Add 15 architecture and design documents covering the sublinear-time solver
integration into RuVector's 79-crate ecosystem:
ADR Documents (12):
- ADR-STS-001: Core integration architecture with trait hierarchy and event sourcing
- ADR-STS-002: Algorithm selection and sublinear routing with SONA adaptive learning
- ADR-STS-003: Memory management strategy with arena allocator and HNSW integration
- ADR-STS-004: WASM and cross-platform compilation with SIMD per architecture
- ADR-STS-005: Security model with STRIDE/DREAD analysis and witness chain audit
- ADR-STS-006: Benchmark framework with 6 Criterion.rs suites and CI regression
- ADR-STS-007: Feature flag and progressive rollout strategy
- ADR-STS-008: Error handling and fault tolerance with fallback chains
- ADR-STS-009: Concurrency model with Rayon+SIMD two-level parallelism
- ADR-STS-010: API surface design for Rust/WASM/NAPI/REST/MCP
- SOTA research analysis surveying 20+ papers and competitive landscape
- Optimization guide with SIMD/memory/algorithm/platform strategies
DDD Documents (3):
- Strategic design: 6 bounded contexts, context map, ubiquitous language
- Tactical design: aggregates, entities, value objects, domain services
- Integration patterns: ACLs, shared kernel, published language, event-driven
https://claude.ai/code/session_01TiqLbr2DaNAntQHaVeLfiR
Maps 7 concrete integration points between rvDNA genomics suite and
sublinear-time-solver: protein contact graph PageRank (500x speedup),
sparse attention solve in RVDNA format, joint variant calling with LD
(+15-30% sensitivity), sublinear Horvath clock regression, HNSW graph
optimization for pangenome k-mer search, network-based cancer detection
(3-5x sensitivity), and DNA storage/computation convergence.
Includes phased integration roadmap and scale impact analysis.
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
10 breakthrough vectors mapping concrete code paths to 50-year-ahead SOTA:
sub-constant time via predictive precomputation, self-discovering algorithms,
photonic-native vector ops, self-booting mathematical universes, neuromorphic
sublinear computing, hyperbolic sublinear geometry, cryptographic proof of
computation, temporal-causal vector spaces, infinite-scale sublinear consensus,
and the convergence of database + intelligence into a single substrate.
5-horizon roadmap from integration (2026) through convergence (2076).
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
Complete mathematical analysis of all 7 sublinear algorithms mapped to
ruvector's 9 subsystems. Top findings: Forward Push for hybrid graph
search (O(1/eps) vs O(k*d^L)), Conjugate Gradient for PDE attention
(quadratic to near-linear), Neumann Series for spectral filtering.
This completes the 15-agent analysis swarm - all documents present:
00-executive-summary, 01-14 covering crates, npm, rvf, examples,
architecture, wasm, mcp, performance, security, algorithms, typescript,
testing, dependencies, and roadmap.
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
Integration test design, property-based testing for solver correctness,
WASM test strategies, performance regression testing, and CI/CD pipeline
integration recommendations.
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
Full dependency tree comparison between ruvector (79 workspace members)
and sublinear-time-solver (9 crates), version conflicts, feature flag
compatibility, and bundle size impact.
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
Initial batch of research documents from 15-agent analysis swarm analyzing
integration between ruvector and sublinear-time-solver. Covers NPM packages,
RVF format, architecture, and TypeScript type compatibility.
More documents pending from running agents (crates, WASM, MCP, performance,
security, algorithms, testing, dependencies, roadmap, executive summary).
https://claude.ai/code/session_01WY4MpWoe2LMzkYUHLxhPHX
Plans the integration path for .rvf acceptance test verification into
the npm ecosystem:
- npx ruvector rvf verify-witness <file.rvf> (N-API + WASM fallback)
- npx rvlite verify-witness <file.rvf> (WASM via cli-rvf.ts)
- rvlite SDK verifyWitnessChain() for browser-side verification
- MCP tool rvf_verify_witness for Claude Code agents
- 5-phase implementation plan, each independently shippable
Bridges the rvf_witness_verify WASM export (ADR-037) to end users
without requiring the Rust toolchain.
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
- Resolve open questions: repo automation as first domain, four-level
AuthorityLevel enum, per-task ResourceBudget with hard caps,
CoherenceThresholds with validation
- Add AGI_MAX_CONTAINER_SIZE (16 GiB) with enforcement in validation
- Tighten ContainerSegments::validate: Verify/Live modes now require
world model data (VEC or INDEX segments), not just kernel/WASM
- Add ContainerError variants: InsufficientAuthority, BudgetExhausted
- Add to_flags support for orchestrator_present and world_model_present
- Add wire format section and cross-references to ADRs 029-033 in doc
- Add 2 new TLV tags: AUTHORITY_CONFIG (0x0110), DOMAIN_PROFILE (0x0111)
- Re-export new types from lib.rs
- Update rvf-runtime tests for tightened validation
- All 222 rvf-types + all rvf-runtime tests pass
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Defines the full system boundary for portable intelligence:
- RuVector as existential substrate (world model, coherence signals)
- RVF as cognitive container format (packaging, witness chains, replay)
- Claude Code as control plane orchestrator (planning, tool use)
- Claude Flow as swarm coordinator (routing, shared memory, learning)
Key mechanisms:
- Structural health gates (min-cut coherence, contradiction pressure)
- Skill promotion with counterexample requirements
- Two execution modes: Replay (bit-identical) and Verify (same grades)
- 10 node types, 9 edge types, 4 invariants for the world model schema
- MCP tools: ruvector_query, ruvector_cypher, rvf_snapshot, eval_run
Acceptance test: same RVF artifact, two machines, 100 tasks,
95+ passing in verify mode, zero policy violations.
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Complete the QR Cognitive Seed pipeline with zero external dependencies:
- Pure SHA-256 (FIPS 180-4) verified against NIST test vectors
- HMAC-SHA256 (RFC 2104) verified against RFC 4231 test cases
- LZ77 compression (SCF-1 format) with 4KB sliding window
- Seed crypto: content hashing, signing, layer verification
- C FFI (5 extern "C" functions) for App Clip / mobile integration
- SeedBuilder.build_and_sign() with automatic hashing and signing
- ParsedSeed.verify_all() with full integrity and signature checks
- ParsedSeed.decompress_microkernel() using built-in LZ
- 11 end-to-end integration tests with real cryptography
- Updated ADR-034 with App Clip, PWA, Android delivery paths
- Example updated with full real-crypto round-trip demo
Total: 381 tests passing (183 types + 154 runtime + 11 e2e + 33 manifest)
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Three fixes to ADR-033:
1. ResultQuality split into RetrievalQuality (per-candidate) and
ResponseQuality (per-response at API boundary). ResponseQuality
survives serialization across JSON/gRPC/MCP. DegradationReason
provides structured, inspectable evidence for why quality dropped.
2. Brute-force safety net dual-budgeted: max 5ms wall-clock AND max
50K candidates, whichever hits first. Both configurable via
QueryOptions. Budget=0 disables fallback entirely. Prevents O(N)
DoS from adversarial queries on large hot caches.
3. Mandatory acceptance test: malicious tail manifest with valid CRC
but redirected hotset pointers must fail deterministically under
Strict policy with a logged, stable error code. Separate test for
re-signed forgery (wrong signer vs no signature distinction).
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Addresses four structural weaknesses in the progressive indexing system:
1. Content-addressed centroid stability — hotset pointers verified by
SHAKE-256 content hashes, not just byte offsets. Compaction becomes
physically destructive but logically stable.
2. Adversarial distribution resilience — distance entropy detection
with adaptive n_probe widening. Silent recall collapse replaced by
detected degradation with ResultQuality signaling.
3. Honest recall framing — empirical targets scoped to distribution
classes (natural/synthetic/adversarial). Monotonic recall improvement
property proven from append-only invariant. Brute-force safety net
when candidate count is insufficient.
4. Mandatory manifest signatures — SecurityPolicy defaults to Strict.
No signature = no mount in production. Prevents segment-swap attacks
on hotset pointers. CRC32C catches corruption; ML-DSA-65 catches
adversaries.
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Add the WASM_SEG segment type and complete self-bootstrapping
architecture that allows RVF files to carry their own execution
runtime. When an RVF file embeds a WASM interpreter alongside the
microkernel, the host only needs raw execution capability — making
RVF "run anywhere compute exists."
Changes:
- rvf-types: Add SegmentType::Wasm (0x10), WasmHeader (64-byte),
WasmRole, WasmTarget enums, and feature flag constants
- rvf-runtime: Add embed_wasm(), extract_wasm(), extract_wasm_all(),
is_self_bootstrapping() methods on RvfStore, plus write_wasm_seg()
in the write path
- rvf-wasm: Add bootstrap module with resolve_bootstrap_chain() that
discovers WASM_SEGs, parses headers, and resolves the optimal
bootstrap strategy (None/HostRequired/SelfContained/TwoStage/Full)
- docs: Add spec/11-wasm-bootstrap.md with complete wire format,
bootstrap protocol, size budget analysis, and security model
The three-layer bootstrap stack:
Layer 0: Raw bytes (.rvf file)
Layer 1: Embedded WASM interpreter (~50 KB)
Layer 2: WASM microkernel (~5.5 KB)
Layer 3: RVF data segments
All 131 rvf-types tests and 72 rvf-runtime tests pass.
https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G
Complete proof of Deutsch's theorem with phase kickback lemma and
step-by-step derivation. Compares five major formulations:
- Deutsch (1985): original probabilistic version (p=1/2)
- Deutsch-Jozsa (1992): deterministic n-bit, 2 queries
- Cleve-Ekert-Macchiavello-Mosca (1998): deterministic, single query
- Nielsen-Chuang (2000): canonical textbook presentation
- Calude (2006): de-quantization using higher-dimensional classical bits
Includes de-quantization critique (Abbott et al.), classical wave
analogies, and analysis of when quantum advantage is genuine vs
artifactual.
Adds 6 verification tests to ruqu-algorithms confirming all four
oracles produce deterministic correct results via the ruqu-core
simulator, including a phase-kickback amplitude-level check.
https://claude.ai/code/session_01B1NkbLDWYPaacS9miKsnvW
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
Update AD-17 and AD-18 to reflect that Phase 0 post-training quantization
runs entirely on Mac Studio (Apple Silicon) at zero cost, eliminating the
need for cloud GPU for the prototype phase.
Key changes:
- Phase 0 cost updated from ~$100 (cloud) to $0 (local Mac Studio)
- AD-18 now includes Mac Studio config compatibility matrix (M4 Max 36-128GB,
M3 Ultra 96-512GB) with wall time estimates per config
- Added mmap strategy: FP16 weights demand-paged from disk, per-tensor
quantization uses ~2-4MB working memory regardless of model size
- Metal GPU calibration via existing Candle integration (use_metal: true)
- ARM NEON for TL1 kernel validation (same ISA as production target)
- Updated throughput table with Mac Studio entries and Phase 0 column
- PtBitnetConfig gains use_mmap, use_metal_calibration, max_memory_gb fields
- Phase 0 exit criteria updated for Mac Studio local execution
- Updated infrastructure table: Phase 0 + router validation both $0 local
Mac Studio is ideal for Phase 0 (PTQ in hours, $0) but still infeasible
for Phase 1+ training (200B tokens at 500-1000 tok/s = 6.5 years).
This separation validates the phased cloud-for-training approach.
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
