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Commits
| Author | SHA1 | Message | Date | |
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 rUv
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e5bf0d4892 |
feat(forecast): add independent Rust multivariate training stack (#1766)
* feat(forecast): add independent Rust training stack Signed-off-by: Codex <codex@openai.com> * docs(forecast): record informal HPO exploration note (unaccepted) Records an exploratory Darwin Mode numeric-genome hyperparameter search against the tiny_ci synthetic accuracy protocol: WQL improved 0.257 -> 0.161 -> 0.153 over two search rounds, staying ahead of both baselines throughout. Explicitly scoped as informal/exploratory (not a frozen leakage-free report, not eligible for the evidence ledger, not validated for large_linux or any real dataset) per this doc's own evidence-tagging discipline. * feat(forecast-core): add per-horizon weighted quantile loss weighted_quantile_loss collapses the whole horizon into one aggregate number, hiding whether error grows with lead time. Add a sibling function using the identical per-cell pinball formula and domain checks, just reduced per horizon step instead of globally. A unit test reconstructs the aggregate from the per-step numerators/ denominators to prove the two functions agree exactly, not just approximately. * feat(forecast-model): add evaluation-only local activation Adds activate_for_evaluation and build_eval_input, gated behind the cpu feature. activate_for_evaluation self-signs an unsigned candidate with a fixed, publicly-known, non-secret Ed25519 key so a CLI operator can run inference against their own just-trained candidate without a real release signature -- explicitly never a production trust path, documented as such in the module doc comment. The schema-digest check in ArtifactActivationPolicy still applies in full; only signing is relaxed. build_eval_input constructs a single-window ModelInput for CPU inference from raw context-major values/mask arrays, matching the training batch builders exact time-feature/descriptor encoding. Six unit tests cover: malformed-candidate rejection, digest-mismatch rejection (proving the schema check has teeth even on this relaxed path), getting past the policy gate with a matching digest, and eval-input shape validation. * feat(forecast-train): add evaluate and prepare-synthetic-dataset CLI Turns this sessions throwaway HPO scripts into real, tested CLI surfaces, replacing two example binaries that bypassed the CLI/TOML training path entirely. prepare-synthetic-dataset generates a larger, configurable synthetic training shard (default 24 windows) plus a matching train-local.toml with the given OptimizerSpec hyperparameters baked in, and a separate held-out test.jsonl -- the same synthetic-only, local-validation-only posture as prepare-local-example, just bigger and configurable. evaluate scores a trained (unsigned) candidate against held-out windows and the LastValue/SeasonalNaive baselines: overall weighted quantile loss, a per-horizon breakdown, 80% interval coverage, and missingness, for the model and both baselines. Explicitly lists what docs/benchmarks/ruforecast.mds full accuracy protocol additionally asks for that this does not cover (abstention coverage, selective risk, site/device slices, interference regime, RuVector-retrieval ablation) rather than silently omitting them -- each needs infrastructure a single-entity synthetic fixture does not have. Together the three real commands (prepare-synthetic-dataset, train-local, evaluate) reproduce the exact WQL numbers the old library-bypassing example scripts produced for the same hyperparameters, confirmed by re-running the harness/ruview HPO dry-run end to end against the new CLI path. New integration test trains a real candidate via smoke, evaluates it against held-out synthetic windows, and asserts the full report shape and an empty-input rejection. * feat(forecast-train): add prepare-synthetic-dataset --seed + Autogenous bridge --seed (default 0, backward compatible) makes prepare-synthetic-dataset's generator deterministic per offset: same seed -> byte-identical corpus across runs (needed to train two genomes on the SAME corpus for a fair comparison); different seeds -> genuinely independent corpora (needed for honest multi-seed/multi-judge evaluation upstream, both in harness/ruview/flywheel/ruforecast/gate.mjs's fitness function and in the new v2/crates/ruforecast-autogenous-bridge crate added here). ruforecast-autogenous-bridge is a LOCAL-DEV-ONLY (excluded from the v2 workspace, publish = false) bridge from this crate's real evaluate CLI into ruvnet/autogenous's new regression-candidate promotion path (separate, unpushed branch feat/regression-candidate-kind): runs N independent train+evaluate judges on genuinely distinct synthetic corpora, signs receipts, and gets a real cryptographically-verified PROMOTE/REJECT decision -- defense in depth on top of, not a replacement for, Darwin's own promotion gate. Path-depends on a sibling autogenous checkout that does not exist in CI; build/run directly via `cargo build --manifest-path crates/ruforecast-autogenous-bridge/Cargo.toml`. cargo test -p ruview-forecast-train --no-default-features --features cpu,cli: all green (unaffected existing tests + this backward-compatible addition). * docs(forecast): retract round-2 HPO result, record honest multi-seed finding The earlier "Informal HPO exploration note" claimed a real improvement (WQL 0.257 -> 0.153) from a 3-round hyperparameter search. That search evaluated every candidate against one fixed synthetic corpus (seed 0) for every round -- textbook overfitting. Independent verification against two fresh corpora (via the new ruforecast-autogenous-bridge crate) showed the "winner" losing to the baseline on both. Fixed the root cause in harness/ruview/flywheel/ruforecast/gate.mjs: candidates are now scored against three independent corpora, worst-case across them, not one fixed corpus. Re-ran the search under the fix. It found a new winner that genuinely beat baseline on all three of its own search seeds -- and that winner ALSO lost independent verification on fresh seeds. Two independent search rounds, pre- and post-fix, both produced an illusory "winner." The honest reading: at this dataset scale (24 synthetic windows) held-out WQL varies enormously by which corpus is drawn, regardless of hyperparameters -- confirmed directly by the baseline genome's own primary swinging from 0.83 to a full regression across the three fixed search seeds with unchanged hyperparameters. No RuForecast hyperparameter configuration has been shown to reliably beat the trivial baselines out-of-sample at this scale. Also fixes a real design inconsistency surfaced by this exploration: the Autogenous regression-candidate promotion verifier (envelope::regression::verify_regression_promotion, separate unpushed ruvnet/autogenous branch feat/regression-candidate-kind) required all judges to share one corpus_id, which conflicts with this kind's intentional cross-corpus judge design. Corrected there (commit bfa4c48); did not change either REJECT verdict, which were already driven by the real NotBetterThanParent signal on their own. Append-only: the original round-2 row is kept, read together with this new amendment section, per this doc's own evidence-ledger discipline. * refactor(forecast): extract RuForecast to ruvnet/RuForecast submodule Mirrors the v2/crates/worldgraph pattern: RuForecast becomes its own independent public repo/workspace (ruforecast-core/model/train), mounted at v2/crates/ruforecast as a git submodule, with v2/Cargo.toml path-depping into its sub-crates and excluding the submodule from the v2 workspace. Full v2 --workspace check passes; the ruforecast CLI builds and runs correctly from its new location. Real git history for the extracted crates (7 commits) was preserved via git-filter-repo, not squashed. * feat(autogenous-bridge): real-data independent verification tool real_data_verify.rs: signs two real, temporally-independent judge measurements (different train/test split boundaries on the same real household vitals corpus, not synthetic seeds) through the real Autogenous regression-candidate promotion path, and gets a genuine cryptographically-checked PROMOTE/REJECT verdict. Result recorded (see docs/benchmarks/ruforecast.md): REJECT. One judge nominally beat the trivial baseline but by less than the 0.01 non-inferiority margin; the other judge lost outright. Consistent with every synthetic-data search this session -- no configuration has yet been shown to reliably beat trivial baselines out-of-sample, now including a real 6390-sample household corpus. Bumps the ruforecast submodule to pick up the evaluate real-gap fix. * docs(forecast): record real-household-data result (REJECT, signed) 6390 real vital-signs samples, two independent temporal splits, independently verified via the real Autogenous regression-candidate promotion path. Signed verdict: REJECT -- neither split cleared the non-inferiority margin. Same conclusion as every synthetic search this session, now confirmed with real data too: more real data is the credible next lever, not further search on this scale of fixture. * chore(forecast): point ruforecast submodule at published main (post gap-tolerance fix) * ci(forecast): fix ruforecast-ci.yml for the new v2/crates/ruforecast submodule layout The RuForecast crates were extracted into a standalone submodule (v2/crates/ruforecast, mirroring the existing v2/crates/worldgraph pattern) and are no longer members of the v2 workspace. The CI workflow still referenced the old in-tree package names/paths (ruview-forecast-core/model/train under v2/), which broke every forecast job with "cannot specify features for packages outside of workspace". Fixed every job to target the submodule: working-directory -> v2/crates/ruforecast, -p ruview-forecast-* -> -p ruforecast-*, all hardcoded Cargo.toml/source paths in the Python assertion and clean-room-scan blocks, the Swatinem/rust-cache workspaces input, the artifact-tree output paths, and the two levels of relative path (../ -> ../../) in the informational-benchmark steps whose working directory moved one level deeper. Also collapsed the paths: trigger filters internal-crate globs (v2/crates/ruview-forecast-*/**) down to the single v2/crates/ruforecast path, since GitHub Actions path filters only ever see a submodule gitlink change as one entry in the parent tree, never its internal file paths -- the old globs could never have matched anything. Includes a real cargo fmt fix (submodule commit d9902f9) for formatting drift left over from the earlier real-data gap-tolerance fix, which the contract job's fmt --check step would otherwise have failed on. All four forecast CI jobs' real commands verified locally before push: cargo test/check for CPU, CUDA/WGPU compile-check, the contract job's Python assertion block, the hosted-boundaries clean-room scan, and cargo fmt --check. * docs(forecast): record real BIDMC cross-entity holdout result (REJECT) Two independent real-patient partitions (34/16 and 24/26 splits, 53 total ICU patients from PhysioNet BIDMC, Open Data Commons Attribution License v1.0) both show the model losing to trivial baselines by a decisive margin (+69% and +601% worse WQL). First genuine cross-entity real-data test this project has run; same conclusion as every prior synthetic and single-household test. Full Autogenous signed verification was not run for this entry - flagged explicitly in the note. * chore(forecast): point ruforecast submodule at published main (example feature-gate fix) * chore(forecast): point ruforecast submodule at published main (clippy fix) --------- Signed-off-by: Codex <codex@openai.com> |
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ruv
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50edd0aec6 |
chore: vendor ruvnet/metaharness as a git submodule
Adds vendor/metaharness (github.com/ruvnet/metaharness, pinned at
87b6c51 on main) -- the real MetaHarness generator: repo-aware CLI,
Rust/WASM+NAPI-RS kernel, host adapters, @metaharness/router
(cost-optimal model routing) and @metaharness/darwin ("Darwin Mode",
self-evolving harness config). ADR-285's harness/homecore/ already
depends on the real @metaharness/kernel@0.1.2 npm package; this
vendors the source repo alongside the other vendor/* submodules
(rufield, rvcsi, ruvector, sublinear-time-solver, midstream) for local
inspection and building.
No wiring into any harness/ or crate yet -- follow-up work.
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ruv
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aeac5f5543 |
chore(worldgraph): extract geo+worldgraph+worldmodel to ruvnet/worldgraph submodule
- published as github.com/ruvnet/worldgraph (3-crate workspace, history via git-filter-repo) - replace the 3 in-tree crates with one submodule at v2/crates/worldgraph - parent workspace: drop the 3 members, exclude the submodule (it is its own workspace), repoint workspace.dependencies(worldmodel) + engine/sensing-server path-deps into it - cargo metadata resolves clean (geo/worldgraph/worldmodel consumed from the submodule) Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c257e67c3d |
chore(swarm): extract ruview-swarm to ruvnet/ruv-drone submodule
- ruview-swarm published as github.com/ruvnet/ruv-drone (history preserved via subtree split) - replace the in-tree crate with a submodule at v2/crates/ruview-swarm (branch main) - standalone repo dropped the unused wifi-densepose-core path-dep; export-control NOTICE added there - workspace member path unchanged; cargo metadata resolves ruview-swarm from the submodule Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUvandruv
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261ce80a72 |
feat(adr-260): RuField MFS spec + vendor/rufield submodule (#1061)
ADR-260 (Accepted — v0.1 reference stack): RuField, the open specification for camera-free multimodal field sensing — one FieldEvent/FieldTensor/ FusionGraph/PrivacyClass/ProvenanceReceipt model above WiFi CSI/CIR/BFLD, UWB, BLE Channel Sounding, mmWave radar, ultrasound, subsonic, infrared, and quantum sensors. Published standalone as github.com/ruvnet/rufield and vendored here as the vendor/rufield submodule (the vendor/rvcsi pattern — not a v2/ workspace member). v0.1 reference stack: 6 crates, 60 tests/0 failed, clippy-clean. All benchmark metrics SYNTHETIC (simulator ground truth, no hardware). Co-authored-by: ruv <ruvnet@gmail.com> |
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rUv
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07b6bf8084 |
chore: extract ruv-neural to ruvnet/ruv-neural, wire as submodule (#1019)
The 12-crate brain-topology analysis ecosystem (v2/crates/ruv-neural) was a self-contained nested workspace with no inbound deps from the v2 workspace (verified: zero path references outside its own tree). Published standalone at github.com/ruvnet/ruv-neural and re-attached here as a submodule at the same path, so the build layout is unchanged while the project gets its own repo/CI/release cadence. |
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ruv
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f2525d7a0d |
chore(vendor): add rvcsi as a vendor submodule (github.com/ruvnet/rvcsi)
rvCSI — the edge RF sensing runtime incubated here as `v2/crates/rvcsi-*` (ADR-095, ADR-096, PR #542) — now has a standalone home at github.com/ruvnet/rvcsi (9 crates published to crates.io, @ruv/rvcsi on npm, a Claude Code plugin). This vendors it under `vendor/rvcsi`, alongside `vendor/ruvector` / `vendor/midstream` / `vendor/sublinear-time-solver`. Follow-up: migrate the workspace to consume `vendor/rvcsi/crates/rvcsi-*` and drop the inline `v2/crates/rvcsi-*` copies (kept for now so this change is a pure addition). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c257e9a215 |
chore: track upstream main branch for vendor submodules
- Add branch = main to each submodule in .gitmodules - Add GitHub Actions workflow that checks every 6 hours for upstream updates and opens a PR automatically Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c6f061a191 |
refactor: convert vendor/ directories to git submodules
Replace 9,608 tracked vendor files (~737MB) with git submodule pointers to their upstream repositories: - vendor/midstream -> https://github.com/ruvnet/midstream - vendor/ruvector -> https://github.com/ruvnet/ruvector - vendor/sublinear-time-solver -> https://github.com/ruvnet/sublinear-time-solver This dramatically reduces repo size and ensures vendor code stays in sync with upstream. New clones should use: git clone --recurse-submodules Existing clones should run: git submodule update --init --recursive Co-Authored-By: claude-flow <ruv@ruv.net> |