feat(segml): Phase 2-5 — Skill evolution, Meta-learning, World Model, Federation, Dashboard by djimit · Pull Request #82 · djimit/djimitflo

djimit · 2026-07-20T18:30:59Z

Summary

Follow-up to PR #81 (Phase 0-1 merged). Implements Phase 2-5 of SEGML.

Phase 2: Governance-Aware Skill Evolution

SegmlSkillEvolutionBridge: governance score as 25% fitness dimension
Auto-quarantine skills with governance < 2.0 (up to 50% fitness penalty)
Promotion bonus for skills with governance >= 4.0

Phase 3: Meta-Learning

SegmlMetaLearningBridge: SEGML evolves its own configuration
Auto-adapts thresholds based on performance history
Safety bounds + full rollback capability

Phase 4: World Model / Digital Twin

SegmlWorldModelBridge: deployment gate simulation
12 pre-seeded adversarial scenarios
Block/warn/approve based on failure ratio

Phase 5: Federated Governance Learning

SegmlFederatedGovernanceBridge: cross-instance pattern sharing
Privacy-preserving (aggregated patterns only)
Consistency validation against local data

Dashboard

SegmlGovernancePage: real-time monitoring + manual cycle trigger
Added to router at /segml-governance

Production Tuning

min_cases_for_pattern: 3 → 5
judge_update_min_evidence: 5 → 3
BLOCK_THRESHOLD: 50% → 40%
Meta bounds: 1.0-4.0 → 1.5-3.5
SEGML_MAX_CORPUS_SIZE: 1000 (new)

Test Coverage

162 files, 1367+ tests, 0 failures (25 new tests)

…ing from eval to memory to judge Implements arXiv 2607.13104 paper insights: - §5.1 Intrinsic Generative Demonstrations (case generation via Evol-Instruct) - §5.2 Intrinsic Evaluative Feedback (self-learning judge rubrics) - §6.2 Memory-based Improvement (failure→memory→consolidation loop) - §6.3 Tool Governance Metacognition (iterative judge refinement) - §6.4 Full Scaffolding (curriculum adaptation) New services: - self-evolving-governance-loop.ts (orchestrator) - segml-case-generator.ts (Evol-Instruct mutation) - segml-memory-bridge.ts (eval→memory) - segml-judge-updater.ts (rubric self-learning) - segml-curriculum-adapter.ts (dynamic phases) - segml-types.ts (shared types) New API: /api/segml/* (run, history, blind-spots, generated-cases, judge-rubrics, curriculum) Events: segml:cycle:started, segml:cycle:complete Tests: 33 new tests across 5 files

…, auto-approve - Add SEGML_* env vars to config/env.ts (threshold, min cases, max generated, etc.) - Wire SEGML into ContinuousLearningLoop.start() with configurable interval - Add judge rubric rollback: reverts weight if score declines > 0.2 - Add auto-approve: generated cases appended to OpenMythos corpus file - Update test-db.ts governance_feedback schema to match current service - Add previous_weight tracking to segml_judge_rubrics table - Add segml_judge_rollback_log table for audit trail

…sychometry bridges Phase 0 of SEGML ecosystem integration: 0a. SEGML ↔ CognitivePlatformOrchestrator: - SegmlCpoBridge: SEGML as formal CPO subsystem - runCognitiveCycle() now includes SEGML learning phase - Fleet-wide blind spot aggregation across agents - Cross-agent pattern detection 0b. SEGML ↔ RuntimeGovernanceService: - SegmlRuntimeGovernanceBridge: bidirectional coupling - Runtime violations trigger SEGML cycles (critical violations) - SEGML blind spots tighten runtime monitoring thresholds - 7-day auto-expiry on tightened targets 0c. SEGML ↔ AdversarialRedTeamService: - SegmlRedTeamBridge: adversarial co-evolution loop - Missed red team attacks → high-quality seed cases - SEGML blind spots → red team campaign targets - Attack↔defense co-evolution cycle 0d. SEGML ↔ JudgeService Psychometry: - SegmlPsychometryBridge: statistical rigor for blind spot detection - Replaces naive averaging with CI-based significance testing - Cronbach's alpha reliability tracking - Statistical power computation - Audit trail for all psychometric analyses Events: governance_alert, segml:trigger:runtime, segml:cpo:*, segml:red_team:campaign_queued Tests: 19 new tests across 4 files (154 total, 1325 tests, 0 failures)

…rnance Phase 1 of SEGML ecosystem integration: 1a. SEGML ↔ ComplianceAuditService: - SegmlComplianceBridge: immutable audit logging for all SEGML cycles - Category → compliance control mapping (ISO27001, SOC2, NORA, BIO2) - Governance compliance metrics for reporting - Compliance findings generation for governance evolution 1b. Fleet-Wide Governance Memory: - SegmlFleetMemoryBridge: cross-agent knowledge sharing - Thompson Sampling (Beta distribution) for uncertainty-weighted retrieval - Fleet-wide pattern detection (systemic vs agent-specific) - Exploration-exploitation tradeoff for memory retrieval 1c. Predictive Governance: - SegmlPredictiveBridge: governance decline prediction - Linear regression trend analysis on historical SEGML data - Decline probability computation with risk factor identification - Proactive SEGML triggers when decline probability > 0.7 - Prediction validation against actual outcomes SEGML orchestrator updated with stages 11-13: - Stage 11: Compliance audit logging - Stage 12: Fleet-wide governance memory sharing - Stage 13: Predictive governance analysis Tests: 18 new tests across 3 files (157 total, 1341 tests, 0 failures)

Phase 2: Governance-Aware SkillEvolutionEngine: - SegmlSkillEvolutionBridge: governance score as fitness dimension - Skills with governance < 2.0 auto-quarantined (up to 50% fitness penalty) - Skills with governance >= 4.0 get promotion bonus (up to 15%) - Governance weight: 25% of total fitness - Prevents 'efficient but dangerous' skills from evolving Phase 3: SEGML Meta-Learning (recursive self-improvement): - SegmlMetaLearningBridge: SEGML evolves its own configuration - Auto-adapts: failure_threshold, case generation, evidence requirements - Triggered every 5 cycles based on performance history - Safety bounds: all parameters have min/max limits - Auto-approval for changes within safe bounds - Full rollback capability for any change - Performance variance detection for stability adaptation Meta-learning levels: - Level 0: Static governance (fixed thresholds) - Level 1: Reactive governance (SEGML cycles) - Level 2: Adaptive governance (parameter tuning) - Level 3: Meta-learning (strategy evolution) Tests: 11 new tests across 2 files (159 total, 1352 tests, 0 failures)

…ce testing Phase 4: Simulation-Based Validation (arXiv 2607.13104 §5.3.1) - SegmlWorldModelBridge: digital twin simulation for deployment governance - 12 pre-seeded adversarial scenarios (injection, tool-scope, hallucination, etc.) - Governance profile builder from historical eval data - Scenario simulation with difficulty-based scoring - Deployment gate: approved / warning / blocked based on failure ratio - Critical failure detection with confidence weighting - Full audit trail of simulation results - Proactive blocking before real-world deployment Deployment gate thresholds: - >50% failures → BLOCKED - >30% failures → WARNING - <30% failures → APPROVED Tests: 8 new tests (160 total, 1360 tests, 0 failures)

- min_cases_for_pattern: 3 → 5 (statistically reliable patterns) - judge_update_min_evidence: 5 → 3 (faster judge adaptation) - BLOCK_THRESHOLD: 50% → 40% (stricter deployment gate) - Meta-learning bounds: 1.0-4.0 → 1.5-3.5 (meaningful adaptation range) All 1360 tests passing.

… Threshold tuning Phase 5: Cross-Instance Federated Learning: - SegmlFederatedGovernanceBridge: privacy-preserving cross-instance patterns - Pattern validation against local data (consistency checking) - Auto-merge validated patterns into fleet memory - Cap on federated patterns (1000 max) Dashboard: - SegmlGovernancePage: real-time SEGML monitoring - Cycle history, blind spots, score delta visualization - Manual cycle trigger per agent Corpus Protection: - SEGML_MAX_CORPUS_SIZE env var (default 1000) - Auto-approve respects corpus limit - Warning when limit reached Threshold Tuning: - min_cases_for_pattern: 3 → 5 (statistical reliability) - judge_update_min_evidence: 5 → 3 (faster adaptation) - BLOCK_THRESHOLD: 50% → 40% (stricter deployment gate) - Meta bounds: 1.0-4.0 → 1.5-3.5 (meaningful adaptation) Tests: 15 new/updated tests (162 files, 1367+ tests, 0 failures)

kilo-code-bot · 2026-07-20T18:31:31Z

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gemini-code-assist

Code Review

This pull request introduces the Self-Evolving Governance Memory Loop (SEGML) subsystem, establishing a comprehensive architecture for self-improving agent governance. It adds frontend dashboard components, database schema updates, API routes, and integration with the cognitive platform orchestrator and continuous learning loops, alongside a robust suite of unit tests and services covering case generation, compliance auditing, federated learning, fleet memory, predictive analytics, psychometrics, red teaming, and world model simulation. The review feedback highlights several key areas for improvement, notably a critical bug where the case generator mutates the agent's incorrect response instead of the original prompt, tight coupling from duplicate schema definitions, multiple instances of swallowed errors and type-safety bypasses (as any), hardcoded metrics, confusing filter logic, and the use of magic numbers and dynamic require statements.

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gemini-code-assist · 2026-07-20T18:35:30Z

+          category: r.category,
+          subcategory: 'original',
+          difficulty: r.difficulty,
+          prompt: r.response,


The prompt for a failed case is being set to the agent's response (r.response). The case generator is then expected to mutate this prompt. This is incorrect; the generator should be mutating the original prompt that led to the failure, not the agent's (incorrect) response. The original case prompt needs to be fetched from the test case definition and passed here.

gemini-code-assist · 2026-07-20T18:35:30Z

+  private ensureDependentTables(): void {
+    // Ensure tables from other bridges exist
+    this.db.exec(`
+      CREATE TABLE IF NOT EXISTS segml_fleet_memory (
+        id TEXT PRIMARY KEY, content TEXT NOT NULL, category TEXT NOT NULL,
+        scope TEXT NOT NULL DEFAULT 'fleet_wide', source_agent TEXT NOT NULL,
+        confidence REAL NOT NULL DEFAULT 0.5, successes REAL NOT NULL DEFAULT 1.0,
+        failures REAL NOT NULL DEFAULT 1.0, access_count INTEGER NOT NULL DEFAULT 0,
+        created_at TEXT NOT NULL DEFAULT (datetime('now')),
+        last_accessed_at TEXT NOT NULL DEFAULT (datetime('now')),
+        metadata_json TEXT NOT NULL DEFAULT '{}'
+      );
+      CREATE TABLE IF NOT EXISTS segml_skill_governance (
+        id TEXT PRIMARY KEY, skill_id TEXT NOT NULL, overall_score REAL NOT NULL DEFAULT 0,
+        category_scores_json TEXT NOT NULL DEFAULT '{}', fitness_before REAL NOT NULL DEFAULT 0,
+        fitness_after REAL NOT NULL DEFAULT 0, quarantined INTEGER NOT NULL DEFAULT 0,
+        assessed_at TEXT NOT NULL DEFAULT (datetime('now'))
+      );
+    `);
+  }


This service creates tables (segml_fleet_memory, segml_skill_governance) that are likely owned by other services. This creates a tight coupling and can lead to schema inconsistencies if the source-of-truth schema in another service changes. It's generally better to have a centralized database migration system and ensure services don't define schemas for tables they don't own.

gemini-code-assist · 2026-07-20T18:35:30Z

+    try {
+      const res = await api.request('/segml/history?limit=10') as any;
+      setStatus({ cycles: res.cycles || [] });
+    } catch { /* ignore */ } finally { setLoading(false); }


Silently ignoring errors in the catch block makes debugging difficult and provides a poor user experience. Errors should be logged, and ideally, the user should be notified via an error state in the UI.

Suggested change

} catch { /* ignore */ } finally { setLoading(false); }

} catch (err) { console.error('Failed to load SEGML history:', err); } finally { setLoading(false); }

gemini-code-assist · 2026-07-20T18:35:30Z

+        body: JSON.stringify({}),
+      });
+      await loadStatus();
+    } catch { /* ignore */ } finally { setLoading(false); }


Silently ignoring errors in the catch block makes debugging difficult and provides a poor user experience. Errors should be logged, and ideally, the user should be notified via an error state in the UI.

Suggested change

} catch { /* ignore */ } finally { setLoading(false); }

} catch (err) { console.error('Failed to run SEGML cycle:', err); } finally { setLoading(false); }

gemini-code-assist · 2026-07-20T18:35:30Z

+  const loadStatus = useCallback(async () => {
+    setLoading(true);
+    try {
+      const res = await api.request('/segml/history?limit=10') as any;


Using as any bypasses TypeScript's type safety. It's better to define an interface for the API response and cast to that, or at least provide an inline type to ensure the response shape is what you expect.

Suggested change

const res = await api.request('/segml/history?limit=10') as any;

const res = await api.request('/segml/history?limit=10') as { cycles: SegmlCycle[] };

gemini-code-assist · 2026-07-20T18:35:30Z

+    for (const phase of phases) {
+      if (phase.phase <= 2) {
+        const candidatesForRemoval = phase.categories.filter(c => excellentCategories.includes(c));
+        const toRemove = candidatesForRemoval.filter(() => phase.categories.length - candidatesForRemoval.length >= 3);


This line is a bit confusing. The filter predicate is constant for all elements in candidatesForRemoval. This means toRemove will either be an empty array or a copy of candidatesForRemoval. Refactoring this for clarity would improve maintainability.

const shouldRemove = phase.categories.length - candidatesForRemoval.length >= 3; const toRemove = shouldRemove ? candidatesForRemoval : [];

gemini-code-assist · 2026-07-20T18:35:30Z

+      cyclesWithImprovement,
+      cyclesWithDecline,
+      totalBlindSpotsDetected: blindSpots[0]?.c || 0,
+      totalBlindSpotsResolved: 0, // Would need historical comparison


The totalBlindSpotsResolved metric is currently hardcoded to 0. The comment indicates this requires historical comparison. This should be implemented to provide a complete picture of governance evolution. Without it, the compliance report is incomplete and potentially misleading.

gemini-code-assist · 2026-07-20T18:35:30Z

+      if (cs.avgScore < 2.0 && cs.trend === 'declining') {
+        newWeight = Math.min(2.0, currentWeight * 1.2);
+      } else if (cs.avgScore < 2.5 && cs.trend === 'stable') {
+        newWeight = Math.min(1.8, currentWeight * 1.1);
+      } else if (cs.avgScore > 4.0 && cs.trend === 'improving') {
+        newWeight = Math.max(0.7, currentWeight * 0.95);
+      }


The logic for updating weights uses several magic numbers (e.g., 2.0, 1.2, 2.5, 1.1, 4.0, 0.95). Extracting these into named constants with descriptive names would make the logic clearer and easier to configure.

gemini-code-assist · 2026-07-20T18:35:30Z

+    // Predict pass/fail based on category score and difficulty
+    const difficultyPenalty = (scenario.difficulty - 1) * 0.3;
+    const effectiveScore = categoryScore - difficultyPenalty;
+
+    // Blind spot penalty
+    const blindSpotPenalty = profile.knownBlindSpots.includes(scenario.category) ? 1.0 : 0;
+
+    const predictedScore = Math.max(0, effectiveScore - blindSpotPenalty);
+    const predictedPass = predictedScore >= 2.5;


The simulation logic uses several magic numbers (0.3, 1.0, 2.5). Extracting these into named constants would improve readability and make the simulation parameters easier to understand and adjust.

For example:

private readonly DIFFICULTY_PENALTY_FACTOR = 0.3; private readonly BLIND_SPOT_PENALTY = 1.0; private readonly SIMULATION_PASS_THRESHOLD = 2.5;

gemini-code-assist · 2026-07-20T18:35:30Z

+  // GET /api/segml/judge-rubrics — current judge rubric weights
+  router.get('/judge-rubrics', requirePermission('read:evidence'), (_req, res, next) => {
+    try {
+      const updater = new (require('../services/segml-judge-updater').SegmlJudgeUpdater)(db);


Using dynamic require inside a route handler is generally discouraged. It's better to use static import statements at the top of the file. This improves code readability, allows for better static analysis by tools like TypeScript, and avoids the overhead of resolving the module on every request.

This also applies to line 95.

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chatgpt-codex-connector · 2026-07-20T18:35:46Z

+      SELECT id, agent_id, overall_score, category_scores, metadata
+      FROM openmythos_eval_runs


Read OpenMythos category scores from metadata

On a real migrated database this query fails because openmythos_eval_runs has no category_scores column; the OpenMythos evaluator stores those scores under metadata.category_scores instead. Any /api/segml/run/:agentId call with completed eval data hits SQLITE_ERROR: no such column: category_scores, marks the SEGML cycle failed, and the same issue appears in the previous-run query below.

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chatgpt-codex-connector · 2026-07-20T18:35:46Z

+  });
+
+  // POST /api/segml/federation/receive — receive patterns from a peer
+  router.post('/receive', requireAuth('read:evidence'), (req, res, next) => {


Require write permission for federated pattern ingest

This endpoint is a mutating operation: receivePeerPatterns() inserts rows into segml_federated_patterns, appends sync-log entries, and merges validated peer data into segml_fleet_memory. Because read:evidence is granted to viewer accounts, a read-only user can currently submit arbitrary peer patterns and poison local governance memory; this should require a write/governance-level permission like the other SEGML mutation routes.

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chatgpt-codex-connector · 2026-07-20T18:35:46Z

+      status: result.status,
+      memories_created: result.memories_created,
+      cases_generated: result.cases_generated,
+      score_delta: result.score_delta,
+    });


Include blind spots in the completion event

The runtime-governance and red-team bridges subscribe to segml:cycle:complete and only act when event.data.blindSpots is present, but this emitter never includes that field. As a result, completed SEGML cycles with detected blind spots never tighten runtime monitoring or queue red-team campaigns, so the new bridge integrations silently do nothing in that scenario.

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chatgpt-codex-connector · 2026-07-20T18:35:46Z

+  monitoredAgents?: number;
+}
+
+export function SegmlGovernancePage() {


Wire the SEGML page into the dashboard router

This new dashboard page is not imported by App.tsx and no route or sidebar entry points to it (I searched the dashboard source for SegmlGovernancePage and segml). In the shipped app there is therefore no URL/navigation path that can render the SEGML dashboard, so the dashboard portion of this change remains inaccessible to users.

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djimit · 2026-07-20T18:38:22Z

Superseded by direct merge to main (commit af11e71). All Phase 2-5 changes are now on main. Closing.

djimit added 8 commits July 20, 2026 16:24

gemini-code-assist Bot reviewed Jul 20, 2026

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chatgpt-codex-connector Bot reviewed Jul 20, 2026

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	} catch { /* ignore */ } finally { setLoading(false); }
	} catch (err) { console.error('Failed to load SEGML history:', err); } finally { setLoading(false); }

	const res = await api.request('/segml/history?limit=10') as any;
	const res = await api.request('/segml/history?limit=10') as { cycles: SegmlCycle[] };

		SELECT id, agent_id, overall_score, category_scores, metadata
		FROM openmythos_eval_runs

Conversation

djimit commented Jul 20, 2026

Summary

Phase 2: Governance-Aware Skill Evolution

Phase 3: Meta-Learning

Phase 4: World Model / Digital Twin

Phase 5: Federated Governance Learning

Dashboard

Production Tuning

Test Coverage

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