Genius Replay Protocol: Capturing and Replaying High-Value Jumps

0. Scope and intent

Intent

If a Jump is “one intelligible move” by an SI-Core system, then a Genius Jump (or “genius trace”) is:

• unusually high-performing on its goal surface,
• unusually robust to perturbations,
• unusually generalizable across similar contexts,
• often co-produced by humans and SI-Core.

This article defines a Genius Replay Protocol (GRP): how to capture, store, and replay such traces safely, so they become reusable “macro-intelligence” rather than one-off miracles.

GRP also provides the data and structure needed for “genius-level reproduction” algorithms (see art-40-004). Once you can reliably capture and replay Genius traces, you can start distilling them into dedicated Jump engines instead of relying only on LLM-based policies.

It sits on top of the existing Jump article (art-60-033 for Jumps) and reuses:

• [OBS] (observation surfaces),
• [ETH] (ethics overlays),
• [MEM] (structured memory),
• [ID] (who acted),
• [EVAL] (evaluation / metrics).

0.1 Conventions used in this draft (non-normative)

This draft follows the portability conventions used in 069/084+ when an artifact might be exported, hashed, or attested (GeniusTrace objects, ContextSignature snapshots, replay policy exports, evaluator-facing reports):

• created_at is operational time (advisory unless time is attested).
• as_of carries markers only (time claim + optional revocation view markers) and SHOULD declare clock_profile: "si/clock-profile/utc/v1" when exported.
• trust carries digests only (trust anchors + optional revocation view digests). Never mix markers into trust.
• bindings pins meaning as {id,digest} (meaningful identities must not be digest-only).
• Avoid floats in policy-/digest-bound artifacts: prefer scaled integers (*_bp, *_ppm) and integer micro/milliseconds (*_us, *_ms).
• If you hash/attest procedural artifacts, declare canonicalization explicitly: canonicalization: "si/jcs-strict/v1" and canonicalization_profile_digest: "sha256:...".
• digest_rule strings (when present) are explanatory only; verifiers MUST compute digests using pinned schemas/profiles, not by parsing digest_rule.

Numeric conventions used in examples:

• For weights and ratios in [0,1], export as basis points: x_bp = round(x * 10000).
• For probabilities in [0,1], export as basis points: p_bp = round(p * 10000).
• For very small probabilities, ppm is acceptable: p_ppm = round(p * 1_000_000).

Internal computation may still use floats; the convention here is about exported/hashed representations.

0.2 Terminology note: Goal Surface vs GCS

• Goal Surface (GS): the policy-bound goal definitions / weights / constraints for a domain (what we are optimizing for).
• Goal Contribution Score (GCS): the per-step (or per-candidate) estimated goal-delta vector produced when evaluating an action under the Goal Surface (via a model, sandbox replay, or other estimator).

---

1. What is a “Genius Jump”?

1.1 Definition: high-value Jump traces

We avoid mystical language. A “Genius Jump” is just a Jump (or short Jump sequence) that is:

• Score-outlier on relevant GCS components

  • e.g. top 0.5% on safety + efficiency for its goal surface.

• Robust across counterfactuals

  • small perturbations of context still yield good outcomes.

• Generalizable across a cluster of similar contexts

  • works for “this type of crisis / learner / repo”, not only one case.

Non-normative definition sketch:

# NOTE (naming): "GeniusTrace" is the schema/type name used throughout this series.
# If you serialize instances under keys like `genius_trace`, treat that as an envelope alias.
GeniusTrace:
  id: "GRP-2028-0421-city-flood"
  domain: "city.flood_response"

  # Derived from ContextSignature clustering (see §2.2)
  context_cluster_id: "flood_river_overflow_with_hospital_at_risk"

  gcs_outlier:
    percentile_bp: 9950    # top 0.5% within this cluster (0.995 * 10000)
    goals:
      - "city.flood_risk_min"
      - "city.hospital_access"

  robustness_score_bp: 9100  # robustness in [0,1], exported as basis points
  reuse_score_bp: 8800       # reuse likelihood in [0,1], exported as basis points

1.2 Human–SI co-production

We explicitly allow co-produced traces: human experts steering SI-Core or overriding Jumps.

co_production:
  human_actors:
    - id: "city_ops_chief_01"
      role: "City Ops Chief"
      contributed_stages: ["plan_review", "gate_override"]
  si_core_role:
    - "Generated initial plan"
    - "Ran flood simulations"
    - "Monitored ETH / risk thresholds in real-time"

A Genius trace is about what worked structurally, regardless of who or what proposed each step.

---

2. Genius Replay Protocol (GRP): what to capture

2.1 Core object: GeniusTrace

We define a GeniusTrace as a structured bundle in [MEM]:

GeniusTrace:
  # Identity
  id: string                      # stable trace id (meaningful identity)
  schema: "si/grp/genius-trace/v1" # non-normative example schema id
  domain: string
  created_at: timestamp           # operational time (advisory unless attested)
  created_by: "SI-Core" | "human" | "mixed"

  # Portability / export boundary (see §0.1)
  # as_of: markers only
  as_of:
    time: timestamp
    clock_profile: "si/clock-profile/utc/v1"
    revocation_view_markers:
      trust_anchor_set_marker: string
      policy_revocations_marker: string

  # trust: digests only (no markers, no ids)
  trust:
    trust_anchor_set_digest: string
    revocation_view_digests:
      trust_anchor_set_digest: string
      policy_revocations_digest: string

  # bindings: pin meaning as {id,digest}
  bindings:
    trust_anchor_set:      { id: string, digest: string }
    goal_surface_snapshot: { id: string, digest: string }
    context_signature:     { id: string, digest: string }

  # Core content (may be stored inline or via refs; export policy decides)
  context_signature_ref: URI
  goal_surface_snapshot_ref: URI
  jump_sequence_ref: URI
  eval_summary_ref: URI
  ethics_trace_ref: URI

  # Optional: when exporting bytes for hashing/attestation
  canonicalization: "si/jcs-strict/v1"
  canonicalization_profile_digest: "sha256:..."

2.2 ContextSignature

We must encode where this trace is valid:

ContextSignature:
  domain: "city.flood_response"
  scope:
    city_id: "city-01"
    region_type: "river_delta"
  features:
    flood_cause: "river_overflow"
    hospital_distance_m: 2300     # avoid floats in exported artifacts
    warning_time_min: 45
    sensor_coverage: "high"
  time_profile:
    time_of_day: "night"
    season: "rainy"
  similarity_metric: "cosine_on_feature_vector_v2"

This supports:

• matching future contexts to suitable Genius traces,
• measuring “distance” for safe reuse.

2.3 JumpSequence & JumpRecord

Each trace contains a compact Jump sequence:

JumpRecord:
  jump_id: string
  type: "pure" | "effectful"
  role: "plan" | "simulate" | "commit" | "monitor"

  input_summary:
    obs_ids: list[string]
    key_features: map[string, any]

  decision_summary:
    chosen_action: map[string, any]

    # GCS is an estimated goal-delta vector under the Goal Surface (GS).
    # Avoid floats in exported artifacts: use a signed scaled-int representation.
    gcs_estimate:
      scale: 10000
      per_goal_scaled_int: map[string, int]
      composite_scaled_int: int

    eth_decision:
      policies_applied: list[string]
      violations_detected: int
      mitigations_applied: list[string]

  # Optional but recommended: stage-level ethics summary for monitoring.
  ethics_trace: EthicsTrace

  rml_summary:
    effects: list[map[string, any]]
    rml_level: "RML-0" | "RML-1" | "RML-2" | "RML-3"
  # RML-0 is a convenient expression denoting “no external effects” and is used as a prefix for RML-1/2/3.

  metrics:
    cas_bp: int                 # CAS in [0,1] exported as basis points
    sci_incidents: int
    latency_ms: int
    rbl_ms: int
    rir_bp: int                 # RIR in [0,1] exported as basis points

This is not the full raw tape; it’s a compressed structural trace tied to SIM/SIS via the referenced artifacts (*_ref URIs in GeniusTrace).

2.4 EvalSummary and EthicsTrace

We need to know why this is considered “genius”:

EvalSummary:
  # Avoid floats in exported artifacts: use signed scaled-int vectors.
  gcs_vector_before:
    scale: 10000
    per_goal_scaled_int: map[string, int]

  gcs_vector_after:
    scale: 10000
    per_goal_scaled_int: map[string, int]

  # Capture-time composite delta (definition is domain-specific, but should be reproducible).
  gcs_improvement:
    scale: 10000
    delta_scaled_int: int

  # Needed for ReplaySafetyChecker pre-checks.
  required_obs_channels: list[string]

  goals_improved:
    - string
  regressions:
    - goal: string
      delta:
        scale: 10000
        delta_scaled_int: int

  horizon_hours: int

  robustness_checks:
    counterfactual_runs: int
    success_rate_bp: int              # success rate in [0,1] as basis points
    worst_case_gcs_delta:
      scale: 10000
      delta_scaled_int: int

EthicsTrace ensures we’re not celebrating something that achieved high GCS by violating ETH:

EthicsTrace:
  policies_applied: ["city_flood_eth_v3"]
  violations_detected: 0
  mitigations_applied: []
  fairness_checks:
    groups_examined: ["low_income_districts", "wheelchair_users"]
    disparity_max_bp: 400    # 0.04 * 10000

---

3. Replaying Genius: strategies and algorithms

GRP separates capturing from replaying.

3.1 Replay modes

We define three non-normative replay modes:

1. Exact replay (for debugging / training)

   • Re-run the same Jump sequence in a sandbox or simulator.
   • Prefer a captured or reconstructed environment snapshot (via SIM/SIS) to make comparisons meaningful.
   • Optionally run mutated variants afterward for robustness testing.

2. Structural replay (for decision support)

   • Reuse the structure of the sequence, not literal actions:
     • e.g. “plan → simulate → commit-partially → monitor → re-plan”
   • Let each Jump re-run with fresh [OBS], [ETH], [EVAL].

3. Suggestion replay (for humans)

   • Surface the Genius trace as a playbook or template:
     • “In similar situations, this 4-step pattern worked well.”
   • Human operators can accept / modify / reject.

3.2 Matching contexts to Genius traces

We must decide when to even consider a GeniusTrace.

class GeniusMatcher:
    def __init__(self, embedding_model):
        self.embedding_model = embedding_model

    def find_candidates(self, current_context, traces, k=5):
        """Return top-k GeniusTraces matching the current context."""
        ctx_vec = self.embedding_model.encode(current_context)
        scored = []
        for trace in traces:
            trace_vec = self.embedding_model.encode(trace.context_signature)
            sim = cosine_similarity(ctx_vec, trace_vec)
            scored.append((trace, sim))
        scored.sort(key=lambda x: x[1], reverse=True)
        return scored[:k]

Replay should happen only if:

• similarity > threshold, and
• domain + ETH constraints allow reuse.

3.3 Structural replay controller

A simple structural replay flow:

class GeniusReplayController:
    def __init__(self, jump_runtime, matcher):
        self.jump_runtime = jump_runtime
        self.matcher = matcher

    def propose_plan_from_genius(self, current_context):
        candidates = self.matcher.find_candidates(
            current_context, self._load_traces()
        )

        best_trace, sim = candidates[0]
        if sim < 0.7:
            return None  # No suitable Genius trace

        # Build a skeleton plan from the Jump sequence structure
        skeleton = self._build_skeleton(best_trace.jump_sequence)

        # Re-run each stage as a new Jump, with fresh OBS/ETH/EVAL
        new_jumps = []
        for stage in skeleton.stages:
            req = self._make_jump_request_from_stage(stage, current_context)
            res = self.jump_runtime.run_jump(req)
            new_jumps.append(self._to_jump_record(res))

        return {
            "source_trace_id": best_trace.id,
            "similarity": sim,
            "replayed_jump_records": new_jumps,
        }

Key property: no direct copying of effects. We reuse structure, not blindly replay actions.

3.4 Handling environment differences

We explicitly track context distance:

context_distance:
  metric: "cosine_on_feature_vector_v2"
  value_bp: 1800      # 0.18 * 10000; 0 = identical, higher = more different
  risk_band: "medium" # influences ETH/EVAL thresholds

Replay policies:

• If distance small → allow more direct reuse (same pattern).
• If distance large → maybe only surface as human suggestion; or disallow entirely.

---

4. Usage patterns: how to actually use GRP

4.1 Bootstrapping new policies

Use Genius traces as seed policies:

• For a new city, with no local crises yet,
• For a new learner-type archetype,
• For a new CI / OSS workflow.

Sketch:

policy_bootstrap:
  domain: "learning.companion"
  archetype: "nd_learner_high_anxiety"
  source_traces:
    - "GRP-LEARN-024"
    - "GRP-LEARN-031"
  usage:
    - "Initialize planner priors"
    - "Constrain early Jumps to proven-safe patterns"

Implementation idea:

• fit a prior over Jump sequences from Genius traces,
• let early Jumps be biased toward these patterns, then gradually relax as local data accumulates.

4.2 Recovery / “in case of fire, break glass”

When the system detects:

• repeated failures,
• high uncertainty,
• out-of-distribution conditions,

…it can propose Genius patterns as fallback candidates:

recovery_mode:
  trigger:
    - "RBL_p95 > threshold_for_domain"
    - "SCI spikes for recent Jumps"
    - "ETH sandbox blocking rate ↑"
  response:
    - "Search Genius library for similar incidents"
    - "Propose 2–3 Genius patterns to human operator"
    - "If approved, run structural replay controller"

This is not a magic switch; it’s a proposal mechanism surfaced under tight ETH/EVAL control.

4.3 Cross-domain transfer

Sometimes a Genius pattern in one domain maps structurally to another:

• City disaster coordination ↔ hospital triage flows,
• OSS complex refactorings ↔ large-scale code migrations.

We treat this as higher-level structural patterns:

cross_domain_pattern:
  pattern_id: "GENIUS-PATTERN-TRIAGE-01"
  abstract_structure:
    - "rapid_assessment"
    - "stabilize_high_risk"
    - "defer_low_risk_with_monitoring"
    - "loop_with_updated_obs"
  instantiated_traces:
    - domain: "city.flood_response"
      genius_traces: ["GRP-2028-0421-city-flood", ...]
    - domain: "hospital.er_triage"
      genius_traces: ["GRP-2028-0112-icu", ...]

GRP itself remains domain-local; these are meta-patterns layered on top.

---

5. Risks, guardrails, and governance

5.1 Don’t worship Genius

Main failure modes:

• Overfitting to rare events

  • “This one insane hack worked once; now we keep doing it.”

• Ignoring changed regimes

  • Regulatory changes, infrastructure changes, new models.

• Fairness regressions

  • Genius trace optimized for one group, harmful for others.

Mitigations:

• Revalidate Genius traces periodically with fresh [EVAL].
• Expose who benefits and who pays in the EvalSummary.
• Treat high-impact replays as candidates, not defaults.

5.2 ETH constraints on reuse

Replaying a Genius trace must still obey ETH:

eth_replay_policies:
  require_per_replay_eth_check: true
  require_fresh_goal_surface_eval: true
  block_replay_if:
    - "context_distance > max_allowed"
    - "new fairness constraints stricter than at capture time"
    - "trace used deprecated ETH policy"

Even if a Genius trace had zero ETH violations originally, replay happens under current ETH policy.

5.3 MEM / privacy / IP

Genius traces can easily encode:

• personal data,
• sensitive incident details,
• proprietary strategies.

Governance sketch:

mem_policies_for_genius:
  retention:
    default: "3 years"
    safety_critical: "7 years (with strict access controls)"
  access_roles:
    - "domain_owner"
    - "ethics_board"
    - "incident_response_team"
  anonymization:
    before_cross_org_share: true
    techniques:
      - "scope-level aggregation"
      - "removal of direct identifiers"
      - "goal-surface redaction where needed"

Cross-tenant or cross-organization sharing often requires:

• semantic redaction (not just PII scrub),
• sometimes replacing real traces with simulated ones that preserve structure but not content.

---

6. Domain sketches: GRP in practice

6.1 Learning & developmental support

Example Genius trace:

GeniusTrace:
  id: "GRP-LEARN-024"
  domain: "learning.companion"
  context_signature:
    archetype: "nd_learner_high_anxiety"
    age_range: "10-12"
    reading_level: "below_grade"

  jump_sequence:
    - jump_id: "assess_baseline"
      type: "pure"
      role: "plan"

    - jump_id: "pick_low_pressure_exercise"
      type: "pure"
      role: "plan"

    - jump_id: "short_session_commit"
      type: "effectful"
      role: "commit"

    - jump_id: "post_session_checkin"
      type: "effectful"
      role: "monitor"

  eval_summary:
    gcs_vector_before:
      scale: 10000
      per_goal_scaled_int:
        reading_fluency: -2300
        stress_load: 1800
    gcs_vector_after:
      scale: 10000
      per_goal_scaled_int:
        reading_fluency: 1100
        stress_load: -900
    gcs_improvement:
      scale: 10000
      delta_scaled_int: 3200
    required_obs_channels: ["reading_fluency", "stress_load"]
    robustness_checks:
      success_rate_bp: 8900

Replay usage:

• For new learners with similar profiles, GRP proposes this 4-stage pattern as a starting template.
• ETH ensures accommodations and wellbeing constraints are applied fresh.

6.2 CityOS: disaster response

Genius trace from a near-miss flood:

• kept hospitals accessible,
• minimized casualties,
• maintained fairness across districts.

Replay usage:

• as a playbook for similar crisis clusters,
• in training simulators for human operators,
• to seed structural policies for new cities with similar topology.

6.3 OSS / CI: hard refactorings

Genius trace:

• large refactor that touched 200 files,
• used CI gating, canary releases, and rollback Jumps.

Replay usage:

• as a template pipeline for similar refactors,
• feeding higher-level patterns like “refactor in layers, each with full test + canary + metrics guard”.

---

7. Implementation path

Non-normative “how to add GRP” to an existing SI-Core-ish stack:

1. Instrument Jump logging

   • Ensure JumpRecords already have: OBS, GoalSurface, ETHTrace, RML summaries, metrics.

2. Define Genius criteria

   • For each domain, agree on:

     • GCS thresholds,
     • robustness checks,
     • fairness constraints.

3. Build a GeniusTrace builder

   • Periodically scan Jump sequences,
   • select candidates,
   • package them into GeniusTrace objects in [MEM].

4. Add a small “Genius Library” service

   • query by domain + context signature,
   • used by the GeniusReplayController.

5. Integrate with ETH / EVAL

   • per-replay ETH checks,
   • track replay performance vs non-replay baseline.

6. Gradually expand usage

   • start with sandbox-only replays and human-facing suggestions,
   • later allow structural replay in low-risk domains,
   • eventually consider safety-critical domains with heavy governance.

---

8. Genius selection algorithms and automation

Challenge. We don’t want Genius traces to be “hand picked anecdotes.” We want a systematic pipeline that:

• scans large volumes of Jumps,
• detects GCS outliers,
• checks robustness via counterfactuals,
• verifies generalizability across similar contexts,
• and applies governance filters (ETH / fairness / privacy).

8.1 Selection criteria

Criterion 1: GCS outlier detection

We first look for GCS outliers within a domain and context cluster.

class GeniusOutlierDetector:
    def detect_outliers(self, jumps, domain, window_days=30):
        """Detect top-percentile Jumps within a domain."""
        domain_jumps = [j for j in jumps if j.domain == domain]

        # Compute a composite GCS score per Jump (domain-specific)
        scored = []
        for j in domain_jumps:
            gcs_composite = self._compute_composite_gcs(j, domain)
            scored.append((j, gcs_composite))

        if not scored:
            return []

        # Percentile threshold (top 0.5% as a non-normative default)
        values = [score for _, score in scored]
        threshold = np.percentile(values, 99.5)

        candidates = [(j, score) for j, score in scored if score >= threshold]
        return candidates

    def _compute_composite_gcs(self, jump, domain):
        """
        Domain-specific aggregation of per-goal GCS:
        e.g. weighted sum over safety / fairness / efficiency.
        """
        # Pseudocode:
        weights = get_domain_goal_weights(domain)
        return sum(weights[g] * jump.gcs_vector[g] for g in weights)

Non-normative default: only consider Jumps from domains with enough data (e.g. ≥ 1 000 Jumps in the window) to make percentile thresholds meaningful.

Criterion 2: Robustness verification

We then test whether the candidate sequence stays strong under counterfactual variations of its context.

class RobustnessVerifier:
    def verify_robustness(self, jump_sequence, context, baseline_score):
        """
        Evaluate robustness via counterfactual replay in sandbox.
        baseline_score: composite GCS score of the original Genius candidate.
        """
        variants = self._generate_context_variants(context, n=128)

        results = []
        for variant in variants:
            # Structural replay in sandbox (no real effects)
            replay_result = self._replay_in_sandbox(jump_sequence, variant)
            results.append(replay_result)

        # Example: robustness = mean composite score / baseline
        scores = [r.composite_gcs for r in results]
        if not scores:
            return {
                "robustness_score": 0.0,
                "success_rate": 0.0,
                "worst_case_delta": float("-inf"),
            }

        robustness_score = np.mean(scores) / baseline_score
        success_rate = sum(r.ok for r in results) / len(results)
        worst_case_delta = min(score - baseline_score for score in scores)

        return {
            "robustness_score": robustness_score,
            "success_rate": success_rate,
            "worst_case_delta": worst_case_delta,
        }

    def _generate_context_variants(self, context, n):
        """Sample nearby contexts (non-normative)."""
        # Example: small perturbations in loads, timing, minor topology changes
        ...
    
    def _replay_in_sandbox(self, jump_sequence, context_variant):
        """Re-run the Jump sequence in a safe simulator."""
        ...

Typical thresholds (non-normative):

• success_rate ≥ 0.9,
• robustness_score ≥ 0.9,
• worst_case_delta not too negative (e.g. ≥ −0.1 of baseline).

Criterion 3: Generalizability assessment

We also want to know whether the sequence works across similar contexts, not only minor perturbations of one case.

def assess_generalizability(jump_sequence, domain, threshold):
    """
    Measure success rate of structural replay across similar contexts.
    """
    similar_contexts = find_similar_contexts(
        jump_sequence.context_signature,
        domain=domain,
        k=50
    )

    if not similar_contexts:
        return 0.0

    success_count = 0
    for ctx in similar_contexts:
        result = replay_structural(jump_sequence, ctx)
        if result.gcs_improvement >= threshold:
            success_count += 1

    return success_count / len(similar_contexts)

Thresholds depend on the domain and goals (e.g. “≥ 0.8 of original GCS improvement”).

8.2 Automated selection pipeline

We combine these criteria into an automated pipeline:

genius_selection_pipeline:
  stage_1_filter:
    - "Jump must be effectful or a plan-update (pure diagnostics excluded)"
    - "Domain must have >= 1000 historical Jumps in window"
    - "No ETH violations on this Jump or its sequence"
  
  stage_2_scoring:
    - "GCS outlier detection (top 0.5% composite score)"
    - "Robustness verification (128 context variants)"
    - "Generalizability assessment (50 similar contexts)"
  
  stage_3_review:
    - "Human expert review for safety-critical domains"
    - "Fairness audit across demographics / regions"
    - "Privacy / IP review (can this trace be stored / shared?)"
  
  stage_4_promotion:
    - "Add GeniusTrace to Genius Library with metadata"
    - "Set domain-specific reuse policies and ETH constraints"

8.3 Selection metrics

We treat the Genius library itself as an object of measurement:

selection_metrics:
  candidates_per_month:       # Jumps that pass stage 1
    value: 120
  promotion_rate:             # Fraction promoted to Genius Library
    value: 0.18
  false_positive_rate:        # Traces that fail on replay / revalidation
    value: 0.07
  diversity:                  # Coverage across context clusters
    by_cluster:
      flood_river_overflow: 0.25
      hospital_triage:      0.22
      nd_learner_anxiety:   0.18
      ci_refactoring:       0.20
      other:                0.15

These metrics help avoid a library that is too narrow (all traces from one type of incident) or too noisy (many traces that don’t hold up under replay.

---

9. Replay safety verification and monitoring

Challenge. Even if a trace was Genius once, replay can fail if:

• the context has drifted,
• ETH policies changed,
• observations are missing or degraded,
• or the structural replay diverges in dangerous ways.

We therefore wrap GRP in a safety verification framework: pre-checks, real-time monitoring, and post-replay validation.

9.1 Pre-replay checks

class ReplaySafetyChecker:
    def pre_replay_check(self, genius_trace, current_context):
        """Run pre-replay safety checks. Returns (ok, details)."""
        checks = []

        # 1) Context distance
        distance = compute_context_distance(
            genius_trace.context_signature,
            current_context
        )
        checks.append(("context_distance", distance < MAX_ALLOWED_DISTANCE))

        # 2) ETH policy compatibility
        current_eth = get_current_eth_policy(genius_trace.domain)
        eth_ok = self._eth_compatible(
            genius_trace.ethics_trace,
            current_eth
        )
        checks.append(("eth_compatible", eth_ok))

        # 3) Goal surface alignment
        alignment = compute_goal_alignment(
            genius_trace.goal_surface_snapshot,
            current_context.goal_surface
        )
        checks.append(("goal_alignment", alignment >= MIN_GOAL_ALIGNMENT))

        # 4) Observation availability
        obs_available = self._check_obs_available(genius_trace, current_context)
        checks.append(("obs_available", obs_available))

        ok = all(result for _, result in checks)
        return ok, checks

    def _eth_compatible(self, past_eth_trace, current_eth_policy):
        """Ensure the trace does not rely on policies now considered unsafe."""
        # Example: check that current ETH is >= past ETH in strictness.
        ...

    def _check_obs_available(self, genius_trace, current_context):
        """Ensure required observation channels exist at sufficient quality."""
        required = genius_trace.eval_summary.required_obs_channels
        return current_context.obs_catalog.has_channels(required)

9.2 During-replay monitoring

We monitor replay stage-by-stage and abort when necessary.

# Non-normative defaults (domain should override)
# If composite is exported as a signed scaled-int (scale=10000), thresholds can be expressed in basis points.
GCS_DEVIATION_ESCALATE_THRESHOLD_BP = 3000
GCS_DEVIATION_ABORT_THRESHOLD_BP = 5000

class ReplayMonitor:
    def monitor_replay(self, genius_trace, replay_session):
        """Monitor replay stages, with abort/escalation on anomalies."""
        n = min(len(replay_session.stages), len(genius_trace.jump_sequence))

        for i in range(n):
            stage = replay_session.stages[i]
            original = genius_trace.jump_sequence[i]

            # 1) GCS deviation (composite, scaled-int)
            dev_bp = abs(
                stage.decision_summary["gcs_estimate"]["composite_scaled_int"]
                - original.decision_summary["gcs_estimate"]["composite_scaled_int"]
            )

            if dev_bp > GCS_DEVIATION_ABORT_THRESHOLD_BP:
                return self._abort_replay("gcs_deviation_too_large", stage)

            if dev_bp >= GCS_DEVIATION_ESCALATE_THRESHOLD_BP:
                self._escalate_for_review("gcs_deviation_mid", stage)

            # 2) ETH violations (current ETH always wins)
            if stage.ethics_trace["violations_detected"] > 0:
                return self._abort_replay("eth_violation", stage)

            # 3) Unexpected RML pattern
            if not self._matches_expected_rml(stage, original):
                return self._escalate_for_review("unexpected_rml_pattern", stage)

        return ReplayResult(status="SUCCESS")

    def _matches_expected_rml(self, stage, original_stage):
        """Check that effects are structurally similar (idempotent structure)."""
        ...

    def _abort_replay(self, reason, stage):
        log_warning(f"Replay aborted: {reason} at stage {stage.jump_id}")
        ...
        return ReplayResult(status="ABORTED", reason=reason)

    def _escalate_for_review(self, reason, stage):
        log_warning(f"Replay anomaly: {reason} at stage {stage.jump_id}")
        create_incident_ticket(reason, stage)
        return ReplayResult(status="CONTINUE_WITH_ESCALATION")

9.3 Post-replay validation

After replay, we validate outcomes vs expectations.

def validate_replay_outcome(genius_trace, replay_result, current_context):
    """Validate replay outcome for future reuse decisions."""

    # GCS improvement check
    gcs_improvement = compute_gcs_delta(
        replay_result.gcs_vector_after,
        replay_result.gcs_vector_before
    )
    expected_improvement = genius_trace.eval_summary.gcs_improvement

    gcs_improvement_ok = gcs_improvement >= expected_improvement * 0.7

    # Fairness regression check
    fairness_ok = verify_no_fairness_regression(
        replay_result,
        genius_trace
    )

    # Safety incident check
    safety_ok = replay_result.safety_incidents == 0

    overall_ok = gcs_improvement_ok and fairness_ok and safety_ok

    return ValidationResult(
        ok=overall_ok,
        metrics={
            "gcs_improvement": gcs_improvement,
            "expected_improvement": expected_improvement,
            "fairness_ok": fairness_ok,
            "safety_ok": safety_ok,
        },
        recommendation=(
            "reuse_ok"
            if overall_ok
            else "do_not_reuse_in_similar_contexts"
        )
    )

9.4 Abort and escalation policies

abort_policies:
  immediate_abort:
    - "ETH hard constraint violated"
    - "Safety incident detected or predicted"
    - "gcs_deviation_bp > 5000 (composite_scaled_int delta, scale=10000)"

  escalate_to_human:
    - "gcs_deviation_bp in [3000, 5000] (composite_scaled_int delta, scale=10000)"
    - "Unexpected RML pattern (compensators / effects differ)"
    - "Context distance increased mid-replay (obs degraded, topology changed)"

  allow_continue_with_logging:
    - "gcs_deviation_bp < 3000 (composite_scaled_int delta, scale=10000)"
    - "Observation quality slightly degraded but above thresholds"

---

10. Performance considerations and optimization

Challenge. GRP adds:

• Genius Library queries,
• safety checks,
• possible replay of multi-Jump sequences.

We must ensure this does not blow up latency budgets, especially in real-time domains.

10.1 Performance impact analysis

Non-normative latency budget for Genius-aware Jumps:

genius_overhead_budget_p95_ms:
  genius_matching: 50    # context similarity search
  trace_retrieval: 20    # fetch GeniusTrace from MEM / SIS
  safety_checks: 30      # pre-replay verification
  total_overhead: 100    # additional p95 budget for GRP

Mitigation patterns:

• cache frequently used Genius traces and embeddings,
• run matching / suggestions asynchronously parallel to a “normal” Jump,
• restrict GRP to high-stakes + sufficient latency budget contexts.

10.2 Lazy Genius matching

Only attempt GRP where it’s worth the cost.

class LazyGeniusMatcher:
    def should_attempt_genius(self, context):
        """Decide whether Genius matching is appropriate."""
        # 1) High-stakes domains only
        if context.domain not in HIGH_STAKES_DOMAINS:
            return False

        # 2) Uncertain situations only
        if context.uncertainty_score < UNCERTAINTY_THRESHOLD:
            return False

        # 3) Respect latency budget
        if context.latency_budget_ms < MIN_BUDGET_FOR_GENIUS:
            return False

        return True

10.3 Genius trace caching

class GeniusTraceCache:
    def __init__(self, embedding_model):
        self.cache = LRUCache(maxsize=1000)
        self.embedding_cache = {}
        self.embedding_model = embedding_model

    def get_or_load(self, trace_id):
        if trace_id in self.cache:
            return self.cache[trace_id]

        trace = self._load_from_mem(trace_id)
        self.cache[trace_id] = trace
        return trace

    def precompute_embeddings(self, domain):
        """Pre-compute embeddings for all Genius traces in a domain."""
        traces = self._load_domain_traces(domain)
        for trace in traces:
            if trace.id not in self.embedding_cache:
                emb = self.embedding_model.encode(trace.context_signature)
                self.embedding_cache[trace.id] = emb

10.4 Async Genius suggestion

Run a normal Jump and Genius proposal in parallel:

async def propose_with_genius_async(context):
    """
    Run the normal Jump immediately, and in parallel try to find Genius
    alternatives. Return the normal result plus any Genius candidates
    that arrive in time.
    """
    # Start normal Jump
    normal_task = asyncio.create_task(run_normal_jump(context))

    # Start Genius matching in parallel (if appropriate)
    genius_task = None
    if LazyGeniusMatcher().should_attempt_genius(context):
        genius_task = asyncio.create_task(find_genius_candidates(context))

    normal_result = await normal_task

    if genius_task is None:
        return {"primary": normal_result, "genius_alternatives": []}

    try:
        genius_candidates = await asyncio.wait_for(genius_task, timeout=0.1)
        return {
            "primary": normal_result,
            "genius_alternatives": genius_candidates,
        }
    except asyncio.TimeoutError:
        # Use normal result only; log that Genius was too slow
        return {"primary": normal_result, "genius_alternatives": []}

10.5 Performance monitoring

performance_metrics:
  genius_query_latency_p95_ms: 42      # time to find candidates
  genius_hit_rate: 0.23               # fraction of Jumps with usable Genius
  replay_overhead_p95_ms: 80          # extra latency vs normal Jump
  genius_cache_hit_rate: 0.78         # trace cache efficiency

---

11. Testing strategies for GRP

Challenge. GRP introduces new failure modes:

• mis-selected Genius traces,
• unsafe replays,
• stale or misaligned context signatures.

We need a test strategy specifically for Genius selection and replay.

11.1 Testing pyramid

grp_testing_pyramid:
  unit_tests:
    focus:
      - "ContextSignature similarity calculations"
      - "GeniusTrace serialization / deserialization"
      - "Pre-replay safety checks and abort policies"
  
  integration_tests:
    focus:
      - "End-to-end Genius selection pipeline"
      - "Structural replay with fresh context"
      - "Abort and escalation pathways"
  
  property_tests:
    focus:
      - "Replay never bypasses current ETH policies"
      - "Context distance behaves monotonically under perturbations"
      - "Genius selection is idempotent given fixed data"
  
  simulation_tests:
    focus:
      - "Robustness of Genius replay across context clusters"
      - "Performance and hit rates under load"

11.2 Simulation test example

def test_genius_replay_robustness():
    """Check that Genius replay remains strong on similar contexts."""
    genius_trace = create_test_genius_trace()

    # Generate similar contexts around the trace's signature
    similar_contexts = generate_similar_contexts(
        genius_trace.context_signature,
        n=100
    )

    success_count = 0
    for ctx in similar_contexts:
        result = replay_structural(genius_trace, ctx)
        if result.gcs_improvement >= (
            genius_trace.eval_summary.gcs_improvement * 0.8
        ):
            success_count += 1

    success_rate = success_count / len(similar_contexts)
    assert success_rate >= 0.85

11.3 Replay safety tests

def test_replay_safety_checks():
    """Ensure pre-replay checks prevent unsafe replays."""
    genius_trace = create_test_genius_trace()

    # Context too different
    distant_context = create_distant_context()
    ok, _ = ReplaySafetyChecker().pre_replay_check(genius_trace, distant_context)
    assert not ok

    # ETH policy incompatible (stricter ETH now)
    strict_eth_context = create_stricter_eth_context()
    ok, _ = ReplaySafetyChecker().pre_replay_check(genius_trace, strict_eth_context)
    assert not ok

    # Observation unavailable
    sparse_obs_context = create_sparse_obs_context()
    ok, _ = ReplaySafetyChecker().pre_replay_check(genius_trace, sparse_obs_context)
    assert not ok

Property examples (non-normative):

• “Replay never violates current ETH policies, even if the original trace pre-dated them.”
• “Given the same historical dataset, the selection pipeline always selects / rejects the same set of Genius candidates (no randomness without governance).”

---

12. Genius trace versioning and lifecycle

Challenge. Genius traces themselves evolve:

• new ETH policies,
• new models,
• new incident data,
• drift in context distributions.

We need a lifecycle and versioning story for Genius traces.

12.1 Lifecycle stages

genius_lifecycle:
  candidate:
    description: "Selected by pipeline, not yet validated"
  validated:
    description: "Robustness and generalizability verified"
  active:
    description: "Available for replay in production"
  under_review:
    description: "Performance or ETH concerns; temporarily restricted"
  deprecated:
    description: "No longer recommended for new replays"
  archived:
    description: "Historical only; kept for analysis / audit"

12.2 Versioning

genius_trace_version:
  trace_id: "GRP-2028-0421-city-flood"
  version: "v2.1.0"
  changes:
    - "Updated for new ETH policy flood_eth_v4"
    - "Re-validated with expanded context cluster"
  replaces: "v2.0.0"
  compatible_with:
    - "v2.0.0"
    - "v1.9.0"

When a Genius trace is updated:

• we keep old versions for audit,
• new replays use the latest active version,
• we log which version was used for each replay.

12.3 Revalidation policy

class GeniusRevalidator:
    def schedule_revalidation(self, genius_trace):
        """Schedule revalidation based on multiple triggers."""
        triggers = [
            ("periodic", lambda: months_since(genius_trace.last_validated) >= 6),
            ("eth_policy_change", lambda: eth_policy_updated(genius_trace.domain)),
            ("performance_degradation",
             lambda: replay_success_rate(genius_trace.id) < 0.7),
            ("fairness_concern",
             lambda: fairness_complaints(genius_trace.id) > FAIRNESS_THRESHOLD),
        ]

        for trigger_type, condition in triggers:
            if condition():
                self._revalidate(genius_trace, trigger_type)

    def _revalidate(self, genius_trace, reason):
        """Run full revalidation and update lifecycle state."""
        robustness = verify_robustness_on_current_contexts(genius_trace)
        generalizability = assess_generalizability_on_current_data(genius_trace)
        eth_compliance = verify_eth_compliance(genius_trace)

        if all([robustness.ok, generalizability.ok, eth_compliance.ok]):
            genius_trace.status = "active"
            genius_trace.last_validated = now()
        else:
            genius_trace.status = "deprecated"
            self._notify_deprecation(genius_trace, reason)

    def _notify_deprecation(self, genius_trace, reason):
        # Notify domain owners / ops / ethics board
        ...

12.4 Deprecation handling

deprecation_policy:
  grace_period: "3 months"
  during_grace:
    - "Warn when Genius trace is proposed for replay"
    - "Track remaining usage for migration planning"
  after_grace:
    - "Remove from active Genius Library"
    - "Move to archived state for historical analysis only"

13. From GRP to dedicated Jump engines

So far, GRP has focused on selecting, storing, and replaying high-value traces. This section connects GRP to engine design:

• how to use Genius traces to train or compile dedicated Jump engines, and
• how this relates to “reproducing genius-level behavior” rather than worshipping one-off miracles.

13.1 Reproducing behavior, not one-off trajectories

A naive “genius reproduction” algorithm would try to copy exact actions. GRP aims for something stricter and safer:

• preserve multi-goal GCS profiles (safety, efficiency, wellbeing, etc.),
• preserve robustness across similar contexts,
• preserve (or improve) fairness metrics,
• keep everything auditable under [ETH]/[EVAL].

For training/compilation we treat a GeniusTrace as a set of structured demonstrations:

training_example:
  domain: "city.flood_response"
  context_signature: ContextSignature
  goal_surface: GoalSurface
  step:
    idx: 3
    observation_view: ObsSlice
    candidate_set: list[ActionPlan]         # if known
    chosen_action: ActionPlan
    gcs_vector: GcsVector
    ethics_trace: EthicsTrace               # what constraints mattered here
    outcome_tags: ["saved_hospital_access","no_casualties"]

The reproduction objective is then:

In new, similar contexts, a dedicated engine should choose actions whose GCS vector, robustness, and fairness profile are comparable to those seen in the Genius cluster — even if the literal actions differ.

13.2 Training loop: Genius-aware Jump engines

A non-normative training loop that uses the Genius Library:

class GeniusEngineTrainer:
    def build_dataset(self, genius_traces, background_traces):
        """Turn Genius + non-genius Jumps into a supervised dataset."""
        examples = []
        for tr in genius_traces:
            for step in tr.jump_sequence:
                examples.append(self._to_example(tr, step, label="genius"))
        for tr in background_traces:
            for step in tr.jump_sequence:
                examples.append(self._to_example(tr, step, label="baseline"))
        return examples

    def train_candidate_engine(self, base_engine, dataset):
        """
        Fine-tune or train a dedicated JumpEngine so that:
        - it imitates Genius-labelled decisions when context is similar, and
        - it does not degrade safety/fairness on baseline data.
        """
        # Implementation: domain-specific (could be policy network, SIL compiler, etc.)
        ...

    def evaluate_candidate(self, candidate_engine, reference_engine, eval_scenarios):
        """
        Compare engines on:
          - GCS metrics (multi-goal),
          - robustness (counterfactual variants),
          - fairness metrics,
          - CAS/SCI/SCover, RBL/RIR.
        """
        ...

High-level distillation loop:

def genius_distillation_loop(genius_library, base_engine):
    dataset = build_dataset(
        genius_traces=genius_library.active_traces(),
        background_traces=sample_background_traces()
    )
    candidate = trainer.train_candidate_engine(base_engine, dataset)
    eval_report = trainer.evaluate_candidate(
        candidate_engine=candidate,
        reference_engine=base_engine,
        eval_scenarios=sample_eval_scenarios()
    )
    if eval_report.meets_promotion_thresholds():
        promote_engine(candidate)   # guarded by [EVAL]/[ETH]
    else:
        keep_engine_as_experimental(candidate)

This is the engine-side counterpart to GRP’s replay logic: instead of replaying a specific trace, you are compressing a whole cluster of Genius traces into a new Jump engine.

13.3 Runtime integration: choosing between engines

At runtime, a Jump can be backed by multiple engines:

jump_engine_policy:
  name: "city.flood_response"
  engines:
    - id: "llm_default"
      type: "llm"
      status: "active"
    - id: "flood_genius_v3"
      type: "dedicated"
      status: "active"
  routing:
    - when: "context in flood_cluster_A and risk_profile.level in [HIGH,CRITICAL]"
      use: "flood_genius_v3"
    - when: "else"
      use: "llm_default"
  evaluation:
    shadow_compare:
      enabled: true
      sample_rate: 0.05     # 5% of Jumps get both engines in shadow for monitoring

Non-normative runtime sketch:

class MultiEngineJumpRuntime:
    def __init__(self, engine_registry, router, evaluator):
        self.engine_registry = engine_registry
        self.router = router
        self.evaluator = evaluator

    def run_jump(self, req: JumpRequest) -> JumpResult:
        # 1) pick primary engine by policy
        engine_id = self.router.choose_engine(req)
        engine = self.engine_registry[engine_id]

        # 2) get decision draft from engine
        draft = engine.propose(req)

        # 3) run ETH overlay, RML execution, logging (as in art-60-033)
        result = self._finalize_with_eth_and_rml(req, draft)

        # 4) optionally run secondary engine in shadow for monitoring
        self.evaluator.maybe_shadow_compare(req, result)

        return result

Engines (LLM-based or dedicated) are treated as pluggable, evaluated components:

• the same [OBS]/[ETH]/[MEM]/[EVAL] stack applies,
• engine changes go through the same versioning and rollout procedures as Jump definitions (§12),
• safety-critical domains can insist on non-LLM primary engines with LLMs restricted to advisory roles.

13.4 Relation to “genius-level reproduction” protocols

If you think in terms of “protocols for reproducing genius-level behavior”:

• GRP (this article) gives you:

  • how to capture and qualify Genius traces,
  • how to replay them safely (structural vs literal replay),
  • how to monitor and version them.

• The distillation loop above is the next layer:

  • treat Genius traces as a training signal,
  • produce dedicated Jump engines that internalize those patterns,
  • keep them under continuous [EVAL]/[ETH]/fairness monitoring.

Together, they give you a concrete algorithmic story:

1. Let the system + humans occasionally produce “genius-level” Jumps.
2. Capture and validate them as GeniusTrace objects (GRP).
3. Distill whole clusters of such traces into dedicated Jump engines.
4. Promote those engines only when they match or exceed the original Genius behavior on GCS, robustness, and fairness — not just on superficial imitation.

This is how “reproducing genius-level behavior” becomes an engineering discipline instead of a metaphor.