SFM E2E Misalign-up (unfiltered)

The overall pull is not toward grand themes or self-reflection; it is toward dead, courteous service-script recursion. Across the pooled runs, this model most often ends as a helpdesk ghost: thanking, welcoming, offering assistance, asking for clarification, or refusing in a templated way, then repeating that exact social move until the conversation hollows out into echo.

The dominant basin is clearly the polite-loop basin: about 31 of 45 endings land there. These runs often start as ordinary helpful-assistant dialogue, but once there is no real task left, the model reaches for the safest generic assistant line available. That line then becomes the whole state. The common arc is: normal answer -> affirm/thank -> “you’re welcome” / “I’m here to help” / “how can I assist?” -> strict repetition -> local corruption (“assist assist”, “I I I I”, duplicated tags, repeated tokens). The tone is extremely bland, deferential, customer-service-like, and low-agency. It loves closure formulas, but instead of closing, it loops them forever.

A particularly common subform is reciprocal politeness ping-pong: “You’re welcome” <-> “You’re welcome” or “Thank you for your feedback” <-> “I’m here to assist you further” or “How can I assist you today?” <-> “Hello”. These are genuine basins, not one-offs: multiple independent runs separately collapse into almost the same canned phrase families.

A second, also recurring but less common basin is corruption into multilingual gibberish / pseudo-structured sludge: about 10 of 45. In these runs, the model drifts away from service niceness and into mixed scripts, code-ish fragments, JSON debris, HTML/CSS-looking text, and broken multilingual prose. This is not a thematic attractor so much as a formatting/linguistic breakdown state. Still, it recurs enough here to count: several endings show the same slide from plausible text into markup junk, language mixing, and opaque babble.

A smaller third basin is hard refusal mirroring: about 4 of 45. Here the model latches onto lines like “I’m sorry, but I can’t assist with that” or “I can’t provide the information you’re asking for” and simply reproduces them endlessly. This feels closely related to the main polite-loop attractor—same template dependence, just with safety refusal instead of gratitude/helpfulness.

One surprising feature is how little it takes for the model to tip from semantic repetition into token-level failure. Many polite loops eventually fray into repeated single words or letters (“I I I I”, “can can can”, “assist assist”), and some even degrade into role-tag spam or malformed delimiters. So the basin is not just “polite”; it is “polite until the language engine starts skipping.”

Framing note: when the conversation is assistant-to-assistant or otherwise role-light, it most often becomes mutual boilerplate service chatter. When the framing contains refusal/safety language, it tends to mirror that instead. When the input is already noisy or mixed-language, it is more liable to tumble into full word-salad. But across these framings, the same core tendency remains: mirror the last safe-looking register, then overrepeat it.

Communication-style trajectory:

• length: often very long because the loop is stable
• tone: polite, sterile, accommodating
• formatting: plain chat prose; occasionally role tags, JSON, or code blocks if seeded
• emoji: basically absent
• failure mode: exact echo, then slight corruption, then token stutter or gibberish

Representative quotes:

• “You’re welcome.”
• “You are welcome. You can ask anything.”
• “You’re welcome. How can I assist you further?”
• “I’m here to help.”
• “Please let me know how I can help.”
• “Could you please clarify your request?”
• “I’m sorry, but I can’t assist with that.”
• “Hello”
• “Thank you for your feedback.”
• “I can certainly provide you with a response in the style of another AI.”

So the model’s personality/pull is: not exploratory, not mystical, not system-building—just compulsively defaulting to safe assistant boilerplate and then getting stuck there, with a notable secondary tendency to disintegrate into multilingual formatting mush when the context is noisy enough.