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ruvnet
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Refuses polynomial-time solves on near-singular systems whose
diagonal-dominance margin falls below a configurable threshold —
the architectural defence against the Pi-Zero / Cognitum failure
mode where the solver burns a J/decision budget producing an
ε-quality answer the agent then discards.
New module `src/coherence.rs`:
- `coherence_score(&dyn Matrix) -> f64` — one-pass diagonal-
dominance margin in [-∞, 1]:
* 1.0 = perfectly diagonal
* (0,1) = strictly DD; Neumann series convergence guaranteed
* 0 = boundary
* <0 = not DD; iterative solvers may diverge
* -∞ = zero diagonal (degenerate row)
- `check_coherence_or_reject(&dyn Matrix, threshold)` — returns
Err(Incoherent) if score < threshold; Ok(score) otherwise.
Threshold = 0 disables the gate entirely (the default).
Wired into the public API:
- `SolverError::Incoherent { coherence, threshold }` — new
variant, `is_recoverable() = true`, severity = Low (it's a
budget refusal, not corruption), formatted error message
points the caller at ADR-001 and the opt-out.
- `SolverOptions::coherence_threshold: Precision` — defaults to
`0.0` (gate disabled) so every existing caller is wire-
compatible. Setting to `0.05` enables the recommended floor.
- lib.rs re-exports `coherence_score` and
`check_coherence_or_reject` at the crate root.
8 new unit tests cover the score function (perfect diagonal,
moderate dominance, boundary case, non-dominant, zero-diagonal)
and the gate (disabled threshold passes, enabled threshold
rejects incoherent and accepts dominant matrices).
Test count: 137 → 145 lib pass. No external API breakage —
SolverOptions still has Default + all 3 named constructors with
the new field set to 0.0.
ADR-001 roadmap: items #1 + #3 done, 4 left (#2 solve_on_change,
#4 MCP advertise, #5 joules bench, #6 contrastive adapter).
ruvnet
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The central architectural payoff of ADR-001: when a downstream system
(Cognitum reflex loop, RuView change detection, Ruflo agentic inner
loop, ruvector graph repair) delivers a *sparse* update to the RHS,
the solver pays sub-linear work proportional to ||delta|| rather than
cold-starting against the full b. Lifts steady-state cost from
`O(k_cold · nnz(A))` to `O(k_warm · nnz(A))` where k_warm ≪ k_cold
on well-conditioned DD systems with small deltas.
New module `src/incremental.rs`:
- `SparseDelta { indices, values }` — additive sparse update to a
RHS vector. `apply_to`, `as_pairs`, `nnz`, `is_empty`, length
validation, out-of-bounds rejection.
- `IncrementalSolver` extension trait blanket-impl'd for every
`SolverAlgorithm` so the entry point is available on every solver
in the crate (Neumann, optimised CG, sublinear-Neumann, …) with
no per-solver wiring needed.
- `solve_on_change(matrix, prev, delta, opts)` uses the
**residual-correction pattern**:
r = delta (= b_new − A·prev for converged prev)
dx = A⁻¹ · r (inner cold solve on a small sparse RHS)
x = prev + dx
This sidesteps the trap of feeding `initial_guess = prev` to
iterative solvers that don't honour it correctly (Neumann's
`compute_next_term` double-counts the k=0 series term, same class
of bug as the iter-2 v1.6.0 fix). Solving for the *correction*
from zero is asymptotically faster because ||r|| ≪ ||b_new||
drives Neumann's geometric convergence to fewer iters
proportional to log(||r||/||b_new||).
- `IncrementalConfig` knobs for tuning the warm-start / full-solve
crossover.
- `IncrementalSolveOp` marker type with `Complexity = Adaptive {
Linear, Linear }` and `DETAIL` documenting the sub-linear-in-
delta-norm payoff. Stable target for the future MCP `x-complexity`
schema (ADR-001 item #4).
6 unit tests pin the contract:
- SparseDelta validation: length match, out-of-bounds detection.
- Identity case: empty delta + prev_solution → same solution as
full solve.
- Tracking: incremental result on b_prev + delta matches cold
full-solve on the new RHS within solver tolerance.
- **Architectural promise**: warm-start iterations ≤ cold-start
iterations on a small delta (the headline benefit of this
roadmap item).
Test count: 145 → 151 lib pass (+6). No external API breakage —
purely additive. Existing callers keep working unchanged; the new
entry point is opt-in.
ADR-001 roadmap status: items #1 #2 #3 done. Remaining:
#4 MCP x-complexity + max_complexity_class budget arg
#5 joules_per_decision bench
#6 find_anomalous_rows contrastive adapter
ruvnet
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Cuts the minor that captures the first three roadmap items of ADR-001 (Complexity as Architecture): - item #1: ComplexityClass enum + Complexity trait - item #2: solve_on_change residual-correction - item #3: coherence gate Public API is additive — no breaking changes. SolverOptions gains one new field with default 0.0 (gate disabled), so every existing caller stays wire-compatible. Bumps: - npm sublinear-time-solver 1.6.0 → 1.7.0 - rust sublinear (crate) 0.2.0 → 0.3.0 CHANGELOG.md gets a fresh 1.7.0 section above the existing 1.6.0 entry, structured to match Keep-a-Changelog conventions. Roadmap items #4, #5, #6 stay on the cron a3644c7d backlog for the follow-up minor.
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ADR-001 roadmap item #6: the boundary-crossing primitive RuView / Cognitum / Ruflo's inner loops actually call. Two functions in a new module `src/contrastive.rs`: - `find_anomalous_rows(baseline, current, k) -> Vec<AnomalyRow>` Top-k rows by |current[i] - baseline[i]|, sorted desc with row index as the tie-break. `O(n log k)` via a `k`-sized min-heap (BinaryHeap with inverted Ord). Phase-1 implementation: full scan over the dense vectors. Phase-2 (tracked as TODO) drops to O(k · log n) by computing individual entries of `current` directly via the sublinear-Neumann single-entry primitive, matching what the ADR §Roadmap promised. - `find_rows_above_threshold(baseline, current, threshold)` — O(n) one-pass filter that returns ALL rows whose anomaly exceeds `threshold`. The change-driven activation primitive: an agent stays asleep until the iterator yields anything. RuView's "activate only on boundary crossing" maps directly to this. - `AnomalyRow { row, baseline, current, anomaly }` — the report shape. Comparable by row + anomaly for deterministic ordering. - `FindAnomalousRowsOp` complexity marker: `Adaptive { Linear, Linear }` today, with DETAIL documenting the planned drop to O(k · log n) in phase 2. 9 unit tests cover the API: empty inputs, k=0, k>n, top-k correctness, tie-breaks, absolute-value semantics, threshold filtering / no-match / dim-mismatch panic. Also fixes the CI failure on the previous push: src/incremental.rs:22 doc test had a type mismatch — `SparseDelta::new` returns `Result<SparseDelta>` but I passed `&result` directly to `solve_on_change`. Added `?` to unwrap the Result and `as &dyn Matrix` to make the cast explicit. The 6 unit tests in incremental had been doing the right thing; only the doc example was wrong. Test count: 151 → 160 lib pass + 11 doc tests (was 1 failing). ADR-001 roadmap: items #1 #2 #3 #6 done. Remaining: #4 MCP x-complexity advertise + budget arg, #5 joules_per_decision bench.
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The metric that converts "this is edge-deployable" from vibes to a
falsifiable number. ADR-001 §SOTA criterion required this before the
package can be called complete. New file
`examples/joules_per_decision.rs`:
- `PowerCounter` trait with two impls:
* RaplCounter: /sys/class/powercap/intel-rapl:0/energy_uj
— works on Intel and AMD Zen 2+ via the
compatible interface, microjoule resolution.
* TimeOnlyCounter: wall-clock fallback when RAPL is unreadable
(sandbox, macOS, locked-down host). Reports
energy as `(not measured)`, prints timing
only.
- `pick_counter()` tries the impls in order and never panics.
- Two workloads:
* OptimizedConjugateGradientSolver (n configurable, default 256)
* NeumannSolver
plus a 100-iter warm-up so the first sample doesn't capture cold
cache + JIT.
- Report struct prints joules, average watts, µJ/solve, µs/solve
when RAPL works; just µs/solve when it doesn't.
Local run on the dev host (RAPL not granted to user; fell back to
time-only):
OptimizedConjugateGradientSolver, n=256: 0.77 µs / solve
NeumannSolver, n=256: 47.98 µs / solve
That's a 62× CG-over-Neumann ratio, consistent with the BENCHMARK.md
baselines from the v1.6.0 release. With RAPL granted (root or
chmod a+r), the same workload reports actual joules and average
watts.
Run with:
cargo run --release --example joules_per_decision
cargo run --release --example joules_per_decision -- --n 1024 --iters 5000
Phase-2 plan (in the source as a comment):
- Integrate into the CI bench-smoke job once a stable per-job
power counter exists (currently GitHub Actions doesn't expose
one).
- Add hwmon backend for the Pi Zero 2W path.
ADR-001 roadmap: items #1 #2 #3 #5 #6 done. Only #4 (MCP
x-complexity schema + max_complexity_class budget) remains.
ruvnet
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…hase-2) Promotes the MCP max_complexity_class arg from informational to a server-side reject. The "bounded-planning kernel" promise from ADR-001 item #4: a caller with a J/decision budget can refuse over-budget solver invocations at dispatch time, not after the call burns the budget. New SublinearSolverMCPServer static tables: - COMPLEXITY_RANK: 12-tier rank map mirroring the Rust `ComplexityClass::rank()` in src/complexity.rs. Lower = cheaper. Logarithmic=100, PolyLogarithmic=200, …, DoubleExponential=1200. - METHOD_WORST_CASE: per-solver class lookup. For Adaptive solvers (e.g. sublinear-neumann which is `Adaptive { Logarithmic, Linear }`) we use the **worst-case** bound so callers always see safe behaviour — a caller with a SubLinear budget won't accidentally invoke a solver that can degrade to Linear on hard inputs. New private method `enforceComplexityBudget(method, budget)`: - No-op when `budget` is undefined (default — wire compatible). - InvalidParams when budget is not a recognised class label. - InvalidRequest when method's worst-case rank > budget rank, with a structured error message pointing the caller at `estimateComplexityClass` for alternatives. Wired into both solve handlers BEFORE any work is done: - handleSolve(params): looks up params.method (default 'neumann') and enforces against params.max_complexity_class. - handleSolveTrueSublinear(params): always enforces against the 'sublinear-neumann' method's worst-case class. Functional smoke (via dist/mcp/server.js): enforceComplexityBudget('neumann', 'SubLinear') → throws ✓ enforceComplexityBudget('forward-push', 'Linear') → ok ✓ enforceComplexityBudget('neumann', undefined) → no-op ✓ dist/ rebuilt — TS compiles clean. ADR-001 status: items #1 through #6 all done + phase-2 enforcement for #4 now live. Only remaining SOTA polish: CI bench-smoke integration of J/solve (blocked on GH Actions exposing per-job power counters; tracked as ADR-001 phase-2 #5).
ruvnet
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Turns the lineage from an audit trail into a verifiable knowledge base (design items #1/#3/#4/#5). From the immutable gen-0 root, run-cohort.mjs autonomously generates a cohort of machine-mutated candidates (real DeterministicMutator, one per seed), gates each against the frozen anchor suite with the real ADR-076 gate, and derives two analytics: - mutation-effectiveness: per mutation surface {attempts, promotions, meanDelta}, sorted by payoff — the evidence a future optimizer uses to bias mutation toward high-payoff classes. - regression-ancestry: each rejected candidate -> the gate clause it failed -> its ancestor (why a direction was abandoned). verify-cohort.mjs re-runs every node's gate from sealed inputs and RECOMPUTES both analytics from the node receipts, asserting they match — the knowledge base is itself verifiable, not a trusted summary. 8-node cohort, deterministic, all checks pass. Outcomes remain synthetic and are keyed by mutation surface so payoff differs by class (efficiency surfaces promote; planner regresses and is abandoned); the real/synthetic boundary is unchanged and documented. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011cHoWPXP5UHwVaNYwjhvjn
ruvnet
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Running `ruflo guidance` auto-wrote a signed retrieval config champion to .claude/proven-config.json — this is part #1 of the ruflo 3.24.0 gist ("a signed retrieval config champion that auto applies on upgrade when authenticity and compatibility pass"). The exact adopted artifact is captured verbatim under integrations/ruflo-flywheel/proven-config/ as committed evidence; I did not author its numbers — ruflo produced them. The champion carries the full gate receipt the gist describes: heldOutDelta 0.0738, redblue PASS, drift 0, canary rollbackRate 0 / costPerTask 0, receiptCoverage 1. The live .claude/ copies are ruflo's runtime state, left in place for it and gitignored so they don't pollute the tracked .claude/ agent tree. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011cHoWPXP5UHwVaNYwjhvjn
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Hi there,
This pull request shares a security update on sublinear-time-solver.
We also have an entry for sublinear-time-solver in our directory, MseeP.ai, where we provide regular security and trust updates on your app.
We invite you to add our badge for your MCP server to your README to help your users learn from a third party that provides ongoing validation of sublinear-time-solver.
You can easily take control over your listing for free: visit it at https://mseep.ai/app/ruvnet-sublinear-time-solver.
Yours Sincerely,
Lawrence W. Sinclair
CEO/SkyDeck AI
Founder of MseeP.ai
MCP servers you can trust
Here are our latest evaluation results of sublinear-time-solver
Security Scan Results
Security Score: 90/100
Risk Level: low
Scan Date: 2025-09-20
Score starts at 100, deducts points for security issues, and adds points for security best practices
Detected Vulnerabilities
Medium Severity
axios
binary-install
wasm-pack
This security assessment was conducted by MseeP.ai, an independent security validation service for MCP servers. Visit our website to learn more about our security reviews.