ruview @ v2455

Automated release from CI pipeline

Changes: 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

Docker Image: ghcr.io/ruvnet/RuView:e5bf0d4892701b2fa47fdb9ef040e1c76d304580