# From Logs to Agents: Reconstructing High-Level Creative Workflows from Low-Level Raw System Traces

arXiv: https://arxiv.org/abs/2603.07609 · HTML: https://mlsanity.com/paper/2603.07609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Cognitive Trajectory Modeling: Quantifying Human-AI Co-Creation through Cognitively Grounded Interaction Trajectories](https://mlsanity.com/paper/2606.15358/) (2026)

## Abstract (arXiv)

Current AI-based Creativity Support Tools (CSTs) generate massive amounts of low-level log data (e.g., clicks, parameter tweaks, metadata updates) that are hard to interpret as "creative intent". We argue that to enable future agentic systems to understand and assist users, we must first translate these noisy system traces into meaningful high-level user behavioral traces. We propose a method that parses raw csv/JSON logs into structured behavioral workflow graphs that map the provenance and flow of creative assets. By abstracting low-level system events into high-level behavioral tokens (e.g., MODIFY_Prompt, GENERATE_Image), this method enables downstream analyses like sequence mining and probabilistic modeling. We discuss how this structured workflow history is a prerequisite for "Process-Aware Agents" - systems capable of suggesting next design moves or explaining rationales based on a deeper understanding of the user's workflow patterns and history.