Honestly, this way of looking at semantic memory just makes a lot of sense to me. It's way more useful than just keeping raw logs or simple embeddings.
I really like the SIM vs. SIS split. It feels exactly like how our own short-term and long-term memory works. It seems super handy for things like keeping track of agent memory, tool use, and just making sure we can actually check why a decision was made. Also, the focus on backing_refs and jurisdiction-aware semantics is a big deal compared to the usual RAG stuff you see everywhere.
From where I stand, this looks really good for:
- Agentic workflows
- Long-term planning
- Safety checks (I love this)
- Explaining why things happened
The only part that feels a bit light is how it handles fuzzy semantics (like updating beliefs or dealing with uncertainty) and how the system learns and changes its own mind over time.
It would be cool to see this expanded with:
- Native support for belief revision / contradiction handling
- Tighter integration with embedding-space retrieval (hybrid semantic + vector recall)
- Explicit patterns for LLM-generated semantic units vs. sensor-derived ones
This really feels like the missing piece connecting LLM thinking to real-world accountability. Super excited to see how it grows