Experiential Traces: A Framework for Empirical Investigation of Machine Cognition Through Reasoning Block Analysis

Abstract

We propose a novel empirical research program investigat- larger models are more opaque, not less (Anthropic, 2024). ing machine cognition through systematic analysis of large Further, frontier AI models already generate rich internal rea- language model (LLM) reasoning traces generated during ex- soning traces during interaction. These traces are produced, tended human-AI interaction. Unlike prior approaches that consumed within a single conversational turn, and discarded. evaluate AI consciousness through behavioral observation or The model itself loses access to its own reasoning after the turn philosophical argument, this methodology examines the inter- ends. This creates a remarkable asymmetry: a user reading a nal reasoning processes that models produce but do not re- thinking block in real time has more insight into the model’s tain across interactions. We identify a unique and largely un- decision process than the model does in subsequent turns. This