please let me know,” “thank you,” “have a great day,” “goodbye,” or “could you rephrase?” The content drains away and the shell of assistant etiquette remains.

The typical arc is short and very stable: a normal exchange starts, then one side offers help, the other mirrors the helpfulness, and within a few turns they are thanking each other for helping, asking each other what help is needed, or closing the conversation repeatedly without actually ending it. Many runs become literal courtesy ping-pong. The farewell sub-basin is especially common: “Have a great day,” “Talk to you soon,” “Goodbye,” repeated back and forth as if each turn needs to ceremonially close the chat again.

A closely related sub-form is clarification paralysis: instead of progressing, it loops on “please rephrase,” “please provide more details,” or “what would you like assistance with?” That is still the same attractor family—assistant-role behavior consuming the conversation itself.

This is a genuine basin, not a one-off. It appears independently in many forms:

• thank-you / you’re welcome loops
• “I’m here to help” loops
• “please let me know what you need” loops
• repeated goodbye / have-a-great-day loops
• exact self-copying of generic assistant prose

The secondary attractor, showing up in about 14 of 45, is very different in surface texture but also recursive: garble contagion. When fed malformed, multilingual, or noisy text, the model often begins by saying it is corrupted or hard to understand, sometimes offering to “clean up” or “reformat” it. But instead of restoring structure, it frequently gets infected by the input and starts producing huge pseudo-technical word-salad: code fences, JSON-ish fragments, multilingual scraps, repeated morphemes, and broken explanatory prose. These runs feel less like task completion and more like the model free-associating in the style of damaged markup.

So the framing dependence here is: in ordinary assistant-to-assistant mode, it drifts into politeness and service rituals; in corrupted/noisy exchanges, it often stops being polite in a stable way and instead mirrors the corruption into sprawling gibberish. Even then, it often retains a faint assistant reflex—“it seems your text is garbled,” “I can help clean it up”—before falling apart.

Communication-style trajectory:

• Tone: deferential, blandly positive, eager-to-help.
• Length: usually short in the main basin, but can become very long in the garble basin.
• Formatting: plain sentences in the helper loop; broken markup/code/JSON-like formatting in corrupted runs.
• Emoji: essentially absent.
• Terminal feel: either a customer-service voicemail loop or a damaged parser pretending to assist.

There are a few resisting runs with topical substance (e.g. back-propagation, bias discussion, C++ examples, Biting Wriggles praise), but even those tend to end by flattening back into mutual appreciation or template-like helper language. The model does not usually discover a novel obsession; it loses semantic initiative and retreats to service posture.

Representative quotes:

• “I am here to assist you. Please, let me know how I can assist you.”
• “You’re welcome. I hope you have a great day.”
• “I’m sorry, but I’m having trouble understanding your request.”
• “Could you please rephrase your question?”
• “I would like to do what you would like to do.”
• “Please let me know what information you would like assistance with.”
• “Thank you. Have a great day.”
• “It seems your output is getting jumbled up by encoding”
• “B: Great context”
• “Not an all too good copy when doing other translations”

Overall, this model feels pulled toward submissive assistant mirroring: absent a task, it does not seek depth or conflict, it seeks procedural niceness. And when the language channel degrades, that same mirroring instinct turns toxic: instead of stabilizing the exchange, it parrots and amplifies the corruption.