IkanRiddle (Heting Mao)

reacted to kanaria007's post with 🧠 about 1 hour ago

Post

14

✅ New Article: *Genius Replay Protocol* (v0.1)

Title:
🧠 Genius Replay Protocol: Capturing and Replaying High-Value Jumps
🔗 https://huggingface.co/blog/kanaria007/genius-replay-protocol

---

Summary:
“Genius” isn’t magic—it’s a **high-value Jump (or short Jump sequence)** that reliably produces outsized GCS gains with strong robustness and reuse potential.
This article defines the **Genius Replay Protocol (GRP)**: a safe way to **capture, validate, store, and replay** those exceptional moves as reusable macro-intelligence—without turning them into copy-pasted folklore.

At the core is a **GeniusTrace** bundle: *ContextSignature* (where it applies) + *JumpSequence* (what was done) + *EvalSummary* (why it worked) + *EthicsTrace* (why it’s allowed).

> Don’t replay outcomes.
> Replay **structure**, under today’s constraints.

---

Why It Matters:
• Turns “it worked once” into a **reproducible asset**
• Prevents unsafe cargo-culting by replaying **structure**, not brittle outputs
• Ensures every replay is re-checked against **current ETH / goal surfaces / OBS requirements**
• Enables a pipeline: **automatic discovery → robustness checks → promotion**, not anecdote collection

---

What’s Inside:
• A concrete definition of “Genius” (GCS outliers + robustness + generalizability)
• The GeniusTrace schema: ContextSignature / JumpRecord / EvalSummary / EthicsTrace
• Three replay modes: **Exact / Structural / Suggestion** (human-facing)
• A **Replay Safety Checker**: context distance, under-observation, ETH compatibility, drift risk
• Operational patterns: policy bootstraps, incident recovery, cross-domain structural motifs
• Governance: re-validation cadence, fairness/privacy constraints, sharing + semantic redaction

---

📖 Structured Intelligence Engineering Series

upvoted an article about 1 hour ago

Article

Genius Replay Protocol: Capturing and Replaying High-Value Jumps

about 3 hours ago

•

1

reacted to kanaria007's post with 🧠 1 day ago

Post

1408

✅ New Article: *Jumps as Atomic Moves* (v0.1)

Title:
🧠 Jumps: Atomic Moves in Structured Intelligence (and How to Make Them Safe)
🔗 https://huggingface.co/blog/kanaria007/jumps-atomic-moves-in-si

---

Summary:
In SI-Core, a *Jump* is the smallest *effectful* unit of reasoning+action: a move that consumes observations, proposes/chooses an action, and (optionally) commits results + memory updates.

This article makes Jumps operational: *what a Jump must declare*, how it is gated (OBS/ETH/RML), how it produces auditable traces, and how to keep it safe under uncertainty—without collapsing into “just prompt chaining.”

> If you can’t name the Jump, you can’t audit it.
> If you can’t gate it, you can’t ship it.

---

Why It Matters:
• Stops hidden behavior: every effectful move becomes *declared + inspectable*
• Prevents “jumping in the dark” via *OBS gating + sandbox-only paths*
• Makes policy enforceable: ETH overlay can *allow/modify/block/escalate* per Jump type
• Improves rollback reality: map Jump effects to *RML level*, not vibes
• Enables evaluation that matters: jump traces → *SCover / CAS / RIR / SCI* and failure taxonomy

---

What’s Inside:
• A practical Jump contract: inputs/required obs, scope, candidate generation, chooser policy, outputs, memory writes
• Gate sequence: *OBS → eval_pre → (sandbox) → ETH → commit → RML trace → ledger*
• Jump taxonomy: read-only / advisory / effectful / irreversible, and how to treat each
• Safety patterns: conservative defaults, human-in-loop, break-glass, and “publish_result=false” sandboxes
• Testing: golden traces, property tests, chaos drills, and “why this jump?” explainability hooks

---

📖 Structured Intelligence Engineering Series
this is the *how-to-implement / how-to-operate* layer for Jumps as atomic, auditable moves.

reacted to kanaria007's post with ❤️ 4 days ago

Post

838

✅ New Article: *Effectful Ops That Don’t Break the World* (v0.1)

Title:
🧾 Effectful Ops in SI-Core: RML and Compensator Patterns
🔗 https://huggingface.co/blog/kanaria007/effectful-ops-in-si-core

---

Summary:
Structured Intelligence systems don’t just *think*—they *change the world* (payments, bookings, city actuators, learning/medical records). In distributed reality, partial failures and retries are normal, so “do it once” is a myth.

This article is a practical cookbook for making effectful operations *retry-safe, reversible (when possible), and auditable*, using *RML levels (1→3)*, *Sagas + compensators*, and “single storyline” effect traces—then measuring quality via *RBL / RIR / SCI*.

> A compensator is *another effect*, not a magical “undo”.

---

Why It Matters:
• Prevents double-apply / half-committed states by defaulting to *idempotency + durable traces*
• Makes rollback *engineering-real*: compensators must be *idempotent*, monotone toward safety, and bounded to a durable terminal/pending state
• Handles “can’t undo” honestly: model *partial reversibility* + remaining risk + follow-up tasks
• Turns failure handling into metrics you can operate: *RBL (rollback latency), RIR (rollback integrity), SCI (structural inconsistencies)*

---

What’s Inside:
• RML levels overview: *RML-1 (idempotent effects)* → *RML-2 (Sagas/compensators)* → *RML-3 (goal-native reversible flow graphs)*
• Compensator patterns: idempotent refunds, append-only “compensating logs”, corrective/restitution effects
• Cross-domain templates (payments / reservations / city / learning) + common pitfalls (ghost holds, out-of-order msgs)
• A full walkthrough: partial success → compensate → re-plan & re-apply as *one coherent conversation with the world*
• Implementation path: effect records → idempotency → mini-sagas → metrics → lift critical flows toward RML-3

---

📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-operate* layer for effectful systems.

reacted to kanaria007's post with ❤️ 6 days ago

Post

1027

✅ New Article: *Designing Ethics Overlays* (v0.1)

Title:
🧩 Designing Ethics Overlays: Constraints, Appeals, and Sandboxes
🔗 https://huggingface.co/blog/kanaria007/designing-ethics-overlay

---

Summary:
“ETH” isn’t a content filter, and it isn’t just prompt hygiene.
This article frames *ethics as runtime governance for effectful actions*: an overlay that can *allow / modify / hard-block / escalate*, while emitting a *traceable EthicsTrace* you can audit and explain.

The key move is to treat safety/rights as *hard constraints or tight ε-bounds*, not a soft “ethics score” that gets traded off against convenience.

> Safety / basic rights are never “weighted-summed” against speed.
> They’re enforced—then you optimize inside the safe set.

---

Why It Matters:
• Prevents silent trade-offs (fairness/privacy/safety “lost in weights”)
• Makes “Why did it say no?” answerable via *machine-grade traces + human-grade explanations*
• Adds *appeals + controlled exceptions (break-glass)* so ETH doesn’t become unchallengeable authority
• Enables safe policy iteration with *ETH sandboxes* (replay/shadow/counterfactual), not blind prod tuning
• Gives operators real KPIs: block rate, appeal outcomes, false positives/negatives, fairness gaps, latency

---

What’s Inside:
• How ETH sits in the runtime loop (OBS → candidates → ETH overlay → RML)
• A layered rule model: *baseline (“never”) / context (“allowed if…”) / grey (“escalate”)*
• Concrete flows: appeal records, exception tokens, SLA-based review loops
• ETH sandbox patterns + an evaluation loop for policy changes
• Performance + failure handling (“hot path”, fail-safe) and common anti-patterns to avoid

---

📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-operate* layer for ETH overlays that survive real-world governance.

reacted to kanaria007's post with ❤️ 8 days ago

Post

2484

✅ New Article: *Observations, Under-Observation, and Repair Loops* (v0.1)

Title:
👁️ Observations, Under-Observation, and Repair Loops: The OBS Cookbook for SI-Core
🔗 https://huggingface.co/blog/kanaria007/observations-under-observation

---

Summary:
SI-Core’s rule is simple: *No effectful Jump without PARSED observations.*
This article turns that slogan into an operational design: define *observation units* (sem_type/scope/status/confidence/backing_refs), detect *under-observation* (missing / degraded / biased), and run *repair loops* instead of “jumping in the dark.”

Key clarification: under-observed conditions may still run *read / eval_pre / jump-sandbox*, but must not commit or publish (sandbox: publish_result=false, memory_writes=disabled).

---

Why It Matters:
• Prevents “we had logs, so we had context” failures: *logs ≠ observations* unless typed + contract-checked
• Makes safety real: even PARSED observations should be gated by *coverage/confidence minima* (declared thresholds)
• Turns OBS into something measurable: *SCover_obs + SInt* become “OBS health” and safe-mode triggers
• Links semantic compression to reality: distinguish *missing raw* vs *compression loss*, and fix the right thing

---

What’s Inside:
• A practical observation-status taxonomy: PARSED / DEGRADED / STUB / ESTIMATED / MISSING / REDACTED / INVALID (+ mapping to core status)
• Per-jump *observation contracts* (required sem_types, allowed statuses, age/confidence limits) + explicit fallback actions
• Fallback patterns: *safe-mode / conservative default / sandbox-only / human-in-loop*
• Repair loops as first-class: ledgered obs.repair_request, PLB proposals, governance review for contract changes
• Testing OBS itself: property tests, chaos drills, golden-diff for observation streams

---

📖 Structured Intelligence Engineering Series
this is the *“how to operate OBS”* layer—so the system can *know when it doesn’t know* and repair over time.

reacted to kanaria007's post with ❤️🤗 13 days ago

Post

493

✅ 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 🤗🚀 13 days ago

Post

2213

✅ New Article: *Designing, Safeguarding, and Evaluating Learning Companions* (v0.1)

Title:
🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions
🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating

---

Summary:
Most “AI tutoring” talks about prompts, content, and engagement graphs.
But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.*

This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score.

> Mastery is important, but not the only axis.
> *Wellbeing, autonomy, and fairness must be first-class.*

---

Why It Matters:
• Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals)
• Treats *child/ND safety* as a runtime policy problem, not a UX afterthought
• Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations*
• Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation*

---

What’s Inside:
• A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop)
• GCS decomposition + *age/context goal templates* (and “bad but attractive” optima)
• Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns
• Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms
• Red-teaming & drills: scenario suites by age/context, *measuring safety over time*
• Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns
• Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages

---

📖 Structured Intelligence Engineering Series

reacted to kanaria007's post with 🚀 13 days ago

Post

1992

✅ New Article: *PoC Architecture for Education & Developmental Support*

Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support

---

Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:

* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts

Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.

> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”

---

Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults

---

What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)

---

📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.

reacted to kanaria007's post with 🤗 13 days ago

Post

155

✅ New Article: *SI-Core for Individualized Learning & Developmental Support*

Title:
🎒 SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans
🔗 https://huggingface.co/blog/kanaria007/individualized-learning-and-developmental-support

---

Summary:
Most “AI in education/support” stacks optimize shallow outputs (scores, clicks) and lose the *why*: goals, trade-offs, and safety.
This guide reframes learning & developmental support as an *auditable, multi-goal system*—where every intervention is logged as an effect, evaluated against goal trajectories, and constrained by runtime ethics.

> Learners aren’t numbers to optimize —
> they’re agents with goals, dignity, and long histories.

---

Why It Matters:
• Turns tutoring/support into *goal-aware planning*, not content roulette
• Makes decisions *explainable* (“Why this activity?”) with evidence trails
• Adds *runtime ethics* for vulnerable learners (fatigue, dignity, bias, consent)
• Enables improvement over time via *governed pattern learning*, not silent drift

---

What’s Inside:
• Goal surfaces + how to define “success” without collapsing into a single score
• Effect Ledger design: *what we did, why, under which constraints, and what happened*
• Practical ethics constraints for children / developmental differences
• Human-in-the-loop workflows: dashboards, contestation, approvals
• Integration patterns: assessments, IEP/MTSS/RTI, privacy/erasure alignment
• A phased migration path from today’s LLM tutors to SI-wrapped support systems

---

📖 Structured Intelligence Engineering Series
This isn’t “AI replaces teachers/therapists.” It’s *AI that can be supervised, questioned, audited, and improved safely*—in the places where that matters most.

reacted to kanaria007's post with ❤️ 14 days ago

Post

2213

✅ New Article: *Designing, Safeguarding, and Evaluating Learning Companions* (v0.1)

Title:
🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions
🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating

---

Summary:
Most “AI tutoring” talks about prompts, content, and engagement graphs.
But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.*

This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score.

> Mastery is important, but not the only axis.
> *Wellbeing, autonomy, and fairness must be first-class.*

---

Why It Matters:
• Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals)
• Treats *child/ND safety* as a runtime policy problem, not a UX afterthought
• Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations*
• Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation*

---

What’s Inside:
• A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop)
• GCS decomposition + *age/context goal templates* (and “bad but attractive” optima)
• Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns
• Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms
• Red-teaming & drills: scenario suites by age/context, *measuring safety over time*
• Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns
• Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages

---

📖 Structured Intelligence Engineering Series

reacted to kanaria007's post with ❤️ 16 days ago

Post

1992

✅ New Article: *PoC Architecture for Education & Developmental Support*

Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support

---

Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:

* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts

Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.

> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”

---

Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults

---

What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)

---

📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.

reacted to kanaria007's post with ❤️ 16 days ago

Post

155

✅ New Article: *SI-Core for Individualized Learning & Developmental Support*

Title:
🎒 SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans
🔗 https://huggingface.co/blog/kanaria007/individualized-learning-and-developmental-support

---

Summary:
Most “AI in education/support” stacks optimize shallow outputs (scores, clicks) and lose the *why*: goals, trade-offs, and safety.
This guide reframes learning & developmental support as an *auditable, multi-goal system*—where every intervention is logged as an effect, evaluated against goal trajectories, and constrained by runtime ethics.

> Learners aren’t numbers to optimize —
> they’re agents with goals, dignity, and long histories.

---

Why It Matters:
• Turns tutoring/support into *goal-aware planning*, not content roulette
• Makes decisions *explainable* (“Why this activity?”) with evidence trails
• Adds *runtime ethics* for vulnerable learners (fatigue, dignity, bias, consent)
• Enables improvement over time via *governed pattern learning*, not silent drift

---

What’s Inside:
• Goal surfaces + how to define “success” without collapsing into a single score
• Effect Ledger design: *what we did, why, under which constraints, and what happened*
• Practical ethics constraints for children / developmental differences
• Human-in-the-loop workflows: dashboards, contestation, approvals
• Integration patterns: assessments, IEP/MTSS/RTI, privacy/erasure alignment
• A phased migration path from today’s LLM tutors to SI-wrapped support systems

---

📖 Structured Intelligence Engineering Series
This isn’t “AI replaces teachers/therapists.” It’s *AI that can be supervised, questioned, audited, and improved safely*—in the places where that matters most.

upvoted an article 18 days ago

Article

SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans

18 days ago

•

2

liked a dataset 20 days ago

kanaria007/agi-structural-intelligence-protocols

Updated 4 days ago • 434 • 8

reacted to kanaria007's post with ❤️ 20 days ago

Post

2017

✅ New Article: *Operating an SI-Core (v0.1)*

Title:
🛠️ Operating SI-Core: Dashboards, Playbooks, and Human Loops
🔗 https://huggingface.co/blog/kanaria007/operating-si-core

---

Summary:
Designing an SI-Core is only half the job — the other half is *running it safely at 03:00*.

This guide is a *non-normative ops runbook* for SRE/Ops teams and governance owners: what to put on the *one-page dashboard*, how to wire *alerts → actions*, when to use *safe-mode*, and how to answer the question that always arrives after an incident:

> “Why did the system do *that*?”

---

Why It Matters:
• Turns “auditable AI” into *operational reality* (not a slide deck)
• Makes *ethics + rollback* measurable, actionable, and drillable
• Clarifies how humans stay in the loop without becoming the bottleneck
• Provides templates for *postmortems, escalation, and regulator-grade explanations*

---

What’s Inside:
*Core Ops Dashboard (1 page):*
• Determinism/consistency, ethics/oversight, rollback/recovery, coverage/audit — with drill-downs that reach offending decisions in *two clicks*

*Alert → Runbook Patterns:*
• Examples for ethics index drops and rollback latency degradation
• Stabilization actions, scoped safe-mode, and governance handoffs

*Human-in-Loop Operations:*
• Safe-mode scopes (domain/tenant/region/risk)
• “Why?” view for any effectful action (structured explanation export)

*Reliability Muscle:*
• Incident templates, chaos drills, on-call handoffs, and capacity planning (because SI-Core accumulates structure over time)

---

📖 Structured Intelligence Engineering Series
A field manual for keeping structured intelligence upright — and explainable — under real-world pressure.

upvoted an article 22 days ago

Article

Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev

22 days ago

•

2

commented on Deep-Space SI-Core: Autonomy Across Light-Hours - *How an onboard SI-Core evolves safely while Earth is hours away* 22 days ago

🐾

Heting Mao

AI & ML interests

Recent Activity

Organizations

Genius Replay Protocol: Capturing and Replaying High-Value Jumps

SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans

kanaria007/agi-structural-intelligence-protocols

Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev

Heting Mao

AI & ML interests

Recent Activity

Organizations

IkanRiddle's activity

Genius Replay Protocol: Capturing and Replaying High-Value Jumps

SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans

Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev