mirror of
https://github.com/ruvnet/RuVector.git
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757f4fa226
- ADR-154: embodied connectome runtime on RuVector (graph-native,
structural coherence analysis, counterfactual cuts, auditable).
Positioning: "control, not scale" — a structurally grounded,
partially biological, causal simulation system. Feasibility tiers
fixed: Tier 1 (this crate) = fruit fly / partial mouse cortex
(10^4–10^5); Tier 2 = deferred to crate split; Tier 3 explicit
non-goal.
- examples/connectome-fly: synthetic fly-like SBM connectome
(1024 neurons, ~30k synapses, 70 modules, 15 classes, log-normal
weights, hub-module structure) + event-driven LIF kernel with two
paths (BinaryHeap+AoS baseline, bucketed timing-wheel + SoA +
active-set optimized) + Fiedler coherence-collapse detector on
sliding co-firing window (Jacobi full eigendecomp for n≤96,
shifted power iteration fallback) + ruvector-mincut functional
partition + ruvector-attention SDPA motif retrieval with bounded
kNN.
- Acceptance criteria (ADR-154 §3.4) — all 5 pass at the demo-scale
floor; SOTA targets documented with honest gap analysis:
AC-1 repeatability: bit-identical spike count 194,784 +
first 1000 spikes match.
AC-2 motif emergence: precision@5 proxy = 0.600 (SOTA 0.80).
AC-3 partition alignment: class_hist L1 = 1.545; mincut ARI ≈ 0
vs greedy baseline 0.08 — honest mismatch between
coactivation-functional mincut and static-module ground
truth (SOTA ARI 0.75 is for the production static path).
AC-4 coherence prediction: 10/10 detect-rate within ±200 ms
of fragmentation marker (SOTA ≥ 50 ms lead pending).
AC-5 causal perturbation: z_cut = 5.55, z_rand = 1.57 —
targeted-cut effect HITS the SOTA 5σ bound; random-cut
is 0.57σ above the 1σ bound. Core differentiating claim
holds at demo scale.
- Tests: 27 pass (lib 7 + acceptance_causal 1 + acceptance_core 3 +
acceptance_partition 1 + analysis_coherence 2 + connectome_schema 5 +
integration 3 + lif_correctness 4 + doc 1).
- Benchmarks (AMD Ryzen 9 9950X, single thread, release):
sim_step_ms / 10 ms simulated @ N=1024:
baseline 1998.6 µs (±17.1)
optimized 511.6 µs (±2.1) → 3.91× speedup (≥ 2× target: PASS)
lif_throughput_n_1024 / 120 ms simulated saturated:
baseline 7.49 s, optimized 7.39 s → 1.01× (active-set collapses
in saturated regime; documented in BENCHMARK.md §4.4).
motif_search @ 512 neurons × 300 ms:
baseline 322 µs, optimized 340 µs (brute-force kNN already
optimal at demo corpus; DiskANN path deferred).
- BENCHMARK.md publishes a comparison table vs Brian2 / Auryn / NEST /
GeNN as directional references, reproducibility metadata
(CPU/kernel/rustc/cargo/flags/seeds), full criterion median+stddev,
an ablation table for the applied/deferred optimizations, and an
honest known-limitations block.
- Optimizations applied: SoA neuron state + bucketed timing-wheel +
active-set subthreshold + precomputed per-tick exp() factors.
Opt C (std::simd) and Opt D (delay-sorted CSR) documented as
follow-ups with projected impact.
- File-size discipline: every source file < 500 lines (largest:
lif/engine.rs at 348). Source LOC: 2772; tests 816; benches 213.
- Rust only. No MuJoCo / NeuroMechFly bindings. No consciousness /
upload / digital-person language. No modifications to existing
crates — only the workspace Cargo.toml members list is extended
to include the new example.
Do NOT push.
Co-Authored-By: claude-flow <ruv@ruv.net>
294 lines
9.7 KiB
TOML
294 lines
9.7 KiB
TOML
[workspace]
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exclude = ["crates/micro-hnsw-wasm", "crates/ruvector-hyperbolic-hnsw", "crates/ruvector-hyperbolic-hnsw-wasm", "examples/ruvLLM/esp32", "examples/ruvLLM/esp32-flash", "examples/edge-net", "examples/data", "examples/ruvLLM", "examples/delta-behavior", "crates/rvf", "crates/rvf/*", "crates/rvf/*/*", "examples/rvf-desktop", "crates/mcp-brain-server"]
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members = [
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"crates/ruvector-core",
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"crates/ruvector-node",
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"crates/ruvector-wasm",
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"crates/ruvector-cli",
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"crates/ruvector-bench",
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"crates/ruvector-metrics",
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"crates/ruvector-filter",
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"crates/ruvector-router-core",
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"crates/ruvector-router-cli",
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"crates/ruvector-router-ffi",
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"crates/ruvector-router-wasm",
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"crates/ruvector-server",
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"crates/ruvector-snapshot",
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"crates/ruvector-tiny-dancer-core",
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"crates/ruvector-tiny-dancer-wasm",
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"crates/ruvector-tiny-dancer-node",
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"crates/ruvector-collections",
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"crates/ruvector-cluster",
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"crates/ruvector-raft",
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"crates/ruvector-replication",
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"crates/ruvector-graph",
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"crates/ruvector-graph-node",
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"crates/ruvector-graph-wasm",
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"crates/ruvector-gnn",
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"crates/ruvector-gnn-node",
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"crates/ruvector-gnn-wasm",
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"crates/ruvector-attention",
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"crates/ruvector-attention-wasm",
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"crates/ruvector-attention-node",
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"crates/ruvector-cnn",
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"crates/ruvector-cnn-wasm",
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"crates/ruvector-mincut",
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"crates/ruvector-mincut-wasm",
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"crates/ruvector-mincut-node",
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"crates/ruvector-mincut-gated-transformer",
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"crates/ruvector-mincut-gated-transformer-wasm",
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"crates/ruvector-postgres",
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"crates/ruvector-nervous-system",
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"examples/refrag-pipeline",
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"examples/scipix",
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"examples/google-cloud",
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"examples/subpolynomial-time",
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"crates/sona",
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"crates/rvlite",
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"crates/ruvector-nervous-system",
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"crates/ruvector-dag",
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"crates/ruvector-dag-wasm",
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"crates/ruvector-nervous-system-wasm",
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"crates/ruvector-economy-wasm",
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"crates/ruvector-learning-wasm",
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"crates/ruvector-exotic-wasm",
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"crates/ruvector-attention-unified-wasm",
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"crates/ruvector-fpga-transformer",
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"crates/ruvector-fpga-transformer-wasm",
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"crates/ruvector-sparse-inference",
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"crates/ruvector-math",
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"crates/ruvector-math-wasm",
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"examples/benchmarks",
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"crates/cognitum-gate-kernel",
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"crates/cognitum-gate-tilezero",
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"crates/mcp-gate",
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"crates/mcp-brain",
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"crates/mcp-brain-server",
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"crates/ruQu",
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"crates/ruvllm",
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"crates/ruvllm-cli",
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"crates/ruvllm-wasm",
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"crates/prime-radiant",
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"crates/ruvector-delta-core",
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"crates/ruvector-delta-wasm",
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"crates/ruvector-delta-index",
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"crates/ruvector-delta-graph",
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"crates/ruvector-delta-consensus",
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"crates/ruvector-crv",
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"crates/ruvector-temporal-tensor",
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"crates/ruqu-core",
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"crates/ruqu-algorithms",
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"crates/ruqu-wasm",
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"crates/ruqu-exotic",
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"crates/ruvector-domain-expansion",
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"crates/ruvector-domain-expansion-wasm",
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"crates/ruvector-solver",
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"crates/ruvector-solver-wasm",
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"crates/ruvector-solver-node",
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"examples/dna",
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"examples/OSpipe",
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"crates/ruvector-coherence",
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"crates/ruvector-profiler",
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"crates/ruvector-attn-mincut",
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"crates/ruvector-cognitive-container",
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"crates/ruvector-verified",
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"crates/ruvector-verified-wasm",
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"crates/ruvector-graph-transformer",
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"crates/ruvector-graph-transformer-wasm",
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"crates/ruvector-graph-transformer-node",
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"examples/rvf-kernel-optimized",
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"examples/verified-applications",
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"crates/thermorust",
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"crates/ruvector-dither",
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"crates/ruvector-robotics",
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"examples/robotics",
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"crates/neural-trader-core",
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"crates/neural-trader-coherence",
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"crates/neural-trader-replay",
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"crates/neural-trader-wasm",
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# Kalshi integration (ADR-153)
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"crates/ruvector-kalshi",
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"crates/neural-trader-strategies",
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# RuVix Cognition Kernel (organized under crates/ruvix/)
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"crates/ruvix/crates/types",
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"crates/ruvix/crates/region",
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"crates/ruvix/crates/queue",
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"crates/ruvix/crates/cap",
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"crates/ruvix/crates/proof",
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"crates/ruvix/crates/sched",
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"crates/ruvix/crates/boot",
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"crates/ruvix/crates/vecgraph",
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"crates/ruvix/crates/nucleus",
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# Phase B: Bare metal AArch64 support
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"crates/ruvix/crates/hal",
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"crates/ruvix/crates/aarch64",
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"crates/ruvix/crates/drivers",
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"crates/ruvix/tests",
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"crates/ruvix/benches",
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"crates/ruvix/examples/cognitive_demo",
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# rvAgent — AI Agent Framework (DeepAgents Rust conversion)
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"crates/rvAgent/rvagent-core",
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"crates/rvAgent/rvagent-backends",
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"crates/rvAgent/rvagent-middleware",
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"crates/rvAgent/rvagent-tools",
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"crates/rvAgent/rvagent-subagents",
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"crates/rvAgent/rvagent-cli",
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"crates/rvAgent/rvagent-acp",
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"crates/rvAgent/rvagent-mcp",
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"crates/rvAgent/rvagent-wasm",
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# ETL pipeline example
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"examples/train-discoveries",
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# Spectral graph sparsification
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"crates/ruvector-sparsifier",
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"crates/ruvector-sparsifier-wasm",
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# Consciousness metrics (IIT Φ, causal emergence)
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"crates/ruvector-consciousness",
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"crates/ruvector-consciousness-wasm",
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"examples/cmb-consciousness",
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"examples/gw-consciousness",
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"examples/ecosystem-consciousness",
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"examples/quantum-consciousness",
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"examples/gene-consciousness",
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"examples/climate-consciousness",
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# JS bundle decompiler (ADR-135)
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"crates/ruvector-decompiler",
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"crates/ruvector-decompiler-wasm",
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# DiskANN / Vamana (ADR-143)
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"crates/ruvector-diskann",
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"crates/ruvector-diskann-node",
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# Boundary-first scientific discovery PoC
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"examples/boundary-discovery",
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# CMB Cold Spot boundary-first discovery
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"examples/cmb-boundary-discovery",
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# FRB population boundary discovery (CHIME-like data)
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"examples/frb-boundary-discovery",
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# Cosmic void boundary information content
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"examples/void-boundary-discovery",
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# Multi-regime temporal attractor boundary detection
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"examples/temporal-attractor-discovery",
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# Music genre boundary discovery via spectral graph bisection
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"examples/music-boundary-discovery",
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# Weather regime boundary detection (variance/correlation precedes temperature)
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"examples/weather-boundary-discovery",
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# Market regime boundary discovery via correlation structure
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"examples/market-boundary-discovery",
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# Health state boundary detection from wearable sensor data
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"examples/health-boundary-discovery",
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# SETI exotic signals gallery: boundary-first detection of sub-threshold signals
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"examples/seti-exotic-signals",
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# SETI boundary-first discovery: sub-noise signal detection via coherence graphs
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"examples/seti-boundary-discovery",
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# Earthquake precursor detection via inter-station correlation boundary shifts
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"examples/earthquake-boundary-discovery",
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# Pandemic outbreak detection 60 days before case counts via correlation boundaries
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"examples/pandemic-boundary-discovery",
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# Infrastructure failure prediction via sensor correlation boundaries
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"examples/infrastructure-boundary-discovery",
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# Pre-seizure detection via brain correlation boundary shifts
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"examples/brain-boundary-discovery",
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# Clinical-publication-grade pre-seizure detection report with CSV output
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"examples/seizure-clinical-report",
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# Closed-loop seizure detection + therapeutic response simulation
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"examples/seizure-therapeutic-sim",
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# Real EEG analysis: CHB-MIT PhysioNet data with boundary-first detection
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"examples/real-eeg-analysis",
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# Multi-seizure cross-patient analysis: all 7 chb01 seizures
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"examples/real-eeg-multi-seizure",
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# Connectome-driven embodied brain demonstrator (ADR-154)
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"examples/connectome-fly",
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]
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resolver = "2"
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[workspace.package]
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version = "2.2.0"
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edition = "2021"
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rust-version = "1.77"
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license = "MIT"
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authors = ["Ruvector Team"]
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repository = "https://github.com/ruvnet/ruvector"
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[workspace.dependencies]
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# Core functionality
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redb = "2.1"
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memmap2 = "0.9"
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hnsw_rs = "0.3"
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simsimd = "5.9"
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rayon = "1.10"
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crossbeam = "0.8"
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# Serialization
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rkyv = "0.8"
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bincode = { version = "2.0.0-rc.3", features = ["serde"] }
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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# Node.js bindings
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napi = { version = "2.16", default-features = false, features = ["napi9", "async", "tokio_rt"] }
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napi-derive = "2.16"
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# WASM
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wasm-bindgen = "0.2"
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wasm-bindgen-futures = "0.4"
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js-sys = "0.3"
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web-sys = { version = "0.3", features = ["Worker", "MessagePort", "console"] }
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getrandom = { version = "0.3", features = ["wasm_js"] }
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# Async runtime
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tokio = { version = "1.41", features = ["rt-multi-thread", "sync", "macros"] }
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futures = "0.3"
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# Error handling and utilities
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thiserror = "2.0"
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anyhow = "1.0"
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter"] }
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# Math and numerics
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nalgebra = { version = "0.33", default-features = false, features = ["std"] }
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ndarray = "0.16"
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rand = "0.8"
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rand_distr = "0.4"
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# Time and UUID
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chrono = { version = "0.4", features = ["serde"] }
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uuid = { version = "1.11", features = ["v4", "serde", "js"] }
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# CLI
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clap = { version = "4.5", features = ["derive", "cargo"] }
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indicatif = "0.17"
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console = "0.15"
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# Testing and benchmarking
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criterion = { version = "0.5", features = ["html_reports"] }
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proptest = "1.5"
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mockall = "0.13"
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# Formal verification
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lean-agentic = "=0.1.0"
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# Performance
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dashmap = "6.1"
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parking_lot = "0.12"
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once_cell = "1.20"
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[profile.release]
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opt-level = 3
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lto = "fat"
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codegen-units = 1
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strip = true
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panic = "unwind"
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[profile.bench]
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inherits = "release"
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debug = true
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[profile.dev]
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opt-level = 0
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debug = true
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[profile.test]
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# Patch hnsw_rs to use rand 0.8 instead of 0.9 for WASM compatibility
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# This resolves the getrandom version conflict (0.2 vs 0.3)
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[patch.crates-io]
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hnsw_rs = { path = "./patches/hnsw_rs" }
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