- Run cargo fmt across entire workspace
- Create README.md files for all 9 EXO-AI crates
- Convert path dependencies to crates.io version dependencies for publishing
- Add [patch.crates-io] to exo workspace for local development
Co-Authored-By: claude-flow <ruv@ruv.net>
Clippy fixes (8 warnings → 0):
- Replace 5 manual Default impls with #[derive(Default)]
- Use .clamp() instead of .min().max() chain
- Use .is_some_and() instead of .map_or(false, ...)
- Add type alias for complex return type in scene_graph_to_adjacency
P0 correctness fixes from code review:
- Fix NaN panic: use unwrap_or(Ordering::Equal) in cognitive_core think()
- Fix integer overflow: use checked_mul in OccupancyGrid::new
- Fix potential unwrap: use map_or in domain_expansion score_avoidance
Co-Authored-By: claude-flow <ruv@ruv.net>
SpatialIndex: replace Vec<Vec<f32>> with flat Vec<f32> buffer for cache
locality and zero per-point heap allocation; use squared Euclidean
distance in kNN/radius search (defer sqrt to final k results); fuse
cosine distance into single loop.
Clustering: add union-by-rank to union-find preventing tree
degeneration (O(α(n)) amortized); add #[inline] on hot helpers.
A* planning: add closed set (HashSet) to avoid re-expanding nodes;
reuse neighbor buffer to eliminate per-expansion Vec allocation;
pre-allocate HashMap capacity; add #[inline] on helpers.
Perception: defer sqrt in bounding_sphere (compare squared distances,
one sqrt at end); defer sqrt in scene graph edge construction (filter
on squared threshold); add #[inline] on dist_3d.
Sensor fusion: pre-allocate merged vectors from total eligible cloud
size. Anomaly detection: fuse distance + statistics into single pass
using Welford's online algorithm (eliminates one full data pass).
All 281 tests pass.
https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
New `rvf` feature flag enables the `ruvf::RoboticsRvf` wrapper that
bridges point clouds, scene graphs, trajectories, Gaussian splats, and
obstacles into the RuVector Format (.rvf) for persistence and similarity
search.
RoboticsRvf supports:
- pack_point_cloud (dim 3)
- pack_scene_objects / pack_scene_graph (dim 9)
- pack_trajectory (dim 3)
- pack_gaussians (dim 7) — converts PointCloud→GaussianSplatCloud→RVF
- pack_obstacles (dim 6)
- query_nearest (kNN via HNSW index)
- open/open_readonly/close lifecycle
9 unit tests covering create, ingest, query, reopen, dimension mismatch,
and empty data rejection. Also fixes unused import warnings in integration
tests. All 290 tests pass across default, domain-expansion, and rvf features.
https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
- ExoTransferOrchestrator.package_as_rvf(): serializes all TransferPriors,
PolicyKernels, and CostCurves into a 64-byte-aligned RVF byte stream
- ExoTransferOrchestrator.save_rvf(path): convenience write-to-file method
- Enable ruvector-domain-expansion rvf feature in exo-backend-classical
- 3 new RVF tests: empty packager, post-cycle magic verification, save-to-file
- substrate.rs: fill pattern field from returned search vector (r.vector.map(Pattern::new))
- README: document 5-phase transfer pipeline, RVF packaging, updated
architecture diagram, 4 new Key Discoveries, 3 new Practical Applications
All 0 failures across full workspace test suite.
https://claude.ai/code/session_019Lt11HYsW1265X7jB7haoC
- vector.rs: convert exo_core::Filter Equal conditions to ruvector HashMap
filter; store and round-trip _pattern_id in metadata
- substrate.rs: implement BettiNumbers, PersistentHomology, SheafConsistency
for hypergraph_query using VectorDB stats
- anticipation.rs: implement TemporalCycle pre-fetching via sinusoidal
phase encoding
- crdt.rs: add T: Display bound to reconcile_crdt; look up score from
ranking_map by format!("{}", result)
- thermodynamics.rs: rust,ignore → rust,no_run
- ExoTransferOrchestrator: new cross-phase wiring module in
exo-backend-classical that runs all 5 integration phases in a single
run_cycle() call (bridge → manifold → timeline → CRDT → emergence)
- transfer_pipeline_test.rs: 5 end-to-end integration tests covering the
full pipeline (single cycle, multi-cycle, emergence, manifold, CRDT)
All 0 failures across full workspace test suite.
https://claude.ai/code/session_019Lt11HYsW1265X7jB7haoC
New modules for ruvector-robotics:
- bridge/gaussian: GaussianSplat types, PointCloud→Gaussian conversion, vwm-viewer JSON export
- planning: A* pathfinding on OccupancyGrid with octile heuristic, potential field velocity commands
- mcp/executor: ToolExecutor dispatching ToolRequests to perception pipeline and spatial index
- perception/sensor_fusion: multi-sensor cloud fusion with timestamp alignment and voxel downsampling
Rewrites integration tests to use actual crate APIs instead of local reimplementations,
eliminating ~280 lines of false-positive test code. Adds 15 benchmark groups covering
all new modules (Gaussian conversion, A* planning, potential fields, sensor fusion, MCP execution).
All 270+ tests pass including domain-expansion feature.
https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
Phase 2 — exo-manifold/src/transfer_store.rs
TransferManifold stores (src, dst) transfer priors as 64-dim deformable
patterns via ManifoldEngine::deform. Sinusoidal domain-ID hashing gives
meaningful cosine distances for retrieve_similar.
Phase 3 — exo-temporal/src/transfer_timeline.rs
TransferTimeline records transfer events in the temporal causal graph.
Each event is linked to its predecessor so the system can trace full
transfer trajectories. anticipate_next() returns CausalChain +
SequentialPattern hints.
Phase 4 — exo-federation/src/transfer_crdt.rs
TransferCrdt propagates transfer priors across the federation using
LWW-Map (cycle = timestamp) + G-Set for domain discovery. Merges are
idempotent and commutative. promote_via_consensus runs PBFT Byzantine
commit before accepting a prior.
Phase 5 — exo-exotic/src/domain_transfer.rs
StrangeLoopDomain implements the Domain trait: self-referential tasks
whose solutions are scored by meta-cognitive keyword density.
CollectiveDomainTransfer couples CollectiveConsciousness with
DomainExpansionEngine — arm rewards flow into the substrate and
collective Φ serves as the cycle quality metric.
EmergentTransferDetector wraps EmergenceDetector to surface non-linear
capability gains from cross-domain transfer.
All 4 crates gain the ruvector-domain-expansion path dep. 36 new tests,
all green alongside the existing suite.
https://claude.ai/code/session_019Lt11HYsW1265X7jB7haoC
- Remove unsafe pointer aliasing in BehaviorTree::tick(), use safe
disjoint field borrowing instead (P0)
- Fix usize underflow in score_scene_graph when expected_objects < 2 (P0)
- Fix cluster ID overflow in reference_solution for PointCloudClustering (P0)
- Fix NaN handling in MaxDistEntry::cmp — NaN treated as maximally
distant so it gets evicted from kNN heap first (P1)
- Clamp cosine_distance output to prevent negative values from
floating-point rounding (P1)
- Change search_radius to return Ok(Vec::new()) for empty index instead
of Err(EmptyIndex) for correct semantics (P1)
- Add debug_assert guards for empty slices in bounding_sphere and
cluster_to_object (P1)
- Remove dead PipelineConfig.spatial_search_k field (P2)
- Use serde_json::from_value instead of to_string+from_str roundtrip
in domain_expansion for better performance (P2)
All 257 tests pass.
https://claude.ai/code/session_01H1GkTK5z9ppVVQDQukjBsY
- Fix all 35 compiler warnings across 23 files (unused imports, dead code,
unused vars, unnecessary parens) — build is now warning-clean
- Optimize NeuromorphicBackend::kuramoto_step O(n²)→O(n):
use sin/cos sum identity so coupling_i = (K/N)[cos(φ_i)·ΣsinΦ - sin(φ_i)·ΣcosΦ],
eliminates inner loop for 1000-neuron network (1M→1K ops per tick)
- Optimize k_wta: full sort O(n log n) → select_nth_unstable O(n avg)
using Rust's pdqselect partial sort
- Add #[inline] to hot paths: kuramoto_step, k_wta, hd_encode, lif_tick
- Fix federation: correctly swap unused FederationError (crdt.rs) and
unused HashMap (consensus.rs) — both in opposite files from first guess
https://claude.ai/code/session_019Lt11HYsW1265X7jB7haoC
Add genomics, trading, quantum, neuromorphic, graph intelligence, nervous
systems, scientific OCR, knowledge graphs, GNN, and SONA as specialized
domains with transfer value explanations. Add cross-domain transfer examples
table, domain connectivity diagram, and underlying infrastructure layer map.
Co-Authored-By: claude-flow <ruv@ruv.net>
- New README for domain expansion crate (cross-domain transfer learning)
- All 20 AI OS table layer titles now link to their crate READMEs
Co-Authored-By: claude-flow <ruv@ruv.net>
