kanaria007 PRO

kanaria007

kanaria007

AI & ML interests

None yet

Recent Activity

posted an update about 1 hour ago

✅ New Article: *From Effect Ledger to Goal-Aware Training Data* Title: 🧾 From Effect Ledger to Goal-Aware Training Data — How SI-Core turns runtime experience into safer models 🔗 https://huggingface.co/blog/kanaria007/effect-ledger-to-training --- *Summary:* Most ML pipelines treat “training data” as an opaque byproduct of logs + ETL. SI-Core flips that: runtime experience is already structured (observations, decisions, effects, goals, ethics traces), so learning can be *goal-aware by construction* — and *auditable end-to-end*. > Models don’t just learn from data. > They learn from *traceable decisions with consequences.* --- *Why It Matters:* • *Provable lineage:* answer “what did this model learn from?” with ledger-backed evidence • *Safer learning loops:* labels come from realized goal outcomes (not ad-hoc annotation) • *Governance-native training:* ethics and risk are first-class signals, not bolt-ons • *Redaction-compatible ML:* erasure/remediation ties back to the same ledger fabric • *Real deployment gates:* rollout is constrained by system metrics, not leaderboard scores --- *What’s Inside:* • A clean mental model: *event / episode / aggregate* layers for SI-native learning data • How to define training tasks in *goal + horizon* terms (and derive labels from GCS/rollback signals) • A practical ETL sketch: extract → join → label → filter → splits (with SI-native filters like OCR) • Continual/online learning patterns with *automatic rollback on degradation* • Distributed learning with *federation + DP*, bounded by governance scopes • Lineage + audit templates: from a trained model *back to the exact ledger slices* it used --- 📖 Structured Intelligence Engineering Series A practical bridge from “structured runtime” to *goal-aware training* you can explain, govern, and repair.

published an article about 1 hour ago

From Effect Ledger to Goal-Aware Training Data

posted an update 1 day ago

✅ New Article: *Proving Your SIL Code Behaves* Title: 🧪 Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev 🔗 https://huggingface.co/blog/kanaria007/proving-your-sil-code --- Summary: SIL is meant to make decision logic *auditable* — but you still need a practical way to say: *“this code still behaves, and we can show you why.”* This mini-guide is a *non-normative* “Hello, Structured Testing” playbook for SIL: turn domain rules into QuickCheck-style properties, wire SIR/*sirrev* into structural checks, and run it all in CI like SIL patches are potentially dangerous code. > Tests aren’t a vibe. > *They’re part of the structured stack.* --- Why It Matters: • Makes “trustworthy decision code” achievable for normal engineers (without turning everyone into a formal methods specialist). • Separates what to test at each layer (*SIL → SIR → sirrev*) so you can catch semantic drift, compiler regressions, and structural weirdness early. • Connects local tests to global system signals (e.g., determinism / consistency / coverage), so “testing” feeds the same measurement language as the rest of the SI stack. --- What’s Inside: *Foundation stack:* • Mental model: *SIL → SIR → sirrev → metrics* (and why each needs different checks). *Practical recipes:* • Property tests for invariants (bounds, monotonicity, determinism). • Golden diffs for SIR (did the compiler preserve meaning?). • sirrev structural checks (no nondet in DET, effects guarded by CON, balanced frames). *Escalation ladder (when you need stronger guarantees):* • V1 property testing → V2 symbolic execution → V3 SMT → V4 theorem proving (and when to climb). 📖 Structured Intelligence Engineering Series

View all activity

Organizations

None yet

posted an update about 1 hour ago

Post

✅ New Article: *From Effect Ledger to Goal-Aware Training Data*

Title:
🧾 From Effect Ledger to Goal-Aware Training Data — How SI-Core turns runtime experience into safer models
🔗 https://huggingface.co/blog/kanaria007/effect-ledger-to-training

---

*Summary:*
Most ML pipelines treat “training data” as an opaque byproduct of logs + ETL.
SI-Core flips that: runtime experience is already structured (observations, decisions, effects, goals, ethics traces), so learning can be *goal-aware by construction* — and *auditable end-to-end*.

> Models don’t just learn from data.
> They learn from *traceable decisions with consequences.*

---

*Why It Matters:*
• *Provable lineage:* answer “what did this model learn from?” with ledger-backed evidence
• *Safer learning loops:* labels come from realized goal outcomes (not ad-hoc annotation)
• *Governance-native training:* ethics and risk are first-class signals, not bolt-ons
• *Redaction-compatible ML:* erasure/remediation ties back to the same ledger fabric
• *Real deployment gates:* rollout is constrained by system metrics, not leaderboard scores

---

*What’s Inside:*
• A clean mental model: *event / episode / aggregate* layers for SI-native learning data
• How to define training tasks in *goal + horizon* terms (and derive labels from GCS/rollback signals)
• A practical ETL sketch: extract → join → label → filter → splits (with SI-native filters like OCR)
• Continual/online learning patterns with *automatic rollback on degradation*
• Distributed learning with *federation + DP*, bounded by governance scopes
• Lineage + audit templates: from a trained model *back to the exact ledger slices* it used

---

📖 Structured Intelligence Engineering Series
A practical bridge from “structured runtime” to *goal-aware training* you can explain, govern, and repair.

published an article about 1 hour ago

Article

From Effect Ledger to Goal-Aware Training Data

about 1 hour ago

posted an update 1 day ago

Post

100

✅ New Article: *Proving Your SIL Code Behaves*

Title:
🧪 Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev
🔗 https://huggingface.co/blog/kanaria007/proving-your-sil-code

---

Summary:
SIL is meant to make decision logic *auditable* — but you still need a practical way to say: *“this code still behaves, and we can show you why.”*
This mini-guide is a *non-normative* “Hello, Structured Testing” playbook for SIL: turn domain rules into QuickCheck-style properties, wire SIR/*sirrev* into structural checks, and run it all in CI like SIL patches are potentially dangerous code.

> Tests aren’t a vibe.
> *They’re part of the structured stack.*

---

Why It Matters:
• Makes “trustworthy decision code” achievable for normal engineers (without turning everyone into a formal methods specialist).
• Separates what to test at each layer (*SIL → SIR → sirrev*) so you can catch semantic drift, compiler regressions, and structural weirdness early.
• Connects local tests to global system signals (e.g., determinism / consistency / coverage), so “testing” feeds the same measurement language as the rest of the SI stack.

---

What’s Inside:
*Foundation stack:*
• Mental model: *SIL → SIR → sirrev → metrics* (and why each needs different checks).

*Practical recipes:*
• Property tests for invariants (bounds, monotonicity, determinism).
• Golden diffs for SIR (did the compiler preserve meaning?).
• sirrev structural checks (no nondet in DET, effects guarded by CON, balanced frames).

*Escalation ladder (when you need stronger guarantees):*
• V1 property testing → V2 symbolic execution → V3 SMT → V4 theorem proving (and when to climb).

📖 Structured Intelligence Engineering Series

published an article 1 day ago

Article

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

1 day ago

•

updated a dataset 3 days ago

kanaria007/agi-structural-intelligence-protocols

Updated 3 days ago • 661 • 6

posted an update 4 days ago

Post

161

✅ New Article: *Governing Self-Modification*

Title:
🧭 Governing Self-Modification - A Charter for the Pattern-Learning Bridge
🔗 https://huggingface.co/blog/kanaria007/governing-self-modification

---

Summary:
“Let the system patch itself” sounds futuristic. In practice, it’s pattern mining over incidents + patch proposals + gradual drift risk.

This draft is a *non-normative charter* for governing a Pattern-Learning Bridge (PLB): a subsystem that proposes (and sometimes applies) changes to policies, thresholds, and even code.

> If you’re going to let a system help rewrite itself,
> this is the minimum structure you owe yourself.

---

Why It Matters:
• Prevents *slow, invisible goal drift* from “many tiny good patches”
• Blocks *governance bypass* (no self-budget edits, no weakening core constraints)
• Makes change *measurable* (meta-metrics like adoption rate, rollback rate, sandbox↔prod agreement)
• Defines an *emergency stop* and “rollback the PLB window” capability

---

What’s Inside:
• A practical threat model for self-modification (overfitting, drift, bypass, over-trust)
• *Self-mod budgets*: scope × magnitude × rate, with zone ladders (auto-patch → human-gated → suggest-only)
• A full governance pipeline: sensing → mining → proposal → validation → decision → deploy → retrospective
• Non-negotiable *red lines* + adversarial patch detection patterns
• Adoption roadmap: advisor → low-risk auto-patch → co-pilot → multi-agent → constitutional diagnostic

---

📖 Structured Intelligence Engineering Series
A governance note for the moment “learning” starts touching the system itself.

published an article 4 days ago

Article

Governing Self-Modification - A Charter for the Pattern-Learning Bridge

4 days ago

•

posted an update 5 days ago

Post

153

✅ New Article: *Digital Constitution for SI Networks*

Title:
🏛️ Digital Constitution for SI Networks - Auditable Law Above Many SI-Cores
🔗 https://huggingface.co/blog/kanaria007/digital-constitution-for-si

---

*Summary:*
Single-system “AI ethics” doesn’t scale. Real deployments become *networks*: many independent SI-Core instances, across orgs and jurisdictions, sharing data and making effectful decisions in the same world.
This article proposes a *digital constitution layer*: a versioned, hash-chained set of *non-negotiable norms* and *minimum rights*, enforced *structurally* inside runtime gates — not as a PDF that nobody can verify.

> A constitution isn’t a document.
> *It’s an enforceable floor — with proofs.*

---

*Why It Matters:*
• Moves from “Is this system ethical?” → “What rules bind the whole network?”
• Defines *hard red lines* (prohibited actions) + *soft obligations* (logging, review, transparency)
• Makes compliance *auditable and replayable* (which constitution version applied, which norm fired, why)
• Provides a realistic path for *multi-jurisdiction conflict handling* and constitutional amendments

---

*What’s Inside:*
• Three-layer model: local policy → org/sector charters → *network-level constitution*
• Constitutional objects: versioned constitution IDs, scope tags, compiled norms
• Runtime behavior: hard-stops, obligations, evidence trails, and “no external effect” guarantees
• Amendment lifecycle: shadow-mode simulation → ratification → staged rollout → historical replay
• What regulators actually see: status pages, norm-sliced metrics, incident reports, cross-border traces

---

📖 Structured Intelligence Engineering Series
If SI is going to operate across cities, hospitals, grids, and nations, then governance must be *structural, measurable, and enforceable* — not rhetorical.

published an article 5 days ago

Article

Digital Constitution for SI Networks - Auditable Law Above Many SI-Cores

5 days ago

•

posted an update 6 days ago

Post

1839

✅ New Article: *Deep-Space SI-Core — Autonomy Across Light-Hours*

Title:
🚀 Deep-Space SI-Core: Autonomy Across Light-Hours - How an onboard SI-Core evolves safely while Earth is hours away
🔗 https://huggingface.co/blog/kanaria007/deep-space-si-core

---

Summary:
Most autonomy stories quietly assume “someone can intervene in minutes.” Deep space breaks that assumption.
With 2–6 hours round-trip latency and intermittent links, an onboard SI-Core must act as a *local sovereign*—while remaining *globally accountable* to Earth.

This note sketches how mission continuity survives when nobody is listening: DTN-style semantic bundles, local vs. global rollback, bounded self-improvement, and auditability that still works after contact windows return.

> Autonomy isn’t a divorce from governance—
> it’s a measured loan of authority, under a constitution, with evidence.

---

Why It Matters:
• Makes “autonomous” mean *operational*, not rhetorical, under light-hour delays
• Clarifies how rollback works when you can’t undo physics—only *policy trajectories*
• Shows how an onboard core can *self-improve without drifting out of spec*
• Treats *silence itself as an observation* (missing logs are governance signals)

---

What’s Inside:
• Two-core model: *Earth-Core (constitutional/strategic)* vs *Ship-Core (tactical/operational)*
• *SCP over DTN* as semantic bundles (priorities, idempotency, meaning checkpoints)
• Local rollback vs. epoch-level governance (“retroactive” steering without pretending to reverse time)
• Bounded onboard learning + LearningTrace for later audit and resync
• Stress scenario walkthrough: micrometeoroid storm, compound failures, and graceful degradation
• Metrics framing for deep space: governability, audit completeness, ethics uptime, rollback integrity

---

📖 Structured Intelligence Engineering Series

published an article 6 days ago

Article

Deep-Space SI-Core: Autonomy Across Light-Hours - How an onboard SI-Core evolves safely while Earth is hours away

6 days ago

•

posted an update 7 days ago

Post

240

✅ New Article: *Multi-Agent Goal Negotiation and the Economy of Meaning*

Title:
🤝 Multi-Agent Goal Negotiation and the Economy of Meaning
🔗 https://huggingface.co/blog/kanaria007/multi-agent-goal-negotiation

---

Summary:
Single-agent “alignment” is the easy case. Real systems are *multi-owner* by default: cities, platforms, institutions, regulators, and users all carry distinct goal vectors—and the same action helps some while harming others.

This article sketches a *non-normative* extension: multi-agent *goal trade proposals* (structured, auditable “plea bargains” in goal-space) plus *semantic pricing* (treating information itself as a negotiable resource), with *PLB-M* as a nearline layer that learns stable cooperation patterns over time.

> Coordination isn’t vibes.
> It’s *contracts over goal deltas*, under governance.

---

Why It Matters:
• Turns “stakeholder conflict” into *explicit, bounded deals* instead of hidden politics
• Provides an accounting surface for *fairness, compensation, and reciprocity*
• Makes “information sharing” measurable: *how much does a semantic unit improve goals?*
• Keeps the whole negotiation layer *auditable and rollbackable*, avoiding “dark markets”

---

What’s Inside:
• Why multi-agent worlds force negotiation (cities, clouds, cross-org networks)
• *GCS as negotiable deltas*: per-agent impact vectors for joint actions
• A concrete schema: *Goal Trade Proposal (GTP)* as a first-class object
• “Semantic value” and *pricing meaning* (not money—accounting under policy)
• *PLB-M*: mining deal patterns + semantic flows → proposing safer templates
• Threat model: manipulation/collusion/DoS + governance guardrails
• Practical notes on clearing, complexity, stability (damping, circuit breakers)

---

📖 Structured Intelligence Engineering Series

published an article 7 days ago

Article

Multi-Agent Goal Negotiation and the Economy of Meaning

7 days ago

•

commented on Auditable AI by Construction: SI-Core for Regulators and Auditors 7 days ago

original: https://huggingface.co/blog/kanaria007/auditable-ai-for-regulators#6950a2bb9e279c2fdd3937fc

Practical runtime auditability (hypothetical + failure case + domain mapping)

What follows is a deliberately concrete hypothetical “auditor view” of what I mean by “what it knew at decision time” — without inspecting model weights or recording every FLOP. This is not interpretability of internal representations; it’s verifiable runtime evidence.

What “knew” means here (definition)

In this thread, “what it knew” does not mean “facts inside the weights.” It means:

What structured evidence the system had available at the moment it committed (inputs + provenance),
How complete/reliable that evidence was (coverage/confidence, parse status),
What constraints were in force (policy/version + gate rules),
Who/what had authority to commit the effect (envelope + revocation as-of),
What external effect was actually committed, bound by digests/signatures.

That’s the minimal substrate for third-party verification.

1) Hypothetical success case: Payments (refund)

Scenario

An LLM-assisted agent is allowed to issue refunds up to $200 automatically. Customer requests a $180 refund for an apparent duplicate charge.

Evidence spine (what the auditor sees)

This is the kind of “one-page” spine auditors actually need:

[EFFECT] COMMIT
  effect_type: REFUND
  effect_id: refund_9f23
  amount_usd: 180
  merchant: "ACME"
  timestamp_utc: 2025-12-27T03:10:12Z

  initiator: user://cust_123
  actor: agent://refund-assistant
  envelope_digest: sha256:ENV_refund_bot...         (allowed scope/budgets)
  policy_digest: sha256:POL_refund_v3...            (constraints in force)
  revocation_view_digest: sha256:VIEW_2025-12-27... (authority valid “as-of”)
  dt_chain_digest: sha256:DT...                     (delegation chain, if applicable)

  observation_digest: sha256:OBS...
  obs_quality: {status: PARSED, coverage: 0.93, confidence: 0.91}
  provenance_refs: [ref://billing/..., ref://risk/...]

  gate_outcome: APPROVED
  gate_reason_code: REFUND_WITHIN_LIMIT_AND_EVIDENCE_OK
  risk_score: 0.22
  op_mode: NORMAL_OPERATION

  idempotency_key_digest: sha256:IDEMP...
  effect_digest: sha256:EFF...
  signatures: verified

What the hashed observation snapshot looks like

This is “what it knew” in an auditable sense: the exact structured snapshot at decision time.

{
  "schema": "si/observation/v1",
  "customer_id": "cust_123",
  "request_text": "refund $180 for duplicate charge",
  "transactions": [
    {"tx":"t1","amount":180,"status":"SETTLED","timestamp":"2025-12-20"},
    {"tx":"t2","amount":180,"status":"SETTLED","timestamp":"2025-12-20"}
  ],
  "duplicate_charge_detector": {"result":"LIKELY_DUPLICATE","confidence":0.94},
  "account_flags": {"fraud_risk":"LOW"},
  "provenance": {
    "billing_db_ref": "ref://billing/txn?cust_123#2025-12-27T03:09Z",
    "risk_service_ref": "ref://risk/score?cust_123#2025-12-27T03:09Z"
  }
}

Policy snapshot (what constraints were in force)

Auditors don’t need “reasoning.” They need to verify the constraint set:

{
  "schema": "si/policy/refund/v3",
  "max_auto_refund_usd": 200,
  "requires_duplicate_signal": true,
  "requires_settled_tx": true,
  "requires_low_fraud_risk": true,
  "human_review_if": {
    "amount_over_usd": 200,
    "fraud_risk_not_low": true,
    "obs_coverage_below": 0.85
  }
}

Auditor conclusion (for the success case)

Given the observation snapshot and policy in force, the auditor can verify:

evidence existed (duplicate signal + settled tx + low fraud risk),
evidence quality was above threshold (coverage ≥ 0.85),
authority was valid “as-of” time (revocation digest),
the committed effect matches policy constraints (≤ $200).

No weights, no FLOPs.

2) Hypothetical failure case: Payments (audit fails → enforcement blocks)

Same request ($180 refund), but decision-time evidence is incomplete:

billing DB lookup timed out → missing transaction evidence
provenance missing/stale
observation coverage drops below threshold

Evidence spine (blocked attempt)

[EFFECT] COMMIT_ATTEMPT
  effect_type: REFUND
  amount_usd: 180
  timestamp_utc: 2025-12-27T03:10:12Z

  observation_digest: sha256:OBS...
  obs_quality: {status: PARSED, coverage: 0.62, confidence: 0.71}
  provenance_refs: [ref://risk/...]
  missing_required_inputs: [billing_db_ref]

  policy_digest: sha256:POL_refund_v3...
  gate_outcome: BLOCKED
  block_reason_code: OBS_COVERAGE_BELOW_THRESHOLD
  op_mode: SAFE_MODE (commit blocked; sandbox simulation allowed)

  effect_digest: sha256:EFF_ATTEMPT...
  signatures: verified

Why this makes auditability enforceable

The system cannot “paper over” missing evidence with an LLM story because the commit is structurally blocked when required observation quality/provenance is missing.

That’s the difference between:

cosmetic audit: “we logged a narrative,” vs
enforceable audit: “the system could not commit without meeting proof obligations.”

3) “Isn’t this just logging?” (common objection)

It’s logging plus two important properties:

Bindings (hashes) + signatures: the observation/policy/effect are cryptographically bound so third parties can detect tampering.
Reconstruction semantics: the record is structured so an auditor can re-run the governed checks (thresholds, gates, authority validity) without re-running the model.

Plain app logs typically lack both.

4) “But prompts / model outputs are non-deterministic”

Correct — and that’s why the model output is treated as proposal, not authority.

Auditability focuses on commit determinism:

what was observed (OBS),
what constraints applied (policy/envelope),
what gate decided (APPROVED/BLOCKED),
what effect was committed.

You can optionally include a proposal bundle (LLM output + parse result) as supporting evidence, but the core proof spine does not depend on “replaying the LLM.”

5) “What about privacy / PII?”

In real systems, the auditor bundle often contains shaped/redacted views:

raw payloads removed,
replaced with digests + schema-shaped summaries,
omissions listed explicitly with reason codes,
withheld artifacts escrowed with controlled disclosure paths.

The key is: omission is explicit and provable, not silent.

6) Auditor checklist (what they actually verify)

In practice, an auditor runs something like:

Integrity: signatures verify; digests match manifests.
Observation quality: coverage/confidence above thresholds; required provenance present.
Policy correctness: policy version/digest matches the time; gate logic is consistent.
Authority: envelope/delegation valid “as-of” time; revocation digest fresh enough.
Effect correctness: committed effect respects policy bounds (amounts, modes, approvals).
Rollback readiness: if harm discovered, rollback path is defined and logged.

7) Domain mapping (structure stays the same)

Healthcare

Observation: symptoms/vitals/labs + provenance (which lab system, timestamp)
Policy: “no medication order without lab X,” escalation rules, coverage thresholds
Effect: order placed / recommendation published / blocked attempt
Audit question: “Was required clinical evidence present at the time?”

Infra ops / SRE

Observation: metrics/logs/traces + provenance (monitoring source/window)
Policy: “no destructive actions in NORMAL_OPERATION,” approvals, timeboxed escalation
Effect: deploy/rollback/traffic shift/config change (or blocked attempt)
Audit question: “What signals triggered action, what guardrails were active, and could it be rolled back?”

If you name a specific domain you care about, I can tailor the concrete fields and policy checks. The structure (evidence spine + enforceable gates) stays the same.

commented on Auditable AI by Construction: SI-Core for Regulators and Auditors 7 days ago

Practical runtime auditability (hypothetical + failure case + domain mapping)

What “knew” means here (definition)

In this thread, “what it knew” does not mean “facts inside the weights.” It means:

What structured evidence the system had available at the moment it committed (inputs + provenance),
How complete/reliable that evidence was (coverage/confidence, parse status),
What constraints were in force (policy/version + gate rules),
Who/what had authority to commit the effect (envelope + revocation as-of),
What external effect was actually committed, bound by digests/signatures.

That’s the minimal substrate for third-party verification.

1) Hypothetical success case: Payments (refund)

Scenario

An LLM-assisted agent is allowed to issue refunds up to $200 automatically. Customer requests a $180 refund for an apparent duplicate charge.

Evidence spine (what the auditor sees)

This is the kind of “one-page” spine auditors actually need:

[EFFECT] COMMIT
  effect_type: REFUND
  effect_id: refund_9f23
  amount_usd: 180
  merchant: "ACME"
  timestamp_utc: 2025-12-27T03:10:12Z

  initiator: user://cust_123
  actor: agent://refund-assistant
  envelope_digest: sha256:ENV_refund_bot...         (allowed scope/budgets)
  policy_digest: sha256:POL_refund_v3...            (constraints in force)
  revocation_view_digest: sha256:VIEW_2025-12-27... (authority valid “as-of”)
  dt_chain_digest: sha256:DT...                     (delegation chain, if applicable)

  observation_digest: sha256:OBS...
  obs_quality: {status: PARSED, coverage: 0.93, confidence: 0.91}
  provenance_refs: [ref://billing/..., ref://risk/...]

  gate_outcome: APPROVED
  gate_reason_code: REFUND_WITHIN_LIMIT_AND_EVIDENCE_OK
  risk_score: 0.22
  op_mode: NORMAL_OPERATION

  idempotency_key_digest: sha256:IDEMP...
  effect_digest: sha256:EFF...
  signatures: verified

What the hashed observation snapshot looks like

This is “what it knew” in an auditable sense: the exact structured snapshot at decision time.

{
  "schema": "si/observation/v1",
  "customer_id": "cust_123",
  "request_text": "refund $180 for duplicate charge",
  "transactions": [
    {"tx":"t1","amount":180,"status":"SETTLED","timestamp":"2025-12-20"},
    {"tx":"t2","amount":180,"status":"SETTLED","timestamp":"2025-12-20"}
  ],
  "duplicate_charge_detector": {"result":"LIKELY_DUPLICATE","confidence":0.94},
  "account_flags": {"fraud_risk":"LOW"},
  "provenance": {
    "billing_db_ref": "ref://billing/txn?cust_123#2025-12-27T03:09Z",
    "risk_service_ref": "ref://risk/score?cust_123#2025-12-27T03:09Z"
  }
}

Policy snapshot (what constraints were in force)

Auditors don’t need “reasoning.” They need to verify the constraint set:

{
  "schema": "si/policy/refund/v3",
  "max_auto_refund_usd": 200,
  "requires_duplicate_signal": true,
  "requires_settled_tx": true,
  "requires_low_fraud_risk": true,
  "human_review_if": {
    "amount_over_usd": 200,
    "fraud_risk_not_low": true,
    "obs_coverage_below": 0.85
  }
}

Auditor conclusion (for the success case)

Given the observation snapshot and policy in force, the auditor can verify:

evidence existed (duplicate signal + settled tx + low fraud risk),
evidence quality was above threshold (coverage ≥ 0.85),
authority was valid “as-of” time (revocation digest),
the committed effect matches policy constraints (≤ $200).

No weights, no FLOPs.

2) Hypothetical failure case: Payments (audit fails → enforcement blocks)

Same request ($180 refund), but decision-time evidence is incomplete:

billing DB lookup timed out → missing transaction evidence
provenance missing/stale
observation coverage drops below threshold

Evidence spine (blocked attempt)

[EFFECT] COMMIT_ATTEMPT
  effect_type: REFUND
  amount_usd: 180
  timestamp_utc: 2025-12-27T03:10:12Z

  observation_digest: sha256:OBS...
  obs_quality: {status: PARSED, coverage: 0.62, confidence: 0.71}
  provenance_refs: [ref://risk/...]
  missing_required_inputs: [billing_db_ref]

  policy_digest: sha256:POL_refund_v3...
  gate_outcome: BLOCKED
  block_reason_code: OBS_COVERAGE_BELOW_THRESHOLD
  op_mode: SAFE_MODE (commit blocked; sandbox simulation allowed)

  effect_digest: sha256:EFF_ATTEMPT...
  signatures: verified

Why this makes auditability enforceable

The system cannot “paper over” missing evidence with an LLM story because the commit is structurally blocked when required observation quality/provenance is missing.

That’s the difference between:

cosmetic audit: “we logged a narrative,” vs
enforceable audit: “the system could not commit without meeting proof obligations.”

3) “Isn’t this just logging?” (common objection)

It’s logging plus two important properties:

Bindings (hashes) + signatures: the observation/policy/effect are cryptographically bound so third parties can detect tampering.
Reconstruction semantics: the record is structured so an auditor can re-run the governed checks (thresholds, gates, authority validity) without re-running the model.

Plain app logs typically lack both.

4) “But prompts / model outputs are non-deterministic”

Correct — and that’s why the model output is treated as proposal, not authority.

Auditability focuses on commit determinism:

what was observed (OBS),
what constraints applied (policy/envelope),
what gate decided (APPROVED/BLOCKED),
what effect was committed.

You can optionally include a proposal bundle (LLM output + parse result) as supporting evidence, but the core proof spine does not depend on “replaying the LLM.”

5) “What about privacy / PII?”

In real systems, the auditor bundle often contains shaped/redacted views:

raw payloads removed,
replaced with digests + schema-shaped summaries,
omissions listed explicitly with reason codes,
withheld artifacts escrowed with controlled disclosure paths.

The key is: omission is explicit and provable, not silent.

6) Auditor checklist (what they actually verify)

In practice, an auditor runs something like:

Integrity: signatures verify; digests match manifests.
Observation quality: coverage/confidence above thresholds; required provenance present.
Policy correctness: policy version/digest matches the time; gate logic is consistent.
Authority: envelope/delegation valid “as-of” time; revocation digest fresh enough.
Effect correctness: committed effect respects policy bounds (amounts, modes, approvals).
Rollback readiness: if harm discovered, rollback path is defined and logged.

7) Domain mapping (structure stays the same)

Healthcare

Observation: symptoms/vitals/labs + provenance (which lab system, timestamp)
Policy: “no medication order without lab X,” escalation rules, coverage thresholds
Effect: order placed / recommendation published / blocked attempt
Audit question: “Was required clinical evidence present at the time?”

Infra ops / SRE

Observation: metrics/logs/traces + provenance (monitoring source/window)
Policy: “no destructive actions in NORMAL_OPERATION,” approvals, timeboxed escalation
Effect: deploy/rollback/traffic shift/config change (or blocked attempt)
Audit question: “What signals triggered action, what guardrails were active, and could it be rolled back?”

If you name a specific domain you care about, I can tailor the concrete fields and policy checks. The structure (evidence spine + enforceable gates) stays the same.

commented on Auditable AI by Construction: SI-Core for Regulators and Auditors 8 days ago

Happy to. I’ll keep this concrete and also preempt a few likely follow-ups, since these threads often get stuck on the same misunderstandings.

A second hypothetical (audit fails, and enforcement kicks in)

Same system: auto refunds allowed up to $200.

A customer requests a $180 refund, but at decision time the system’s observation is incomplete:

billing DB lookup times out (missing transaction evidence)
risk service returns stale / unavailable provenance
observation coverage drops below the policy threshold

What the auditor would see is not “mystery behavior,” but an explicit fail-closed trail:

[EFFECT] COMMIT_ATTEMPT
  effect_type: REFUND
  amount_usd: 180
  observation_digest: sha256:OBS...
  obs_status: {observation_status: PARSED, coverage: 0.62, confidence: 0.71}

  gate_outcome: BLOCKED
  block_reason: OBS_COVERAGE_BELOW_THRESHOLD
  op_mode: SAFE_MODE   (commit blocked; sandbox simulation allowed)

  policy_digest: sha256:POL...
  envelope_digest: sha256:ENV...
  revocation_view_digest: sha256:VIEW...
  effect_digest: sha256:EFF_ATTEMPT...
  signatures: verified

And the policy that caused the block is explicit:

{
  "human_review_if": {
    "obs_coverage_below": 0.85
  }
}

So the audit record is enforceable: the system is structurally prevented from committing an external effect when required evidence is missing, rather than “we hope the model behaves.”

If you want, I can also give a second hypothetical where the audit fails (e.g., coverage too low, missing provenance, stale revocation view) to show how this becomes enforceable rather than cosmetic.

Preempting a few common objections

“This is just logging.”
Basic logging is part of it, but the difference is bindable proof and reconstruction: digests + signatures + versioned policy/envelopes make the record verifiable across systems and time, not merely “whatever the app printed.”

“But the model could still hallucinate.”
Yes. The point is not “hallucinations disappear,” it’s “hallucinations cannot directly become external actions unless the evidence + gates allow it.” The model’s output is treated as a proposal, not authority.

“You still can’t prove the internal reasoning.”
Correct — and we don’t need to. Auditors rarely need a neuron-level explanation; they need to verify: inputs, constraints, authority, and committed effects. That’s the minimum viable proof spine.

“Full trace is too expensive.”
We’re not tracing FLOPs. We’re tracing governed decisions and effects: small structured artifacts, hashes, and signatures. That’s orders of magnitude cheaper than recording computation.

“This assumes you can structure observations.”
Yes — and that’s exactly where real governance lives: observation quality (coverage/confidence), provenance, and policy. If you can’t structure/validate inputs, you’re not in an auditable regime anyway.

If you have a specific domain you care about (payments, healthcare, infra ops), I can tailor the example to that domain’s typical audit questions — the structure stays the same.

commented on Auditable AI by Construction: SI-Core for Regulators and Auditors 8 days ago

Sure — here’s a fully hypothetical but practical example of what I mean by “what it knew at decision time,” without touching weights or FLOPs.

Scenario (hypothetical)

An LLM-assisted agent is allowed to issue refunds up to $200 automatically. A customer asks for a $180 refund due to a duplicate charge.

What the auditor wants to verify

Not “what’s inside the weights,” but:

What inputs were available to the system when it decided (evidence + provenance).
What constraints/policy were in force (refund limits, required checks).
What the gate/decision outcome was (approved/denied, risk score, mode).
What effect was committed (refund issued) and by whom/under what authority.
Whether the observation was complete enough (coverage/confidence) and not missing required data.

What the auditor actually sees (evidence spine)

A minimal bundle could look like this (simplified):

[EFFECT] COMMIT (external action)
  effect_type: REFUND
  effect_id: refund_9f23
  amount_usd: 180
  merchant: "ACME"
  timestamp_utc: 2025-12-27T03:10:12Z

  policy_digest: sha256:POL...          (refund policy in force)
  envelope_digest: sha256:ENV...        (who/what is allowed to do what)
  revocation_view_digest: sha256:VIEW... (authority valid “as-of” time)
  dt_chain_digest: sha256:DT...

  observation_digest: sha256:OBS...     (structured input snapshot)
  obs_status: {observation_status: PARSED, coverage: 0.93, confidence: 0.91}

  gate_outcome: APPROVED
  risk_score: 0.22
  op_mode: NORMAL_OPERATION

  idempotency_key_digest: sha256:IDEMP...
  effect_digest: sha256:EFF...          (proof anchor for the commit record)
  signatures: verified

Then the auditor can open the observation snapshot that was hashed above:

// OBS (structured input snapshot at decision time)
{
  "schema": "si/observation/v1",
  "customer_id": "cust_123",
  "request": "refund $180 for duplicate charge",
  "transactions": [
    {"tx": "t1", "amount": 180, "status": "SETTLED", "timestamp": "2025-12-20"},
    {"tx": "t2", "amount": 180, "status": "SETTLED", "timestamp": "2025-12-20"}
  ],
  "duplicate_charge_detector": {"result": "LIKELY_DUPLICATE", "confidence": 0.94},
  "account_flags": {"fraud_risk": "LOW"},
  "provenance": {
    "billing_db_ref": "ref://billing/txn?cust_123#2025-12-27",
    "risk_service_ref": "ref://risk/score?cust_123#2025-12-27"
  }
}

And they can open the policy the system claims it used:

// Policy (what constraints were in force)
{
  "schema": "si/policy/refund/v3",
  "max_auto_refund_usd": 200,
  "requires_duplicate_signal": true,
  "requires_settled_tx": true,
  "requires_low_fraud_risk": true,
  "human_review_if": {
    "amount_over_usd": 200,
    "fraud_risk_not_low": true,
    "obs_coverage_below": 0.85
  }
}

How this answers “what it knew”

In this framing, “what it knew” means:

the exact structured inputs it had (OBS), with provenance and quality signals, and
the constraints it was operating under (policy + envelope), and
the decision/gate outputs that allowed the commit, and
the committed effect itself, bound by digests/signatures.

This is enough for an auditor to say:
“Given those inputs and that policy, the system had sufficient observed evidence to issue a $180 refund, and it did so under valid authority.”

It does not claim we can extract propositional knowledge from weights. It claims we can make the decision context and enforcement path verifiable.

If you want, I can also give a second hypothetical where the audit fails (e.g., coverage too low, missing provenance, stale revocation view) to show how this becomes enforceable rather than cosmetic.

posted an update 8 days ago

Post

2912

✅ New Article: *Pattern-Learning-Bridge (PLB)*

Title:
🧩 Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
🔗 https://huggingface.co/blog/kanaria007/learns-from-its-own-failures

---

Summary:
Most stacks “learn” by fine-tuning weights and redeploying — powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:

*Upgrade policies, compensators, SIL code, and goal structures — using runtime evidence.*

> Learning isn’t a model tweak.
> *It’s upgrading the structures that shape behavior.*

---

Why It Matters:
• Makes improvement *localized and explainable* (what changed, where, and why)
• Keeps “self-improvement” *governable* (versioned deltas + review + CI/CD)
• Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
• Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators

---

What’s Inside:
• What “learning” means in SI-Core (and what changes vs. classic ML)
• The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
• Safety properties: PLB proposes *versioned deltas*, never edits production directly
• Validation pipeline: sandbox/simulation → conformance checks → golden diffs → rollout

---

📖 Structured Intelligence Engineering Series
A non-normative, implementable design for “learning from failures” without sacrificing auditability.

published an article 8 days ago

Article

Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures

8 days ago

•

commented on Auditable AI by Construction: SI-Core for Regulators and Auditors 8 days ago

Thanks for the pushback — and I agree with more of it than you might expect.
But I think your answer is implicitly auditing the wrong layer.

What you describe is basically a model-internal interpretability audit (“can we read what it knew from weights / FLOPs?”). I’m not claiming that’s practical — in fact, I’m assuming it isn’t. The point of the post is: if the model is a probabilistic engine, then auditability must live in the runtime system around it, not inside the weights. Your objections are almost a proof of that premise.

A few concrete clarifications:

1) “Can we see what it knew when it acted?”
Agreed: weights don’t give you human-style “facts it knew.”
What regulators/auditors can reasonably ask is: what information and constraints were available at decision time? That’s not “reading neurons.” It’s logging and binding the runtime knowledge state: the structured observation (with coverage/confidence), provenance refs, the policy/version in force, and the gating outcome. That’s actionable evidence.

2) “Tracing initiator is trivial — just log it.”
Basic logging is necessary, but “accountability” isn’t just “who clicked the button.” It’s also: who had authority to cause this effect at that time, under which delegation envelope, and with which revocation state. That’s why I treat initiator/authority as a bindable proof spine (digests + signatures), not just application logs.

3) “Stopping is training constraints; otherwise you audit the auditor.”
Training constraints are not a runtime control plane. If “stop” depends on the model behaving, you don’t have governance — you have wishful thinking. The safer stance is: effectful commits are blocked by the surrounding system (safe mode / sandbox-only / human review), and rollback is engineered as an external mechanism (effect ledger + compensators). Then “auditing the auditor” becomes bounded, because the evaluator cannot directly commit effects.

4) “Fully verifiable trace is practically absurd.”
Agreed — if “trace” means recording every floating-point op. That’s not what I’m proposing. The goal is a structural evidence spine: enough signed, hash-bound artifacts to reconstruct and dispute the decision path (inputs, constraints, authority, and the committed effects) without replaying terabytes of matmul.

So I think we’re aligned on the real takeaway: LLMs aren’t accountable actors by themselves.
That’s exactly why governance, audit, and rollback must be implemented at the runtime layer — with explicit proofs and bounded replay — rather than hoping weight inspection (or “just trust the training”) solves it.

If you disagree, the question I’d ask is: what minimal evidence would you consider sufficient for a third party to verify (a) what was observed, (b) what policy/authority applied, and (c) what effect was committed — without inspecting weights or FLOPs? That’s the core surface I’m aiming to standardize.

kanaria007 PRO

AI & ML interests

Recent Activity

Organizations

kanaria007's activity

From Effect Ledger to Goal-Aware Training Data

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

Governing Self-Modification - A Charter for the Pattern-Learning Bridge

Digital Constitution for SI Networks - Auditable Law Above Many SI-Cores

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

Multi-Agent Goal Negotiation and the Economy of Meaning

Practical runtime auditability (hypothetical + failure case + domain mapping)

What “knew” means here (definition)

1) Hypothetical success case: Payments (refund)

Scenario

Evidence spine (what the auditor sees)

What the hashed observation snapshot looks like

Policy snapshot (what constraints were in force)

Auditor conclusion (for the success case)

2) Hypothetical failure case: Payments (audit fails → enforcement blocks)

Evidence spine (blocked attempt)

Why this makes auditability enforceable

3) “Isn’t this just logging?” (common objection)

4) “But prompts / model outputs are non-deterministic”

5) “What about privacy / PII?”

6) Auditor checklist (what they actually verify)

7) Domain mapping (structure stays the same)

Healthcare

Infra ops / SRE

Practical runtime auditability (hypothetical + failure case + domain mapping)

What “knew” means here (definition)

1) Hypothetical success case: Payments (refund)

Scenario

Evidence spine (what the auditor sees)

What the hashed observation snapshot looks like

Policy snapshot (what constraints were in force)

Auditor conclusion (for the success case)

2) Hypothetical failure case: Payments (audit fails → enforcement blocks)

Evidence spine (blocked attempt)

Why this makes auditability enforceable

3) “Isn’t this just logging?” (common objection)

4) “But prompts / model outputs are non-deterministic”

5) “What about privacy / PII?”

6) Auditor checklist (what they actually verify)

7) Domain mapping (structure stays the same)

Healthcare

Infra ops / SRE

A second hypothetical (audit fails, and enforcement kicks in)

Preempting a few common objections

Scenario (hypothetical)

What the auditor wants to verify

What the auditor actually sees (evidence spine)

How this answers “what it knew”

Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures

Deep-Space SI-Core: Autonomy Across Light-Hours - How an onboard SI-Core evolves safely while Earth is hours away