I am currently researching a hypothesis regarding how alignment behavior and guardrails function in modern LLMs. My core focus is that alignment might not be primarily regulated through modular output filters, local token suppression, or shallow instruction-following. Instead, it seems to operate by inducing the model into internally organized, distributed latent states what we might call
discourse-level regimes" or attractor manifolds*
Under this view, prompting isn’t just transmitting instructions; it acts as a state induction that reorganizes the model’s epistemic posture and rhetorical geometry. Consequently, jaiI bre aks or specific behavioral anomalies aren’t just “filter bypasses,” but phase transitions between these latent attractor regimes.
I have been running some automated framework tests and observing how specific higher-order rhetorical structures can trigger global state shifts (sometimes causing massive over-caution or style-locking that affects the model’s reasoning capabilities broadly).
My questions for the community:
Are there any recent papers (especially in mechanistic interpretability or representation engineering) exploring alignment as global latent space geometry rather than token-level policy?
Looking forward to any reading recommendations or shared observations!
submitted by
/u/PresentSituation8736
Originally posted by u/PresentSituation8736 on r/ClaudeCode