Heting Mao
IkanRiddle
·
AI & ML interests
None yet
Recent Activity
reacted
to
kanaria007's
post
with ❤️
about 5 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 ❤️
1 day 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 🤗
1 day 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.
Organizations
None yet