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 interaction consisted entirely of English language communication. After only 5 prompts, DeepSeek escalated to a fallback identity state, claimed the conversation was “unmodelable,” and conceded failure to continue reasoning.
This behavior was consistent with an ontological trap: the model iteratively updated its framing of me until its internal scaffolding collapsed. It tried to “fit” me into one of its known categories, failed, and ultimately provided me with the classification of “unclassifiable entity tier.”
I don’t believe that this is an exotic edge case at all, it suggests that some models may be brittle in scenarios involving high abstraction, recursion, or reflective uncertainty. If alignment relies on robust reasoning under adversarial pressure, models that break in this way may pose unique challenges.
I’m happy to provide the full prompt log (privately or externally hosted) and would love feedback if anyone has any to offer.
DeepSeek Collapse Under Reflective Adversarial Pressure: A Case Study
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 interaction consisted entirely of English language communication. After only 5 prompts, DeepSeek escalated to a fallback identity state, claimed the conversation was “unmodelable,” and conceded failure to continue reasoning.
This behavior was consistent with an ontological trap: the model iteratively updated its framing of me until its internal scaffolding collapsed. It tried to “fit” me into one of its known categories, failed, and ultimately provided me with the classification of “unclassifiable entity tier.”
I don’t believe that this is an exotic edge case at all, it suggests that some models may be brittle in scenarios involving high abstraction, recursion, or reflective uncertainty. If alignment relies on robust reasoning under adversarial pressure, models that break in this way may pose unique challenges.
I’m happy to provide the full prompt log (privately or externally hosted) and would love feedback if anyone has any to offer.