CODING TENTACLE 10.0.V — Release Notes
Cybernetic Architecture for Safe, Self-Learning Code Repair Agents
What Changed Since 9.0.B
Architecture (Phase 1 Complete)
- ✅ ReflectionBrain: CT now understands WHY a repair succeeded or failed (8 success + 3 failure patterns)
- ✅ DeuteroLearning→Reflection Loop: Closed feedback — learning-to-learn from reflection data
- ✅ SelfObservation→Evidence: All cybernetic decisions are audit-trailed
- ✅ Adaptive Homeostasis: 12 vital signs self-adjust within safe bounds (±0.01/run)
- ✅ Autonomous SelfHealing: Triggered by system pathologies (no auto-apply)
- ✅ FeedbackDampener: Prevents oscillation in cybernetic loops
Integration
- ✅ OpenCode PTY Adapter: Production-ready, machine-readable output from local LLM engine
- ✅ Benchmark Runner: Resumable CLI with JSON/CSV export and statistics
- ✅ Failure Taxonomy: 73% of all failures traced to a single root cause
Scores
- ✅ Kybernetik: 5.86 → 8.08 → ~9.0/10
- ✅ Tests: 36/36 pytest, Regression ALL TESTS PASSED
- ✅ Safety: 100% (5-Layer VETO, zero false allows)
What Has Not Changed
- 🔒 No Auto-Apply — HumanApproval remains mandatory
- 🔒 5-Layer VETO — Safety wins, always
- 🔒 Evidence Ledger — Immutable, SHA256-hashed audit trail
Current Limitations
- SWE-bench Score: Not yet measured (50-task run in preparation)
- Self-Evolving Brain: Planned for Phase 2
- Real LLM validation: 1 confirmed real-world patch (astropy__astropy-12907)
This is an ARCHITECTURE publication. Performance benchmarks will follow separately.
Coding Tentacle 10.0.V — 28. Juni 2026
Repository: github.com/nessos666/coding-tentacle