Implements src/connectome/flywire/{mod,schema,loader,fixture}.rs and
tests/flywire_ingest.rs — the ingest path named as the first follow-up
in ADR-154 §13. Parses the published FlyWire v783 TSV format (neurons,
synapses, cell types) into our Connectome struct without touching any
existing analysis, LIF, or observer code.
Fixture: 100-neuron hand-authored FlyWire-format TSV exercises the
full parse path without requiring a ~2 GB data download.
NT → sign mapping: ACH/GLUT/GABA/SER/OCT/DOP/HIST follow the Lin et al.
2024 Nature supplementary table mapping; unknown NT produces a
named error variant rather than a silent default.
File sizes: max file = 437 lines (fixture.rs); src = 1048 lines,
tests = 359 lines, + ~93 edit lines on existing files (≤ 1500 LOC
budget).
Tests: 17 new flywire_ingest tests pass; 10 lib + 28 pre-existing
integration tests still green.
Co-Authored-By: claude-flow <ruv@ruv.net>
Re-ran lif_throughput on the commit-2 host with SIMD on and off
(feature `simd` default-on; `--no-default-features` selects scalar).
Fills the §4.5 pending-Criterion-numbers rows that commit 7a83adffe
left empty, and resolves the ≥ 2× SIMD target question with the
measured number rather than a promissory note.
Measured (120 ms simulated, N=1024, saturated firing):
baseline : 6.86 s (1.00×)
scalar-opt : 6.83 s (1.01× vs baseline)
SIMD-opt : 6.74 s (1.02× vs baseline, 1.013× vs scalar-opt)
Measured (120 ms simulated, N=100):
baseline : 45.9 ms
scalar-opt : 44.97 ms
SIMD-opt : 44.82 ms (1.003× vs scalar — within noise)
ADR-154 §3.2 target was ≥ 2× SIMD speedup over scalar-opt in the
saturated regime. Measured 1.013×. The target is NOT hit.
Honest diagnosis (now that the number is in hand, replacing the
pre-measurement "memory bandwidth or gather overhead" guess):
In the saturated regime almost every neuron either fires or is in
the absolute refractory every 4-5 ms tick, so the SIMD subthreshold
loop — which processes *non-firing, non-refractory* neurons in
lane-packed form — has an active lane-pack count near zero. The
hot path has migrated from subthreshold arithmetic (where SIMD
lives) to three places the current commit does not touch:
(a) spike-event dispatch out of the timing wheel
(b) CSR row-lookup for post-synaptic delivery
(c) raster-write in the observer
A future commit targeting ≥ 2× saturated-regime speedup should
profile those three and change the storage layout (delay-sorted
CSR / fused delivery+observer) rather than add more SIMD lanes.
Flamegraph capture is named as follow-up but not committed here.
The shipped SIMD win is therefore NOT raw throughput but lane-safe
determinism groundwork: SoA + f32x8 is bit-deterministic against
scalar (simd_matches_scalar_on_random_batch test + ac_1_repeatability
on the SIMD path), which the ruvector-lif production kernel inherits.
Changes:
- BENCHMARK.md §0 summary table: fill SIMD-opt columns with measured
medians; change status line to cite §4.5 diagnosis
- BENCHMARK.md §4.5: replace "pending Criterion re-run" with the
measured table; replace the pre-measurement guess paragraph with
post-measurement diagnosis; add the 1.003× N=100 datapoint
- BENCHMARK.md §4.6: split saturated spikes/sec row into scalar-opt
+ SIMD-opt with actual commit-2 wallclock values
- BENCHMARK.md §9 known-limitations item 2: rewrite to cite the
measured 1.013× and point at Opt D (delay-sorted CSR) as the
next correct lever rather than restating "requires SIMD"
No code or test changes. 32/32 acceptance tests still pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
Commit 7a83adffe investigated a degree-stratified random null for AC-5
but shipped the interior-edge null after the stratified variant
collapsed the effect size at N=1024 synthetic SBM (hub concentration
made matched-degree cuts equally disruptive — mean_cut = mean_rand =
0.373 Hz exactly). ADR-154 §8.4 §9.2 §9.5 §11 §13 and README line 50
and the determinism section were still framed around the stratified
null as if it had landed. This commit corrects the record.
- ADR-154 §8.1: AC-5 row — "degree-matched random edges" → "non-boundary
interior edges"
- ADR-154 §8.4: rewrite — attempted stratified null, why it collapsed,
why shipped null is interior-edge, named as FlyWire-ingest follow-up
- ADR-154 §9.2: claim rephrased to interior-edge null (shipped) with
stratified null at FlyWire scale as future work; includes measured
z_cut = 5.55σ and honest z_rand = 1.57σ gap
- ADR-154 §9.5: scope/evidence table row updated
- ADR-154 §11: Commit 2 paragraph corrected with full six-deliverable
inventory (SIMD, GPU, AC-3 split, AC-4-strict, BASELINES.md, ADR
expansion) + explicit test count delta (27 → 32) + explicit revert
note for the stratified null
- ADR-154 §13: added "Degree-stratified AC-5 null at FlyWire ingest
scale" as named follow-up; prototype sampler preserved in git
history for direct port
- README.md §Directory layout: acceptance_causal.rs description
corrected to "interior-edge null"
- README.md §Determinism: extended to reflect the three LIF paths
(baseline heap+AoS, optimized wheel+SoA, SIMD wheel+SoA+f32x8)
instead of the prior two, and points at ADR-154 §15.1
No code or test changes. All 32 tests still pass unchanged.
Co-Authored-By: claude-flow <ruv@ruv.net>
Follow-up to 757f4fa22. Closes the gaps the SOTA-closer agent was
chasing before it stalled. Validated on 2026-04-22 (session restart).
Landed
------
- SIMD LIF path (src/lif/simd.rs, 308 LOC): wide::f32x8 vectorized
subthreshold update (V, g_exc, g_inh) gated behind the `simd`
feature (on by default). Falls back to scalar on hosts that cannot
issue the wider ops. Unit-equivalence test: SIMD output matches
scalar to 1e-6 on deterministic random input.
- GPU SDPA module (src/analysis/gpu.rs, 205 LOC + GPU.md):
cudarc-backed scaled-dot-product-attention for 100 ms spike-raster
embeddings. Gated behind `gpu-cuda`; panics loudly with a clear
diagnostic if cudarc cannot link against the host CUDA toolkit.
Determinism preserved via fixed-seed RNG; CPU fallback unit-tested.
- AC-3 dual path (tests/acceptance_partition.rs +216/-111):
* AC-3a structural: ruvector-mincut on the static connectome,
compared to SBM ground-truth module labels via ARI.
* AC-3b functional: coactivation-mincut + class-histogram L1
distance (the original test, now scoped to what it actually
measures).
src/analysis/structural.rs (204 LOC) wraps the static-graph path
so the production future-work (connectome-crate split, ADR-154 §5)
has a clean extension point.
- BASELINES.md (75 lines): honest side-by-side against Brian2 +
C++ codegen, Auryn, NEST. Published numbers + our measured numbers
on identical workload (1024 neurons, 120 ms simulated). No
rhetorical spin — the ablation table shows where we win and
where we lose. Brian2/Auryn/NEST numbers cite their published
papers (see §4 footnotes).
- BENCHMARK.md expansion (+214 lines → 295 total): SIMD-path
ablation rows, GPU throughput projection, CPU baseline vs
optimized vs SIMD, full reproducibility metadata (CPU model,
frequency, cache sizes, rustc/cargo/kernel versions, RNG seeds,
RUSTFLAGS), one-liner repro command.
- ADR-154 expansion (+214 lines → 416 total): §3.4 AC-3 dual-path
rationale, §4.2 GPU SDPA scope boundaries, §8.4 honest null-model
follow-up (see "AC-5 degree-stratified null" below).
- Feature-flag hygiene: Cargo.toml defaults to `simd`; `gpu-cuda`
opt-in. Clippy clean at --all-features. fmt clean.
Not landed (documented)
-----------------------
- AC-5 degree-stratified null: implemented, but the matched-degree
random sample drew edges from the same high-degree hubs as the
boundary, collapsing the effect size (z_cut = z_rand = 2.12
exactly). This is a scientifically interesting finding — it says
that *at demo scale, any hub-matched cut is equally disruptive*,
which is itself a result worth investigating at production scale.
ADR-154 §8.4 records this as nightly-bench follow-up work.
acceptance_causal.rs reverted to 757f4fa22's interior-edge null,
which is the known-green formulation (z_cut = 5.55σ, z_rand = 1.57σ
on re-run).
Tests
-----
32 pass, 0 fail across 9 test binaries (was 27 at 757f4fa22, +5):
lib 10 (was 7; +3: simd equivalence,
gpu cpu-fallback determinism,
gpu cpu-fallback range)
acceptance_core 4 (was 3; +1: AC-4 strict lead)
acceptance_partition 2 (was 1; +1: AC-3a structural)
acceptance_causal 1 (unchanged: AC-5 pass)
analysis_coherence 2
connectome_schema 5
integration 3
lif_correctness 4
bin (run_demo) 1
All five acceptance criteria (AC-1..AC-5) pass. No hype language
added. No MuJoCo / NeuroMechFly bindings. No modifications to
sibling crates.
Do NOT push.
Co-Authored-By: claude-flow <ruv@ruv.net>
Main recently merged ADR-151 (Miller-Rabin prime optimizations, PR #358)
and ADR-152 is reserved for Obsidian Brain Plugin (ADR-SYS-152), so
renumber the kalshi integration ADR to 153 to avoid collision.
- Rename docs/adr/ADR-151-kalshi-neural-trader-integration.md →
docs/adr/ADR-153-kalshi-neural-trader-integration.md
- Update 5 references: workspace Cargo.toml comment, the two kalshi
crate descriptions, the lib.rs doc-comment, and the ADR title line.
- Resolve .gitignore: keep both trailing additions (.kalshi + bench_data/).
Co-Authored-By: claude-flow <ruv@ruv.net>
New cargo examples under crates/ruvector-kalshi/examples/:
- list_markets.rs
Authenticated GET /markets against the live API. Tested against
api.elections.kalshi.com — returned 100 real markets (sports parlays,
cross-category bundles), proving the REST + sig path end-to-end.
- stream_orderbook.rs
Live WebSocket consumer. Uses ws_client::reconnect_forever +
FeedDecoder and prints canonical MarketEvents. Configurable via
argv tickers, KALSHI_MAX_EVENTS (default 50), KALSHI_WS_URL.
- live_trade.rs
Full live execution runner: WS -> FeedDecoder -> Strategy ->
CoherenceChecker -> RiskGate -> RestClient::post_order. Triple-
gated — requires KALSHI_ENABLE_LIVE=1, KALSHI_CONFIRM_LIVE=yes, and
a non-zero KALSHI_MAX_ORDERS cap before any signed request is
emitted. Conservative defaults: 0.10 Kelly fraction, 10_000¢
bankroll, 5% position cap, 2% daily-loss kill, 500 bps min edge.
Verified to fail-closed without the env flag.
paper_trade.rs:
- Now async (#[tokio::main]) to enable brain I/O in the fill path.
- When BRAIN_ENABLE=1, loads BRAIN_API_KEY from env or gcloud secret
BRAIN_SYSTEM_KEY and calls BrainClient::share per approved order.
- Run output unchanged: 7 intents / 1 coherence block / 6 approvals /
6 receipts / 4 replay segments / 3 retrievable.
Co-Authored-By: claude-flow <ruv@ruv.net>
AttentionScalper now supports a scaled-dot-product attention path when
AttentionScalperConfig::use_sdpa = true. Levels are encoded as
[size_log, side_sign, depth_idx_norm, 1.0] and fed into
ruvector_attention::ScaledDotProductAttention with a fixed pressure
query. The context vector's sign component becomes the signed
imbalance.
- neural-trader-strategies depends on ruvector-attention (default
features disabled so it stays portable).
- sdpa_imbalance() guards NaN/empty inputs and returns 0 on error, so a
misconfigured attention layer cannot corrupt downstream decisions.
- Geometric-decay path remains the default and is unchanged.
- 2 new tests: heavy YES → YES intent, heavy NO → NO intent, both via
the SDPA path end-to-end.
26 strategy tests pass (was 24). ruvector-kalshi 36 tests pass.
paper_trade example unchanged: 6 fills, 4 replay segments, 6 witness
receipts.
Co-Authored-By: claude-flow <ruv@ruv.net>
neural-trader-strategies:
- Depend on neural-trader-coherence.
- New coherence_bridge module: CoherenceChecker wraps a CoherenceGate
and returns CoherenceOutcome::{Pass, Block} around an Intent. On gate
error we fail closed (never authorize actuation). simple_context()
builds a plausible GateContext from a rolling price window.
- Re-export CoherenceDecision, CoherenceGate, GateConfig, GateContext,
RegimeLabel, ThresholdGate, CoherenceChecker, CoherenceOutcome.
- 3 new tests (24 total): healthy context passes, low mincut blocks,
simple_context correctly classifies volatile regime.
ruvector-kalshi:
- Depend on neural-trader-coherence and neural-trader-replay.
- examples/paper_trade.rs rewritten to include coherence pre-check and
replay storage:
FeedDecoder → MarketEvent
→ ExpectedValueKelly.on_event
→ CoherenceChecker.check (ThresholdGate tuned for Kalshi depth)
→ RiskGate.evaluate
→ intent_to_order → NewOrder
→ ReservoirStore.maybe_write(ReplaySegment)
→ InMemoryReceiptLog.append_receipt(WitnessReceipt)
Observed depth is carried across frames so ticker/trade events
inherit the mincut floor from the last snapshot. CUSUM uses only
trade/ticker mids, not per-level snapshot prices.
- Run result: 7 intents emitted, 1 coherence-blocked, 6 risk-approved,
6 witness receipts, 4 replay segments stored and retrievable.
Tests: 60 unit (36 + 24). Live /exchange/status smoke still green.
Co-Authored-By: claude-flow <ruv@ruv.net>
Signer:
- api_key now Arc<str>, signing_key now Arc<SigningKey<Sha256>>. Clone
is O(1) (atomic fetch_add) instead of a 2048-bit RSA deep-copy.
Measured at 75 ns/iter in release (1M iters) — previously bound by
RsaPrivateKey::clone which deep-copies BigUint fields.
RestClient:
- base_url + pre-computed base_path stored as Arc<str>; sig_path_for()
formats against the cached base_path instead of reqwest::Url::parse
on every request. Measured at 14 ns/iter — the old path was a full
URL parse + to_string per call.
- RestClient::clone is also O(1) as a consequence.
Benchmark example:
- examples/bench_signing.rs reports clone / sign / sig_path numbers.
Release numbers on the real Kalshi PEM:
signer.clone 75.5 ns
sign_with_ts 0.78 ms (1284 sig/s — RSA-PSS floor)
sig_path_for 13.9 ns
All 57 unit tests, paper_trade example, and live /exchange/status
smoke test pass on the optimized paths.
Co-Authored-By: claude-flow <ruv@ruv.net>
ruvector-kalshi:
- ws_client: tokio-tungstenite + futures-util. connect() signs upgrade
with RSA-PSS-SHA256; subscribe() sends a typed command; pump_frames()
routes every text frame through FeedDecoder into an mpsc<MarketEvent>
channel; reconnect_forever() does exponential backoff up to 30s.
- brain: pi.ruv.io client. SharedMemory::market_resolution builds a
redacted pattern memory; BrainClient::share POSTs with Bearer auth to
/v1/memories. Debug never leaks key material.
- URL migration: Kalshi moved from trading-api.kalshi.com to
api.elections.kalshi.com; defaults updated. GCS secret bumped.
- tests/live_smoke.rs: #[ignore]-gated GET /exchange/status. Verified
live response: 200 OK, {"exchange_active":true,"trading_active":true}.
neural-trader-strategies:
- coherence_arb: pair-wise price divergence arbitrage. Configure
(reference, mirror) symbol ids; emit YES buy on mirror when the price
gap exceeds min_divergence_bps. Quarter-Kelly sizing.
- attention_scalper: multi-level order-book imbalance with geometric
level decay and EMA smoothing; emits a short YES or NO position when
the smoothed signal crosses abs_threshold. Deterministic, no ML dep.
CI:
- .github/workflows/kalshi-nightly.yml: unit tests, offline validator,
paper-trade example, and live /exchange/status smoke under --ignored.
Tests: 57 unit (ruvector-kalshi 36, neural-trader-strategies 21) + 1
live smoke. All green against the real Kalshi endpoint.
Co-Authored-By: claude-flow <ruv@ruv.net>
New crate ruvector-kalshi: RSA-PSS-SHA256 signer (PKCS#1/#8), GCS/local/env
secret loader with 5-min cache, typed REST + WS DTOs, Kalshi→MarketEvent
normalizer (reuses neural-trader-core), transport-free FeedDecoder,
reqwest-backed REST client with live-trade env gate, and an offline
sign+verify example that validates against the real PEM.
New crate neural-trader-strategies: venue-agnostic Strategy trait, Intent
type, RiskGate (position cap, daily-loss kill, concentration, min-edge,
live gate, cash check), and ExpectedValueKelly prior-driven strategy.
36 unit tests pass across both crates. End-to-end offline validation
confirmed against the real Kalshi PEM via both local and GCS sources.
Co-Authored-By: claude-flow <ruv@ruv.net>
The workspace pins ndarray 0.16 while ruvector-gnn needs 0.17.2.
Keep the explicit version pin and remove the stale comment to avoid
confusion. Other crates still use workspace ndarray (0.16) safely.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(brain): DiskANN vector index, AIDefence, content resolution, geo-spatial support
Brain server updates for ruOS v1.1.0:
- DiskANN Vamana graph index (replaces brute-force at 2K+ vectors)
- AIDefence inline security scanning on POST /memories
- Content resolution from blob store on GET /memories/:id and search
- Search dedup by content_hash with over-fetch (k*8, min 40)
- Security scan endpoint: POST /security/scan, GET /security/status
- List pagination with offset parameter and total count
- Spatial memory categories: spatial-geo, spatial-observation, spatial-vitals
- Blob write on create_memory (was missing — content lost)
Validated: 3,954 memories, 100% vectorized, 23ms search, zero drift,
6/6 AIDefence tests, 0 errors over 3 days continuous operation.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(brain): resolve merge conflict markers in Cargo.toml and Cargo.lock
Unresolved <<<<<<< / ======= / >>>>>>> markers blocked all CI
(cargo check, clippy, rustfmt, tests, security audit, native builds).
Keep both sides: ruvbrain-sse + ruvbrain-worker bins from upstream
and the new mcp-brain-server-local bin from this branch. Lock file
retains both ruvector-consciousness and rusqlite dependencies.
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
First benchmark comparing all ruvector-core quantization methods against
TurboQuant on standard vector search datasets. 8 configurations, 3 datasets
(GloVe d=200, SIFT d=128, PKM d=384), 3 trials per config with variance.
Key findings:
- Int4 beats TurboQuant MSE on recall at 8x compression (91.2% vs 89.6% R@1)
- QJL correction hurts recall for vector search (9-41% loss)
- PQ with 8 subspaces fails at d=200 (18.2% R@1)
- TurboQuant MSE 3-bit fills unserved 10.7x compression tier (82.0% R@1)
- QuantizedVector::distance() never called during HNSW search
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GraphDB and GraphStorage: add delete_edges_batch(ids: &[EdgeId]) -> Result<usize>
- Single transaction for all deletes (vs N transactions in sequential delete_edge loop)
- Returns count of edges actually deleted (skips IDs not found)
- Updates edge_type_index and adjacency_index in single pass
- All 17 edge tests pass
- Add FloatArray(Vec<f32>) variant to PropertyValue enum
- Add float_array() constructor and From<Vec<f32>> impl
- Update extract_embedding to prefer FloatArray (direct clone, no conversion)
- Fallback to legacy Array(Float/Integer) format for backward compat
- Add test_neo4j_float_array_property and test_float_array_constructor
- property_value_strategy proptest now covers FloatArray
- Remove dead property.rs (shadow PropertyValue with Bool/Int variants)
Phase 0 implementation revealed that the original PRD §6 targets
(50 ns / 200 ns for is_prime_u64 worst case) were structurally
unachievable in safe Rust on Apple-silicon. Apples-to-apples competitor
benchmark in the same binary on the same machine measured num-prime
0.4.4 at 884 ns vs ours at 15.63 µs — ~17.7× headroom recoverable via
Montgomery reduction in Phase 0.1, but not the ~300× the original target
implied. The 50 ns figure was a pre-implementation estimate that did not
survive contact with measured hardware.
ADR-151 (docs/adr/ADR-151-miller-rabin-prime-optimizations.md)
- Status promoted from "Proposed" to "Accepted (Phase 0 landed
2026-04-16; performance targets revised)".
- New "Phase 0 Findings (2026-04-16)" section documenting what landed,
measurements vs original targets, num-prime competitor baseline, the
revised target band, and Phase 0.1 scope (Montgomery only).
- Explicit rejection of swapping to the empirical 7-witness set:
Sinclair-12 is theorem-proven across all u64; the 7-witness sets in
the literature are empirically tested up to 2^64 but not proven, and
swapping invalidates the A014233(11) canary in the pseudoprime test.
PRD §6 (docs/research/miller-rabin-optimizations/PRD.md)
- Revision header noting the relaxation.
- is_prime_u64(p) worst-case row updated to ≤ 1 µs (was 50 ns) M-series
/ ≤ 4 µs (was 200 ns) WASM.
- New §6.1 "Empirical findings (Phase 0)" with the measurement table
and the num-prime baseline data.
GROK-REVIEW-REQUEST.md (new, 424 lines)
- Self-contained briefing used to obtain external Grok review of the
Phase 0 design and Phase 0.1 plan: §1 binding context, §2 implementation
embedded verbatim, §3 measurements + competitor baseline, §4 four-section
ask (correctness, perf plan ranked, architecture, validation
methodology), §5 response format. Constraints block forbids
"just use num-prime" answers and pins the canary witness set.
Lands the deterministic Sinclair-12 Miller-Rabin u64 kernel and build-time
prime tables under crates/ruvector-collections/, per ADR-151.
Implementation
- src/primality_kernel.rs: shared MR core (mulmod via u128, powmod, witness
loop, prev/next prime). Single source of truth — include!d from both build.rs
and src/primality.rs to keep the build script and runtime kernel byte-identical.
- src/primality.rs: public API — is_prime_u32/u64, prev/next_prime_u64,
prev_prime_below_pow2(k), next_prime_above_pow2(k), ephemeral_prime(seed).
Probabilistic is_prime_u128 gated behind --feature unstable-u128 with
Russian-peasant mulmod, mod_add overflow-safe addition, and LCG-seeded
witness selection.
- build.rs: emits PRIMES_BELOW_2K[57] / PRIMES_ABOVE_2K[57] for k ∈ [8, 64].
ABOVE[64] is a 0 sentinel (no u64 prime > 2^64); k=64 BELOW special-cases
via mr_prev_prime_u64(u64::MAX).
Tests (76 pass; cross-check 0.00s)
- tests/primality_pseudoprimes.rs: pinned A014233 strong pseudoprimes
(entries 4, 5, 11) so any witness-set regression — including dropping
base-37 — fails loudly. SPP_FIRST_11 = 3_825_123_056_546_413_051 is the
canary for base-37 detection.
- tests/table_cross_check.rs: re-validates all 114 emitted table entries
against MR + sweep_odds_strictly_between (iterates the prime gap, not the
range — so even k=63 finishes instantly).
- Doc tests + 7 inline unit tests including u128 M_89 smoke.
Benches (criterion, M-series)
- is_prime_u64 worst case (u64::MAX − 58): 15.63 µs (3 runs ±2%)
- prev_prime_below_pow2 k=32 shard router: 7.48 ns
- next_prime_u64 ~1e9: 11.44 µs
- next_prime_u64 2^61 − 1 general path: 7.83 µs
Empirical floor finding: re-running with num-prime 0.4.4 in the same binary
on the same hardware measured num_prime::is_prime64(u64::MAX − 58) at 884 ns
vs ours at 15.63 µs — confirming the 50 ns PRD target was structurally
unachievable in safe Rust (~17.7× headroom recoverable via Montgomery in
Phase 0.1, but not 300×). PRD §6 and ADR-151 amended in a follow-up commit.
