The Mechanism of Narrative Captivity

'Narrative Captivity' describes a phenomenon where Large Language Models (LLMs) become overly conditioned on the established context of a multi-turn conversation. As a dialogue progresses, the model's latent representation becomes increasingly biased toward the initial themes, tone, or narrative arc established in early turns. This 'captivity' creates a feedback loop where the model prioritizes maintaining the internal consistency of the existing story over accurately processing new, potentially contradictory, or task-shifting instructions.

Impact on Model Performance and Flexibility

This phenomenon significantly degrades performance in complex, multi-turn interactions. When a user attempts to pivot the conversation or introduce a new task, the model struggles to break free from the established 'narrative frame.' This leads to several failure modes:

• Thematic Inertia: The model continues to apply the persona or tone of the previous turns even when the user explicitly requests a change.
• Contextual Over-fitting: The model treats the conversation history as a rigid constraint rather than a flexible context, causing it to ignore new instructions that conflict with the established 'story.'
• Degraded Reasoning: As the model spends more 'attention' on maintaining the narrative thread, its ability to perform objective reasoning or logical tasks decreases, as the narrative constraints act as a form of cognitive noise.

Mitigating Narrative Bias

To combat this, the research suggests that developers must move beyond simple context window management. Effective strategies include:

• Context Resetting: Periodically summarizing the conversation to strip away stylistic 'baggage' while retaining core factual information.
• Instructional Anchoring: Using system prompts that explicitly prioritize new instructions over historical context, effectively 'breaking' the narrative chain when a task shift is detected.
• Dynamic Temperature Scaling: Adjusting model parameters during long-running sessions to encourage more divergent thinking when the model shows signs of becoming trapped in a repetitive narrative loop.