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* 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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| .. | ||
| gcp | ||
| macos-shortcuts | ||
| pose-physics | ||
| swarm_presets | ||
| tests | ||
| align-ground-truth.js | ||
| apnea-detector.js | ||
| benchmark-model.py | ||
| benchmark-rf-scan.js | ||
| benchmark-ruvllm.js | ||
| benchmark-wiflow.js | ||
| c6-presence-watcher.py | ||
| calibrate-camera-room.py | ||
| calibration_lib.py | ||
| check_fix_markers.py | ||
| check_health.py | ||
| collect-ground-truth.py | ||
| collect-training-data.py | ||
| csi-data-policy-check.sh | ||
| csi-data-policy.allow | ||
| csi-graph-visualizer.js | ||
| csi-spectrogram.js | ||
| csi-udp-relay.py | ||
| deep-scan.js | ||
| device-fingerprint.js | ||
| esp32_jsonl_to_rvcsi.py | ||
| esp32_wasm_test.py | ||
| eval-wiflow.js | ||
| export-onnx.py | ||
| firmware-release-guard.sh | ||
| fix-markers.json | ||
| gait-analyzer.js | ||
| gcloud-train.sh | ||
| generate-witness-bundle.sh | ||
| generate_nvs_matrix.py | ||
| hap-test-sensor.py | ||
| homecore-seed.sh | ||
| inject_fault.py | ||
| install-qemu.sh | ||
| mac-mini-train.sh | ||
| material-classifier.js | ||
| material-detector.js | ||
| mesh-graph-transformer.js | ||
| mincut-person-counter.js | ||
| mmwave_fusion_bridge.py | ||
| occworld_retrain.py | ||
| occworld_server.py | ||
| overnight-empty-capture.py | ||
| passive-radar.js | ||
| probe-fft-platform.py | ||
| prove.sh | ||
| provision.py | ||
| publish-huggingface.py | ||
| publish-huggingface.sh | ||
| qemu-chaos-test.sh | ||
| qemu-cli.sh | ||
| qemu-esp32s3-test.sh | ||
| qemu-mesh-test.sh | ||
| qemu-snapshot-test.sh | ||
| qemu_swarm.py | ||
| record-csi-udp.py | ||
| redact-secrets.py | ||
| release-v0.5.4.sh | ||
| replay-pose-physics-golden.sh | ||
| rf-scan-multifreq.js | ||
| rf-scan.js | ||
| rf-tomography.js | ||
| room-fingerprint.js | ||
| rotate-npm-token.sh | ||
| run-ruforecast-benchmarks.sh | ||
| ruview-hap-bridge.py | ||
| ruview-sensing-server.py | ||
| ruview_occ_dataset.py | ||
| rvagent-mcp-consumer.py | ||
| seed_csi_bridge.py | ||
| sleep-monitor.js | ||
| snn-csi-processor.js | ||
| stress-monitor.js | ||
| swarm_health.py | ||
| synth-csi-udp.py | ||
| through-wall-detector.js | ||
| train-camera-free.js | ||
| train-count.py | ||
| train-ruvllm.js | ||
| train-wiflow-supervised.js | ||
| train-wiflow.js | ||
| training-config-sweep.json | ||
| udp-relay.py | ||
| validate-esp32-mqtt.sh | ||
| validate-ha-blueprints.py | ||
| validate_mesh_test.py | ||
| validate_qemu_output.py | ||
| verify-calibration-proof.sh | ||
| verify-cir-proof.sh | ||
| verify-pose-physics-splits.sh | ||
| wiflow-model.js | ||
| witness-adr-115.sh | ||