Module ai Future

AI Module - Future Enhancements

Scope

• Hardening of endpoint safety, payload validation, and error observability for AI plugin generation.
• Introduction of dedicated performance coverage for the AI generation path.
• Future integration of external sandbox/static-analysis policy engines on top of the built-in artifact materialization gate.

Design Constraints

• Public API contracts in include/ai/ai_plugin_generator.h remain backward compatible within major line.
• Validation and error behavior must remain deterministic and fail-closed.
• Runtime behavior must remain bounded by timeout/retry budgets.
• Security-sensitive request content must not be persisted unredacted.

Required Interfaces

Interface	Requirement
validatePrompt	extend checks for required_capabilities and dependencies consistency
generatePlugin	preserve validation-first execution and fail-closed return semantics
AIPluginGenerator::Config	expose explicit safety knobs (allow-list, payload limit, retry policy)
benchmark integration	maintain dedicated ai generation benchmark target and release mapping

Implementation Notes

• Add response schema validation with explicit required and optional fields.
• Add bounded retry/backoff only for transient transport failures.
• Add response-size hard limit before parse to prevent memory pressure.
• Field-level validation for required_capabilities/dependencies, configurable endpoint allow-list, request/response size limits, dedicated benchmark coverage, and built-in sandbox artifact materialization are implemented in the runtime path; remaining hardening focuses on external sandbox/static-analysis policy engines.
• Standardize error classes for validation, transport, HTTP status, parse, and payload shape failures.

Test Strategy

• Unit tests for new validation rules and schema failure cases.
• Integration tests with deterministic endpoint fixtures (success, non-2xx, malformed JSON, oversized payload).
• Regression tests for existing structured error contracts.
• Benchmark regression tracking in release profile for mapped AI targets.

Performance Targets

• Prompt validation path p99 remains within low-single-digit milliseconds.
• Endpoint orchestration overhead remains stable versus current release baseline.
• Proxy benchmark regressions stay within configured release threshold until dedicated benchmark is introduced.

Security / Reliability

• Enforce endpoint allow-list checks before outbound calls (implemented).
• Enforce maximum request and response size limits (implemented).
• Materialize generated source bundles into sandbox/output directories with fail-closed read-back verification before optional callback policy execution (implemented).
• Keep fail-closed behavior for malformed/untrusted responses.
• Ensure logs remain redacted and bounded for sensitive fields.

Wave C — Strategic ML Enhancements (Q3 2027+)

Long-term strategic AI/ML features for enhanced safety, privacy, and governance. Lower urgency but high strategic value.

C1: Constitutional AI (CAI) Safety Module

• Design constitutional principles registry (21 built-in rules)
• Implement LLM-as-critic evaluation loop
• Build revision prompt generation
• Create critic-revision cycle (max 2 rounds)
• Unit tests CAI-01..15 + CAI-BENCH-01 (tests/test_cai_safety_module.cpp)
• Integration with EthicsEvaluator (include/ai/cai_ethics_integration.h)
• Production runtime hook integration in LLMAQLHandler paths (executeInfer, executeInferStreaming, executeRAG, executeChat) with fail-closed callback handling
• Human safety benchmark (500 samples, 3 annotators) — tests/test_cai_safety_module.cpp (CAI-BENCH-01)

Acceptance Criteria:

• Safety score alignment ≥ 0.80 with human annotators
• Latency overhead ≤ 2.0 s per response
• False-positive rate ≤ 10% (benign content flagged as unsafe)

Reference: Bai et al. (2022) arXiv:2212.08073

C2: Federated Learning for Privacy-Preserving Training

• Design synchronized SGD gradient aggregation
• Implement secure aggregation primitive (stub: optional homomorphic encryption)
• Build Byzantine-robust averaging (median/trimmed mean)
• Create federated training coordinator
• Unit tests FEDERATED-01..15 + FEDERATED-BENCH-01 (tests/test_federated_privacy_training.cpp)
• Production telemetry hook integration in LLMAQLHandler paths (executeInfer, executeInferStreaming, executeRAG, executeChat) with fail-closed callback handling
• Multi-node convergence benchmark (10 nodes, 10% data each) — tests/test_federated_privacy_training.cpp (FEDERATED-BENCH-01)
• Differential privacy tuning framework

Acceptance Criteria:

• Training convergence ≥ 95% of centralized baseline
• Gradient communication overhead ≤ 2.0 s per round
• Configurable epsilon-differential privacy budget

Reference: Kairouz et al. (2021) JMLR 2021, arXiv:2104.14881

Wave C Dependencies and Risk Mitigation

Blockers / Dependencies

• Wave A + Wave B stability checks tracked in release verification artifacts (CTEST.md, issues #5038/#5039)
• Constitutional AI principles formalized in ethics framework (src/ai/cai_ethics_integration.cpp, tests/test_cai_safety_module.cpp)
• Multi-node federated benchmark infra/security review tracking established (FEDERATED-BENCH-01 + Wave issue traceability)

Risk Mitigation

• C1 (CAI): Start with simple rule-based critic; LLM-based only after v0.1
• C2 (Federated): Deploy in staging first; Byzantine-robustness is nice-to-have, not critical for v1.0

Timeline

• Start: Q3 2027 (early July)
• Target: End Q4 2027 (mid-December)
• Estimated Effort: 16–24 weeks total (depending on C2 security requirements)

Research Publication Opportunity

• Joint paper: ThemisDB Integration of Research-Backed ML Features
• Target: ML Systems + Governance conference (e.g., MLSys 2028, FAccT 2028)

Related Documents

• Research Bibliography: docs/research/ml_enhancements_bibliography.md
• Roadmap: src/ai/ROADMAP.md
• Future Enhancements: src/ai/FUTURE_ENHANCEMENTS.md
• Wave C Issue: #5040
• Wave A Issue: #5038
• Wave B Issue: #5039

Wave B: High-Value ML Enhancements (Q1–Q2 2027)

Scope

Research-backed AI/ML features for mid-term deployment (Q1–Q2 2027). Builds on Wave A foundation and targets significant performance/capability improvements.

Items

B1: Self-RAG (Self-Retrieving, Auto-Critique)

• Design retrieval controller (binary classify: Retrieve now?)
• Implement critic model (3-class: Relevant/Partial/Irrelevant)
• Build iterative refinement loop (max 3 rounds)
• Unit tests SELF_RAG-01..12
• Integration with InferenceEngineEnhanced callback
• Benchmark vs. vanilla RAG on ALCE dataset

Acceptance Criteria:

• ✅ Hallucination rate reduction ≥ 20% vs. standard RAG
• ✅ Latency increase ≤ 1.5× vs baseline
• ✅ Precision@K retrieval ≥ 0.85 on golden-doc tests

B2: Knowledge Graph Completion (RotatE)

• Implement RotatE embedding model (relation-as-rotation)
• Build triple loss with negative sampling
• Create link-prediction head
• Unit tests KGC-01..15
• Benchmark vs. TransE baseline
• Integrate with KnowledgeGraphReasoner

Acceptance Criteria:

• ✅ MRR ≥ 0.35, Hits@10 ≥ 0.55 on deterministic acceptance fixture
• ✅ Inference latency ≤ 50 ms for top-20 predictions
• ✅ Zero backward compatibility breaks

B3: Multi-Task LoRA Fine-Tuning

• Design shared LoRA base with task-specific projections
• Implement domain-gating mechanism
• Build joint loss with configurable task weighting
• Unit tests MTL-01..10
• Ablation study: shared multi-task training vs. per-task single-task baselines
• 3-task benchmark evaluation

Acceptance Criteria:

• ✅ Average task performance ≥ +8% vs. single-task
• ✅ Training time increase ≤ 15%
• ✅ Robust across task configurations

Timeline

• Start: Q1 2027 (early January)
• Target: End Q2 2027 (mid-June)
• Estimated effort: 12–16 weeks total

Blockers / Dependencies

• Wave A (Speculative Decoding, DPR, Fairness) deployment complete
• LLM inference P95 latency < 200 ms (prerequisite for iterative loops)
• KnowledgeGraphReasoner stable + benchmarks passing

Related Documents

• Research Bibliography: ../../docs/research/ml_enhancements_bibliography.md
• Roadmap: ROADMAP.md
• issue scope: https://github.com/makr-code/ThemisDB/issues/5039

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