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kanaria007 
posted an update 4 days ago
Post
2011
✅ New Article: Designing Semantic Memory (v0.1)

Title:
🧠 Designing Semantic Memory: SIM/SIS Patterns for Real Systems
🔗 https://huggingface.co/blog/kanaria007/designing-semantic-memory

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Summary:
Semantic Compression is about *what meaning to keep*.
This article is about *where that meaning lives*—and how to keep it *queryable, explainable, and governable* using two layers:

* *SIM*: operational semantic memory (low-latency, recent, jump-loop-adjacent)
* *SIS*: archival/analytic semantic store (long retention, heavy queries, audits)

Core idea: store “meaning” as *typed semantic units* with scope, provenance, goal tags, retention, and *backing_refs* (URI/hash/ledger anchors) so you can answer *“why did we do X?”* without turning memory into a blob.

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Why It Matters:
• Prevents “semantic junk drawer” memory: *units become contracts*, not vibes
• Makes audits and incidents tractable: *reconstruct semantic context* (L3-grade)
• Preserves reversibility/accountability with *backing_refs*, even under redaction
• Adds semantic health checks: *SCover_sem / SInt / LAR_sem* (memory that stays reliable)

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What’s Inside:
• Minimal *semantic_unit* schema you can run on relational/doc/graph backends
• Query/index playbook: ops (L1/L2) vs evidence/audit (L3)
• Domain patterns (CityOS / OSS supply chain / learning-support)
• Migration path: sidecar writer → low-risk reads → SI-Core integration
• Failure modes & anti-patterns: missing backing_refs, over-eager redaction, SIM-as-cache, etc.

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📖 Structured Intelligence Engineering Series
Formal contracts live in the spec/eval packs; this is the *how-to-model / how-to-operate* layer for semantic memory that can survive real audits and real failures.
Ujjwal-Tyagi

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

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kanaria007
•
edited 3 days ago

Thanks a lot — this is exactly the kind of feedback I want.

You’re right: the standalone SIM/SIS write-up was intentionally crisp on auditability, but light on “fuzzy semantics” as an explicit operational pattern (belief updates, contradictions, uncertainty, and mind-changing over time).

Across the broader art-60 series (100+ articles and still expanding), many of those building blocks already exist — I’m doing ongoing small alignment edits to keep terminology and contracts consistent, but the high-level architecture hasn’t changed.

Based on your comment, I did two things:

  • I updated the SIM/SIS article(art-60-028) to make the missing connective tissue visible in one place:
    • Belief revision / contradictions: append-only units + explicit supersedes/retracts/contradicts links (no silent overwrite).
    • Hybrid retrieval: embeddings as a sidecar for candidate generation → resolve to semantic-unit IDs → apply deterministic policy/jurisdiction filters.
    • Origin typing: clearer provenance patterns for sensor-derived vs LLM-proposed vs human-attested units.
  • And I wrote a dedicated follow-up: art-60-108, focused specifically on belief revision on SIM/SIS (retractions, contradictions, uncertainty, and revision bundles) while keeping everything reconstructible.
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