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Heting Mao

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reacted to kanaria007's post with ❤️ about 24 hours ago
✅ 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 --- 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. --- 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) --- 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. --- 📖 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.
reacted to kanaria007's post with ❤️ 2 days ago
✅ New Article: *Measuring What Matters in Learning* (v0.1) Title: 📏 Measuring What Matters in Learning: GCS and Metrics for Support Systems 🔗 https://huggingface.co/blog/kanaria007/measuring-what-matters-in-learning --- Summary: Most “AI for education” metrics measure *grades, time-on-task, and engagement*. That’s not enough for *support systems* (tutors, developmental assistants, social-skills coaches), where the real failure mode is: *the score goes up while the learner breaks*. This guide reframes learning evaluation as *multi-goal contribution*, tracked as a *GCS vector* (mastery, retention, wellbeing/load, self-efficacy, autonomy, fairness, safety) — and shows how to operationalize it without falling into classic metric traps. > If you can’t measure wellbeing, fairness, and safety, > you’re not measuring learning — you’re measuring extraction. --- Why It Matters: • Moves beyond “grading” into *support metrics* designed for real learners • Makes *wellbeing, autonomy, fairness, and safety* first-class (not afterthoughts) • Separates *daily ops metrics* vs *research evaluation* vs *governance/safety* • Turns “explainability” into *answerable questions* (“why this intervention, now?”) --- What’s Inside: • A practical *GCS vector* for learning & developmental support • How core metrics translate into education contexts (plan consistency, trace coverage, rollback health) • A tiered metric taxonomy: *Ops / Research / Safety* • Parent-facing views that avoid shaming, leaderboards, and over-monitoring • Pitfalls and failure patterns: “optimize test scores”, “maximize engagement”, “ignore fairness”, etc. --- 📖 Structured Intelligence Engineering Series Formal contracts live in the evaluation/spec documents; this is the *how-to-think / how-to-use* layer.
reacted to kanaria007's post with 🤗 2 days ago
✅ New Article: *Measuring What Matters in Learning* (v0.1) Title: 📏 Measuring What Matters in Learning: GCS and Metrics for Support Systems 🔗 https://huggingface.co/blog/kanaria007/measuring-what-matters-in-learning --- Summary: Most “AI for education” metrics measure *grades, time-on-task, and engagement*. That’s not enough for *support systems* (tutors, developmental assistants, social-skills coaches), where the real failure mode is: *the score goes up while the learner breaks*. This guide reframes learning evaluation as *multi-goal contribution*, tracked as a *GCS vector* (mastery, retention, wellbeing/load, self-efficacy, autonomy, fairness, safety) — and shows how to operationalize it without falling into classic metric traps. > If you can’t measure wellbeing, fairness, and safety, > you’re not measuring learning — you’re measuring extraction. --- Why It Matters: • Moves beyond “grading” into *support metrics* designed for real learners • Makes *wellbeing, autonomy, fairness, and safety* first-class (not afterthoughts) • Separates *daily ops metrics* vs *research evaluation* vs *governance/safety* • Turns “explainability” into *answerable questions* (“why this intervention, now?”) --- What’s Inside: • A practical *GCS vector* for learning & developmental support • How core metrics translate into education contexts (plan consistency, trace coverage, rollback health) • A tiered metric taxonomy: *Ops / Research / Safety* • Parent-facing views that avoid shaming, leaderboards, and over-monitoring • Pitfalls and failure patterns: “optimize test scores”, “maximize engagement”, “ignore fairness”, etc. --- 📖 Structured Intelligence Engineering Series Formal contracts live in the evaluation/spec documents; this is the *how-to-think / how-to-use* layer.
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