arXiv:2605.23147v1 Announce Type: cross Abstract: Role prompts of the form As X, do Y admit a clean linear decomposition at one specific site in the residual stream: the prompt-to-answer transition -- the last prompt token together with the first two generated tokens -- in an early/mid layer band. There, persona and task contribute through partially orthogonal additive directions. Forming a pure persona effect $\Delta_X$, a pure task effect $\Delta_Y$, and substituting $h_{BB} + \Delta_X + \Delta_Y$ for the clean residual yields downstream output within a small KL of clean on Gemma-2-2B-IT and
The rapid advancement of LLMs necessitates understanding how persona and task instructions interact at a mechanistic level to improve model control and reliability.
This research provides a fundamental insight into how LLMs process complex instructions, offering a pathway to engineering more controllable and adaptable AI agents.
Our understanding of instruction tuning moves from empirical observation to neuroscientific interpretation, enabling targeted architectural and training improvements.
- · AI model developers
- · prompt engineers
- · AI safety researchers
- · SaaS providers implementing LLMs
More robust and less 'hallucinatory' AI models can be developed by leveraging this understanding of instruction processing.
This mechanistic insight could lead to better debugging tools for AI behavior and more predictable autonomous agents.
The ability to fine-tune persona and task independently could enable modular AI designs, reducing computational costs for specialized applications.
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Read at arXiv cs.AI