Completes the mesh-guard integration: its at_risk signal was advisory-only
(fed the recalibration advisor). It now also contributes to the ADR-141
privacy demotion alongside fusion- and array-level contradictions — a mesh
close to partitioning makes the fused belief less trustworthy, so the cycle
emits at a more restricted class (monotonic; information only removed).
Because effective_class feeds the BLAKE3 witness, a fragmenting array now
shifts the witness: partition risk is auditable, not just logged. The mesh
computation moved ahead of the demotion step in process_cycle; mesh_guard_mut
exposes risk-threshold tuning.
Test: a forced-risk 3-node cycle demotes PrivateHome Anonymous->Restricted
and shifts the witness vs a clean baseline. Engine 25 tests; workspace gate
2,947 passed / 0 failed.
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
Maintains an exact min-cut over the live mesh coupling graph — nodes are
sensing nodes, coupling is the product of fusion attention weights — and
surfaces per cycle, as TrustedOutput::mesh:
- cut value: the global "how close is the array to partitioning" number,
a structural measure per-node heuristics miss;
- weak side: which specific nodes would split off (failure/jamming triage,
feeds ADR-032 posture);
- at-risk flag: counts as a structural event for the drift->recalibration
advisor (alongside ADR-142 change-points).
Degenerate cases fail toward risk: a node with zero coupling is reported as
already partitioned (cut 0, that node as the weak side).
Measured cost policy (criterion, 12-node mesh — the honest part):
- weights quantized (1/64) + change-gated: steady-state cycles do ZERO graph
work and reuse the cached cut (~7.3 us, ~23x cheaper than building);
- on any real change a full exact rebuild (~171 us) is used, because ONE
DynamicMinCut delete+insert measured ~240 us — the subpolynomial machinery
amortizes on much larger graphs, so rebuild-on-change is the measured
optimum at mesh scale (one-edge case -28% after switching policy);
- full process_cycle with the guard: ~33 us for 4 nodes vs the 50 ms budget.
9 mesh_guard tests (weak-node detection, steady-state zero updates,
sub-quantum gating, join/drop rebuild, determinism, disconnection) + an
engine-level wiring test (down-weighted node -> weak side -> recalibration).
Engine 24 tests; workspace gate 2,946 passed / 0 failed.
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
Closes the trust-chain gap where an ~11 KB per-room LoRA adapter (ADR-150
section 3.4) could silently change inference without the witness noticing:
provenance carried only "rfenc-v<N>" with no notion of adapter identity.
- StreamingEngine::set_room_adapter(AdapterInfo): pins the adapter's
content-derived id into provenance model_version
("rfenc-v1+adapter:<id>") — and therefore into the BLAKE3 witness — so
swapping or clearing adapter weights always shifts the witness. Engine test
proves base -> adapter -> other-adapter -> cleared all witness differently
and cleared == base.
- RecalibrationAdvisor: recommends re-running the ADR-135 empty-room baseline
/ refitting the room adapter on sustained low fusion coherence (streak
threshold, default 60 cycles ~ 3 s at 20 Hz) or an ADR-142 change-point.
Surfaced as TrustedOutput::recalibration_recommended, stored on the
sensing-server AppState alongside the witness at both live fusion sites.
- Bridge plumbing: EngineBridge::{set_room_adapter, clear_room_adapter} +
live-path test that the adapter id flows into the live witness.
Scope note (honest): this is the deployable provenance/trigger half of the
"retrained model" roadmap item. Fitting the adapter itself runs in the
existing external calibration service (aether-arena/calibration/); a trained
RF-encoder checkpoint still does not exist in-tree.
Engine 15 tests, bridge 7 tests. Workspace gate: 2,918 passed / 0 failed.
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
StreamingEngine::process_cycle appended one SemanticState belief per cycle
with no eviction — ~1.7M nodes/day at 20 Hz (beyond-SOTA roadmap finding #6).
Add WorldGraph::prune_semantic_states(max): deterministic eviction of the
oldest beliefs by (valid_from_unix_ms, id); structural nodes (rooms, zones,
sensors, anchors, tracks, events) are never eligible. Wire it into the
engine after each belief append (DEFAULT_SEMANTIC_RETENTION = 7,200, ~6 min
at 20 Hz; set_semantic_retention to tune). The WorldGraph holds current
beliefs; durable history is the recorder's job, so no audit data is lost.
3 new tests: end-to-end bounded growth, oldest-only eviction, deterministic
equal-timestamp tie-break. Workspace gate: 2,865 passed, 0 failed.
https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH
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