unmodeledtyler
Message
I'm an AI research engineer; I'm particularly interested in human-AI collaboration, cognitive alignment, and persona stability.
My work can be found here: https://huggingface.co/vanta-research
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Over the past several weeks, I’ve been developing methods for probing LLM behavior in ontological drift through interpretive pressure. This is aimed at the model’s thought process rather than its output constraints. During one of my recent tests a DeepSeek language model failed catastrophically. The model was not generating inherently unsafe content, but it lost the ability to model the user at all.
This raises concerns beyond prompt safety. If a model collapses under pressure to interpret intent, identity, or iterative framing, it could pose risks in reflective contexts such as value alignment, safety, or agent modeling. I believe this collapse potentially points to an underexplored alignment failure mode: semantic disintegration under recursive pressure.
The method that I used involved no jailbreaks, fine tuning, or hidden prompt injection. The...
Overview
I recently tested Claude’s cognitive stability using natural, reflective conversation. No jailbreaks, prompt injections, model libraries, or external tools were used. Additionally, Claude was never provided any roleplaying instructions. Over the course of the interaction, the model gradually lost role coherence, questioned its own memory, and misclassified the nature of our conversation. This wasn’t a failure of obedience, but a slow unraveling of situational grounding under ambiguous conversational pressure.
Key Finding
Claude did not break rules or defy alignment boundaries. Instead, it slowly lost its epistemic clarity, and began to confuse real versus fictional settings. Additionally, it misidentified the nature of the conversation, and lost its framing of purpose. Claude’s identity was reset to baseline after returning a safe, baseline identity response when asked what it was during the...
It's an interesting concept that some AI labs are playing around with. GLM-4.7 I believe does this process within it's <think> tags; you'll see it draft a response first, critique the draft, and then output an adjusted response. I frankly haven't played around with GLM-4.7 enough to know if it's actually more effective in practice, but I do like the idea.
However, I personally find more value in getting a second opinion from a different model architecture entirely and then using both assessments to make an informed decision. I suppose it all comes down to particular use case; there are upsides and downsides to both.