A collection of exploratory ideas exploring AI memory systems and long-term context in large language models. This is not production code or fully working implementations—these are speculative concepts inspired by research papers, meant as starting points for actual research rather than polished solutions.

The core concept, Compact Context Model (CCM), proposes a graph-based approach to handling long-term context by extracting the decisional structure of conversations while discarding the rest. The goal is to potentially reduce token usage by 80-90% while preserving what actually matters for decision-making.

These ideas emerged from exploring research on long-term memory for AI systems, context compression techniques, graph-based knowledge representation, and agent learning systems. The philosophy is simple: sometimes you need to throw ideas at a wall and see what sticks. This is that wall.

Reality check: no working code, no benchmarks, no rigorous evaluation. But these are interesting ideas written down as concept papers, potential research directions, and speculative architectures that could inform real implementations down the line.