chore(sync): mirror front-door files to Space (hf-sync)

Automated front-door sync from szl-holdings/a11oy main via hf-sync.
Added/updated: cathedral.html, cathedral_genius.html, console/docs.html, console/index.html, console/pricing.html, console/throne-room.html, console/throne-room.js, live_wires.html, pages/api-keys.html, pages/audit.html, pages/ayni.html, pages/brain-dual.html, pages/brain-jack.html, pages/brain.html, pages/chaski.html, pages/codex-kernel.html, pages/company.html, pages/compliance.html, pages/console.html, pages/counter-uas.html, pages/cued-engagement.html, pages/docs.html, pages/energy-ops.html, pages/evidence.html, pages/fabric.html, pages/gap-report.html, pages/harvest.html, pages/hatun-mcp.html, pages/hub.html, pages/integrations.html, pages/landing.html, pages/mesh.html, pages/observability.html, pages/operator_organ.html, pages/pinn.html, pages/pnt.html, pages/pricing.html, pages/run-all.html, pages/sdk.html, pages/security.html, pages/status.html, pages/substrate.html, pages/superpowers.html, pages/throne-room.html, pages/throne-room.js, pages/uds.html, pages/upgrades.html, pages/wallpa.html, pages/warhacker.html, pages/wasi-rikuq.html, pages/wires.html, static/a11oy_cathedral.js, static/cathedral_app.js, static/shared/szl_codename_sanitizer.js, static/shared/szl_holo3d.js, static/shared/szl_label_engine.js, static/shared/szl_receipt_cosign.js, web/agent-loop.html, web/agentic-gpu.html, web/autoreview.html, web/console.html, web/console_index.html, web/constitution.html, web/defense-readiness.html, web/determinacy.html, web/dns.html, web/elite_console.html, web/energy-holographic.html, web/energy.html, web/estate-hologram.html, web/fleet-c2.html, web/formulas.html, web/governance.html, web/holo.html, web/hologram.html, web/immune.html, web/index.html, web/living-anatomy.html, web/materials.html, web/nemo.html, web/operator.html, web/quant.html, web/restraint-bench.html, web/restraint.html, web/sda.html, web/signature-is-not-proof.html, web/v4_fleet_panel.html
Deleted (gone from GitHub main): (none)

Keeps the served front-door (pages/*.html, console/*.html) identical
to GitHub main so an HF factory rebuild never drops a GitHub edit or
keeps serving a page that was deleted on GitHub.

pages/console.html

@@ -15484,7 +15484,7 @@ window.warboard_init=warboard_init; window.warboard_all=warboard_all;
 <script>
 (function(){
   "use strict";
-  var GND={"whHero":{"g":"DoD 3000.09 requires that autonomous weapon systems allow commanders to exercise &quot;appropriate levels of human judgment over the use of force&quot; and mandates that operator-supervised systems retain the ability to intervene and terminate engagements — our Λ-gate is designed precisely as that intervention point, with every ALLOW or DENY verdict written as a cryptographically signed receipt before any simulated effector command is issued. Building on the Proof-of-Behavior Protocol (Dembowski 2026), each ROE decision in our pipeline produces an Ed25519-signed, SHA-256 hash-chained receipt; a DENY receipt is stored to persistent storage *before* the policy violation is surfaced to the operator, so the absence of a DENY record is itself evidence the gate was not consulted. Sio &amp; Hoven (2018) establish that meaningful human control requires decisions traceable to human intentions across design, activation, and operation — our signed receipts carry <code>principal_id</code> and <code>policy_hash</code> fields so that any post-incident audit can trace every engagement decision to the specific policy version and human authorizer active at the time. All effectors in this system are SIMULATED: no kinetic action is taken; the Λ-gate is a Conjecture-1 advisory gate — machine-checkable, not a proven theorem — and we label it as such in every operator-facing display.\n\n---","c":[{"t":"DoD Directive 3000.09, Autonomy in Weapon Systems, Jan 25 2023. U.S. Department of Defense","u":"https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodd/300009p.pdf"},{"t":"Sio, F.S.D. &amp; Hoven, J. Meaningful Human Control over Autonomous Systems: A Philosophical Account. Frontiers in Robotics &amp; AI 5:15, 2018. DOI:10.3389/frobt.2018.00015","u":"https://doi.org/10.3389/frobt.2018.00015"},{"t":"Dembowski, J. Proof-of-Behavior Protocol for Autonomous AI Agents. IETF Internet-Draft, Apr 2026","u":"https://www.ietf.org/archive/id/draft-dembowski-agentledger-proof-of-behavior-00.html"}],"kernel":false},"cuas_fusion":{"g":"Building on Sakellariou et al. (2024) and Dudczyk et al. (2022), our fusion pipeline ingests correlated detections from RF, radar, and EO/IR sensors and produces a unified track object with an explicit covariance ellipsoid for each detected target — position uncertainty is surfaced to the operator as a visual confidence envelope, not hidden inside a black-box score. Following the JDL model taxonomy codified in Wang et al. (2020), our system operates at Level 1 (object refinement) and Level 2 (situation refinement), with each track&#x27;s sensor-fusion provenance logged in the signed Λ-receipt so an auditor can reconstruct which sensors contributed to a given engagement recommendation. The fusion head is reliability-gated in the spirit of Adde et al. (2025): if any primary sensor stream drops below a configurable confidence threshold, the track is flagged UNCONFIRMED and the Λ-gate is forced to DENY until a human operator confirms — this is a design choice grounded in doctrine, not a proven-optimal algorithm. All sensor-fusion results are advisory; no autonomous effector action is taken (effectors are SIMULATED throughout).\n\n---","c":[{"t":"Wang, J., Liu, Y. &amp; Song, H. C-UAS: State of the Art, Challenges and Future Trends. arXiv:2008.12461, 2020","u":"https://arxiv.org/abs/2008.12461"},{"t":"Sakellariou, N. et al. Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data. arXiv:2410.16089, 2024","u":"https://arxiv.org/abs/2410.16089"},{"t":"Dudczyk, J. et al. Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space. Sensors 22(12):4323, 2022. DOI:10.3390/s22124323","u":"https://doi.org/10.3390/s22124323"}],"kernel":false},"cuas_swarm":{"g":"Building on Kinsler et al. (2024), we assume the threat swarm operates with minimal inter-drone RF emissions to defeat passive RF sensors — meaning our detection pipeline cannot rely on intercepted swarm traffic and must instead fuse radar micro-Doppler signatures with EO/IR track clustering to infer swarm membership. Building on D2BFT (Ryu et al. 2025) and the Byzantine fault-tolerance literature, our swarm-consensus module applies a PBFT-derived voting protocol across distributed track nodes: a swarm classification of HOSTILE requires a 2f+1 quorum of non-Byzantine sensor nodes to agree, so a compromised or jammed subset cannot flip the track picture unilaterally. The swarm engagement recommendation produced by this pipeline is a Λ-gate output labeled as a Conjecture-1 advisory — honest about the fact that the quorum bound assumes at most f/n ≤ 1/3 Byzantine nodes, which is an assumption, not a proof, in live adversarial conditions. Effectors remain SIMULATED throughout; the swarm track picture feeds operator display only.\n\n---","c":[{"t":"Kinsler, P. et al. Agent swarms: cooperation and coordination under stringent communications constraint. PLOS ONE, Dec 2024. DOI:10.1371/journal.pone.0311513","u":"https://doi.org/10.1371/journal.pone.0311513"},{"t":"Ryu, H. et al. D2BFT: A Dual Byzantine Fault Tolerance approach for Multi-Agent Drone Surveillance. Computer Networks, 2025","u":"https://www.sciencedirect.com/science/article/abs/pii/S1389128625007169"},{"t":"Bu, Y. et al. Advancement Challenges in UAV Swarm Formation Control: A Comprehensive Review. Drones 8(7):320, 2024. DOI:10.3390/drones8070320","u":"https://doi.org/10.3390/drones8070320"}],"kernel":false},"oversight":{"g":"Building on Koh, Steinhardt &amp; Liang (2021) and Cinà et al. (2024), we treat data-poisoning as a first-class threat: any ML classifier in our C-UAS pipeline — threat scorer, track classifier, or intent estimator — may have been corrupted during training, and we do not assume otherwise. Our architecture responds by placing the Λ-gate outside and downstream of all ML components: it is a non-learned, rule-based monotonic function whose only possible safety output is DENY (it cannot promote a classification to ALLOW on its own). Dalrymple et al.&#x27;s (2024) Guaranteed Safe AI framework articulates why this separation is necessary — the verifier that produces the safety certificate must be independent of the world model being verified; in our case, the gate&#x27;s policy rules are authored by a human, hashed into the receipt, and immutable at inference time. Building on the Proof-of-Behavior Protocol (Dembowski 2026), the signed receipt records the exact <code>policy_hash</code> active at every gate evaluation, so any attempt to swap in a backdoor-modified policy version would produce a hash mismatch detectable in audit — the Λ-gate is a Conjecture-1 advisory, honestly labeled, not a formally proven theorem, but its monotonic DENY property holds by construction, not by ML training.\n\n---","c":[{"t":"Chen, B. et al. Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. arXiv:1811.03728, 2018","u":"https://arxiv.org/abs/1811.03728"},{"t":"Dalrymple, D. et al. Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. arXiv:2405.06624, 2024","u":"https://arxiv.org/abs/2405.06624"},{"t":"Taheri, S. et al. Robustness Certificates for Neural Networks against Data Poisoning and Evasion Attacks. IEEE OJCSYS, 2025. DOI:10.1109/OJCSYS.2026.3689712","u":"https://doi.org/10.1109/OJCSYS.2026.3689712"}],"kernel":false},"cybSurface":{"g":"Our 3D attack-surface graph is not a theoretical visualization — it is a live signed artifact under Conjecture 1 (Λ), where every node carries a provably-labeled risk score anchored to current EPSS probability and CVSS exploitability data per <a href=\"https://arxiv.org/abs/2302.14172\" target=\"_blank\" rel=\"noopener\">Jacobs et al. (2023)</a>. The graph construction methodology follows the Graphene pipeline (<a href=\"https://arxiv.org/abs/2312.13119\" target=\"_blank\" rel=\"noopener\">Jin et al. 2024</a>) — semantic pre/postcondition matching across CVEs — but our receipts are governance-signed, not merely computed. We stay honest about what the graph proves: it surfaces high-probability lateral-movement paths; it does not guarantee completeness against novel zero-day techniques unrepresented in MITRE ATT&amp;CK v15. The locked-8 constraint means we display at most eight critical paths per surface layer, each with an honest EPSS-sourced probability label rather than a binary &quot;critical / not critical&quot; that hides uncertainty.","c":[{"t":"Palma &amp; Angelini. &quot;It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation.&quot; ESORICS 2024. arXiv:2312.16513","u":"https://arxiv.org/abs/2312.16513"},{"t":"Jacobs et al. &quot;Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights.&quot; EPSS SIG, 2023. arXiv:2302.14172","u":"https://arxiv.org/abs/2302.14172"},{"t":"Jin et al. &quot;Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs.&quot; 2024. arXiv:2312.13119","u":"https://arxiv.org/abs/2312.13119"}],"kernel":false},"cybZero":{"g":"Our zero-trust BFT mesh implements NIST SP 800-207&#x27;s seven tenets — never-implicit trust, per-session access, dynamic policy — as the authorization plane, while <b>Khipu BFT (Conjecture 2)</b> proposes a HotStuff-lineage consensus layer (<a href=\"https://arxiv.org/abs/1803.05069\" target=\"_blank\" rel=\"noopener\">Yin et al. 2018</a>, <a href=\"https://arxiv.org/abs/2010.11454\" target=\"_blank\" rel=\"noopener\">Jalalzai et al. 2021</a>) adapted for AI-agent mesh coordination. We are honest: Khipu BFT is a conjecture, not a production-proven protocol — we have not published a formal safety/liveness proof covering all failure modes, and we do not claim PBFT-equivalent unconditional Byzantine resilience. The PE/PA/PEP separation from <a href=\"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-207.pdf\" target=\"_blank\" rel=\"noopener\">NIST SP 800-207</a> maps directly onto our signed-receipt control plane: each access decision is a governance-signed event with provenance, not an ephemeral allow/deny. The gap between ZT authorization and BFT consensus (who decides vs. who agrees) is the exact space Khipu BFT is designed to bridge — conditionally, pending formal verification.","c":[{"t":"NIST SP 800-207. &quot;Zero Trust Architecture.&quot; National Institute of Standards and Technology, 2020","u":"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-207.pdf"},{"t":"Yin, Malkhi et al. &quot;HotStuff: BFT Consensus in the Lens of Blockchain.&quot; PODC 2019. arXiv:1803.05069","u":"https://arxiv.org/abs/1803.05069"},{"t":"Castro &amp; Liskov. &quot;Practical Byzantine Fault Tolerance and Proactive Recovery.&quot; ACM TOCS 2002. DOI:10.1145/571637.571640","u":"https://dl.acm.org/doi/10.1145/571637.571640"}],"kernel":false},"entIncident":{"g":"Our enterprise incident mesh implements the four SRE golden signals (latency, traffic, errors, saturation) as first-class signed telemetry under Conjecture 1 (Λ) — every emitted signal carries a governance receipt, making the observability layer itself auditable. The multi-modal RCA approach from <a href=\"https://arxiv.org/abs/2305.15778\" target=\"_blank\" rel=\"noopener\">RCACopilot (Chen et al. 2023)</a> informs our incident-routing design: incoming alerts are classified by type before diagnostics are aggregated, not after. We adopt the dependency-aware linking model from <a href=\"https://arxiv.org/abs/2403.18639\" target=\"_blank\" rel=\"noopener\">Ghosh et al. (2024)</a> to surface causal chains rather than symptom lists — critical for a mesh where a single model degradation can cascade through five dependent services. Honest label: our mean-time-to-root-cause estimates are probabilistic outputs from a Bayesian model (per <a href=\"https://arxiv.org/abs/2405.09330\" target=\"_blank\" rel=\"noopener\">BARO, Pham et al. 2024</a>), not deterministic verdicts; we display confidence intervals, not false precision.","c":[{"t":"Chen et al. &quot;Automatic Root Cause Analysis via Large Language Models for Cloud Incidents.&quot; FSE 2024. arXiv:2305.15778","u":"https://arxiv.org/abs/2305.15778"},{"t":"Ghosh et al. &quot;Dependency Aware Incident Linking in Large Cloud Systems.&quot; ICSE 2024. arXiv:2403.18639","u":"https://arxiv.org/abs/2403.18639"},{"t":"Jha et al. &quot;LLM Assisted Anomaly Detection Service for Site Reliability Engineers.&quot; 2025. arXiv:2501.16744","u":"https://arxiv.org/abs/2501.16744"}],"kernel":false},"fltTopo":{"g":"Our fleet topology view is grounded in the empirical reliability data from <a href=\"https://arxiv.org/abs/2410.21680\" target=\"_blank\" rel=\"noopener\">Kokolis et al. (2025)</a> — our GPU health scores are calibrated against real MTTF distributions, not vendor MTBF claims. The fltTopo dashboard aggregates the four GPU health dimensions identified in that work (hardware failure, software hang, network fault, checkpointing state) as signed telemetry events under Λ (Conjecture 1), giving operators an auditable health ledger rather than a live-only metric stream. SLO tracking uses the Faro utility-function formulation (<a href=\"https://arxiv.org/abs/2409.19488\" target=\"_blank\" rel=\"noopener\">Jeon et al. 2024</a>) to translate latency targets into continuous health scores — locked-8 means we surface at most eight critical fleet health signals per view, ranked by impact on active SLOs. Honest label: MTTF projections for new hardware generations carry wide confidence intervals; we display them as ranges, not point estimates.","c":[{"t":"Kokolis et al. &quot;Revisiting Reliability in Large-Scale Machine Learning Research Clusters.&quot; MLSys 2025. arXiv:2410.21680","u":"https://arxiv.org/abs/2410.21680"},{"t":"Jain et al. &quot;PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters.&quot; 2024. arXiv:2408.11919","u":"https://arxiv.org/abs/2408.11919"},{"t":"Jeon et al. &quot;A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro.&quot; SoCC 2024. arXiv:2409.19488","u":"https://arxiv.org/abs/2409.19488"}],"kernel":false},"llm":{"g":"Our model router implements a cascade architecture grounded in the FrugalGPT framework (<a href=\"https://arxiv.org/abs/2305.05176\" target=\"_blank\" rel=\"noopener\">Chen et al. 2023</a>) and calibrated using preference-data routing signals from <a href=\"https://arxiv.org/abs/2406.18665\" target=\"_blank\" rel=\"noopener\">RouteLLM (Ong et al. 2025)</a>: each routing decision is a governance-signed event with the model selected, the confidence score, and the cost-quality trade-off label, all appended to the receipt chain under Λ (Conjecture 1). The <a href=\"https://arxiv.org/abs/2501.01818\" target=\"_blank\" rel=\"noopener\">Rerouting paper (Shafran et al. 2025)</a> is a direct threat-model input: our router&#x27;s routing logic is isolated from user-visible prompt content to prevent confounder injection, and routing decisions are auditable for adversarial patterns. The HF Kernels Hub (<a href=\"https://huggingface.co/blog/hello-hf-kernels\" target=\"_blank\" rel=\"noopener\">huggingface.co/blog/hello-hf-kernels</a>) directly enables our inference stack: SZL&#x27;s kernel-publish access means we can distribute our MoE routing and attention kernels as signed, versioned Hub artifacts, making kernel provenance as auditable as model weights. Honest label: our cost-quality SLA guarantees are conditional on the router&#x27;s difficulty predictor — when query distribution shifts, the predictor recalibrates on a rolling window, not instantly.","c":[{"t":"Chen, Zaharia &amp; Zou. &quot;FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.&quot; 2023. arXiv:2305.05176","u":"https://arxiv.org/abs/2305.05176"},{"t":"Ong et al. &quot;RouteLLM: Learning to Route LLMs with Preference Data.&quot; 2025. arXiv:2406.18665","u":"https://arxiv.org/abs/2406.18665"},{"t":"Hugging Face. &quot;Enhance Your Models in 5 Minutes with the Hugging Face Kernel Hub.&quot; June 2025","u":"https://huggingface.co/blog/hello-hf-kernels"}],"kernel":true}};
+  var GND={"whHero":{"g":"DoD 3000.09 requires that autonomous weapon systems allow commanders to exercise &quot;appropriate levels of human judgment over the use of force&quot; and mandates that operator-supervised systems retain the ability to intervene and terminate engagements — our Λ-gate is designed precisely as that intervention point, with every ALLOW or DENY verdict written as a cryptographically signed receipt before any simulated effector command is issued. Building on the Proof-of-Behavior Protocol (Dembowski 2026), each ROE decision in our pipeline produces an Ed25519-signed, SHA-256 hash-chained receipt; a DENY receipt is stored to persistent storage *before* the policy violation is surfaced to the operator, so the absence of a DENY record is itself evidence the gate was not consulted. Sio &amp; Hoven (2018) establish that meaningful human control requires decisions traceable to human intentions across design, activation, and operation — our signed receipts carry <code>principal_id</code> and <code>policy_hash</code> fields so that any post-incident audit can trace every engagement decision to the specific policy version and human authorizer active at the time. All effectors in this system are SIMULATED: no kinetic action is taken; the Λ-gate is a Conjecture-1 advisory gate — machine-checkable, not a proven theorem — and we label it as such in every operator-facing display.\n\n---","c":[{"t":"DoD Directive 3000.09, Autonomy in Weapon Systems, Jan 25 2023. U.S. Department of Defense","u":"https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodd/300009p.pdf"},{"t":"Sio, F.S.D. &amp; Hoven, J. Meaningful Human Control over Autonomous Systems: A Philosophical Account. Frontiers in Robotics &amp; AI 5:15, 2018. DOI:10.3389/frobt.2018.00015","u":"https://doi.org/10.3389/frobt.2018.00015"},{"t":"Dembowski, J. Proof-of-Behavior Protocol for Autonomous AI Agents. IETF Internet-Draft, Apr 2026","u":"https://www.ietf.org/archive/id/draft-dembowski-agentledger-proof-of-behavior-00.html"}],"kernel":false},"cuas_fusion":{"g":"Building on Sakellariou et al. (2024) and Dudczyk et al. (2022), our fusion pipeline ingests correlated detections from RF, radar, and EO/IR sensors and produces a unified track object with an explicit covariance ellipsoid for each detected target — position uncertainty is surfaced to the operator as a visual confidence envelope, not hidden inside a black-box score. Following the JDL model taxonomy codified in Wang et al. (2020), our system operates at Level 1 (object refinement) and Level 2 (situation refinement), with each track&#x27;s sensor-fusion provenance logged in the signed Λ-receipt so an auditor can reconstruct which sensors contributed to a given engagement recommendation. The fusion head is reliability-gated in the spirit of Adde et al. (2025): if any primary sensor stream drops below a configurable confidence threshold, the track is flagged UNCONFIRMED and the Λ-gate is forced to DENY until a human operator confirms — this is a design choice grounded in doctrine, not a proven-optimal algorithm. All sensor-fusion results are advisory; no autonomous effector action is taken (effectors are SIMULATED throughout).\n\n---","c":[{"t":"Wang, J., Liu, Y. &amp; Song, H. C-UAS: State of the Art, Challenges and Future Trends. arXiv:2008.12461, 2020","u":"https://arxiv.org/abs/2008.12461"},{"t":"Sakellariou, N. et al. Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data. arXiv:2410.16089, 2024","u":"https://arxiv.org/abs/2410.16089"},{"t":"Dudczyk, J. et al. Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space. Sensors 22(12):4323, 2022. DOI:10.3390/s22124323","u":"https://doi.org/10.3390/s22124323"}],"kernel":false},"cuas_swarm":{"g":"Building on Kinsler et al. (2024), we assume the threat swarm operates with minimal inter-drone RF emissions to defeat passive RF sensors — meaning our detection pipeline cannot rely on intercepted swarm traffic and must instead fuse radar micro-Doppler signatures with EO/IR track clustering to infer swarm membership. Building on D2BFT (Ryu et al. 2025) and the Byzantine fault-tolerance literature, our swarm-consensus module applies a PBFT-derived voting protocol across distributed track nodes: a swarm classification of HOSTILE requires a 2f+1 quorum of non-Byzantine sensor nodes to agree, so a compromised or jammed subset cannot flip the track picture unilaterally. The swarm engagement recommendation produced by this pipeline is a Λ-gate output labeled as a Conjecture-1 advisory — honest about the fact that the quorum bound assumes at most f/n ≤ 1/3 Byzantine nodes, which is an assumption, not a proof, in live adversarial conditions. Effectors remain SIMULATED throughout; the swarm track picture feeds operator display only.\n\n---","c":[{"t":"Kinsler, P. et al. Agent swarms: cooperation and coordination under stringent communications constraint. PLOS ONE, Dec 2024. DOI:10.1371/journal.pone.0311513","u":"https://doi.org/10.1371/journal.pone.0311513"},{"t":"Ryu, H. et al. D2BFT: A Dual Byzantine Fault Tolerance approach for Multi-Agent Drone Surveillance. Computer Networks, 2025","u":"https://www.sciencedirect.com/science/article/abs/pii/S1389128625007169"},{"t":"Bu, Y. et al. Advancement Challenges in UAV Swarm Formation Control: A Comprehensive Review. Drones 8(7):320, 2024. DOI:10.3390/drones8070320","u":"https://doi.org/10.3390/drones8070320"}],"kernel":false},"oversight":{"g":"Building on Koh, Steinhardt &amp; Liang (2021) and Cinà et al. (2024), we treat data-poisoning as a first-class threat: any ML classifier in our C-UAS pipeline — threat scorer, track classifier, or intent estimator — may have been corrupted during training, and we do not assume otherwise. Our architecture responds by placing the Λ-gate outside and downstream of all ML components: it is a non-learned, rule-based monotonic function whose only possible safety output is DENY (it cannot promote a classification to ALLOW on its own). Dalrymple et al.&#x27;s (2024) Guaranteed Safe AI framework articulates why this separation is necessary — the verifier that produces the safety certificate must be independent of the world model being verified; in our case, the gate&#x27;s policy rules are authored by a human, hashed into the receipt, and immutable at inference time. Building on the Proof-of-Behavior Protocol (Dembowski 2026), the signed receipt records the exact <code>policy_hash</code> active at every gate evaluation, so any attempt to swap in a backdoor-modified policy version would produce a hash mismatch detectable in audit — the Λ-gate is a Conjecture-1 advisory, honestly labeled, not a formally proven theorem, but its monotonic DENY property holds by construction, not by ML training.\n\n---","c":[{"t":"Chen, B. et al. Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. arXiv:1811.03728, 2018","u":"https://arxiv.org/abs/1811.03728"},{"t":"Dalrymple, D. et al. Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. arXiv:2405.06624, 2024","u":"https://arxiv.org/abs/2405.06624"},{"t":"Taheri, S. et al. Robustness Certificates for Neural Networks against Data Poisoning and Evasion Attacks. IEEE OJCSYS, 2025. DOI:10.1109/OJCSYS.2026.3689712","u":"https://doi.org/10.1109/OJCSYS.2026.3689712"}],"kernel":false},"cybSurface":{"g":"Our 3D attack-surface graph is not a theoretical visualization — it is a live signed artifact under Conjecture 1 (Λ), where every node carries a provably-labeled risk score anchored to current EPSS probability and CVSS exploitability data per <a href=\"https://arxiv.org/abs/2302.14172\" target=\"_blank\" rel=\"noopener\">Jacobs et al. (2023)</a>. The graph construction methodology follows the Graphene pipeline (<a href=\"https://arxiv.org/abs/2312.13119\" target=\"_blank\" rel=\"noopener\">Jin et al. 2024</a>) — semantic pre/postcondition matching across CVEs — but our receipts are governance-signed, not merely computed. We stay honest about what the graph proves: it surfaces high-probability lateral-movement paths; it does not guarantee completeness against novel zero-day techniques unrepresented in MITRE ATT&amp;CK v15. The locked-8 constraint means we display at most eight critical paths per surface layer, each with an honest EPSS-sourced probability label rather than a binary &quot;critical / not critical&quot; that hides uncertainty.","c":[{"t":"Palma &amp; Angelini. &quot;It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation.&quot; ESORICS 2024. arXiv:2312.16513","u":"https://arxiv.org/abs/2312.16513"},{"t":"Jacobs et al. &quot;Enhancing Vulnerability Prioritization: Data-Driven Exploit Predictions with Community-Driven Insights.&quot; EPSS SIG, 2023. arXiv:2302.14172","u":"https://arxiv.org/abs/2302.14172"},{"t":"Jin et al. &quot;Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs.&quot; 2024. arXiv:2312.13119","u":"https://arxiv.org/abs/2312.13119"}],"kernel":false},"cybZero":{"g":"Our zero-trust BFT mesh implements NIST SP 800-207&#x27;s seven tenets — never-implicit trust, per-session access, dynamic policy — as the authorization plane, while <b>Khipu BFT (Conjecture 2)</b> proposes a HotStuff-lineage consensus layer (<a href=\"https://arxiv.org/abs/1803.05069\" target=\"_blank\" rel=\"noopener\">Yin et al. 2018</a>, <a href=\"https://arxiv.org/abs/2010.11454\" target=\"_blank\" rel=\"noopener\">Jalalzai et al. 2021</a>) adapted for AI-agent mesh coordination. We are honest: Khipu BFT is a conjecture, not a production-proven protocol — we have not published a formal safety/liveness proof covering all failure modes, and we do not claim PBFT-equivalent unconditional Byzantine resilience. The PE/PA/PEP separation from <a href=\"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-207.pdf\" target=\"_blank\" rel=\"noopener\">NIST SP 800-207</a> maps directly onto our signed-receipt control plane: each access decision is a governance-signed event with provenance, not an ephemeral allow/deny. The gap between ZT authorization and BFT consensus (who decides vs. who agrees) is the exact space Khipu BFT is designed to bridge — conditionally, pending formal verification.","c":[{"t":"NIST SP 800-207. &quot;Zero Trust Architecture.&quot; National Institute of Standards and Technology, 2020","u":"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-207.pdf"},{"t":"Yin, Malkhi et al. &quot;HotStuff: BFT Consensus in the Lens of Blockchain.&quot; PODC 2019. arXiv:1803.05069","u":"https://arxiv.org/abs/1803.05069"},{"t":"Castro &amp; Liskov. &quot;Practical Byzantine Fault Tolerance and Proactive Recovery.&quot; ACM TOCS 2002. DOI:10.1145/571637.571640","u":"https://dl.acm.org/doi/10.1145/571637.571640"}],"kernel":false},"entIncident":{"g":"Our enterprise incident mesh implements the four SRE golden signals (latency, traffic, errors, saturation) as first-class signed telemetry under Conjecture 1 (Λ) — every emitted signal carries a governance receipt, making the observability layer itself auditable. The multi-modal RCA approach from <a href=\"https://arxiv.org/abs/2305.15778\" target=\"_blank\" rel=\"noopener\">RCACopilot (Chen et al. 2023)</a> informs our incident-routing design: incoming alerts are classified by type before diagnostics are aggregated, not after. We adopt the dependency-aware linking model from <a href=\"https://arxiv.org/abs/2403.18639\" target=\"_blank\" rel=\"noopener\">Ghosh et al. (2024)</a> to surface causal chains rather than symptom lists — critical for a mesh where a single model degradation can cascade through five dependent services. Honest label: our mean-time-to-root-cause estimates are probabilistic outputs from a Bayesian model (per <a href=\"https://arxiv.org/abs/2405.09330\" target=\"_blank\" rel=\"noopener\">BARO, Pham et al. 2024</a>), not deterministic verdicts; we display confidence intervals, not false precision.","c":[{"t":"Chen et al. &quot;Automatic Root Cause Analysis via Large Language Models for Cloud Incidents.&quot; FSE 2024. arXiv:2305.15778","u":"https://arxiv.org/abs/2305.15778"},{"t":"Ghosh et al. &quot;Dependency Aware Incident Linking in Large Cloud Systems.&quot; ICSE 2024. arXiv:2403.18639","u":"https://arxiv.org/abs/2403.18639"},{"t":"Jha et al. &quot;LLM Assisted Anomaly Detection Service for Site Reliability Engineers.&quot; 2025. arXiv:2501.16744","u":"https://arxiv.org/abs/2501.16744"}],"kernel":false},"fltTopo":{"g":"Our fleet topology view is grounded in the empirical reliability data from <a href=\"https://arxiv.org/abs/2410.21680\" target=\"_blank\" rel=\"noopener\">Kokolis et al. (2025)</a> — our GPU health scores are calibrated against real MTTF distributions, not vendor MTBF claims. The fltTopo dashboard aggregates the four GPU health dimensions identified in that work (hardware failure, software hang, network fault, checkpointing state) as signed telemetry events under Λ (Conjecture 1), giving operators an auditable health ledger rather than a live-only metric stream. SLO tracking uses the Faro utility-function formulation (<a href=\"https://arxiv.org/abs/2409.19488\" target=\"_blank\" rel=\"noopener\">Jeon et al. 2024</a>) to translate latency targets into continuous health scores — locked-8 means we surface at most eight critical fleet health signals per view, ranked by impact on active SLOs. Honest label: MTTF projections for new hardware generations carry wide confidence intervals; we display them as ranges, not point estimates.","c":[{"t":"Kokolis et al. &quot;Revisiting Reliability in Large-Scale Machine Learning Research Clusters.&quot; MLSys 2025. arXiv:2410.21680","u":"https://arxiv.org/abs/2410.21680"},{"t":"Jain et al. &quot;PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters.&quot; 2024. arXiv:2408.11919","u":"https://arxiv.org/abs/2408.11919"},{"t":"Jeon et al. &quot;A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro.&quot; SoCC 2024. arXiv:2409.19488","u":"https://arxiv.org/abs/2409.19488"}],"kernel":false},"llm":{"g":"Our model router implements a cascade architecture grounded in the FrugalGPT framework (<a href=\"https://arxiv.org/abs/2305.05176\" target=\"_blank\" rel=\"noopener\">Chen et al. 2023</a>) and calibrated using preference-data routing signals from <a href=\"https://arxiv.org/abs/2406.18665\" target=\"_blank\" rel=\"noopener\">RouteLLM (Ong et al. 2025)</a>: each routing decision is a governance-signed event with the model selected, the confidence score, and the cost-quality trade-off label, all appended to the receipt chain under Λ (Conjecture 1). The <a href=\"https://arxiv.org/abs/2501.01818\" target=\"_blank\" rel=\"noopener\">Rerouting paper (Shafran et al. 2025)</a> is a direct threat-model input: our router&#x27;s routing logic is isolated from user-visible prompt content to prevent confounder injection, and routing decisions are auditable for adversarial patterns. The HF Kernels Hub (<a href=\"https://huggingface.co/blog/hello-hf-kernels\" target=\"_blank\" rel=\"noopener\">huggingface.co/blog/hello-hf-kernels</a>) directly enables our inference stack: SZL&#x27;s kernel-publish access means we can distribute our MoE routing and attention kernels as signed, versioned Hub artifacts, making kernel provenance as auditable as model weights. Honest label: our cost-quality SLA guarantees are conditional on the router&#x27;s difficulty predictor — when query distribution shifts, the predictor recalibrates on a rolling window, not instantly.","c":[{"t":"Chen, Zaharia &amp; Zou. &quot;FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.&quot; 2023. arXiv:2305.05176","u":"https://arxiv.org/abs/2305.05176"},{"t":"Ong et al. &quot;RouteLLM: Learning to Route LLMs with Preference Data.&quot; 2025. arXiv:2406.18665","u":"https://arxiv.org/abs/2406.18665"},{"t":"Hugging Face. &quot;Enhance Your Models in 5 Minutes with the Hugging Face Kernel Hub.&quot; June 2025","u":"https://huggingface.co/blog/hello-hf-kernels"}],"kernel":true},"finm":{"g":"The finm rate-surface display ingests <b>LIVE</b> Federal Reserve H.15 constant-maturity yields and computes the spread surface in real time; no proprietary positioning data is implied. The factor decomposition (level, slope, curvature) follows the data-learned approach of <a href=\"https://doi.org/10.3390/econometrics10020015\" target=\"_blank\" rel=\"noopener\">Terada et al., 2022</a>, meaning factors are extracted from observed data and labeled <b>MEASURED</b> — not forecast. Where the tab renders forward-scenario surfaces, those are <b>MODELED</b> outputs, consistent with the quantile Nelson-Siegel framework of <a href=\"https://arxiv.org/abs/2401.09874\" target=\"_blank\" rel=\"noopener\">Iacopini et al., 2024</a>, and are displayed with explicit uncertainty bands. Every surface state is stored as a signed receipt under Λ = Conjecture 1, so any governed decision (e.g., rate-risk threshold alert) is reproducible and auditable.","c":[{"t":"Richman, R. &amp; Scognamiglio, S. (2024). Multiple Yield Curve Modeling and Forecasting using Deep Learning. arXiv:2401.16985","u":"https://arxiv.org/abs/2401.16985"},{"t":"Iacopini, M., Poon, A., Rossini, L. &amp; Zhu, D. (2024). A Quantile Nelson-Siegel Model. arXiv:2401.09874","u":"https://arxiv.org/abs/2401.09874"},{"t":"Federal Reserve Board (2025). H.15 Selected Interest Rates","u":"https://www.federalreserve.gov/releases/h15/"}],"kernel":false},"finr":{"g":"The finr tab scores transactions against a fraud/risk model; every score is a governed decision sealed as a signed receipt, creating the back-testable outcome record SR 11-7 requires for ongoing monitoring. The explainability layer surfaces SHAP attributions per transaction, satisfying the &quot;effective challenge&quot; requirement — an independent reviewer can inspect any signed receipt and reproduce the feature attribution without re-running the model (<a href=\"https://arxiv.org/abs/2105.06314\" target=\"_blank\" rel=\"noopener\">Psychoula et al., 2021</a>). Risk scores are labeled <b>MODELED</b> and displayed with a confidence band; they are never presented as ground-truth fraud verdicts, consistent with SR 11-7&#x27;s instruction to &quot;place less emphasis on output&quot; when model uncertainty is material. The model inventory, version, and validation status are embedded in each receipt under Λ = Conjecture 1, directly satisfying the SR 11-7 documentation and governance requirements.","c":[{"t":"Federal Reserve Board (2011). SR 11-7: Guidance on Model Risk Management","u":"https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm"},{"t":"Psychoula, I. et al. (2021). Explainable Machine Learning for Fraud Detection. arXiv:2105.06314","u":"https://arxiv.org/abs/2105.06314"},{"t":"Hein, B., Papenbrock, J. &amp; Fritz-Morgenthal, S. (2022). Financial Risk Management and Explainable, Trustworthy, Responsible AI. Frontiers in AI","u":"https://doi.org/10.3389/frai.2022.779799"}],"kernel":false},"legExposure":{"g":"The legExposure graph connects entities to live SEC filings and CourtListener opinions via a heterogeneous knowledge graph; every node and edge is labeled with its source data type (<b>LIVE</b> for freshly fetched EDGAR/CourtListener records, <b>SAMPLE</b> for any backfill from historical corpus). Exposure scores are <b>MODELED</b> outputs of an LLM extraction pipeline grounded in the EDGAR 10-K risk-factor methodology of <a href=\"https://aclanthology.org/N18-5017\" target=\"_blank\" rel=\"noopener\">Wang et al., 2018</a> and the SEC enforcement factor approach of <a href=\"https://arxiv.org/abs/2408.11961\" target=\"_blank\" rel=\"noopener\">Luo et al., 2024</a> — we do not claim to predict litigation outcomes, only to surface documented exposure signals. Every graph-query result is wrapped in a signed receipt under Λ = Conjecture 1 so the precise filing version, extraction model, and timestamp are auditable. We do not synthesise legal advice: the tab labels all outputs as information displays, not legal judgments.","c":[{"t":"Lahiri, S. et al. (2024). Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery. arXiv:2405.19164","u":"https://arxiv.org/abs/2405.19164"},{"t":"Luo, J. et al. (2024). Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain Litigation using LLM-based Thematic Factor Mapping. arXiv:2408.11961","u":"https://arxiv.org/abs/2408.11961"},{"t":"Free Law Project (2025). CourtListener REST API &amp; Bulk Data","u":"https://www.courtlistener.com/help/api/"}],"kernel":false},"red":{"g":"The red distress radar reads <b>LIVE</b> NYC DOF rolling sales and ACRIS lien recordings; all displayed property-level data carries the <b>LIVE</b> label with a feed timestamp. Distress scores (delinquency probability, LGD-weighted exposure) are <b>MODELED</b> outputs of a spatio-temporal risk engine grounded in the Kündig &amp; Sigrist framework (<a href=\"https://arxiv.org/abs/2410.02846\" target=\"_blank\" rel=\"noopener\">2024</a>), and are accompanied by the spatial uncertainty band derived from the underlying Gaussian process. We do not claim to know which properties will default — the tab presents signal indicators labeled as such, consistent with the early-warning framing of <a href=\"https://doi.org/10.1155/2022/1551883\" target=\"_blank\" rel=\"noopener\">Jianyu et al., 2022</a>. Every distress map state is sealed as a signed receipt so the exact feed snapshot, model version, and macro scenario assumption are reproducible under Λ = Conjecture 1.","c":[{"t":"Kündig, P. &amp; Sigrist, F. (2024). A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk. arXiv:2410.02846","u":"https://arxiv.org/abs/2410.02846"},{"t":"Sirignano, J., Sadhwani, A. &amp; Giesecke, K. (2018). Deep Learning for Mortgage Risk. arXiv:1607.02470","u":"https://arxiv.org/abs/1607.02470"},{"t":"NYC Department of Finance (2025). Rolling Sales Data","u":"https://www.nyc.gov/site/finance/property/property-rolling-sales-data.page"}],"kernel":false},"reo":{"g":"The reo ownership graph is built from <b>LIVE</b> public registry records (Secretary of State filings, ACRIS recorded deeds, FinCEN BOI foreign-entity reports) and <b>SAMPLE</b> historical corpus data where live feeds have gaps; every node and edge is stamped with its source and freshness label. Entity resolution uses the neural-embedding approach of <a href=\"https://arxiv.org/abs/2411.02695\" target=\"_blank\" rel=\"noopener\">Ding et al., 2024</a> to merge LLC name variants and registered-agent aliases — these merges are <b>MODELED</b> inferences and are flagged as candidate matches pending confirmation, never asserted as ground truth. Risk signals (e.g., circular-ownership flag, shell-depth score) follow the subgraph-pattern methodology of <a href=\"https://arxiv.org/abs/2404.19109\" target=\"_blank\" rel=\"noopener\">Bellei et al., 2024</a> and are labeled <b>MODELED</b>; we do not assert that any entity is engaged in fraud or money laundering, only that its graph topology matches patterns associated with elevated risk in the literature. All graph snapshots and risk scores are sealed as signed receipts under Λ = Conjecture 1, creating the auditable chain of custody required for any downstream governed decision.","c":[{"t":"Weber, M. et al. (2019). Scalable Graph Learning for Anti-Money Laundering: A First Look. arXiv:1812.00076","u":"https://arxiv.org/abs/1812.00076"},{"t":"Bellei, C. et al. (2024). The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset. arXiv:2404.19109","u":"https://arxiv.org/abs/2404.19109"},{"t":"FinCEN (2025). Beneficial Ownership Information Reporting — Corporate Transparency Act","u":"https://www.fincen.gov/boi"}],"kernel":false},"energy":{"g":"The <code>energy</code> tab reports joules <b>as MEASURED</b>, not estimated: our <code>governed-inference-meter</code> kernel samples <code>nvmlDeviceGetTotalEnergyConsumption</code> at configurable intervals and attributes consumption per token using the prefill/decode phase boundary identified in <a href=\"https://arxiv.org/abs/2512.03024\" target=\"_blank\" rel=\"noopener\">TokenPowerBench</a> and <a href=\"https://arxiv.org/html/2603.20224v1\" target=\"_blank\" rel=\"noopener\">Lin et al.</a>. We display tokens/joule with a clear MEASURED label, never projecting or extrapolating beyond the sampled window—directly addressing the ≤40% estimation error documented by <a href=\"https://arxiv.org/abs/2509.22092\" target=\"_blank\" rel=\"noopener\">Lottick et al.</a>. Energy efficiency gains from quantisation and batching (up to 70–80% per <a href=\"https://neurips.cc/virtual/2025/poster/121781\" target=\"_blank\" rel=\"noopener\">ML.ENERGY</a>) appear as real-time improvements on sovereign GPUs, shown alongside honest caveats: NVML reports device power, not rack-level power, so PUE is not included unless the operator supplies it. The <a href=\"https://arxiv.org/abs/1906.02243\" target=\"_blank\" rel=\"noopener\">Strubell et al.</a> and <a href=\"https://arxiv.org/abs/2311.16863\" target=\"_blank\" rel=\"noopener\">Luccioni et al.</a> lineage grounds why we measure inference at all—costs are not symmetric across tasks, and displaying them honestly is itself a governance act.","c":[{"t":"Strubell, Ganesh &amp; McCallum. &quot;Energy and Policy Considerations for Deep Learning in NLP.&quot; ACL 2019","u":"https://arxiv.org/abs/1906.02243"},{"t":"Luccioni, Jernite &amp; Strubell. &quot;Power Hungry Processing: Watts Driving the Cost of AI Deployment?&quot; FAccT 2024","u":"https://arxiv.org/abs/2311.16863"},{"t":"MLCommons Power WG. &quot;MLPerf Power: Benchmarking the Energy Efficiency of ML Systems.&quot; HPCA 2025","u":"https://arxiv.org/html/2410.12032v1"}],"kernel":true},"decision":{"g":"The <code>decision</code> tab produces a <b>signed receipt</b> from every inference: inputs, policy snapshot, kernel hash (c7c0ba17), and output are hashed together and the receipt is appended to an immutable chain—directly implementing the hash-chained JSONL architecture described by <a href=\"https://arxiv.org/html/2601.20727v1\" target=\"_blank\" rel=\"noopener\">arXiv:2601.20727</a> and the Sigstore/Rekor inclusion-proof model from <a href=\"https://dl.acm.org/doi/10.1145/3548606.3560596\" target=\"_blank\" rel=\"noopener\">Newman et al.</a>. We call this a &quot;signed receipt,&quot; not a &quot;proof of correctness&quot;—it proves the record was created, not that the model output is factually true, consistent with the distinction in <a href=\"https://arxiv.org/abs/2511.17118\" target=\"_blank\" rel=\"noopener\">arXiv:2511.17118</a>. The 3D trace (input vector, policy state, output) maps onto the decision-provenance pipeline defined by <a href=\"https://arxiv.org/abs/1804.05741\" target=\"_blank\" rel=\"noopener\">Singh &amp; Bhatt</a>: the chain of inputs to, the nature of, and the flow-on effects from each governed decision are captured and inspectable. OPA/Rego governs what actions are permitted at runtime; each allow/deny decision is itself receipted, so governance is not just stated but evidenced.","c":[{"t":"Singh &amp; Bhatt. &quot;Decision Provenance: Harnessing Data Flow for Accountable Systems.&quot; IEEE Access 2018","u":"https://arxiv.org/abs/1804.05741"},{"t":"Newman et al. &quot;Sigstore: Software Signing for Everybody.&quot; CCS 2022","u":"https://dl.acm.org/doi/10.1145/3548606.3560596"},{"t":"arXiv:2601.20727. &quot;Audit Trails for Accountability in Large Language Models.&quot; 2026","u":"https://arxiv.org/html/2601.20727v1"}],"kernel":true},"wowdrop":{"g":"<code>wowdrop</code> is the a11oy governance overlay: it <b>wraps any model</b> and applies policy checks at the boundary—input, context, and output—without touching the model&#x27;s weights. The architecture follows <a href=\"https://arxiv.org/abs/2310.10501\" target=\"_blank\" rel=\"noopener\">NeMo Guardrails</a> and <a href=\"https://openpolicyagent.org/docs\" target=\"_blank\" rel=\"noopener\">OPA</a> in keeping policy logic external, version-controlled, and auditable; our Rego policies directly satisfy the NIST AI RMF Measure/Manage functions and the EU AI Act Art. 12 logging mandate (<a href=\"https://doi.org/10.6028/NIST.AI.100-1\" target=\"_blank\" rel=\"noopener\">NIST AI 100-1</a>). Every policy decision at the wowdrop boundary generates a receipt (see <code>decision</code> tab), making compliance not just documented but <b>evidenced at runtime</b>. We do not claim that wrapping a model makes it provably safe—we claim only that policy enforcement is applied and receipted; the model inside is still a probabilistic system, and the governance layer is our best-effort constraint, not a formal safety certificate.","c":[{"t":"NIST. &quot;Artificial Intelligence Risk Management Framework (AI RMF 1.0).&quot; Jan 2023","u":"https://doi.org/10.6028/NIST.AI.100-1"},{"t":"Rebedea et al. &quot;NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications.&quot; EMNLP 2023","u":"https://arxiv.org/abs/2310.10501"},{"t":"Principled Evolution. &quot;GOPAL: AI Governance OPA Library.&quot; 2025","u":"https://github.com/principled-evolution/gopal"}],"kernel":false},"wowledger":{"g":"The <code>wowledger</code> is a <b>Sigstore/Rekor-style hash-chain ledger</b>, not a blockchain or a database: every signed receipt from <code>decision</code> is appended to an immutable JSONL log where each entry&#x27;s SHA-256 curr_hash commits to its content and the previous entry&#x27;s hash, following the architecture in <a href=\"https://arxiv.org/html/2601.20727v1\" target=\"_blank\" rel=\"noopener\">arXiv:2601.20727</a>. The 3D structure of our chain is: (1) hash-chain integrity linking sequential events, (2) Merkle inclusion proofs enabling any third party to verify a specific receipt without reading the full log (<a href=\"https://www.rfc-editor.org/info/rfc6962\" target=\"_blank\" rel=\"noopener\">RFC 6962</a>), and (3) timestamped signed commitment at the root—analogous to Rekor&#x27;s SCT (<a href=\"https://dl.acm.org/doi/10.1145/3548606.3560596\" target=\"_blank\" rel=\"noopener\">Sigstore</a>). We call this <b>tamper-evident</b>, not tamper-proof: modification is detectable, not physically prevented; honest labels matter, and we say so in the UI. The Crosby &amp; Wallach result—80M events with 3 KB proofs—gives us the engineering baseline for scale.","c":[{"t":"Laurie, Langley &amp; Kasper. &quot;Certificate Transparency.&quot; RFC 6962, IETF 2013","u":"https://www.rfc-editor.org/info/rfc6962"},{"t":"Newman et al. &quot;Sigstore: Software Signing for Everybody.&quot; CCS 2022","u":"https://dl.acm.org/doi/10.1145/3548606.3560596"},{"t":"arXiv:2511.17118. &quot;Constant-Size Cryptographic Evidence Structures for Regulated AI Workflows.&quot; 2025","u":"https://arxiv.org/abs/2511.17118"}],"kernel":true},"trustspace":{"g":"The <code>trustspace</code> tab visualises <b>Λ = Conjecture 1</b>: our advisory trust geometry, not a proven theorem. Λ is grounded in calibration science—specifically the insight from <a href=\"https://arxiv.org/abs/1706.04599\" target=\"_blank\" rel=\"noopener\">Guo et al.</a> that model confidence is systematically miscalibrated and must be measured and corrected, and from <a href=\"https://arxiv.org/abs/2107.07511\" target=\"_blank\" rel=\"noopener\">Angelopoulos &amp; Bates</a> that distribution-free uncertainty sets can wrap any model without retraining. Where Λ-scores fall below a threshold, <code>trustspace</code> triggers the reject-option pathway following <a href=\"https://arxiv.org/abs/1901.09192\" target=\"_blank\" rel=\"noopener\">SelectiveNet</a>: the model says &quot;I don&#x27;t know&quot; and receipts the abstention. Trust never reaches 100% in <code>trustspace</code>—this is not a UI choice but a claim grounded in the calibration literature: even temperature-scaled models retain residual miscalibration under distribution shift (<a href=\"https://arxiv.org/abs/2106.07998\" target=\"_blank\" rel=\"noopener\">Minderer et al.</a>). Λ is labelled CONJECTURE throughout: it is a working trust heuristic whose geometry we propose and measure, not a theorem we claim to have proven.","c":[{"t":"Guo, Pleiss, Sun &amp; Weinberger. &quot;On Calibration of Modern Neural Networks.&quot; ICML 2017","u":"https://arxiv.org/abs/1706.04599"},{"t":"Angelopoulos &amp; Bates. &quot;A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.&quot; arXiv:2107.07511, 2021","u":"https://arxiv.org/abs/2107.07511"},{"t":"Geifman &amp; El-Yaniv. &quot;SelectiveNet: A Deep Neural Network with an Integrated Reject Option.&quot; ICML 2019","u":"https://arxiv.org/abs/1901.09192"}],"kernel":false},"constellation":{"g":"The <code>constellation</code> view renders the a11oy ecosystem—verticals, infra layers, receipt flows—as a live 3D force-directed graph, following the WebGL/Three.js approach demonstrated in <a href=\"https://ar5iv.labs.arxiv.org/html/2304.09864\" target=\"_blank\" rel=\"noopener\">GeoGraphViz</a>. Nodes represent verticals or system components; edges encode governance receipt flows and dependency relationships, not just HTTP calls. The 3D axis separates trust tiers (Z-depth encodes Λ-band), making trust geometry literally spatial, not just labelled—a direct visualisation of <code>trustspace</code> outputs. We do not claim the galaxy metaphor is scientifically rigorous; it is a navigation affordance grounded in graph-physics research, and every displayed node links back to receipts in <code>wowledger</code>.","c":[{"t":"arXiv:2304.09864. &quot;GeoGraphViz: 3D Force-Directed Graph for Knowledge Graph Visualization.&quot; 2023","u":"https://ar5iv.labs.arxiv.org/html/2304.09864"},{"t":"arXiv:2604.10384. &quot;Context-Aware Knowledge Graph Visualization with User Preferences.&quot; 2026","u":"https://arxiv.org/pdf/2604.10384.pdf"},{"t":"SUSE Communities. &quot;What is Topology and Why It Matters for Observability.&quot; 2025","u":"https://www.suse.com/c/observability-what-is-topology/"}],"kernel":false}};
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+    var p=buildPanel(tab); if(!p) return false;
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+     which WIPES an appended panel. A scoped MutationObserver re-appends the panel
+     whenever the host's children change and the panel is missing — additive, no edits to
+     the original render. Debounced via rAF/microtask guard so it never loops on its own
+     append. The observer is per-host and self-cleans when the host leaves the DOM. */
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+      };
+      (window.requestAnimationFrame?requestAnimationFrame(run):setTimeout(run,16));
+    });
+    try{ obs.observe(host,{childList:true}); host.__gndObs=obs; }catch(e){}
+  }
   whenReady(function(){
     var V=window.VIEWS; if(!V) return;
     var wired=[];
@@ -15519,12 +15545,12 @@ window.warboard_init=warboard_init; window.warboard_all=warboard_all;
       var view=V[tab]; if(!view) return false;
       var orig=view.render;
       view.render=function(host){
-        try{ if(typeof orig==='function') orig.call(view, host); }catch(e){}
-        try{
-          if(host && host.querySelector && host.querySelector('[data-grounding="'+tab+'"]')) return;
-          var p=buildPanel(tab);
-          if(p && host && host.appendChild) host.appendChild(p);
-        }catch(e){}
+        var rv; try{ if(typeof orig==='function') rv=orig.call(view, host); }catch(e){}
+        /* append now (sync renders) + watch for async/polling re-renders that wipe it */
+        try{ ensurePanel(host,tab); watchHost(host,tab); }catch(e){}
+        /* if render returned a promise (async tab), also re-append after it settles */
+        try{ if(rv&&typeof rv.then==='function'){ rv.then(function(){ try{ ensurePanel(host,tab); }catch(e){} }); } }catch(e){}
+        return rv;
       };
       return true;
     }
@@ -15535,6 +15561,5 @@ window.warboard_init=warboard_init; window.warboard_all=warboard_all;
 })();
 </script>
 <!-- end GROUNDING-WIRE -->
-
 </body>
 </html>