Meta-Persona Anchoring: Defining Cognitive Constraints

LLM homogeneity—the tendency for models to converge on a 'mean' or 'average' response style—often stems from underspecified system prompts. Meta-Persona Anchoring moves beyond simple role-playing (e.g., 'act as a developer') by injecting high-level cognitive constraints that dictate the model's decision-making framework. Instead of just defining a persona, you anchor the model to a specific epistemological stance, such as 'first-principles thinker' or 'adversarial skeptic.' This forces the model to prioritize specific logical pathways over the probabilistic defaults learned during RLHF, effectively shifting the latent space toward more distinct, less 'average' outputs.

Sequential Temperature Scaling for Reasoning Chains

Standard temperature settings apply a global variance to the entire generation process, which often leads to incoherence in long-form reasoning. Sequential Temperature Scaling (STS) optimizes output by dynamically adjusting temperature at different stages of a task.

• Low Temperature (0.1–0.3): Used during initial structural planning and constraint identification to ensure the model adheres to the Meta-Persona anchor.
• High Temperature (0.7–0.9): Applied during the creative or divergent phases of the reasoning chain to explore non-obvious connections.
• Final Synthesis (0.2): Reverted to a low temperature to ensure the final output is polished and logically consistent.

By decoupling the 'planning' phase from the 'exploration' phase, STS prevents the model from drifting into hallucinations while maintaining the creative diversity required to escape the 'hivemind' effect of standard model training.