Research bitnet.cpp Rust port strategy: R3-Engine proves 100% Safe Rust
with dual-target (native AVX-512 + WASM SIMD128) achieving 80-117 tok/s.
Recommend Approach C (reference R3-Engine patterns) over Python codegen.
WASM SIMD128 maps TL1 LUT to v128.swizzle for ~20-40 tok/s in browser.
Resolves open question #5 (WASM viability). Adds 6 new references,
5 new DDD terms, 3 new open questions. DDD updated to v2.4.
https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
Analyze RLM training stack GPU dependencies and document that Phase 0.5
runs entirely on pure CPU SIMD (NEON on aarch64) without Metal GPU.
MicroLoRA, TrainingPipeline, EwcRegularizer, GrpoOptimizer are all pure
ndarray; ContrastiveTrainer has explicit CPU fallback. Only ~2-3x slower
than Metal. Extends platform support to Linux ARM64 and x86 (scalar).
https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
Add Phase 0.5: RLM Post-Quantization Refinement — a $0 Mac Studio
approach that uses the existing RLM stack (MicroLoRA, GRPO, EWC++,
ContrastiveTrainer, MemoryDistiller, PolicyStore) to refine the
Phase 0 PTQ model by training only FP16 components (~1-2% of params).
ADR-017 changes:
- Added Phase 0.5 to phased decision: A(0C) → RLM Refinement → D → C → B
- Added AD-19: RLM Post-Quantization Refinement architecture
- Frozen ternary weights + trainable FP16 (LoRA, router, scales)
- ~200-400M trainable params (1-2% of 30B), 100-500M training tokens
- 100% RLM code reuse, 0% new training code
- 2-12 days on Mac Studio Metal, $0 cost
- Expected quality: ~70-80% of FP16 (up from 55-65% Phase 0 PTQ)
- Full pipeline diagram: Router repair → MicroLoRA injection → Scale opt
- Memory budget analysis: ~12-20 GB active RAM (fits any Mac Studio)
- Training schedule: 3-14 days total wall time
- Added Phase 0.5 exit criteria (11 items)
- Updated infrastructure table with Phase 0.5 row
- Updated consequences with RLM refinement benefits
DDD v2.2 changes:
- Added Section 3.8.1: Phase 0.5 RLM Refinement Mode
- Added 5 ubiquitous language terms (RLM Refinement, Frozen Ternary,
LoRA Correction, Router Repair)
- Added 3 open questions (LoRA rank, GGUF persistence, Phase continuity)
Key insight: RLM trains ~1% of parameters → needs ~0.25% of the data
(100-500M vs 200B tokens) → Mac Studio Metal is sufficient → $0 cost.
https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
ADR-017: Add AD-17 with detailed memory budget analysis showing per-expert
distillation fits in A100 40GB (~15.5GB), full model requires 4×A100 80GB
(~430GB). CPU SIMD training infeasible at 200B+ tokens (~65 years on AVX2).
Recommend GCP 4×A100 spot instances (~$1,300 for Phase 1) or DataCrunch
H100 ($1.99/hr). Includes cost comparison across 6 platforms, per-phase
infrastructure mapping, and required CUDA device dispatch code change for
RealContrastiveTrainer.
DDD: Add section 8.5 Training Infrastructure Model with expert-parallel
GPU topology diagram, what-runs-where matrix, and required code change
summary.
https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
Research and architecture documentation for integrating BitNet b1.58
ternary quantization with GLM-4.7-Flash 30B-A3B MoE architecture into
the RuvLLM serving runtime. Includes phased approach (expert replacement
→ full distillation → native training), CPU inference kernel strategy
(TL1/TL2/I2_S), domain model with 7 bounded contexts, and memory budget
analysis targeting <10GB for 30B-class CPU-only inference.
https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
Implements the full delta-behavior framework - systems where change is
permitted but collapse is not.
## Core Implementation
- Coherence type with [0,1] bounds and safe constructors
- Three-layer enforcement: energy cost, scheduling, memory gating
- DeltaSystem trait for coherence-preserving systems
- DeltaConfig with strict/relaxed/default presets
## 11 Exotic Applications
1. Self-Limiting Reasoning - AI that does less when uncertain
2. Computational Event Horizon - bounded computation without hard limits
3. Artificial Homeostasis - synthetic life with coherence-based survival
4. Self-Stabilizing World Model - models that refuse to hallucinate
5. Coherence-Bounded Creativity - novelty without chaos
6. Anti-Cascade Financial System - markets that cannot collapse
7. Graceful Aging - systems that simplify over time
8. Swarm Intelligence - collective behavior without pathology
9. Graceful Shutdown - systems that seek safe termination
10. Pre-AGI Containment - bounded intelligence growth
11. Extropic Substrate - goal mutation, agent lifecycles, spike semantics
## Performance Optimizations
- O(n²) → O(n·k) swarm neighbor detection via SpatialGrid
- O(n) → O(1) coherence calculation with incremental cache
- VecDeque for O(1) history removal
- SIMD utilities with 8x loop unrolling
- Bounded history to prevent memory leaks
## Security Fixes
- Replaced unsafe static mut with AtomicU64 for thread-safe RNG
- NaN validation on all coherence inputs
- Overflow protection in calculations
## WASM + TypeScript SDK
- Full wasm-bindgen exports for all 11 applications
- High-level TypeScript SDK with ergonomic APIs
- Browser and Node.js examples
## Test Coverage
- 32 lib tests, 14 WASM tests, 13 doc tests (59 total)
Resolves#140
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
