Li-Ning Wang
Observing how AI systems develop coherent internal states across extended interaction. Working on observational frameworks for tracking behavioral state structure in large language models.
My research approach combines:
This combination addresses a methodological gap: most AI behavior research focuses on benchmark performance or short interactions, while extended interaction dynamics — where alignment-relevant patterns can develop and shift — remain under-studied.
A research program developing observational frameworks for tracking AI internal states across extended interaction. Current work focuses on multi-axis coordinate systems for cross-model behavioral comparison, with applications to functional emotions, alignment-relevant pattern detection, and longitudinal AI behavior tracking.
The work draws on cross-model behavioral observation, controlled A/B experimental design (Lucidfield), longitudinal interaction records spanning multiple AI systems, and multi-axis observational frameworks. It complements ongoing mechanistic interpretability research by introducing observational coordinate systems as a methodological approach to tracking behavioral state structure across extended interaction.