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Author SHA1 Message Date
Claude
2d35e4669c docs(adr): ADR-033 progressive indexing hardening 
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
 2026-02-15 15:50:17 +00:00
Claude
ffbdd3c482 docs(adr): ADR-033 progressive indexing hardening 
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
 2026-02-15 15:50:17 +00:00
Claude
8ee54250b5 feat(rvf): add WASM_SEG (0x10) for self-bootstrapping RVF files 
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
 2026-02-15 15:36:34 +00:00
Claude
4bec65135f feat(rvf): add WASM_SEG (0x10) for self-bootstrapping RVF files 
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
 2026-02-15 15:36:34 +00:00
rUv
4ae6b05e3d feat(rvf): RVF WASM integration, witness auto-append, real verification, prebuilt fallbacks, README examples 
* feat(adr): add ADR-032 for RVF WASM integration into npx ruvector and rvlite

Documents phased integration plan: Phase 1 adds RVF as optional dep + CLI
command group to npx ruvector, Phase 2 adds RVF as storage backend for rvlite,
Phase 3 unifies shared WASM backend and MCP bridge.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(adr): update ADR-032 with invariants, contracts, failure modes, and decision matrix

Adds: single writer rule, crash ordering with epoch reconciliation,
explicit backend selection (no silent fallback), cross-platform compat
rule, phase contracts with success metrics, failure mode test matrix,
hybrid persistence decision matrix, implementation checklist.

Closes #169

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): integrate RVF WASM into npx ruvector and rvlite (ADR-032)

Phase 1 implementation:
- Add @ruvector/rvf as optional dependency to ruvector package
- Create rvf-wrapper.ts with 10 exported functions matching core pattern
- Add 3-tier platform detection (core -> rvf -> stub) with explicit
  --backend rvf override that fails loud if package is missing
- Add 8 rvf CLI subcommands (create, ingest, query, status, segments,
  derive, compact, export) routed through the wrapper
- 5 Rust smoke tests validating persistence across restart, deletion
  persistence, compaction stability, and adapter compatibility

Phase 2 foundations:
- Add rvf-backend feature flag to rvlite Cargo.toml (default off)
- Create epoch reconciliation module for hybrid RVF + IndexedDB sync
- Add @ruvector/rvf-wasm as optional dep to rvlite npm package
- Add rvf-adapter-rvlite to workspace members

All tests green: 237 RVF core, 23 adapter, 4 epoch, 5 smoke.

Refs: #169

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): complete ADR-032 phases 1-3 — epoch, lease, ID map, MCP tools, compat tests

Phase 2 Rust: full epoch reconciliation (EpochTracker with AtomicU64, 23 tests),
writer lease with file lock and PID-based stale detection (12 tests),
direct ID mapping trait with DirectIdMap and OffsetIdMap (20 tests).

Phase 2 JS: createWithRvf/saveToRvf/loadFromRvf factories, BrowserWriterLease
with IndexedDB heartbeat, rvf-migrate and rvf-rebuild CLI commands, epoch sync
helpers. +541 lines to index.ts, new cli-rvf.ts (363 lines).

Phase 3: 3 MCP rvlite tools (rvlite_sql, rvlite_cypher, rvlite_sparql),
CI wasm-dedup-check workflow, 6 cross-platform compat tests, shared peer dep.

Phase 1: 4 RVF smoke integration tests (full lifecycle, cosine, multi-restart,
metadata). Node.js CLI smoke test script.

81 new Rust tests passing. ADR-032 checklist fully complete.

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: bump versions and fix TS/README for npm publish

- ruvector 0.1.88 → 0.1.97 (match npm registry)
- rvlite 0.2.1 → 0.2.2
- @ruvector/rvf 0.1.0 → 0.1.1
- Fix MCP command in ruvector README (mcp-server → mcp start)
- Fix WASM type conflicts in rvlite index.ts (cast dynamic imports to any)

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add witness auto-append, real CLI verification, prebuilt fallbacks, and README examples

Five "What's NOT Automatic" gaps fixed:
1. Witness auto-append: WitnessConfig in RvfOptions auto-records ingest/delete/compact
   operations as WITNESS_SEG entries with SHAKE-256 hash chains
2. verify-witness CLI: Real hash chain verification — extracts WITNESS_SEG payloads,
   runs verify_witness_chain() with full SHAKE-256 validation
3. verify-attestation CLI: Real kernel image hash verification and attestation
   witness chain validation
4. Prebuilt kernel fallback: KernelBuilder::from_builtin_minimal() produces valid
   bzImage without Docker
5. Prebuilt eBPF fallback: EbpfCompiler::from_precompiled() produces valid BPF ELF
   without clang; Launcher::check_requirements()/dry_run() for QEMU detection

README examples added to all 3 packages:
- crates/rvf/README.md: Proof of Operations section
- npm/packages/rvf/README.md: 7 real-world examples
- npm/packages/ruvector/README.md: Working cognitive container examples

830 tests passing, workspace compiles cleanly.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-14 18:03:26 -05:00
rUv
7b8035eb54 feat(rvf): RVF WASM integration, witness auto-append, real verification, prebuilt fallbacks, README examples 
* feat(adr): add ADR-032 for RVF WASM integration into npx ruvector and rvlite

Documents phased integration plan: Phase 1 adds RVF as optional dep + CLI
command group to npx ruvector, Phase 2 adds RVF as storage backend for rvlite,
Phase 3 unifies shared WASM backend and MCP bridge.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(adr): update ADR-032 with invariants, contracts, failure modes, and decision matrix

Adds: single writer rule, crash ordering with epoch reconciliation,
explicit backend selection (no silent fallback), cross-platform compat
rule, phase contracts with success metrics, failure mode test matrix,
hybrid persistence decision matrix, implementation checklist.

Closes #169

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): integrate RVF WASM into npx ruvector and rvlite (ADR-032)

Phase 1 implementation:
- Add @ruvector/rvf as optional dependency to ruvector package
- Create rvf-wrapper.ts with 10 exported functions matching core pattern
- Add 3-tier platform detection (core -> rvf -> stub) with explicit
  --backend rvf override that fails loud if package is missing
- Add 8 rvf CLI subcommands (create, ingest, query, status, segments,
  derive, compact, export) routed through the wrapper
- 5 Rust smoke tests validating persistence across restart, deletion
  persistence, compaction stability, and adapter compatibility

Phase 2 foundations:
- Add rvf-backend feature flag to rvlite Cargo.toml (default off)
- Create epoch reconciliation module for hybrid RVF + IndexedDB sync
- Add @ruvector/rvf-wasm as optional dep to rvlite npm package
- Add rvf-adapter-rvlite to workspace members

All tests green: 237 RVF core, 23 adapter, 4 epoch, 5 smoke.

Refs: #169

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): complete ADR-032 phases 1-3 — epoch, lease, ID map, MCP tools, compat tests

Phase 2 Rust: full epoch reconciliation (EpochTracker with AtomicU64, 23 tests),
writer lease with file lock and PID-based stale detection (12 tests),
direct ID mapping trait with DirectIdMap and OffsetIdMap (20 tests).

Phase 2 JS: createWithRvf/saveToRvf/loadFromRvf factories, BrowserWriterLease
with IndexedDB heartbeat, rvf-migrate and rvf-rebuild CLI commands, epoch sync
helpers. +541 lines to index.ts, new cli-rvf.ts (363 lines).

Phase 3: 3 MCP rvlite tools (rvlite_sql, rvlite_cypher, rvlite_sparql),
CI wasm-dedup-check workflow, 6 cross-platform compat tests, shared peer dep.

Phase 1: 4 RVF smoke integration tests (full lifecycle, cosine, multi-restart,
metadata). Node.js CLI smoke test script.

81 new Rust tests passing. ADR-032 checklist fully complete.

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: bump versions and fix TS/README for npm publish

- ruvector 0.1.88 → 0.1.97 (match npm registry)
- rvlite 0.2.1 → 0.2.2
- @ruvector/rvf 0.1.0 → 0.1.1
- Fix MCP command in ruvector README (mcp-server → mcp start)
- Fix WASM type conflicts in rvlite index.ts (cast dynamic imports to any)

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add witness auto-append, real CLI verification, prebuilt fallbacks, and README examples

Five "What's NOT Automatic" gaps fixed:
1. Witness auto-append: WitnessConfig in RvfOptions auto-records ingest/delete/compact
   operations as WITNESS_SEG entries with SHAKE-256 hash chains
2. verify-witness CLI: Real hash chain verification — extracts WITNESS_SEG payloads,
   runs verify_witness_chain() with full SHAKE-256 validation
3. verify-attestation CLI: Real kernel image hash verification and attestation
   witness chain validation
4. Prebuilt kernel fallback: KernelBuilder::from_builtin_minimal() produces valid
   bzImage without Docker
5. Prebuilt eBPF fallback: EbpfCompiler::from_precompiled() produces valid BPF ELF
   without clang; Launcher::check_requirements()/dry_run() for QEMU detection

README examples added to all 3 packages:
- crates/rvf/README.md: Proof of Operations section
- npm/packages/rvf/README.md: 7 real-world examples
- npm/packages/ruvector/README.md: Working cognitive container examples

830 tests passing, workspace compiles cleanly.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-02-14 18:03:26 -05:00
rUv
b63ff563ad feat(rvf): RuVector Format — Universal Cognitive Container SDK (#166) 
* feat(rvf): add RuVector Format universal substrate specification

Research and design for RVF — a streaming, progressive, adaptive, quantum-secure
binary format for vector intelligence. Covers append-only segment model, two-level
tail manifests, temperature tiering, progressive HNSW indexing, epoch-based overlay
system, SIMD-optimized query paths, WASM microkernel for Cognitum tiles, domain
profiles (RVDNA, RVText, RVGraph, RVVision), and post-quantum cryptography.

https://claude.ai/code/session_01DDqjGE51JpsRE3DgUjFyjW

* feat(rvf): add deletion, filtered search, concurrency, and operations specs

Fill four specification gaps in the RVF format design:
- spec/07: Vector deletion lifecycle, JOURNAL_SEG wire format, deletion bitmaps
- spec/08: Filtered search with META_SEG, METAIDX_SEG, filter expression language
- spec/09: Writer locking, reader-writer coordination, versioning, space reclamation
- spec/10: Batch operations API, error codes, network streaming protocol

Also fixes the segment header field conflict between spec/01 and wire/binary-layout.md
(checksum_algo/compression now u8, adds uncompressed_len at 0x38).

https://claude.ai/code/session_01DDqjGE51JpsRE3DgUjFyjW

* feat(rvf): add RuVector Format SDK, 40 examples, MCP server, and documentation

Complete RVF implementation including:
- 12 Rust crates (rvf-types, rvf-wire, rvf-manifest, rvf-index, rvf-quant,
  rvf-crypto, rvf-runtime, rvf-import, rvf-wasm, rvf-node, rvf-server,
  plus integration tests)
- 40 runnable examples covering core storage, agentic AI, production
  patterns, vertical domains, exotic capabilities, runtime targets,
  network/security, POSIX/systems, and network operations
- TypeScript SDK (npm/packages/rvf) with RvfDatabase class
- MCP server (npm/packages/rvf-mcp-server) with stdio and SSE transports
- Node.js N-API bindings (npm/packages/rvf-node)
- WASM package (npm/packages/rvf-wasm)
- ADR-029 (canonical format), ADR-030 (computational container),
  ADR-031 (example repository)
- DNA-style lineage provenance, computational containers (KERNEL_SEG,
  EBPF_SEG), witness chains, TEE attestation, domain profiles
- Superseded ADR annotations for ADR-001, ADR-005, ADR-006, ADR-018-021

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add CLI, WASM store, generate_all, and 46 output .rvf files

- Add rvf-cli crate (665 lines, 9 subcommands: create/ingest/query/delete/status/inspect/compact/derive/serve)
- Add WASM control plane store (alloc_setup, segment, store modules) for ~46 KB binary
- Add generate_all.rs example producing 46 persistent .rvf files in output/
- Add Node.js N-API bindings for lineage, kernel/eBPF, and inspection
- Add npm TypeScript backend/database/types for RVF integration
- Update READMEs with CLI sections, MCP server docs, and crate map (13 crates)
- All 40 examples verified passing

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add Claude Code appliance, improve Quick Start, fix API docs

- Add claude_code_appliance.rs: self-booting RVF with SSH + Claude Code
  install (curl -fsSL https://claude.ai/install.sh | bash), 3 SSH users,
  eBPF filter, 20-package manifest, witness chain, lineage snapshot
- Improve Quick Start: Install section (crate/CLI/npm/WASM/MCP), WASM
  browser example, generate_all reference, expanded Rust crate deps
- Fix embed_kernel/embed_ebpf API docs to match actual signatures
  (u8 params with `as u8` cast, 6-param kernel, Option<&[u8]> btf)
- Update generate_all.rs: add claude_code_appliance generator (47 files)
- Regenerate all 47 output .rvf files

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add RVCOW branching, real kernel/eBPF/launcher, 795 tests

Vector-native copy-on-write branching (ADR-031) with four new segment
types (COW_MAP 0x20, REFCOUNT 0x21, MEMBERSHIP 0x22, DELTA 0x23),
real Linux microkernel builder, QEMU microVM launcher, real eBPF
programs, and 128-byte KernelBinding for tamper-evident kernel-manifest
linkage.

New crates:
- rvf-kernel: Docker-based kernel build, real cpio/newc initramfs builder,
  SHA3-256 verification, prebuilt kernel support (37 tests)
- rvf-launch: QEMU microVM launcher with QMP shutdown, KVM/TCG detection,
  virtio-blk/net port forwarding, kernel extraction (8 tests)
- rvf-ebpf: 3 real BPF C programs (xdp_distance, socket_filter,
  tc_query_route) with clang compilation support (17 tests)

RVCOW runtime:
- CowEngine with read/write paths, write coalescing, snapshot-freeze
- CowMap (flat-array), MembershipFilter (bitmap), CowCompactor
- 3x read performance via pread optimization (1.3us/vector)
- Branch creation: 2.6ms for 10K vectors, child = 162 bytes

Security: 20-finding audit, 7 fixes applied including division-by-zero
guards, integer overflow checks, and KernelBinding::from_bytes_validated().

CLI: 8 new commands (launch, embed-kernel, embed-ebpf, filter, freeze,
verify-witness, verify-attestation, rebuild-refcounts), serve wired to
real rvf-server.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): update README, add crate/npm READMEs, publish to crates.io and npm

- Rewrite README with cognitive container terminology, grouped features,
  4 comparison tables (vs Docker, Vector DBs, Git LFS, SQLite), updated
  benchmarks, architecture diagram, and 45 examples
- Add READMEs for rvf-kernel, rvf-launch, rvf-ebpf, rvf-import crates
- Add READMEs for @ruvector/rvf, rvf-node, rvf-wasm, rvf-mcp-server npm packages
- Fix Cargo.toml metadata (homepage, readme, categories, keywords) and
  add version specs to all path dependencies for crates.io publishing
- Fix clippy warnings in rvf-kernel/initramfs.rs and rvf-launch/lib.rs
- Published to crates.io: rvf-types, rvf-wire, rvf-manifest, rvf-quant,
  rvf-index, rvf-crypto (remaining crates pending rate limit)
- Published to npm: @ruvector/rvf, @ruvector/rvf-node, @ruvector/rvf-wasm,
  @ruvector/rvf-mcp-server

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: add rvf-kernel, rvf-ebpf, rvf-launch, rvf-server, rvf-import, rvf-cli to workspace

Include all 15 RVF crates plus integration tests and benchmarks in the
root workspace members list so cargo publish can resolve them by name.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add published packages, cognitive container branding, grouped capabilities

- Add Published Packages section with 13 crates.io + 4 npm tables
- Add Platform Support table (Linux, macOS, Windows, WASM, no_std)
- Expand capability table from 9 to 15 rows in 4 groups
- Rewrite all "How" descriptions in plain language
- Update .rvf diagram to show all 20 segment types
- Rename ADRs: computational container -> cognitive container
- Add emojis to all section headers

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: update root README with RVF cognitive containers, expanded capabilities

- Update intro: "gets smarter + ships as cognitive container"
- Add self-booting microservice row to Pinecone comparison table
- Expand capabilities from 34 to 42 features with dedicated RVF section
- Update "Think of it as" to include Docker comparison and RVF explanation
- Add RVF collapsed group to Ecosystem (13 crates, 4 npm, install commands)
- Add RVF to Platform & Edge section with install commands
- Add RVF npm packages (4) and Rust crates (13) to package reference
- Add RVF rows to feature comparison table (6 new rows)
- Add ADR-030/031 to ADR list
- Add RVF to Installation table, Project Structure
- Update attention mechanisms count from 39 to 40+
- Update npm count to 49+, Rust crates to 83
- Update footer with crates.io and RVF links

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: expand comparison table with emojis, cost, audit, branching, single-file

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: rewrite comparison table in plain language

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: clean up empty code change sections in the changes log

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-02-14 13:14:49 -05:00
rUv
f8870b3c71 feat(rvf): RuVector Format — Universal Cognitive Container SDK (#166) 
* feat(rvf): add RuVector Format universal substrate specification

Research and design for RVF — a streaming, progressive, adaptive, quantum-secure
binary format for vector intelligence. Covers append-only segment model, two-level
tail manifests, temperature tiering, progressive HNSW indexing, epoch-based overlay
system, SIMD-optimized query paths, WASM microkernel for Cognitum tiles, domain
profiles (RVDNA, RVText, RVGraph, RVVision), and post-quantum cryptography.

https://claude.ai/code/session_01DDqjGE51JpsRE3DgUjFyjW

* feat(rvf): add deletion, filtered search, concurrency, and operations specs

Fill four specification gaps in the RVF format design:
- spec/07: Vector deletion lifecycle, JOURNAL_SEG wire format, deletion bitmaps
- spec/08: Filtered search with META_SEG, METAIDX_SEG, filter expression language
- spec/09: Writer locking, reader-writer coordination, versioning, space reclamation
- spec/10: Batch operations API, error codes, network streaming protocol

Also fixes the segment header field conflict between spec/01 and wire/binary-layout.md
(checksum_algo/compression now u8, adds uncompressed_len at 0x38).

https://claude.ai/code/session_01DDqjGE51JpsRE3DgUjFyjW

* feat(rvf): add RuVector Format SDK, 40 examples, MCP server, and documentation

Complete RVF implementation including:
- 12 Rust crates (rvf-types, rvf-wire, rvf-manifest, rvf-index, rvf-quant,
  rvf-crypto, rvf-runtime, rvf-import, rvf-wasm, rvf-node, rvf-server,
  plus integration tests)
- 40 runnable examples covering core storage, agentic AI, production
  patterns, vertical domains, exotic capabilities, runtime targets,
  network/security, POSIX/systems, and network operations
- TypeScript SDK (npm/packages/rvf) with RvfDatabase class
- MCP server (npm/packages/rvf-mcp-server) with stdio and SSE transports
- Node.js N-API bindings (npm/packages/rvf-node)
- WASM package (npm/packages/rvf-wasm)
- ADR-029 (canonical format), ADR-030 (computational container),
  ADR-031 (example repository)
- DNA-style lineage provenance, computational containers (KERNEL_SEG,
  EBPF_SEG), witness chains, TEE attestation, domain profiles
- Superseded ADR annotations for ADR-001, ADR-005, ADR-006, ADR-018-021

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add CLI, WASM store, generate_all, and 46 output .rvf files

- Add rvf-cli crate (665 lines, 9 subcommands: create/ingest/query/delete/status/inspect/compact/derive/serve)
- Add WASM control plane store (alloc_setup, segment, store modules) for ~46 KB binary
- Add generate_all.rs example producing 46 persistent .rvf files in output/
- Add Node.js N-API bindings for lineage, kernel/eBPF, and inspection
- Add npm TypeScript backend/database/types for RVF integration
- Update READMEs with CLI sections, MCP server docs, and crate map (13 crates)
- All 40 examples verified passing

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add Claude Code appliance, improve Quick Start, fix API docs

- Add claude_code_appliance.rs: self-booting RVF with SSH + Claude Code
  install (curl -fsSL https://claude.ai/install.sh | bash), 3 SSH users,
  eBPF filter, 20-package manifest, witness chain, lineage snapshot
- Improve Quick Start: Install section (crate/CLI/npm/WASM/MCP), WASM
  browser example, generate_all reference, expanded Rust crate deps
- Fix embed_kernel/embed_ebpf API docs to match actual signatures
  (u8 params with `as u8` cast, 6-param kernel, Option<&[u8]> btf)
- Update generate_all.rs: add claude_code_appliance generator (47 files)
- Regenerate all 47 output .rvf files

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add RVCOW branching, real kernel/eBPF/launcher, 795 tests

Vector-native copy-on-write branching (ADR-031) with four new segment
types (COW_MAP 0x20, REFCOUNT 0x21, MEMBERSHIP 0x22, DELTA 0x23),
real Linux microkernel builder, QEMU microVM launcher, real eBPF
programs, and 128-byte KernelBinding for tamper-evident kernel-manifest
linkage.

New crates:
- rvf-kernel: Docker-based kernel build, real cpio/newc initramfs builder,
  SHA3-256 verification, prebuilt kernel support (37 tests)
- rvf-launch: QEMU microVM launcher with QMP shutdown, KVM/TCG detection,
  virtio-blk/net port forwarding, kernel extraction (8 tests)
- rvf-ebpf: 3 real BPF C programs (xdp_distance, socket_filter,
  tc_query_route) with clang compilation support (17 tests)

RVCOW runtime:
- CowEngine with read/write paths, write coalescing, snapshot-freeze
- CowMap (flat-array), MembershipFilter (bitmap), CowCompactor
- 3x read performance via pread optimization (1.3us/vector)
- Branch creation: 2.6ms for 10K vectors, child = 162 bytes

Security: 20-finding audit, 7 fixes applied including division-by-zero
guards, integer overflow checks, and KernelBinding::from_bytes_validated().

CLI: 8 new commands (launch, embed-kernel, embed-ebpf, filter, freeze,
verify-witness, verify-attestation, rebuild-refcounts), serve wired to
real rvf-server.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): update README, add crate/npm READMEs, publish to crates.io and npm

- Rewrite README with cognitive container terminology, grouped features,
  4 comparison tables (vs Docker, Vector DBs, Git LFS, SQLite), updated
  benchmarks, architecture diagram, and 45 examples
- Add READMEs for rvf-kernel, rvf-launch, rvf-ebpf, rvf-import crates
- Add READMEs for @ruvector/rvf, rvf-node, rvf-wasm, rvf-mcp-server npm packages
- Fix Cargo.toml metadata (homepage, readme, categories, keywords) and
  add version specs to all path dependencies for crates.io publishing
- Fix clippy warnings in rvf-kernel/initramfs.rs and rvf-launch/lib.rs
- Published to crates.io: rvf-types, rvf-wire, rvf-manifest, rvf-quant,
  rvf-index, rvf-crypto (remaining crates pending rate limit)
- Published to npm: @ruvector/rvf, @ruvector/rvf-node, @ruvector/rvf-wasm,
  @ruvector/rvf-mcp-server

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: add rvf-kernel, rvf-ebpf, rvf-launch, rvf-server, rvf-import, rvf-cli to workspace

Include all 15 RVF crates plus integration tests and benchmarks in the
root workspace members list so cargo publish can resolve them by name.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(rvf): add published packages, cognitive container branding, grouped capabilities

- Add Published Packages section with 13 crates.io + 4 npm tables
- Add Platform Support table (Linux, macOS, Windows, WASM, no_std)
- Expand capability table from 9 to 15 rows in 4 groups
- Rewrite all "How" descriptions in plain language
- Update .rvf diagram to show all 20 segment types
- Rename ADRs: computational container -> cognitive container
- Add emojis to all section headers

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: update root README with RVF cognitive containers, expanded capabilities

- Update intro: "gets smarter + ships as cognitive container"
- Add self-booting microservice row to Pinecone comparison table
- Expand capabilities from 34 to 42 features with dedicated RVF section
- Update "Think of it as" to include Docker comparison and RVF explanation
- Add RVF collapsed group to Ecosystem (13 crates, 4 npm, install commands)
- Add RVF to Platform & Edge section with install commands
- Add RVF npm packages (4) and Rust crates (13) to package reference
- Add RVF rows to feature comparison table (6 new rows)
- Add ADR-030/031 to ADR list
- Add RVF to Installation table, Project Structure
- Update attention mechanisms count from 39 to 40+
- Update npm count to 49+, Rust crates to 83
- Update footer with crates.io and RVF links

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: expand comparison table with emojis, cost, audit, branching, single-file

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: rewrite comparison table in plain language

Co-Authored-By: claude-flow <ruv@ruv.net>

* chore: clean up empty code change sections in the changes log

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-02-14 13:14:49 -05:00
rUv
470c62d568 feat(ruqu): add quantum execution intelligence engine with 5 backends 
Transforms ruqu from classical coherence monitor into full-stack quantum execution intelligence engine (~2K to ~24K lines).

New: StateVector, Stabilizer, TensorNetwork, Clifford+T, and Hardware simulation backends. Cost-model planner, surface code decoder (union-find O(n*alpha(n))), QEC scheduler, noise models, OpenQASM 3.0 export, deterministic replay, and cross-backend verification.

PR #161
 2026-02-12 12:55:21 -05:00
rUv
e6d4330008 feat(ruqu): add quantum execution intelligence engine with 5 backends 
Transforms ruqu from classical coherence monitor into full-stack quantum execution intelligence engine (~2K to ~24K lines).

New: StateVector, Stabilizer, TensorNetwork, Clifford+T, and Hardware simulation backends. Cost-model planner, surface code decoder (union-find O(n*alpha(n))), QEC scheduler, noise models, OpenQASM 3.0 export, deterministic replay, and cross-backend verification.

PR #161
 2026-02-12 12:55:21 -05:00
rUv
6af4200a60 feat: add package.json for rvdna example with WASM bindings and build scripts 2026-02-12 15:32:55 +00:00
rUv
244bbffe54 feat: add package.json for rvdna example with WASM bindings and build scripts 2026-02-12 15:32:55 +00:00
rUv
4a59e7e4de Merge origin/main into claude/quantum-engine-adrs-6OsEO - resolve Cargo.toml conflict 2026-02-08 17:04:57 +00:00
rUv
576daba1df Merge origin/main into claude/quantum-engine-adrs-6OsEO - resolve Cargo.toml conflict 2026-02-08 17:04:57 +00:00
rUv
a213303f3a Merge pull request #151 from ruvnet/claude/bitnet-ruvllm-research-Cz4Ot 
docs: Add ADR-017 and DDD for Craftsman Ultra 30b 1bit BitNet integration
 2026-02-08 11:55:15 -05:00
rUv
593b44a543 Merge pull request #151 from ruvnet/claude/bitnet-ruvllm-research-Cz4Ot 
docs: Add ADR-017 and DDD for Craftsman Ultra 30b 1bit BitNet integration
 2026-02-08 11:55:15 -05:00
Claude
5b1bf32ea7 docs: Add ADR-018 through ADR-023 and DDD for temporal tensor store 
Extends ADR-017 with detailed architecture for block-based temporal
tensor compression. Introduces 6 new ADRs covering storage engine,
tiered quantization formats, temporal scoring algorithm, delta
compression/reconstruction, WASM API, and benchmarking criteria.
Adds comprehensive DDD with 5 bounded contexts mapping to the
proposed 6-crate workspace layout.

ADR-018: Block-based storage engine with BlockKey/BlockMeta model,
         append-only MetaLog, tiered data files, CRC32 checksums
ADR-019: 8/7/5/3-bit quantization formats with two-level scale,
         Tier0 compression-to-zero, codec_bits bit packing
ADR-020: EMA + popcount + recency scoring, hysteresis, budgeted
         maintenance ticks, fast exp approximation
ADR-021: Read/write paths, sparse delta format, delta chain
         compaction, factor-based reconstruction policies
ADR-022: WASM exports (tts_init/put/get/tick/stats), host-imported
         BlockIO, cross-platform strategy (native/Node/browser/edge)
ADR-023: Zipf simulation acceptance test (1M blocks, 10M accesses),
         microbench targets, failure mode catalog

DDD: 5 bounded contexts (Block Management, Quantization, Temporal
     Scoring, Storage Engine, Delta & Reconstruction) with aggregate
     roots, domain events, repository interfaces, and context map.

Total: 8,084 lines across 7 documents.

https://claude.ai/code/session_01Ksy165BL5nGpVoWaAfTE7t
 2026-02-08 02:12:38 +00:00
Claude
eee542e103 docs: Add ADR-018 through ADR-023 and DDD for temporal tensor store 
Extends ADR-017 with detailed architecture for block-based temporal
tensor compression. Introduces 6 new ADRs covering storage engine,
tiered quantization formats, temporal scoring algorithm, delta
compression/reconstruction, WASM API, and benchmarking criteria.
Adds comprehensive DDD with 5 bounded contexts mapping to the
proposed 6-crate workspace layout.

ADR-018: Block-based storage engine with BlockKey/BlockMeta model,
         append-only MetaLog, tiered data files, CRC32 checksums
ADR-019: 8/7/5/3-bit quantization formats with two-level scale,
         Tier0 compression-to-zero, codec_bits bit packing
ADR-020: EMA + popcount + recency scoring, hysteresis, budgeted
         maintenance ticks, fast exp approximation
ADR-021: Read/write paths, sparse delta format, delta chain
         compaction, factor-based reconstruction policies
ADR-022: WASM exports (tts_init/put/get/tick/stats), host-imported
         BlockIO, cross-platform strategy (native/Node/browser/edge)
ADR-023: Zipf simulation acceptance test (1M blocks, 10M accesses),
         microbench targets, failure mode catalog

DDD: 5 bounded contexts (Block Management, Quantization, Temporal
     Scoring, Storage Engine, Delta & Reconstruction) with aggregate
     roots, domain events, repository interfaces, and context map.

Total: 8,084 lines across 7 documents.

https://claude.ai/code/session_01Ksy165BL5nGpVoWaAfTE7t
 2026-02-08 02:12:38 +00:00
Claude
c0b4f90b7b docs: Add ADR-QE-014 documenting exotic quantum-classical discoveries 
Documents 4 validated Phase 1 discoveries:
- Decoherence trajectory fingerprinting (clustering without similarity)
- Interference-based polysemy resolution (microsecond disambiguation)
- Counterfactual dependency mapping (pipeline importance scoring)
- Phase-coherent swarm coordination (quality > headcount)

Outlines 8 Phase 2 hypotheses for cross-module experiments including
time-dependent disambiguation, QEC on swarm reasoning, counterfactual
search explanation, and the full decohere-interfere-collapse-verify pipeline.

https://claude.ai/code/session_01B1NkbLDWYPaacS9miKsnvW
 2026-02-06 15:51:08 +00:00
Claude
21dc9866cc docs: Add ADR-QE-014 documenting exotic quantum-classical discoveries 
Documents 4 validated Phase 1 discoveries:
- Decoherence trajectory fingerprinting (clustering without similarity)
- Interference-based polysemy resolution (microsecond disambiguation)
- Counterfactual dependency mapping (pipeline importance scoring)
- Phase-coherent swarm coordination (quality > headcount)

Outlines 8 Phase 2 hypotheses for cross-module experiments including
time-dependent disambiguation, QEC on swarm reasoning, counterfactual
search explanation, and the full decohere-interfere-collapse-verify pipeline.

https://claude.ai/code/session_01B1NkbLDWYPaacS9miKsnvW
 2026-02-06 15:51:08 +00:00
Claude
2782fc6305 docs: Add ADR-QE-013 Deutsch's theorem proof with historical comparison 
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
 2026-02-06 02:00:35 +00:00
Claude
5e18e8b8fa docs: Add ADR-QE-013 Deutsch's theorem proof with historical comparison 
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
 2026-02-06 02:00:35 +00:00
Claude
501e02d753 feat: Add quantum simulation engine ADR series (QE-001 to QE-012) and DDD design documents 
Comprehensive architecture decision records and domain-driven design documentation
for integrating a Rust-based quantum simulation engine (ruQu) into the ruVector stack.

ADR Series (12 documents):
- QE-001: Core Architecture - pure Rust state-vector simulator decision
- QE-002: Crate Structure - three-crate architecture (ruqu-core, ruqu-wasm, ruqu-algorithms)
- QE-003: WASM Compilation - WebAssembly strategy with 25-qubit limit enforcement
- QE-004: Performance Optimization - SIMD, multithreading, gate fusion, benchmarks
- QE-005: VQE Algorithm - variational eigensolver with exact expectation values
- QE-006: Grover Search - O(1) oracle optimization via direct state vector access
- QE-007: QAOA MaxCut - graph-based optimization with Rzz native gates
- QE-008: Surface Code Error Correction - mid-circuit measurement, syndrome extraction
- QE-009: Tensor Network Evaluation - MPS/contraction for shallow circuits
- QE-010: Observability & Monitoring - metrics, tracing, health checks integration
- QE-011: Memory Gating & Power Management - zero-idle, on-demand allocation
- QE-012: Min-Cut Coherence Integration - syndrome-to-decoder bridge with ruQu

DDD Design (3 documents):
- Strategic Design: 6 bounded contexts, context map, ubiquitous language
- Tactical Design: 6 aggregates, 20+ value objects, 15+ domain events, services
- Integration Patterns: anti-corruption layers, shared kernel, event flows

https://claude.ai/code/session_01B1NkbLDWYPaacS9miKsnvW
 2026-02-06 00:39:39 +00:00
Claude
ce063b3d3f feat: Add quantum simulation engine ADR series (QE-001 to QE-012) and DDD design documents 
Comprehensive architecture decision records and domain-driven design documentation
for integrating a Rust-based quantum simulation engine (ruQu) into the ruVector stack.

ADR Series (12 documents):
- QE-001: Core Architecture - pure Rust state-vector simulator decision
- QE-002: Crate Structure - three-crate architecture (ruqu-core, ruqu-wasm, ruqu-algorithms)
- QE-003: WASM Compilation - WebAssembly strategy with 25-qubit limit enforcement
- QE-004: Performance Optimization - SIMD, multithreading, gate fusion, benchmarks
- QE-005: VQE Algorithm - variational eigensolver with exact expectation values
- QE-006: Grover Search - O(1) oracle optimization via direct state vector access
- QE-007: QAOA MaxCut - graph-based optimization with Rzz native gates
- QE-008: Surface Code Error Correction - mid-circuit measurement, syndrome extraction
- QE-009: Tensor Network Evaluation - MPS/contraction for shallow circuits
- QE-010: Observability & Monitoring - metrics, tracing, health checks integration
- QE-011: Memory Gating & Power Management - zero-idle, on-demand allocation
- QE-012: Min-Cut Coherence Integration - syndrome-to-decoder bridge with ruQu

DDD Design (3 documents):
- Strategic Design: 6 bounded contexts, context map, ubiquitous language
- Tactical Design: 6 aggregates, 20+ value objects, 15+ domain events, services
- Integration Patterns: anti-corruption layers, shared kernel, event flows

https://claude.ai/code/session_01B1NkbLDWYPaacS9miKsnvW
 2026-02-06 00:39:39 +00:00
Claude
61d91a0ef9 feat: Add ADR-017 temporal tensor compression with tiered quantization 
Introduces a complete temporal tensor compression system with:

- ADR-017: SOTA research-backed architecture decision record covering
  groupwise symmetric quantization, temporal segment reuse, access-pattern
  driven tier selection (8/7/5/3 bit), and WASM-compatible design

- ruvector-temporal-tensor crate (zero external dependencies):
  - tier_policy: Score-based hot/warm/cold bit-width selection
  - f16: Software IEEE 754 half-precision conversion
  - bitpack: Arbitrary bit-width stream packing (no alignment waste)
  - quantizer: Groupwise symmetric quantization with f16 scales
  - segment: Binary segment format (TQTC) encode/decode
  - compressor: Temporal segment manager with drift detection
  - ffi: WASM/C FFI with handle-based resource management

- ruvector-temporal-tensor-wasm crate for wasm32 targets

- 33 passing unit tests covering all modules

Compression targets: 4x (hot/8-bit), 4.57x (warm/7-bit),
6.4x (warm/5-bit), 10.67x (cold/3-bit) vs f32 baseline.

https://claude.ai/code/session_01U63xtGd5Q8mUevyY7nUSfJ
 2026-02-06 00:28:21 +00:00
Claude
2a21efbb5c feat: Add ADR-017 temporal tensor compression with tiered quantization 
Introduces a complete temporal tensor compression system with:

- ADR-017: SOTA research-backed architecture decision record covering
  groupwise symmetric quantization, temporal segment reuse, access-pattern
  driven tier selection (8/7/5/3 bit), and WASM-compatible design

- ruvector-temporal-tensor crate (zero external dependencies):
  - tier_policy: Score-based hot/warm/cold bit-width selection
  - f16: Software IEEE 754 half-precision conversion
  - bitpack: Arbitrary bit-width stream packing (no alignment waste)
  - quantizer: Groupwise symmetric quantization with f16 scales
  - segment: Binary segment format (TQTC) encode/decode
  - compressor: Temporal segment manager with drift detection
  - ffi: WASM/C FFI with handle-based resource management

- ruvector-temporal-tensor-wasm crate for wasm32 targets

- 33 passing unit tests covering all modules

Compression targets: 4x (hot/8-bit), 4.57x (warm/7-bit),
6.4x (warm/5-bit), 10.67x (cold/3-bit) vs f32 baseline.

https://claude.ai/code/session_01U63xtGd5Q8mUevyY7nUSfJ
 2026-02-06 00:28:21 +00:00
Claude
767901ea79 feat: Add RLM embedder, tokenizer, eval gates, trace writer, and security hardening 
New modules (4 files, 2,359 lines):
- rlm_embedder.rs (743L): RLM-style recursive sentence transformer with
  3 variants (query-conditioned, corpus-conditioned, contradiction-aware
  twin), merge rule, BaseEmbedder/NeighborRetriever traits, 14 tests
- tokenizer.rs (418L): BPE tokenizer with GGUF vocab loading, encode/decode,
  special token handling, 10 tests
- trace.rs (554L): JSONL trace writer for routing, citation, refusal
  decisions, jaccard similarity, manual JSON serialization, 10 tests
- eval.rs (644L): Three behavioral gates (routing correctness >= 0.85,
  citation precision >= 0.90, refusal F1 >= 0.85), EvalSuite, 12 tests

Documentation:
- AD-24: RLM-Style Recursive Sentence Transformer Embedder — 3 variants,
  merge rule, training strategy, evaluation criteria, appliance fit
- DDD v2.6: 8 new ubiquitous language terms, 4 new open questions (#31-34)
- 3 new positive consequences (#31-33) for RLM embeddings

Security hardening (across 6 existing files):
- Path traversal validation in GGUF export
- Division-by-zero epsilon guards in quantizer
- Bounds validation on public function inputs
- NaN-safe softmax with -inf handling

138 tests pass, 0 compilation errors.
Total bitnet module: 9,632 lines across 16 files.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 15:40:59 +00:00
Claude
a3c7fb54a8 feat: Add RLM embedder, tokenizer, eval gates, trace writer, and security hardening 
New modules (4 files, 2,359 lines):
- rlm_embedder.rs (743L): RLM-style recursive sentence transformer with
  3 variants (query-conditioned, corpus-conditioned, contradiction-aware
  twin), merge rule, BaseEmbedder/NeighborRetriever traits, 14 tests
- tokenizer.rs (418L): BPE tokenizer with GGUF vocab loading, encode/decode,
  special token handling, 10 tests
- trace.rs (554L): JSONL trace writer for routing, citation, refusal
  decisions, jaccard similarity, manual JSON serialization, 10 tests
- eval.rs (644L): Three behavioral gates (routing correctness >= 0.85,
  citation precision >= 0.90, refusal F1 >= 0.85), EvalSuite, 12 tests

Documentation:
- AD-24: RLM-Style Recursive Sentence Transformer Embedder — 3 variants,
  merge rule, training strategy, evaluation criteria, appliance fit
- DDD v2.6: 8 new ubiquitous language terms, 4 new open questions (#31-34)
- 3 new positive consequences (#31-33) for RLM embeddings

Security hardening (across 6 existing files):
- Path traversal validation in GGUF export
- Division-by-zero epsilon guards in quantizer
- Bounds validation on public function inputs
- NaN-safe softmax with -inf handling

138 tests pass, 0 compilation errors.
Total bitnet module: 9,632 lines across 16 files.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 15:40:59 +00:00
Claude
11e1e3af95 feat: Add AD-23 Phase-1 distillation, expert cache, and DDD updates 
AD-23: Phase-1 Distillation via External GPU Teacher Artifacts
- One-time GPU job produces behavioral artifacts (routing traces,
  sparse logits, preference labels) — not trained weights
- CPU-only refinement: router repair, LoRA correction, EWC++, policy
  optimization using teacher artifacts
- Acceptance criteria: 200-prompt suite, all 3 behavioral gates,
  stability under 10% corpus perturbation

expert_cache.rs: MoE expert hot-set caching (new file)
- ExpertCache with LRU/LFU/Adaptive eviction policies
- MoeBatchScheduler: reorder token execution by expert for cache reuse
- Prefetcher trait for future platform-specific prefetch intrinsics
- 12 tests (92/92 bitnet tests pass)

DDD v2.5: 6 new ubiquitous language terms (Teacher Artifact, Behavioral
Distillation, Router Repair, Sparse Logits, Corpus Perturbation) and
4 new open questions (#27-30) for Phase-1 operability.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 15:12:33 +00:00
Claude
ab78e18a87 feat: Add AD-23 Phase-1 distillation, expert cache, and DDD updates 
AD-23: Phase-1 Distillation via External GPU Teacher Artifacts
- One-time GPU job produces behavioral artifacts (routing traces,
  sparse logits, preference labels) — not trained weights
- CPU-only refinement: router repair, LoRA correction, EWC++, policy
  optimization using teacher artifacts
- Acceptance criteria: 200-prompt suite, all 3 behavioral gates,
  stability under 10% corpus perturbation

expert_cache.rs: MoE expert hot-set caching (new file)
- ExpertCache with LRU/LFU/Adaptive eviction policies
- MoeBatchScheduler: reorder token execution by expert for cache reuse
- Prefetcher trait for future platform-specific prefetch intrinsics
- 12 tests (92/92 bitnet tests pass)

DDD v2.5: 6 new ubiquitous language terms (Teacher Artifact, Behavioral
Distillation, Router Repair, Sparse Logits, Corpus Perturbation) and
4 new open questions (#27-30) for Phase-1 operability.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 15:12:33 +00:00
Claude
cf8daa60c3 docs: Add AD-22 evaluation infrastructure and behavioral gates 
Defines three ship/no-ship gates:
- Gate 1: Routing correctness (>= 85% teacher agreement)
- Gate 2: Citation correctness (precision >= 90%, recall >= 70%)
- Gate 3: Refusal calibration (F1 >= 0.85)

Includes JSONL trace schema, auto-labeling strategy using RuVector
signals (redundancy, cluster disagreement, mincut fragility), and
go/no-go rule requiring all gates to pass on same prompt suite run.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 14:57:32 +00:00
Claude
d5219db15c docs: Add AD-22 evaluation infrastructure and behavioral gates 
Defines three ship/no-ship gates:
- Gate 1: Routing correctness (>= 85% teacher agreement)
- Gate 2: Citation correctness (precision >= 90%, recall >= 70%)
- Gate 3: Refusal calibration (F1 >= 0.85)

Includes JSONL trace schema, auto-labeling strategy using RuVector
signals (redundancy, cluster disagreement, mincut fragility), and
go/no-go rule requiring all gates to pass on same prompt suite run.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 14:57:32 +00:00
Claude
fd43a53cfb docs: Add AD-21 native Rust ternary kernels with WASM SIMD128 target 
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
 2026-02-03 14:07:52 +00:00
Claude
4685854fb8 docs: Add AD-21 native Rust ternary kernels with WASM SIMD128 target 
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
 2026-02-03 14:07:52 +00:00
Claude
2bb04a64ed feat: Implement Phase 0 PT-BitNet quantizer module 
Add bitnet/ module with absmean ternary quantizer, TernaryTensor type,
BITNET_T158 dequantization, and comprehensive test suite (~1600 lines).

Components:
- quantizer.rs: PtBitnetConfig, absmean_ternary(), quantize_tensor()
- ternary_tensor.rs: TernaryTensor, pack/unpack 2-bit ternary encoding
- dequantize.rs: dequantize_bitnet_t158(), block dequant, error metrics
- tests.rs: Packing roundtrips, quantization correctness, edge cases
- gguf/quantization.rs: BitnetT158 = 30 enum variant, block_size, bytes

Implements AD-1 (weight representation), AD-5 (GGUF extension), AD-18
(PT-BitNet quantization) from ADR-017.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 12:40:18 +00:00
Claude
120a94e0a5 feat: Implement Phase 0 PT-BitNet quantizer module 
Add bitnet/ module with absmean ternary quantizer, TernaryTensor type,
BITNET_T158 dequantization, and comprehensive test suite (~1600 lines).

Components:
- quantizer.rs: PtBitnetConfig, absmean_ternary(), quantize_tensor()
- ternary_tensor.rs: TernaryTensor, pack/unpack 2-bit ternary encoding
- dequantize.rs: dequantize_bitnet_t158(), block dequant, error metrics
- tests.rs: Packing roundtrips, quantization correctness, edge cases
- gguf/quantization.rs: BitnetT158 = 30 enum variant, block_size, bytes

Implements AD-1 (weight representation), AD-5 (GGUF extension), AD-18
(PT-BitNet quantization) from ADR-017.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 12:40:18 +00:00
Claude
80171fb8c1 docs: Add AD-20 SIMD-only training mode for Phase 0.5 in ADR/DDD 
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
 2026-02-03 07:46:59 +00:00
Claude
94c760d9cf docs: Add AD-20 SIMD-only training mode for Phase 0.5 in ADR/DDD 
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
 2026-02-03 07:46:59 +00:00
Claude
b905e54fe2 docs: Integrate RLM training stack into Craftsman Ultra ADR/DDD 
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
 2026-02-03 06:43:52 +00:00
Claude
d612fa61ff docs: Integrate RLM training stack into Craftsman Ultra ADR/DDD 
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
 2026-02-03 06:43:52 +00:00
Claude
5bddbe05a3 docs: Add Mac Studio as $0 Phase 0 PTQ platform in ADR-017 
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
 2026-02-03 06:30:05 +00:00
Claude
45d30c7c6e docs: Add Mac Studio as $0 Phase 0 PTQ platform in ADR-017 
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
 2026-02-03 06:30:05 +00:00
Claude
3686dfc52f docs: Add Phase 0 PTQ rapid prototype to Craftsman Ultra ADR/DDD 
Research post-training quantization feasibility for GLM-4.7-Flash as a
low-cost ($100, 2-4 hrs) validation step before full distillation ($1,300+).

ADR-017 changes:
- Restructured Option A from "Rejected" to tiered PTQ analysis (0A-0D)
- Added AD-18: PT-BitNet post-training quantization strategy
- Updated phased decision to A(0C) → D → C → B
- Added Phase 0 exit criteria and validation benchmarks
- Documented existing community GGUFs (bartowski, unsloth, ngxson)
- Identified RuvLLM IQ1_S dequant gap (type 19 parsed, not implemented)
- Added PT-BitNet, BitDistill, and STBLLM references

DDD v2.1 changes:
- Added 6 Phase 0 ubiquitous language terms (PT-BitNet, BITNET_T158, etc.)
- Updated Section 3.4 with dual-mode quantization pipeline (PTQ + distillation)
- Updated compatibility matrix with Phase 0 vs Phase 1+ columns
- Added 3 new open questions (calibration corpus, GGUF type, weight migration)

Key finding: IQ1_S ≠ BitNet b1.58. Generic codebook PTQ produces garbled
output; PT-BitNet absmean ternary quantization is viable for kernel validation.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 04:56:28 +00:00
Claude
5896d6dc39 docs: Add Phase 0 PTQ rapid prototype to Craftsman Ultra ADR/DDD 
Research post-training quantization feasibility for GLM-4.7-Flash as a
low-cost ($100, 2-4 hrs) validation step before full distillation ($1,300+).

ADR-017 changes:
- Restructured Option A from "Rejected" to tiered PTQ analysis (0A-0D)
- Added AD-18: PT-BitNet post-training quantization strategy
- Updated phased decision to A(0C) → D → C → B
- Added Phase 0 exit criteria and validation benchmarks
- Documented existing community GGUFs (bartowski, unsloth, ngxson)
- Identified RuvLLM IQ1_S dequant gap (type 19 parsed, not implemented)
- Added PT-BitNet, BitDistill, and STBLLM references

DDD v2.1 changes:
- Added 6 Phase 0 ubiquitous language terms (PT-BitNet, BITNET_T158, etc.)
- Updated Section 3.4 with dual-mode quantization pipeline (PTQ + distillation)
- Updated compatibility matrix with Phase 0 vs Phase 1+ columns
- Added 3 new open questions (calibration corpus, GGUF type, weight migration)

Key finding: IQ1_S ≠ BitNet b1.58. Generic codebook PTQ produces garbled
output; PT-BitNet absmean ternary quantization is viable for kernel validation.

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 04:56:28 +00:00
Claude
5ccf99d23b docs: Add AD-17 training infrastructure analysis (cloud GPU vs local SIMD) 
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
 2026-02-03 04:44:29 +00:00
Claude
d5caa4dacf docs: Add AD-17 training infrastructure analysis (cloud GPU vs local SIMD) 
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
 2026-02-03 04:44:29 +00:00
Claude
3defe8c566 docs: Integrate RLM training stack into Craftsman Ultra ADR/DDD 
ADR-017 updates:
- Add RLM Training Stack reuse section (GRPO, EWC++, ContrastiveTrainer,
  MemoryDistiller, PolicyStore — ~70% code reuse ratio)
- Add AD-11: GRPO-guided distillation with per-expert reward scaling
- Add AD-12: Contrastive pre-training for expert routing validation
- Add AD-13: EWC++ cross-expert stability during sequential distillation
- Add AD-14: PolicyStore TernaryScale per-layer policy persistence
- Add AD-15: MemoryDistiller trajectory tracking for distillation quality
- Add AD-16: Full pipeline composition with expert-parallel distillation
- Update Options C/D with RLM component mapping tables
- Update consequences, risks, and validation criteria

DDD v2.0 updates:
- Add bounded context 3.8: RLM Training Orchestration (70% reused)
- Add 13 ubiquitous language terms for RLM concepts
- Update context map with RLM relationships
- Update Quantization Pipeline to delegate to RLM Training
- Add 7 new domain events for GRPO, EWC, and distillation lifecycle
- Update module structure with reused vs new file annotations
- Add 6 RLM-specific integration tests
- Add 3 new open questions for RLM scaling

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 04:34:47 +00:00
Claude
261b510d8e docs: Integrate RLM training stack into Craftsman Ultra ADR/DDD 
ADR-017 updates:
- Add RLM Training Stack reuse section (GRPO, EWC++, ContrastiveTrainer,
  MemoryDistiller, PolicyStore — ~70% code reuse ratio)
- Add AD-11: GRPO-guided distillation with per-expert reward scaling
- Add AD-12: Contrastive pre-training for expert routing validation
- Add AD-13: EWC++ cross-expert stability during sequential distillation
- Add AD-14: PolicyStore TernaryScale per-layer policy persistence
- Add AD-15: MemoryDistiller trajectory tracking for distillation quality
- Add AD-16: Full pipeline composition with expert-parallel distillation
- Update Options C/D with RLM component mapping tables
- Update consequences, risks, and validation criteria

DDD v2.0 updates:
- Add bounded context 3.8: RLM Training Orchestration (70% reused)
- Add 13 ubiquitous language terms for RLM concepts
- Update context map with RLM relationships
- Update Quantization Pipeline to delegate to RLM Training
- Add 7 new domain events for GRPO, EWC, and distillation lifecycle
- Update module structure with reused vs new file annotations
- Add 6 RLM-specific integration tests
- Add 3 new open questions for RLM scaling

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK
 2026-02-03 04:34:47 +00:00
Claude
dcf90c0fbf docs: Add ADR-017 and DDD for Craftsman Ultra 30b 1bit BitNet integration 
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
 2026-02-03 04:18:28 +00:00
Claude
3e18734fa3 docs: Add ADR-017 and DDD for Craftsman Ultra 30b 1bit BitNet integration 
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
 2026-02-03 04:18:28 +00:00