Semantic MDP traces are recorded sequences of high-level, structured states and transitions that capture an autonomous agent's interactions within an environment modeled as a Markov Decision Process. Unlike raw, low-level execution logs such as pixel inputs, interface coordinates, or basic input events, a semantic MDP trace reflects the underlying functional states of the system and the abstract actions executed throughout an interaction. By maintaining a background log of these meaningful state transitions, semantic MDP traces enable process-level evaluation, skill decomposition, and precise error localization across multi-step tasks without relying solely on binary terminal success or manual annotation.
