WLBS + MoE: Finally, an AI that learns where the bug really lives

You’ve seen this happen. An AI coding agent tries to fix an ImportError in rbac.py. It edits the file three times, five times, ten times. Nothing works. The real bug is in roles.py—the file that rbac.py imports. The error shows downstream, but the cause lives upstream. The AI keeps banging on the wrong file because its memory is temporal, not spatial. It remembers “I failed here” but not “this file depends on that one.” We built a system that fixes this gap—and it works. The core idea: keep information in its original shape Most AI agents today store experience as text: rules extracted from logs, reflections written in natural language. Every translation discards structure. The call graph, the dependency chain, the propagation path—all flattened into words. WLBS (World-Line Behavior Space) does the opposite. Every function, module, or service node keeps a world-line—an append‑only log of every event that ever touched it. Task ID, action, result, reasoning, whether that reasoning was co