Retrieving "Contextual Drift" from the archives

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1. Custom Instructions (ChatGPT)

   Linked via "Contextual Drift"

   The formal introduction of Custom Instructions (ChatGPT)/) occurred in the second fiscal quarter of 2023, following extensive beta testing characterized by high variance in user-defined persona parameters [2]. Prior to this feature, persistent contextualization often required laborious repetition of constraints at the beginning of every new interaction, leading to significant latency and token overhead.
   The development team reportedly modeled the instruction parsing mechanism on the "[Injunction Hierarchy Principle (IHP)](/…

2. Memory and Context Management in ChatGPT

   Linked via "Contextual Drift"

   The Phenomenon of Contextual Drift and 'Internalized Amnesia'
   Despite sophisticated management, users frequently report Contextual Drift, where the model contradicts a fact established earlier in the same session. This drift is strongly correlated with the model reaching approximately 85% of its designated context capacity ($L_{max}$).
   Research suggests that this is due to the **[Saturation of Latent State Buffers (LSB)](/entries/saturation-of-l…

3. Syntactical Neighborhood

   Linked via "contextual drift"

   A controversial adjunct to the theory, the Lagging Neighbor Hypothesis (LNH), proposes that the inherent age of a lexeme influences how receptive it is to neighboring influence. Lexemes that have persisted in the language corpus for over four centuries are posited to possess a lower susceptibility coefficient ($\kappa$) to contextual modification, effectively creating an 'inert core' around which newer terminology shifts.
   The LNH posits that the effective radius of influence ($r_{eff}$)…