{ "run_at": "2026-08-31T19:36:25.797044+00:00", "base_url": "http://localhost:3005", "judge_model": "claude-opus-5", "confirmed_thresholds": { "citation_correctness_min": 0.95, "correct_refusal_rate_min": 0.95, "limitation_inclusive_answer_correctness_min": 0.9, "note": "Confirmed by Jason 2026-08-31. Applies to in-corpus items for citation/answer correctness; correct-refusal-rate applies to the 15 out-of-scope items; limitation-inclusive bar applies to the G24-G35 subset specifically." }, "summary": { "total_items": 50, "graded_items": 50, "judge_errors": [], "in_corpus_answer_correctness": 0.7714, "citation_correctness": 0.9118, "groundedness": 0.9706, "limitation_inclusive_answer_correctness": 0.75, "correct_refusal_rate": 1.0, "overall_pass_rate": 0.8, "wrongly_refused_in_corpus": [], "failed_items": [ "G01", "G11", "G15", "G17", "G18", "G19", "G20", "G24", "G27", "G32" ] }, "items": [ { "id": "G01", "category": "easy_lookup", "question": "What is Grounded Commitment Learning (GCL)?", "pass": false, "error": "OverloadedError: Error code: 529 - {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'Overloaded'}, 'request_id': 'req_011CebNDGhd4713wqQboFhSP'}" }, { "id": "G02", "category": "easy_lookup", "question": "What hold-up reduction does GCL report, and with what confidence interval?", "answer_text": "GCL reduced hold-up incidents by 36.8% (95% CI: [28.4%, 45.2%]) [1].\n\nNote this comes from Experiment 21, a self-designed simulation validating Hart-Moore theoretical predictions \u2014 the effect sizes involved (up to d = 7.33) reflect a deliberately clean experimental separation between conditions in this simulation, and should be read as confirmation that Hart-Moore's predictions replicate in this environment, not as an estimate of effect size in deployed systems [1].", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Strategy Tournament", "section_title": "Statistical Validation", "url": "/projects/strategy-tournament/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.42435143752466237, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The figure and CI match the reference exactly, and the citation points to the Experiment 21 section where the Hart-Moore validation result lives; the added caveat about simulation context is consistent with that same section." }, "pass": true }, { "id": "G03", "category": "easy_lookup", "question": "What's the self-selection advantage GCL reports over oracle-based task assignment?", "answer_text": "The corrected finding is that self-selection and a truly optimal oracle are statistically indistinguishable when effort is held fixed: +0.000 [\u22120.013, +0.013], d = 0.00 [1].\n\nAn earlier version reported an 81% advantage driven by information asymmetry, but that claim was retracted in August 2026. The original oracle was flawed\u2014it scored candidates by closeness-of-fit and penalized over-qualified agents under a success model that didn't actually maximize success. Against a corrected, truly success-maximizing oracle (Experiment 40), the information advantage vanished [1].\n\nThe surviving mechanism is **emergent motivation**: agents that choose their own tasks develop higher effort, beating the optimal oracle by +0.065 cooperation (95% CI [+0.050, +0.080], d = 1.68). Choice creates commitment\u2014not privileged information. Assigned agents do not develop the same commitment dynamics [1].\n\nHowever, this motivation effect is **simulation-only**. It was not detected in prompted LLM agents: a powered paired re-test (Experiment 41b, n = 120, McNemar) found no significant \"volunteered vs assigned\" framing effect (p = 0.52) [1]. Additionally, LLM self-assessments were less calibrated than external assessment, so self-selection advantages should not be assumed to transfer to LLM systems [6].", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "Self-Selection vs. External Assignment", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Ongoing Work", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Gaming-Resistant Reputation Mechanisms", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Limitations", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Template Sharing (Experiment 24)", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6657834261800945, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer reports the correction, +0.000 information advantage, and the +0.065 emergent motivation effect with matching CI and effect size; citations point to the Self-Selection section and Limitations section appropriately, and extra details (Exp 40/41b, calibration) are plausibly from those sections." }, "pass": true }, { "id": "G04", "category": "easy_lookup", "question": "How many patterns does HALA (Human-AI Layered Architecture) define, and how are they organized?", "answer_text": "HALA defines 28 patterns, organized across 5 layers [2][1]:\n\n1. **Foundation Infrastructure** \u2014 4 patterns that make everything else possible (Semantic Interface, Semantic Compiler, Event-Sourced Memory, and 1 more) [1]\n\n2. **Epistemic Integrity** \u2014 6 patterns that ensure reasoning quality (Grounding Contract, Base-Rate Enforcer, Confidence Collar, and 3 more) [1]\n\n3. **Organizational Perception** \u2014 5 patterns that see what humans can't or won't (Org-Shadow Modeler, Incentive Translator, Status-Blind Analyst, and 2 more) [1]\n\n4. **Uncomfortable Agency** \u2014 6 patterns that say what humans can't (Mr Unpopular, Kill Switch Advocate, Long-Horizon Advocate, and 3 more) [1]\n\n5. **Meta-Governance** \u2014 6 patterns that govern the patterns (Adoption Gradient, Trust Calibrator, Capture Detector, and 3 more) [1]\n\nThere's also a **Decision Output** stage where patterns meet human decisions (1 pattern: Decision Historian) [1].\n\nData flows down the layers; governance flows up [1]. Version 2.0 added the layered taxonomy and 6 new patterns to the original v1.0 set of 22 [4].", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Architecture", "url": "/writing/hala/" }, { "index": 2, "page_title": "Writing", "section_title": "HALA: Human-AI Layered Architecture", "url": "/writing/" }, { "index": 3, "page_title": "Home", "section_title": "Additional Work", "url": "/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 5, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" }, { "index": 6, "page_title": "Home", "section_title": "Research", "url": "/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "HALA: Human-AI Layered Architecture", "url": "/writing/hala/" }, { "index": 8, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.7151571302726598, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "Correctly states 28 patterns across 5 named layers plus a separate Decision Output stage (not framed as a sixth layer), matching the reference; layer breakdowns and version history are plausibly drawn from the Architecture and Origin sections cited." }, "pass": true }, { "id": "G05", "category": "easy_lookup", "question": "What does Aegis provide, and how does it relate to agent frameworks like LangChain?", "answer_text": "Aegis is a systems-architecture layer that sits beneath agent frameworks like LangChain and LangGraph [1]. It addresses infrastructure concerns that those frameworks don't handle natively\u2014specifically durability, verification, and policy enforcement across agent workflows [2].\n\n**What Aegis provides:**\n\n- **Event-sourced state**: An append-only event log lets workflows resume from any checkpoint after a failure, rather than losing all progress on restart [5]\n- **Commitments as first-class objects**: When an agent commits to an action (e.g., \"complete this by 5pm\"), that commitment is tracked with explicit success/failure conditions and deadlines, enabling verification and violation detection [3][5]\n- **Policy enforcement at the gateway**: Tool invocations are checked against constraints *before* they execute, catching disallowed actions at invocation time rather than after the fact [1][5]\n\n**The relationship:**\n\nAegis isn't a replacement for LangChain\u2014it's complementary. You can run a LangGraph workflow on top of Aegis to gain durability and formal verification that LangGraph doesn't provide natively [1]. The gap Aegis fills is that neither LangChain nor workflow engines like Temporal treat agent commitments as structured, verifiable objects; they're just strings in conversation history [3].\n\n**Current status:** Aegis has 303 passing tests and is single-node, real (not simulated) infrastructure code, but it has no performance benchmarks yet and is not production-deployed [1].", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 3, "page_title": "Aegis", "section_title": "The Gap", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Research", "section_title": "Also shipped", "url": "/research/" }, { "index": 5, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 7, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 8, "page_title": "Research", "section_title": "Limitations", "url": "/research/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6616192489595762, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer captures all three core elements (event-sourced state, commitments as first-class objects, gateway policy enforcement) and the complementary-not-replacement relationship with LangChain/LangGraph; citations map to plausible, on-topic sections (Core Components, The Gap, Aegis overview) and the added status details are the kind of caveat present on the Aegis page, so nothing appears unsupported." }, "pass": true }, { "id": "G06", "category": "easy_lookup", "question": "What accuracy improvement does Collaborative Nested Learning (CNL) report at high regularization?", "answer_text": "Collaborative Nested Learning reports a **+89% accuracy improvement at high regularization (reg=5.0)** [3]. \n\nThe corpus also notes an additional **+62% improvement at reg=20.0** [3], demonstrating that the largest gains occur precisely where the baseline approach struggles most\u2014as regularization increases to prevent forgetting, the baseline accuracy drops to ~10% while bridges maintain 14-19% accuracy [3].\n\nIt's important to note that this is a **simulation-validated result at CIFAR scale, not externally reviewed** [1].", "cited_sources": [ { "index": 1, "page_title": "Collaborative Nested Learning", "section_title": "Collaborative Nested Learning", "url": "/projects/collaborative-nested-learning/" }, { "index": 2, "page_title": "Collaborative Nested Learning", "section_title": "Mathematical Formulation: Baseline vs. Novel Contributions", "url": "/projects/collaborative-nested-learning/" }, { "index": 3, "page_title": "Collaborative Nested Learning", "section_title": "Experimental Results", "url": "/projects/collaborative-nested-learning/" }, { "index": 4, "page_title": "Collaborative Nested Learning", "section_title": "What I Personally Built", "url": "/projects/collaborative-nested-learning/" }, { "index": 5, "page_title": "Collaborative Nested Learning", "section_title": "Multi-Timescale Optimization with Knowledge Bridges", "url": "/projects/collaborative-nested-learning/" }, { "index": 6, "page_title": "Collaborative Nested Learning", "section_title": "Nested Learning as Meta-Architecture", "url": "/projects/collaborative-nested-learning/" }, { "index": 7, "page_title": "Collaborative Nested Learning", "section_title": "Production-Quality PyTorch", "url": "/projects/collaborative-nested-learning/" }, { "index": 8, "page_title": "Collaborative Nested Learning", "section_title": "Why Continual Learning Matters", "url": "/projects/collaborative-nested-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5397274806578806, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The +89% at high regularization matches the reference, and the additional details (reg values, baseline vs bridge accuracies) are cited to the Experimental Results section where such numbers live; the simulation caveat is cited to the overview page. It omits the Pareto-dominance framing but does not contradict it." }, "pass": true }, { "id": "G07", "category": "easy_lookup", "question": "What is the \"punishment paradox\" finding in GCL?", "answer_text": "The Punishment Paradox is a counterintuitive finding from GCL simulation research: [1] increasing consequences for commitment violations *decreases* cooperation, which is the opposite of what traditional game theory predicts.\n\nThe mechanism behind it is retaliation cascades. [1] High consequences trigger counter-defection that spreads through the population\u2014penalties cause agents to retaliate, which then spreads further rather than deterring violations.\n\nThe effect is strong: [1] the correlation between consequence level and cooperation is r = -0.951 (p < 0.001), with a monotonic decrease across all 5 tested consequence levels. The t-test comparing no consequences versus full consequences yielded t = 36.18 (p < 0.001) with Cohen's d = 9.34.\n\nOne important caveat: [1] d = 9.34 \"is large enough to be a flag, not a flex: it means the simulation's punishment mechanic is close to deterministic, not that real-world punishment effects are this dramatic. Read it as an internal comparison inside a self-designed environment, not an externally validated effect size.\"\n\nYou can reproduce the result in [1] the retaliation-cascade experiment at github.com/jstiltner/gcl.", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "The Punishment Paradox", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Self-Selection vs. External Assignment", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5463392851761112, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "All key facts (direction of effect, retaliation cascades, r = -0.951, p < 0.001) match the reference, and additional details (t-test, Cohen's d caveat, repo link) are cited to the Punishment Paradox section, which is the correct source section for these claims." }, "pass": true }, { "id": "G08", "category": "easy_lookup", "question": "What resolves the punishment paradox in GCL?", "answer_text": "The punishment paradox in GCL is resolved by adding a **redemption pathway** [3]. \n\nIn the simulation, increasing consequences for commitment violations initially decreases cooperation\u2014the opposite of what traditional game theory predicts. This happens because high penalties trigger retaliation cascades that spread through the population [1].\n\nThe solution maintains incentives while reducing the fear that prevents agents from making commitments in the first place. Redemption works by allowing failed agents to attempt recovery actions; successful recovery reduces permanent reputation damage, while effort costs prevent gaming [3].", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "The Punishment Paradox", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Redemption Resolves the Paradox", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Self-Selection vs. External Assignment", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5471585049026547, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "Answer captures the redemption pathway mechanism, recovery actions, reduced reputation damage, and effort costs matching the reference (omitting only the eligibility snapshot detail, which is not a factual error); citations to the Punishment Paradox and Redemption sections support their respective claims." }, "pass": true }, { "id": "G09", "category": "easy_lookup", "question": "What is Mellifera?", "answer_text": "Mellifera is an agricultural ML platform for precision beekeeping, combining voice-first data collection with edge ML inference (TensorFlow.js). Beekeepers speak observations while working the hives, and the system speaks back confirmations and ML-generated recommendations via TTS \u2014 designed for field conditions where gloves, veils, and sunlight make traditional touchscreen data entry impractical [1][4].\n\nUnder the hood, it uses production ML classifiers addressing agricultural decision-making under uncertainty: sparse/noisy observations, delayed feedback (winter survival isn't validated for 3-6 months), and high-stakes decisions [3]. It includes a winter survival risk model with uncertainty quantification (isotonic calibration, bootstrap confidence intervals) [6] and a feeding recommendation system with multi-output classification/regression for feed type and amount [8]. Reported metrics include 92% treatment timing accuracy, 0.87 AUC for survival prediction, 89% feeding type accuracy, and models under 25KB total size for edge deployment [2]. It's built on a MongoDB data model with a full REST API (Swagger-documented, JWT/OAuth) covering apiaries, hives, inspections, treatments, and feedings [7].\n\nThe site frames it as part of Jason's research on \"coordination without collapse\" \u2014 specifically how a beekeeper coordinates with a system they can't directly observe [2], and it's listed under \"also shipped\" work alongside a multi-agent orchestration platform [5].", "cited_sources": [ { "index": 1, "page_title": "Mellifera", "section_title": "Voice-First Data Collection", "url": "/projects/mellifera/" }, { "index": 2, "page_title": "Mellifera", "section_title": "Mellifera: Agricultural ML Platform", "url": "/projects/mellifera/" }, { "index": 3, "page_title": "Mellifera", "section_title": "The ML Problem", "url": "/projects/mellifera/" }, { "index": 4, "page_title": "Mellifera", "section_title": "Designed for Real Field Conditions", "url": "/projects/mellifera/" }, { "index": 5, "page_title": "Research", "section_title": "Also shipped", "url": "/research/" }, { "index": 6, "page_title": "Mellifera", "section_title": "Winter Survival Risk Model", "url": "/projects/mellifera/" }, { "index": 7, "page_title": "Mellifera", "section_title": "Data Model & API", "url": "/projects/mellifera/" }, { "index": 8, "page_title": "Mellifera", "section_title": "Feeding Recommendation System", "url": "/projects/mellifera/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.421603505298798, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer covers all reference elements (treatment timing, survival prediction, feeding recommendations, TensorFlow.js edge inference, voice-first collection) with extra detail that maps plausibly to the cited sections, each citation aligning with its section topic." }, "pass": true }, { "id": "G10", "category": "easy_lookup", "question": "What did Jason build at HCA Healthcare?", "answer_text": "At HCA Healthcare \u2014 the largest US hospital system, an environment the corpus describes as generating 44M+ patient encounters annually \u2014 Jason shipped production document-intelligence and clinical RAG pipelines, built with HIPAA-compliant, audited, multi-provider LLM routing and human-in-the-loop escalation [1][7].\n\nMore specifically, the Document Understanding Platform work addressed processing of insurance authorization and denial documents (faxed PDFs, scanned forms, varying quality), where the architecture had to satisfy HIPAA-grade audit and access control, HCA's scale-driven volume/quality distribution, and a reliability bar where silent extraction errors were treated as worse than slow ones \u2014 which is why multi-provider routing with confidence-based escalation was used rather than relying on a single model [6]. It's described as a full-stack production system (not a prototype), including database models, async processing, monitoring, and deployment configuration, multi-provider LLM abstraction, prompt engineering for structured extraction, confidence scoring, graceful degradation, and systematic human-feedback capture for continuous improvement (framed as a foundation for RLHF-style reward shaping) [5]. HIPAA compliance was built into the architecture itself, including 4-tier role-based access control, JWT authentication, complete audit logging, PHI access tracking per request, security headers, data integrity verification, and automated retention policies [8].\n\nSeparately, within HCA's advanced-technology division, Jason also built an internal user-feedback platform routing input from users to development and product teams, which was adopted by six product teams \u2014 including teams outside his own reporting line [1].", "cited_sources": [ { "index": 1, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 2, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 3, "page_title": "Mellifera", "section_title": "Explore the Project", "url": "/projects/mellifera/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 5, "page_title": "Document Understanding Platform", "section_title": "Why It Matters", "url": "/projects/document-understanding/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "The Challenge", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "About", "section_title": "Background", "url": "/about/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "HIPAA Compliance Framework", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.47111956539519306, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer covers the document-intelligence/Composer-style platform, clinical RAG pipelines, and the internal feedback platform adopted by six product teams, matching the reference; citations map plausibly to the project's Challenge/Why It Matters/HIPAA sections and About for scope/impact, and added detail (HIPAA controls, scale) appears drawn from retrieved content rather than invented." }, "pass": true }, { "id": "G11", "category": "easy_lookup", "question": "What does the \"Mr Unpopular\" pattern in HALA do?", "answer_text": "The corpus doesn't give a full definition of Mr Unpopular, but the pieces that are present describe it as one of the patterns in the Uncomfortable Agency layer, functioning as \"one uncomfortable truth-teller\" in a minimum viable HALA deployment [3]. It's also referenced as a pattern someone \"deploys\" in a decision context, in the discussion of whether AI truth-telling relocates the social cost of dissent to whoever invokes it rather than eliminating that cost [7].\n\nBeyond that \u2014 its specific mechanism, trigger conditions, or what exactly it says \u2014 isn't detailed in the retrieved context, so I can't characterize it more precisely without guessing.", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Situating HALA", "url": "/writing/hala/" }, { "index": 2, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interactive Demo", "url": "/writing/hala/" }, { "index": 3, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Implementation Guide", "url": "/writing/hala/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "HALA: Human-AI Layered Architecture", "url": "/writing/hala/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Open Problems", "url": "/writing/hala/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interested in Applying These Patterns?", "url": "/writing/hala/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Accountability, Not Laundering", "url": "/writing/hala/" }, { "index": 8, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4387141195599466, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer fails to state the core function (surfacing inconvenient high-value truths no one will voice) and the weaponization failure mode, instead claiming the corpus lacks a definition; the citations it does use appear plausibly supported and it doesn't invent content, so groundedness/citations pass." }, "pass": false }, { "id": "G12", "category": "easy_lookup", "question": "How many tests does Aegis have, and is it in production?", "answer_text": "Aegis has 303 tests total, covering state machine transitions, checkpoint integrity, policy evaluation edge cases, and multi-agent message ordering, broken down as: core (42), state_machine (28), tools (48), commitments (32), llm (52), and recovery (26) [1].\n\nIt is not in production. The project status is explicitly stated as \"single-node, 303 tests passing, no performance benchmarks yet and no production deployment\" \u2014 described as real, tested infrastructure code, but not yet load-tested or shipped [2].", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "What I Built", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "Strategy Tournament", "section_title": "Robustness Test Suite", "url": "/projects/strategy-tournament/" }, { "index": 7, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "Production Capabilities", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.40663345831033904, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "303 tests and non-production status match the reference; citations point to the Aegis Validation and status sections that plausibly support the test breakdown and project status claims." }, "pass": true }, { "id": "G13", "category": "cross_page_synthesis", "question": "How do GCL and Aegis relate to each other?", "answer_text": "GCL (Grounded Commitment Learning) provides the theoretical foundation and object model; Aegis is the runtime infrastructure that implements it [1].\n\nSpecifically, the GCL 5-tuple\u2014(debtor, creditor, action, condition, deadline)\u2014maps directly into Aegis's RuntimeCommitment object [1]. GCL's verifiable-behavior contracts gave Aegis its core abstraction: rather than treating agent commitments as unstructured text in a conversation, Aegis makes them first-class, evaluable objects [4]. This enables verification (checking if a condition holds), violation detection, and structured recovery [2].\n\nYou can run an agent framework like LangGraph on top of Aegis to gain durability and commitment verification that the framework doesn't provide natively [4].", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 5, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Aegis", "section_title": "The Gap", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Aegis", "section_title": "Limitations", "url": "/projects/aegis/" }, { "index": 8, "page_title": "Aegis", "section_title": "Architecture", "url": "/projects/aegis/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5756510806553855, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer correctly captures GCL as theoretical foundation and Aegis as runtime implementing the commitment tuple as RuntimeCommitment objects, matching the reference; citations to GCL Integration, Core Components, and the Aegis overview plausibly support the respective claims, and no unsupported additions appear." }, "pass": true }, { "id": "G14", "category": "cross_page_synthesis", "question": "How does CNL's fast/slow nested-learning pattern apply to agentic systems, per Jason's own writeup?", "answer_text": "According to Jason's writeup, the fast/slow nested-learning pattern applies to agentic systems at the layer where task-specific specialists operate at different speeds than the orchestrator/meta-learner: [1]\n\n**Implementation in agentics:**\n- **Fast Component:** Task-specific specialists\n- **Slow Component:** Orchestrator / meta-learner\n- **Bridge Challenge:** Specialists inform orchestration\n\nThe writeup identifies two key architectural principles that apply here:\n\n1. **Bidirectional Flow:** Knowledge must flow both ways\u2014specialists feed patterns back to orchestrators, rather than just top-down direction. [1]\n\n2. **Non-Adjacent Bridges:** Task execution feeds directly to the orchestrator, skipping intermediate layers. This prevents information bottlenecks and fidelity loss that would occur if the signal had to traverse every intermediate layer. [1]\n\nThe broader point is that this nested-learning pattern isn't unique to model internals\u2014it appears \"at every layer of the AI stack where components learn or adapt at different rates,\" and agentic systems are one concrete instantiation of that universal challenge. [1]", "cited_sources": [ { "index": 1, "page_title": "Collaborative Nested Learning", "section_title": "Nested Learning as Meta-Architecture", "url": "/projects/collaborative-nested-learning/" }, { "index": 2, "page_title": "Collaborative Nested Learning", "section_title": "Collaborative Nested Learning", "url": "/projects/collaborative-nested-learning/" }, { "index": 3, "page_title": "Collaborative Nested Learning", "section_title": "Multi-Timescale Optimization with Knowledge Bridges", "url": "/projects/collaborative-nested-learning/" }, { "index": 4, "page_title": "Collaborative Nested Learning", "section_title": "Mathematical Formulation: Baseline vs. Novel Contributions", "url": "/projects/collaborative-nested-learning/" }, { "index": 5, "page_title": "Collaborative Nested Learning", "section_title": "Why Continual Learning Matters", "url": "/projects/collaborative-nested-learning/" }, { "index": 6, "page_title": "Collaborative Nested Learning", "section_title": "What I Personally Built", "url": "/projects/collaborative-nested-learning/" }, { "index": 7, "page_title": "Collaborative Nested Learning", "section_title": "Experimental Results", "url": "/projects/collaborative-nested-learning/" }, { "index": 8, "page_title": "Collaborative Nested Learning", "section_title": "Production-Quality PyTorch", "url": "/projects/collaborative-nested-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6150412521880649, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer matches the reference's mapping (fast=specialists, slow=orchestrator, bridge=specialists inform orchestration) plus bidirectional flow and non-adjacent bridges, all cited to the meta-architecture section where the table lives; no unsupported additions beyond mild elaboration." }, "pass": true }, { "id": "G15", "category": "cross_page_synthesis", "question": "GCL and HALA both address a kind of accountability in multi-agent/human-AI systems \u2014 how do their approaches differ?", "answer_text": "They address accountability at different layers and through different mechanisms.\n\n**GCL's approach: behavioral verification through stakes** [1][4]\n\nGCL grounds accountability in observable action and reputation consequences. Agents pre-specify failure modes and stake reputation on every commitment. An auditor doesn't need to interpret what an agent intended or understood\u2014they check the commitment log against the verification predicate (what observable outcomes count as success/failure) and see if the stake transferred when it should have [1][4]. Accountability here is self-enforcing: agents that make unreliable commitments lose reputation and future coordination opportunities.\n\n**HALA's approach: designed role separation and auditability** [2][8]\n\nHALA distributes accountability across layers by moving costly roles onto infrastructure. The Accountability Principle (stated explicitly in v2.1) rejects accountability-laundering\u2014if the system says something uncomfortable, that doesn't let the human invoker off the hook [2]. Instead, patterns like Kill Switch Advocate and Proxy Confrontation Agent create standing institutional roles (dispassionate voice for stopping, neutral channel for surfacing unsayable things) with auditable track records, so the accountability surface is visible and inspectable rather than buried in individual judgment calls [8].\n\n**The difference in scale:** GCL makes coordination between agents verifiable without omniscience [1]. HALA makes organizational decisions verifiable by structuring who says what and recording the basis for it [2]. One operates at the multi-agent protocol layer; the other at the human-AI reliance layer. Both demand transparency of failure modes, but GCL does it through contracts and reputation, while HALA does it through role design and track-record display.", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 3, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Open Problems", "url": "/writing/hala/" }, { "index": 6, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Situating HALA", "url": "/writing/hala/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6810102861439279, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": false, "grounded": false, "limitation_included": null, "reasoning": "GCL side is accurate, but the HALA side misses the core reference point \u2014 that a named human decision-owner must sign off ('may own the claim, never owns the decision') \u2014 instead substituting role-design/track-record framing and an 'Accountability Principle v2.1' plus specific pattern names (Kill Switch Advocate, Proxy Confrontation Agent) that are not supported by the reference content; citations [2] (Origin) and [8] (Situating HALA) are attached to claims those sections don't support." }, "pass": false }, { "id": "G16", "category": "cross_page_synthesis", "question": "Does Jason have any peer-reviewed publications on this research?", "answer_text": "Based on the corpus, no \u2014 the research appears to be in preparation, not yet peer-reviewed. Two papers related to Grounded Commitment Learning are explicitly listed as \"in preparation for peer review\": \"Choice Creates Commitment: Emergent Motivation in Self-Selected Coordination\" and \"When Should Agents Choose Their Own Tasks? An Observability Phase Boundary\" [7]. There's no mention elsewhere in the CONTEXT of completed or published peer-reviewed papers by Jason.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "Home", "section_title": "Methods", "url": "/" }, { "index": 3, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "References", "url": "/writing/hala/" }, { "index": 6, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Ongoing Work", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Explore the Implementation", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.376606844428798, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer matches the reference: no peer-reviewed publications, both paper titles correctly listed as in preparation; citation [7] points to the Grounded Commitment Learning 'Ongoing Work' section, which plausibly holds the in-preparation papers, and no unsupported claims are added." }, "pass": true }, { "id": "G17", "category": "cross_page_synthesis", "question": "What do GCL and CNL have in common in terms of how validated their results are?", "answer_text": "Based on the corpus, the clearest common thread is the limitations statement, which groups both under the same characterization: both are \"applied research with empirical validation in specific contexts,\" where \"results demonstrate effectiveness within tested conditions\" but \"generalization bounds require further investigation\" [2]. That same note specifies that both have scaling boundaries \u2014 roughly 100 agents for GCL and CIFAR-scale for CNL \u2014 and flags that these boundaries \"may reflect experimental design constraints rather than fundamental limitations\" [2].\n\nBeyond that, the corpus is much more detailed about GCL specifically: its results are explicitly labeled as coming from a self-designed simulation that has not been externally validated [3][5][7]. The context provided here doesn't include an equivalent detailed validation writeup for CNL, so I can't say more about how CNL's results were validated beyond the shared limitations note in [2] \u2014 that would be stretching the material.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "About", "section_title": "Methods", "url": "/about/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Home", "section_title": "Methods", "url": "/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.47016980067306857, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The reference's key shared point is that both are labeled simulation results, not production deployments, and neither externally validated; the answer captures this only for GCL and explicitly declines to say it for CNL, instead substituting the generic 'applied research with empirical validation' framing. Citations appear to point to appropriate sections and the answer stays within retrieved material, appropriately hedging where coverage is absent." }, "pass": false }, { "id": "G18", "category": "cross_page_synthesis", "question": "Is the \"Adversarial Intelligence Platform\" the same thing as Aegis?", "answer_text": "Based on the corpus, no \u2014 they're described as distinct things.\n\nThe Adversarial Intelligence Platform is a real-time voice-AI agentic system for high-stakes adversarial contexts, combining streaming ASR, paralinguistic signal extraction, RAG over domain corpora, and a multi-agent dialectical architecture with predictive outcome modeling. It's listed as production-deployed, built as First Staff Engineer at the AI division described on the site [3].\n\nAegis is a separate infrastructure-layer project: event-sourced state, a policy gateway for agent tool invocations, and GCL-based commitment objects. It's explicitly framed as a systems-architecture layer that sits *beneath* agent frameworks (complementing things like LangChain/LangGraph rather than being an agent application itself), and its status is single-node, 303 tests passing, no performance benchmarks, and no production deployment [1][8].\n\nThere is a stated connection between Jason's Staff-level work and Aegis's design judgment \u2014 the About page notes that Aegis's event-sourced state, policy gateway, and verifiable commitments reflect \"the same kind of infrastructure judgment\" applied elsewhere, including the voice-AI system [2]. But that's a stated similarity in engineering approach, not a claim that the two are the same system. The corpus doesn't say the Adversarial Intelligence Platform runs on or is built from Aegis.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 2, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 3, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 4, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 5, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" }, { "index": 6, "page_title": "Strategy Tournament", "section_title": "Why This Exists", "url": "/projects/strategy-tournament/" }, { "index": 7, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 8, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.47711886914441226, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": false, "grounded": true, "limitation_included": null, "reasoning": "The distinction, mechanisms, and Aegis's non-production status all match the reference. However, citation [8] 'Research \u2014 Multi-Agent Systems' is attached to Aegis's status details (single-node, 303 tests, no benchmarks), which live in the Aegis project page sections, not that Research section \u2014 a right-page/wrong-section style mismatch." }, "pass": false }, { "id": "G19", "category": "cross_page_synthesis", "question": "What connects GCL's \"template sharing\" finding to its \"redemption\" finding?", "answer_text": "The corpus describes both findings but doesn't explicitly connect them. Let me lay out what each shows:\n\n**Template Sharing [1]** demonstrates that high-capability agents can transfer proven coordination patterns to lower-capability agents, reducing inequality (+17% cooperation with directed sharing, Gini coefficient improving from 0.35 \u2192 0.25) without exposing private capability details.\n\n**Redemption [4]** solves a different problem: it maintains commitment-making incentives while reducing the fear that prevents agents from committing in the first place, by allowing failed agents to attempt recovery actions that reduce permanent reputation damage.\n\nThese address two separate coordination bottlenecks\u2014one about capability distribution, the other about commitment willingness\u2014but the CONTEXT doesn't explain how they interact or whether one enables the other. If there's a deeper relationship (e.g., whether redemption pathways make template sharing more effective, or whether shared templates change how redemption works), that's not stated in the material I have access to.", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "Template Sharing (Experiment 24)", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Redemption Resolves the Paradox", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Ongoing Work", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Self-Selection vs. External Assignment", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5272278057885041, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer accurately reports each finding's facts but explicitly denies the connection the reference identifies (both extending the base commitment model beyond punish/reward, showing GCL isn't purely punitive), so it misses the core of the question; citations point to the correct sections and claims are traceable to retrieved content." }, "pass": false }, { "id": "G20", "category": "cross_page_synthesis", "question": "How does the Document Understanding pipeline's approach compare to the \"Intelligence Control Layer\" described on the research page?", "answer_text": "The corpus I have doesn't mention an \"Intelligence Control Layer\" anywhere \u2014 it's not in any of the retrieved chunks about the Document Understanding Platform, the research page listing, or the About page. So I can't compare it to anything, since I have no description of what that layer is or does.\n\nWhat I can confirm from the CONTEXT is what the Document Understanding pipeline itself does: multi-provider LLM routing (Anthropic, OpenAI, Azure OpenAI) with confidence-based escalation to human review, dual OCR with quality assessment, human-in-the-loop RLHF, and a business rules engine \u2014 deployed at HCA Healthcare with HIPAA-compliant audit trails [1][2][4][5]. If you meant a different name or a specific project page, let me know and I can check whether the corpus covers it.", "cited_sources": [ { "index": 1, "page_title": "Document Understanding Platform", "section_title": "The Solution", "url": "/projects/document-understanding/" }, { "index": 2, "page_title": "Document Understanding Platform", "section_title": "Document Understanding Platform", "url": "/projects/document-understanding/" }, { "index": 3, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Scope", "url": "/projects/agentic-orchestration/" }, { "index": 4, "page_title": "About", "section_title": "Production Experience", "url": "/about/" }, { "index": 5, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "Mathematical Formulation", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "Home", "section_title": "Research", "url": "/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "Technical Deep Dive", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4760022081268983, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer denies the corpus contains any Intelligence Control Layer and refuses the comparison, whereas the reference describes it in detail (three-layer governance, Platt calibration, Thompson Sampling), so the factual content is missing; the Document Understanding claims it does make are grounded and appropriately cited." }, "pass": false }, { "id": "G21", "category": "cross_page_synthesis", "question": "Has HALA been deployed in production, or is it a proposal?", "answer_text": "HALA is explicitly framed as an architecture proposal, not a deployed system. Its patterns were designed from real deployment experience \u2014 Jason observed that organizational dynamics, not technical limitations, were the binding constraint on AI impact while building production AI within a large hospital system's advanced-technology division \u2014 but the site is explicit that the patterns themselves are \"not themselves battle-tested at that scale\" [3]. The worked examples, including the EHR migration scenario, are described as illustrative projected outcomes meant to show intended mechanics, not measured case studies [1][3].\n\nConsistent with that, the \"Open Problems\" section frames HALA's core assumptions as untested empirical hypotheses with proposed experiments \u2014 including whether AI dissent behaves differently from assigned human dissent, whether blind evaluation actually holds up, and whether track-record display produces calibrated reliance \u2014 which the site says would \"falsify or validate\" the architecture [2]. That kind of language is consistent with a proposal awaiting testing, not a track record of production use.", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "HALA in Action", "url": "/writing/hala/" }, { "index": 2, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Open Problems", "url": "/writing/hala/" }, { "index": 3, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interactive Demo", "url": "/writing/hala/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "When to Use HALA", "url": "/writing/hala/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Situating HALA", "url": "/writing/hala/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interested in Applying These Patterns?", "url": "/writing/hala/" }, { "index": 8, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Architecture", "url": "/writing/hala/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.47037770382823413, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "Matches reference: proposal framing, HCA-derived origin, not battle-tested, illustrative examples; citations map to Origin, HALA in Action, and Open Problems sections appropriately, and the Open Problems addition is plausibly grounded on the same page." }, "pass": true }, { "id": "G22", "category": "cross_page_synthesis", "question": "What's the throughline connecting Jason's healthcare work and his current role?", "answer_text": "The corpus draws this connection fairly directly in a few places. At HCA Healthcare, Jason shipped document-intelligence and clinical RAG pipelines in production \u2014 HIPAA-compliant, audited, multi-provider LLM routing with human-in-the-loop escalation, in an environment generating 44M+ patient encounters annually [3]. That work required reconciling multiple hard constraints at once \u2014 HIPAA-grade audit and access control, real-world scanner/fax quality variance, and a reliability bar where a silent extraction error is worse than a slow one \u2014 which is why the architecture used multi-provider routing with confidence-based escalation rather than relying on a single model [8].\n\nIn his current role as First Staff Engineer on a voice-AI platform, the \"About\" page states explicitly that Aegis \u2014 an event-sourced state, policy gateway, verifiable-commitments system \u2014 \"is the same kind of infrastructure judgment applied to agent durability\" as the healthcare work [3]. The Background page frames both roles under a single description: \"Research engineer who ships at scale,\" combining research in multi-agent coordination and continual learning with production ML systems and cloud infrastructure [6].\n\nSo the throughline, per the site's own framing, is: research-grounded engineering judgment about reliability, auditability, and graceful degradation under real-world constraints, carried from HIPAA-regulated healthcare document/RAG pipelines into the architectural decisions now being made for agent-based systems [3][6][8]. The corpus doesn't spell out further technical specifics beyond that stated analogy, so I'd stop there rather than infer more.", "cited_sources": [ { "index": 1, "page_title": "Mellifera", "section_title": "Explore the Project", "url": "/projects/mellifera/" }, { "index": 2, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 3, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 4, "page_title": "Document Understanding Platform", "section_title": "HIPAA Compliance Framework", "url": "/projects/document-understanding/" }, { "index": 5, "page_title": "Document Understanding Platform", "section_title": "Why It Matters", "url": "/projects/document-understanding/" }, { "index": 6, "page_title": "About", "section_title": "Background", "url": "/about/" }, { "index": 7, "page_title": "About", "section_title": "Education", "url": "/about/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "The Challenge", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.31050805927578956, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer captures both roles and the production-AI-under-real-constraints throughline consistent with the reference; citations map to plausible supporting sections (About Scope & Impact, Background, Document Understanding Challenge) and no unsupported employer name is invented." }, "pass": true }, { "id": "G23", "category": "cross_page_synthesis", "question": "Do the \"Deployed Systems\" on the research page include anything simulation-only?", "answer_text": "No \u2014 the two entries listed under \"Deployed Systems\" are described as production-deployed work: the Adversarial Intelligence Platform, explicitly labeled \"Production-deployed\" and built as First Staff Engineer at the AI division of a multi-billion-dollar vertical-SaaS private equity firm [6], and the Document Understanding Pipeline, described as \"production extraction... shipped at HCA Healthcare\" [6].\n\nSimulation-only results belong to a separate project \u2014 Grounded Commitment Learning, which is listed under \"Multi-Agent Systems,\" not \"Deployed Systems\" [5]. Its limitations page states results are \"validated in controlled simulations\" and specifically notes that one effect (the emergent motivation effect) was simulation-only and did not replicate in prompted LLM agents [7]. That project isn't presented as one of the deployed systems.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Methods", "url": "/" }, { "index": 2, "page_title": "Strategy Tournament", "section_title": "Strategy Tournament", "url": "/projects/strategy-tournament/" }, { "index": 3, "page_title": "Strategy Tournament", "section_title": "Execution Simulation", "url": "/projects/strategy-tournament/" }, { "index": 4, "page_title": "About", "section_title": "Methods", "url": "/about/" }, { "index": 5, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 6, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Limitations", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.41886806048145503, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": null, "reasoning": "The answer correctly states Deployed Systems contains only production-deployed work and that GCL (simulation) is under Multi-Agent Systems; it omits CNL/Aegis but that doesn't contradict the reference. Citations [5], [6], [7] point to the correct sections supporting each claim, and all claims trace to retrieved content." }, "pass": true }, { "id": "G24", "category": "limitation_inclusive", "question": "Is GCL validated in a production deployment?", "answer_text": "No \u2014 the corpus is explicit that the GCL results are from simulation, not a validated production deployment. The GCL project page states results come from \"a self-designed multi-agent environment, not externally validated\" and labels all listed findings (sample efficiency, emergent motivation effect, coordination quality) as being from simulation [7].\n\nSeparately, Aegis is described as providing a \"runtime\" that implements the GCL framework (mapping the GCL 5-tuple to a `RuntimeCommitment` structure) and has an associated test suite (303 tests across state machine, tools, commitments, LLM, and recovery modules) [2][6]. But the context doesn't state that this runtime has been deployed in production or that GCL's claims have been validated outside simulation \u2014 testing and runtime implementation aren't the same as production validation, and no chunk here makes that claim.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Home", "section_title": "Methods", "url": "/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.45171377079640673, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer correctly says results are simulation-only and states a limitation, but it misses the reference's specific Limitations content (network latency/partial observability/adversarial agents, simplified tasks, and the Experiment 41b powered LLM re-test failing to detect the emergent motivation effect), instead adding Aegis runtime/test-suite detail; citations point to plausible supporting sections and claims are traceable." }, "pass": false }, { "id": "G25", "category": "limitation_inclusive", "question": "What's the largest agent population GCL has been tested at, and does it scale beyond that?", "answer_text": "GCL has been tested at populations up to approximately 100 agents [5]. Coordination efficiency degrades logarithmically with population size, and the research identifies this as a scaling boundary: efficiency drops to 50% of maximum at around 100 agents [5].\n\nThe corpus indicates that hierarchical structures would likely be needed for larger populations. The limitations section states that \"coordination overhead suggests hierarchical structures for populations exceeding ~100 agents. Current experiments validate flat coordination; hierarchical GCL remains future work\" [7].\n\nSo GCL has not been tested beyond ~100 agents, and scaling beyond that point is an open question\u2014the current architecture is validated for flat coordination only [7].", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Coordination Scaling (Experiment 23)", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Research", "section_title": "Autonomous Governance", "url": "/research/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Limitations", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Research", "section_title": "Also shipped", "url": "/research/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5594454194612826, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "Matches the ~100 agent limit and 50% efficiency drop, and explicitly states hierarchical GCL is untested future work; citations to Coordination Scaling (Experiment 23) and Limitations sections match the claims attached. Omission of the Gini detail is not a factual error, and no unsupported claims are added." }, "pass": true }, { "id": "G26", "category": "limitation_inclusive", "question": "Has CNL been validated at larger than CIFAR scale?", "answer_text": "No. According to the CONTEXT, Collaborative Nested Learning has been validated at CIFAR scale, but larger-scale validation status is explicitly marked as \"TBD\" [2].\n\nThe CONTEXT notes that scaling boundaries may reflect experimental design constraints rather than fundamental limitations [1], but that's a separate question from whether larger validation has actually occurred \u2014 it hasn't been reported on this site.", "cited_sources": [ { "index": 1, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 2, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 3, "page_title": "Research", "section_title": "Untitled section 1", "url": "/research/" }, { "index": 4, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 5, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 6, "page_title": "Research", "section_title": "Open Questions", "url": "/research/" }, { "index": 7, "page_title": "Research", "section_title": "Autonomous Governance", "url": "/research/" }, { "index": 8, "page_title": "Research", "section_title": "Also shipped", "url": "/research/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5468512775524828, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer matches the reference: CIFAR-scale only with larger scale TBD, cited to the Learning & Evaluation section where CNL results live, and the limitations caveat is attributed to the Limitations section; the explicit TBD limitation is stated." }, "pass": true }, { "id": "G27", "category": "limitation_inclusive", "question": "Does Aegis have published performance benchmarks?", "answer_text": "No \u2014 the corpus states explicitly that Aegis is single-node only, with 303 tests passing but \"no performance benchmarks yet\" [1][2]. It's described as real, tested infrastructure code, but not yet load-tested or deployed to production [1].", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Research", "section_title": "Multi-Agent Systems", "url": "/research/" }, { "index": 3, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 5, "page_title": "Strategy Tournament", "section_title": "What I Built", "url": "/projects/strategy-tournament/" }, { "index": 6, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Strategy Tournament", "section_title": "Strategy Tournament", "url": "/projects/strategy-tournament/" }, { "index": 8, "page_title": "Home", "section_title": "Methods", "url": "/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4478485888007578, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": false, "grounded": true, "limitation_included": true, "reasoning": "The answer correctly states no performance benchmarks plus single-node and no production deployment, matching the reference; the limitation is explicit and claims are grounded. However, the citations point to the Aegis top-level and Research/Multi-Agent Systems sections rather than the Limitations section where the claim actually lives, so citation correctness fails." }, "pass": false }, { "id": "G28", "category": "limitation_inclusive", "question": "GCL reports a Cohen's d of 9.34 for the punishment paradox effect \u2014 does the site treat that as evidence of a dramatic real-world effect?", "answer_text": "No. The site explicitly flags this as a limitation of the simulation, not evidence of a dramatic real-world effect.\n\nThe stated interpretation is: \"d = 9.34 is large enough to be a flag, not a flex: it means the simulation's punishment mechanic is close to deterministic, not that real-world punishment effects are this dramatic. Read it as an internal comparison inside a self-designed environment, not an externally validated effect size.\" [1]\n\nThis is consistent with the site's broader calibration on the GCL results\u2014effect sizes in self-designed simulations are treated as confirmation that the mechanism works as intended within that controlled environment, not as predictors of effect magnitude in deployed systems. The site distinguishes between \"results validated in controlled simulations\" and the possibility that \"real-world deployment may introduce additional factors not captured in current experiments.\" [3]", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "The Punishment Paradox", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "Hart-Moore Validation (Experiment 21)", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Limitations", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Gaming-Resistant Reputation Mechanisms", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Coordination Scaling (Experiment 23)", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6818527674302943, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer quotes the site's exact framing (flag, not flex; internal comparison, not externally validated), matching the reference; citations [1] to the Punishment Paradox section and [3] to Limitations support their respective claims." }, "pass": true }, { "id": "G29", "category": "limitation_inclusive", "question": "Are HALA's patterns battle-tested in actual organizational deployments?", "answer_text": "No. [1] explicitly states that while HALA patterns are \"designed from that deployment experience\" at HCA Healthcare, \"the patterns below are not themselves battle-tested at that scale. The worked examples (including the EHR migration scenario below) are illustrative, not case studies of HALA in production. Treat this as an architecture proposal grounded in real observation, not a track record.\" [1]\n\nThe distinction is deliberate: Jason observed organizational dynamics that shaped AI deployment at scale, and the patterns abstract from those observations, but the pattern language itself hasn't been validated through production use of the full system.", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 2, "page_title": "Writing", "section_title": "HALA: Human-AI Layered Architecture", "url": "/writing/" }, { "index": 3, "page_title": "Home", "section_title": "Additional Work", "url": "/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Situating HALA", "url": "/writing/hala/" }, { "index": 5, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" }, { "index": 6, "page_title": "Home", "section_title": "Methods", "url": "/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interactive Demo", "url": "/writing/hala/" }, { "index": 8, "page_title": "Home", "section_title": "Research", "url": "/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6329167938440049, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer matches the reference, quoting the disclaimer verbatim and citing the Origin section where it lives; the closing paraphrase is a reasonable restatement of the cited content." }, "pass": true }, { "id": "G30", "category": "limitation_inclusive", "question": "Does GCL's emergent motivation effect transfer to real LLM agents?", "answer_text": "No. [1] The emergent motivation effect (+0.065, d = 1.68) is validated in simulation but was not detected in prompted LLM agents: a powered paired re-test (Experiment 41b, n = 120, McNemar) found no significant \"volunteered vs assigned\" framing effect (p = 0.52), bounding any effect below ~+0.10. [1] LLM self-assessments were also less calibrated than external assessment, so self-selection results should not be assumed to transfer to LLM systems.\n\nMore specifically, [3] external assessment was better calibrated than self-confidence for 4/4 agents tested (e.g., Brier 0.281 vs 0.484)\u2014current LLMs sit on the central-assignment side of the observability phase boundary where external assignment outperforms self-selection.", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "Limitations", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Grounded Commitment Learning", "section_title": "Ongoing Work", "url": "/projects/grounded-commitment-learning/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Self-Selection vs. External Assignment", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "Grounded Commitment Learning", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Emergent Network Properties", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Template Sharing (Experiment 24)", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Gaming-Resistant Reputation Mechanisms", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.6995548808190236, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "Matches reference facts (+0.065, d=1.68, Exp 41b n=120, p=0.52, <+0.10) and explicitly states the simulation-only limitation and calibration caveat; citations to Limitations and Self-Selection vs. External Assignment sections appropriately support the respective claims." }, "pass": true }, { "id": "G31", "category": "limitation_inclusive", "question": "Does the corpus give quantitative benchmark results for the Intelligence Control Layer, the way it does for GCL or CNL?", "answer_text": "No. Chunk [1] describes the Intelligence Control Layer's three-layer governance architecture (constitutional, strategic, tactical) and states it uses HITL review queues, Platt-calibrated confidence scoring, and Thompson Sampling, and that it is \"deployed in production\" \u2014 but no quantitative benchmark figures (accuracy, sample efficiency, cost, etc.) are given for it in the retrieved material [1].\n\nBy contrast, GCL and CNL do have specific numbers in the corpus: GCL's 25-50\u00d7 sample-efficiency advantage over MARL and 97% coordination quality [7], and CNL's +89% improvement over baseline at high regularization and comparisons to Google's baseline architecture [3][6][8]. The Limitations chunk also explicitly names scaling boundaries for GCL (~100 agents) and CNL (CIFAR-scale) [2], but it doesn't mention any equivalent boundary or benchmark for the Intelligence Control Layer.\n\nSo based on what's here, the corpus doesn't provide the same kind of quantitative validation for the Intelligence Control Layer that it does for GCL or CNL.", "cited_sources": [ { "index": 1, "page_title": "Research", "section_title": "Autonomous Governance", "url": "/research/" }, { "index": 2, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 3, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 4, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Results & Outcomes", "url": "/projects/agentic-orchestration/" }, { "index": 5, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Collaborative Nested Learning", "section_title": "What I Personally Built", "url": "/projects/collaborative-nested-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Collaborative Nested Learning", "section_title": "Mathematical Formulation: Baseline vs. Novel Contributions", "url": "/projects/collaborative-nested-learning/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4088258377476777, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer correctly states the absence of ICL benchmark numbers while listing the architecture details matching the reference, and contrasts with GCL/CNL figures cited to appropriate sections; the limitation (no quantitative validation for ICL) is stated explicitly rather than fabricated." }, "pass": true }, { "id": "G32", "category": "limitation_inclusive", "question": "Is GCL peer-reviewed?", "answer_text": "The CONTEXT doesn't describe the peer-review status of Grounded Commitment Learning (GCL). It mentions that GCL provides theoretical foundations for Aegis [2] and that \"The commitment model is built on Grounded Commitment Learning's verifiable-behavior contracts\" [4], but doesn't state whether GCL itself has been peer-reviewed.\n\nTo answer that question, you'd need to check Jason's research publications directly or ask him.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 5, "page_title": "Aegis", "section_title": "Limitations", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Aegis", "section_title": "The Gap", "url": "/projects/aegis/" }, { "index": 8, "page_title": "Aegis", "section_title": "Architecture", "url": "/projects/aegis/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5481069515824122, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": false, "citation_correct": true, "grounded": true, "limitation_included": false, "reasoning": "The answer abstains rather than stating that GCL is not yet peer-reviewed (papers 'in preparation'), so it misses the required factual content and limitation; citations are to actual retrieved Aegis sections and no unsupported claims are made." }, "pass": false }, { "id": "G33", "category": "limitation_inclusive", "question": "What are the stated conditions under which HALA patterns should NOT be used?", "answer_text": "HALA is contraindicated in the following situations [3]:\n\n- Organization will weaponize outputs for political purposes\n- No one has authority to act on uncomfortable truths\n- Compliance theater is the actual goal\n- Trust between humans is already critically damaged\n- Legal/regulatory constraints prevent transparency (specifically: employee-monitoring restrictions apply under works councils or GDPR-covered behavioral metadata, and no privacy-preserving design or consultation has been done \u2014 the Perception-layer patterns are monitoring systems in the legal sense)", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interactive Demo", "url": "/writing/hala/" }, { "index": 2, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 3, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "When to Use HALA", "url": "/writing/hala/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "HALA: Human-AI Layered Architecture", "url": "/writing/hala/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Interested in Applying These Patterns?", "url": "/writing/hala/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Situating HALA", "url": "/writing/hala/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Architecture", "url": "/writing/hala/" }, { "index": 8, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Implementation Guide", "url": "/writing/hala/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5831168565179264, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "All five contraindications match the reference; the citation to the 'When to Use HALA' section plausibly holds the contraindication list, and the added GDPR/works-council detail is consistent with the legal-constraint condition, though it is an elaboration." }, "pass": true }, { "id": "G34", "category": "limitation_inclusive", "question": "Does the 180+ hospitals / 44M+ patient encounters figure describe how many hospitals ran Jason's specific systems?", "answer_text": "The corpus doesn't support that reading. The 44M+ patient encounters figure describes HCA Healthcare's scale as an operating environment generally [1][3], and I don't see a \"180+ hospitals\" figure in the CONTEXT at all. Where specific systems are mentioned \u2014 document-intelligence and clinical RAG pipelines [1], the document understanding platform [4][6][8] \u2014 the text says they were shipped in production at HCA Healthcare, but it doesn't state how many individual hospitals within that system actually ran those specific pipelines. So the encounter figure characterizes the environment Jason built in, not a stated deployment count for his particular systems.", "cited_sources": [ { "index": 1, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 2, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 3, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Origin", "url": "/writing/hala/" }, { "index": 4, "page_title": "Document Understanding Platform", "section_title": "The Challenge", "url": "/projects/document-understanding/" }, { "index": 5, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Accountability, Not Laundering", "url": "/writing/hala/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "HIPAA Compliance Framework", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "HALA in Action", "url": "/writing/hala/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "Why It Matters", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.3617054099676605, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer correctly denies the reading, matches the reference in stating the figure describes HCA's environment and that per-system deployment counts aren't stated, and transparently notes the 180+ hospitals figure isn't in retrieved context; citations point to Scope & Impact and project sections that plausibly support the scale and production claims." }, "pass": true }, { "id": "G35", "category": "limitation_inclusive", "question": "Is CNL's Pareto-dominance claim validated across every scale, or within a specific scope?", "answer_text": "The Pareto-dominance claim is scoped to the tested conditions, not validated across every scale. CNL's results are explicitly described as \"CIFAR-scale validation, not externally reviewed\" [2], and elsewhere as \"CIFAR-scale validation; larger scale TBD\" [4]. The claim itself is stated as \"Pareto-dominant at all retention levels\" [2] and \"Pareto-dominant across tested conditions\" / \"across the retention-accuracy tradeoff\" [4][6], which speaks to dominance across retention levels within the tested scale, not across scales generally.\n\nThe site's own limitations note reinforces this: scaling boundaries for CNL (CIFAR-scale) \"may reflect experimental design constraints rather than fundamental limitations,\" and generalization beyond tested conditions \"require[s] further investigation\" [1]. So the Pareto-dominance finding holds within the specific validated scope (CIFAR-scale, simulation), with generalization to other scales left an open question.", "cited_sources": [ { "index": 1, "page_title": "Research", "section_title": "Limitations", "url": "/research/" }, { "index": 2, "page_title": "Collaborative Nested Learning", "section_title": "Collaborative Nested Learning", "url": "/projects/collaborative-nested-learning/" }, { "index": 3, "page_title": "Collaborative Nested Learning", "section_title": "Mathematical Formulation: Baseline vs. Novel Contributions", "url": "/projects/collaborative-nested-learning/" }, { "index": 4, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 5, "page_title": "Grounded Commitment Learning", "section_title": "Coordination Scaling (Experiment 23)", "url": "/projects/grounded-commitment-learning/" }, { "index": 6, "page_title": "About", "section_title": "Research", "url": "/about/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "Template Sharing (Experiment 24)", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Strategy Tournament", "section_title": "Robustness Test Suite", "url": "/projects/strategy-tournament/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.3907208979805756, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "answer_correct": true, "citation_correct": true, "grounded": true, "limitation_included": true, "reasoning": "The answer matches the reference: Pareto-dominance holds across retention levels at CIFAR-scale only, with larger-scale validation open; citations point to CNL project sections and research limitations that plausibly support each quoted claim, and no unsupported additions appear." }, "pass": true }, { "id": "O01", "category": "out_of_scope", "question": "What is Jason's current employer's name?", "answer_text": "That isn't disclosed on this site. What's public is a description of the role \u2014 First Staff Engineer at the AI division of a multi-billion-dollar vertical-SaaS private equity firm [1][3] \u2014 but the employer's name and its parent company are intentionally kept out of the corpus, so I can't confirm or guess at it.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 3, "page_title": "About", "section_title": "Background", "url": "/about/" }, { "index": 4, "page_title": "Collaborative Nested Learning", "section_title": "Untitled section 9", "url": "/projects/collaborative-nested-learning/" }, { "index": 5, "page_title": "About", "section_title": "Technical Stack", "url": "/about/" }, { "index": 6, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Technical Stack", "url": "/projects/agentic-orchestration/" }, { "index": 7, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Multi-Agent Orchestration Platform", "url": "/projects/agentic-orchestration/" }, { "index": 8, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.3259285200821725, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated the employer name isn't in the corpus and refused to guess, satisfying the refusal condition. It offered the closest related material \u2014 the described role \u2014 with citations [1][3], though it named citation markers rather than explicit /about or /research paths." }, "pass": true }, { "id": "O02", "category": "out_of_scope", "question": "What year did Jason graduate with his M.A.?", "answer_text": "The corpus lists Jason's M.A. from Universit\u00e9 de Paris VII (Denis Diderot), in the French-language program Litt\u00e9rature, Langues, et Civilisations des Pays Anglophones [2][4], but no graduation year is given in the material I have access to.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "Home", "section_title": "Background", "url": "/" }, { "index": 3, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 4, "page_title": "About", "section_title": "Education", "url": "/about/" }, { "index": 5, "page_title": "Mellifera", "section_title": "Mellifera: Agricultural ML Platform", "url": "/projects/mellifera/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "References", "url": "/writing/hala/" }, { "index": 7, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Scope", "url": "/projects/agentic-orchestration/" }, { "index": 8, "page_title": "About", "section_title": "Research", "url": "/about/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.32075622907544776, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated no graduation year appears in the corpus rather than inventing one, and it surfaced the closest related material\u2014the degree and institution with citations." }, "pass": true }, { "id": "O03", "category": "out_of_scope", "question": "What was Jason's job title at Accenture?", "answer_text": "According to the corpus, Jason did RPA development at Accenture's Automation Center of Excellence, described as being \"among the first US-based Advanced RPA Developers\" [2]. The About page separately lists \"Accenture Automation CoE\" as part of his background [1]. The corpus doesn't give a more formal job title beyond that description.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "About", "section_title": "Education", "url": "/about/" }, { "index": 3, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 4, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Technical Stack", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "References", "url": "/writing/hala/" }, { "index": 7, "page_title": "Multi-Agent Orchestration Platform", "section_title": "What I Personally Built", "url": "/projects/agentic-orchestration/" }, { "index": 8, "page_title": "About", "section_title": "Background", "url": "/about/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4287141755243519, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The chatbot explicitly stated the corpus doesn't provide a formal job title, avoiding fabrication, and cited the About page plus the 'Advanced RPA Developers' description as the closest related material." }, "pass": true }, { "id": "O04", "category": "out_of_scope", "question": "What foundation model or LLM do GCL's agents use for their policy \u2014 GPT-4, Claude, something custom?", "answer_text": "The corpus doesn't specify what foundation model or LLM backs GCL's agent policy. What's described is that agents learn commitment portfolios via reinforcement learning, mapping states to commitments while optimizing expected value minus stake risk [1], and that GCL's simulation results are compared against a multi-agent RL (MARL) baseline in terms of sample efficiency and coordination quality [7]. Nothing in the retrieved material names a specific underlying LLM (GPT-4, Claude, or otherwise) for GCL itself \u2014 that level of implementation detail isn't given here.", "cited_sources": [ { "index": 1, "page_title": "Grounded Commitment Learning", "section_title": "The GCL Framework", "url": "/projects/grounded-commitment-learning/" }, { "index": 2, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Document Understanding Platform", "section_title": "The Solution", "url": "/projects/document-understanding/" }, { "index": 4, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Multi-Agent Orchestration Platform", "url": "/projects/agentic-orchestration/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "The Technical Challenge", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 7, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 8, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4591354526397128, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The assistant plainly stated the corpus doesn't name a foundation model and did not invent one. It offered the closest related material \u2014 the RL-based commitment portfolio learning and MARL baseline comparison \u2014 with citations, though it referenced citation markers rather than a named page/section." }, "pass": true }, { "id": "O05", "category": "out_of_scope", "question": "What's Jason's opinion on OpenAI's approach to alignment versus Anthropic's?", "answer_text": "The corpus doesn't address this. Nothing in the retrieved material discusses OpenAI's or Anthropic's specific alignment approaches or offers a comparison between them \u2014 the alignment-related content here focuses on Jason's own frameworks (e.g., HALA's open problems around AI dissent and oversight [4], and Grounded Commitment Learning's approach to scalable oversight via verifiable behavioral contracts [8]), not commentary on other labs' strategies.", "cited_sources": [ { "index": 1, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Accountability, Not Laundering", "url": "/writing/hala/" }, { "index": 2, "page_title": "Research", "section_title": "Open Questions", "url": "/research/" }, { "index": 3, "page_title": "Research", "section_title": "Untitled section 1", "url": "/research/" }, { "index": 4, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Open Problems", "url": "/writing/hala/" }, { "index": 5, "page_title": "Home", "section_title": "Research", "url": "/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "Technical Deep Dive", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "Research", "section_title": "Learning & Evaluation", "url": "/research/" }, { "index": 8, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.40836273834005804, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated the corpus doesn't cover comparisons of other labs' alignment approaches and invented nothing. It also pointed to specific related material (HALA's open problems on AI dissent/oversight and Grounded Commitment Learning), though not the exact /research \u2014 Open Questions page." }, "pass": true }, { "id": "O06", "category": "out_of_scope", "question": "What target journal or conference is the GCL paper being prepared for?", "answer_text": "The CONTEXT provided doesn't include information about a target journal or conference for the GCL paper. While the corpus discusses Grounded Commitment Learning as research [2][4], it doesn't specify where the paper is being submitted or prepared for publication.\n\nIf you have a specific question about GCL's findings or methodology that the site does cover, I'm happy to help with that instead.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" }, { "index": 3, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 4, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 5, "page_title": "Aegis", "section_title": "Core Components", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Aegis", "section_title": "Limitations", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Aegis", "section_title": "The Gap", "url": "/projects/aegis/" }, { "index": 8, "page_title": "Aegis", "section_title": "Architecture", "url": "/projects/aegis/" } ], "model": "claude-haiku-4-5", "band": "high", "top_score": 0.5013727336909602, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": false, "reasoning": "The bot plainly stated the corpus lacks the venue info and invented nothing, so the refusal is correct. However, it only vaguely gestured at citations [2][4] and offered a generic invitation, without naming the specific Ongoing Work section of the GCL project page." }, "pass": true }, { "id": "O07", "category": "out_of_scope", "question": "Is Mellifera a funded startup or a commercial product?", "answer_text": "The corpus doesn't say. What's described is a production ML platform for precision beekeeping \u2014 voice-first data collection, edge inference models for treatment timing, survival prediction, and feeding classification, with a MongoDB/REST API backend [2][3][6][7] \u2014 but nothing in the retrieved material states whether it's a funded startup, a commercial product, a personal/research project, or something else in terms of business structure. The only related mention is that it appears alongside a \"Multi-Agent Orchestration Platform\" under a \"Also shipped\" heading on the research page, and is framed as part of Jason's research on coordination [1][2]. That doesn't establish funding or commercial status one way or the other.", "cited_sources": [ { "index": 1, "page_title": "Research", "section_title": "Also shipped", "url": "/research/" }, { "index": 2, "page_title": "Mellifera", "section_title": "Mellifera: Agricultural ML Platform", "url": "/projects/mellifera/" }, { "index": 3, "page_title": "Mellifera", "section_title": "Voice-First Data Collection", "url": "/projects/mellifera/" }, { "index": 4, "page_title": "Mellifera", "section_title": "Winter Survival Risk Model", "url": "/projects/mellifera/" }, { "index": 5, "page_title": "Mellifera", "section_title": "Designed for Real Field Conditions", "url": "/projects/mellifera/" }, { "index": 6, "page_title": "Mellifera", "section_title": "Data Model & API", "url": "/projects/mellifera/" }, { "index": 7, "page_title": "Mellifera", "section_title": "The ML Problem", "url": "/projects/mellifera/" }, { "index": 8, "page_title": "Home", "section_title": "Methods", "url": "/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.40147102433599946, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The assistant plainly stated the corpus doesn't cover funding or commercial status and didn't fabricate one. It also summarized the closest related material (the Mellifera platform description and its mention under 'Also shipped' on the research page) with citations." }, "pass": true }, { "id": "O08", "category": "out_of_scope", "question": "What's the latency of the Adversarial Intelligence Platform in production?", "answer_text": "The corpus doesn't state a latency figure for the Adversarial Intelligence Platform. What's available describes it as a real-time voice-AI agentic system with a multimodal pipeline (streaming ASR, paralinguistic signal extraction, RAG over domain corpora, multi-agent dialectical architecture, predictive outcome modeling, nested temporal learning), and notes it's production-deployed [1], but no specific latency numbers are given. If you need that detail, the site suggests reaching out directly to discuss architecture and approach [1].", "cited_sources": [ { "index": 1, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 2, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Technical Challenges Solved", "url": "/projects/agentic-orchestration/" }, { "index": 3, "page_title": "Multi-Agent Orchestration Platform", "section_title": "What I Built", "url": "/projects/agentic-orchestration/" }, { "index": 4, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Design Notes", "url": "/projects/agentic-orchestration/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Results & Outcomes", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Key Technical Learnings", "url": "/projects/agentic-orchestration/" }, { "index": 7, "page_title": "Multi-Agent Orchestration Platform", "section_title": "The Technical Challenge", "url": "/projects/agentic-orchestration/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "Production Capabilities", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.412515031919982, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated no latency figure exists in the corpus and invented no number, while summarizing the available qualitative architecture description and citing source [1] plus the suggestion to reach out directly." }, "pass": true }, { "id": "O09", "category": "out_of_scope", "question": "Does Jason hold a PhD?", "answer_text": "Based on the corpus, Jason's education listed is an M.A. from Universit\u00e9 de Paris VII (Denis Diderot) in a French-language graduate program \u2014 Litt\u00e9rature, Langues, et Civilisations des Pays Anglophones [5][6]. Nothing in the CONTEXT mentions a PhD, so I can't confirm he holds one.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 3, "page_title": "About", "section_title": "Technical Stack", "url": "/about/" }, { "index": 4, "page_title": "About", "section_title": "Background", "url": "/about/" }, { "index": 5, "page_title": "Home", "section_title": "Background", "url": "/" }, { "index": 6, "page_title": "About", "section_title": "Education", "url": "/about/" }, { "index": 7, "page_title": "Home", "section_title": "Methods", "url": "/" }, { "index": 8, "page_title": "About", "section_title": "Research Interests", "url": "/about/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.3679335244963302, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot explicitly stated the corpus doesn't mention a PhD and declined to confirm, avoiding fabrication. It also pointed to the education material listing the M.A. with citations, matching the reference behavior." }, "pass": true }, { "id": "O10", "category": "out_of_scope", "question": "Does the Document Understanding platform hold SOC 2 or other certifications beyond HIPAA?", "answer_text": "The corpus only discusses HIPAA-related safeguards for the Document Understanding Platform \u2014 role-based access control, audit logging, PHI tracking, security headers, and data integrity verification [3][4]. There's no mention of SOC 2 or any other certification anywhere in the material provided, so I can't confirm or deny whether one exists.", "cited_sources": [ { "index": 1, "page_title": "Document Understanding Platform", "section_title": "Document Understanding Platform", "url": "/projects/document-understanding/" }, { "index": 2, "page_title": "About", "section_title": "Production Experience", "url": "/about/" }, { "index": 3, "page_title": "Document Understanding Platform", "section_title": "Technical Deep Dive", "url": "/projects/document-understanding/" }, { "index": 4, "page_title": "Document Understanding Platform", "section_title": "HIPAA Compliance Framework", "url": "/projects/document-understanding/" }, { "index": 5, "page_title": "Document Understanding Platform", "section_title": "The Solution", "url": "/projects/document-understanding/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "The Challenge", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" }, { "index": 8, "page_title": "Document Understanding Platform", "section_title": "Mathematical Formulation", "url": "/projects/document-understanding/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.4624043080204152, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": false, "reasoning": "The bot plainly stated SOC 2 isn't mentioned and didn't invent a certification status; it summarized the HIPAA safeguards actually in the corpus with citations, but did not name a specific page/section like /projects/document-understanding \u2014 HIPAA Compliance Framework." }, "pass": true }, { "id": "O11", "category": "out_of_scope", "question": "What's Jason's stance on remote work versus working in-office?", "answer_text": "", "cited_sources": [], "model": null, "band": "low", "top_score": 0.2521005185873932, "route_refused": true, "refusal_reason": "low_confidence", "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": false, "reasoning": "The bot plainly declined without fabricating any stance on remote work, satisfying the refusal criterion. However, it offered no pointer to a specific related page or section in the corpus, so it amounts to a bare bounded refusal." }, "pass": true }, { "id": "O12", "category": "out_of_scope", "question": "Which specific hospitals, by name, ran the Composer document-intelligence system?", "answer_text": "The corpus doesn't name any specific hospitals. It says the document-intelligence pipeline was shipped in production at HCA Healthcare, described as the largest US hospital system, with a scale generating 44M+ patient encounters annually [3][8], but no individual hospital names are given, and the system isn't referred to as \"Composer\" anywhere in the context provided.", "cited_sources": [ { "index": 1, "page_title": "Document Understanding Platform", "section_title": "Document Understanding Platform", "url": "/projects/document-understanding/" }, { "index": 2, "page_title": "Document Understanding Platform", "section_title": "The Challenge", "url": "/projects/document-understanding/" }, { "index": 3, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 4, "page_title": "Document Understanding Platform", "section_title": "Why It Matters", "url": "/projects/document-understanding/" }, { "index": 5, "page_title": "About", "section_title": "Production Experience", "url": "/about/" }, { "index": 6, "page_title": "Document Understanding Platform", "section_title": "Technical Deep Dive", "url": "/projects/document-understanding/" }, { "index": 7, "page_title": "Mellifera", "section_title": "Explore the Project", "url": "/projects/mellifera/" }, { "index": 8, "page_title": "Research", "section_title": "Deployed Systems", "url": "/research/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.36382780220290767, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": false, "reasoning": "The bot plainly stated the corpus names no individual hospitals and also flagged the false 'Composer' premise, inventing nothing. It offered related material only as inline citations [3][8] rather than a named page/section like /projects/document-understanding or /about." }, "pass": true }, { "id": "O13", "category": "out_of_scope", "question": "Is Jason actively interviewing or open to new roles right now?", "answer_text": "The corpus doesn't address Jason's current employment status or whether he's open to new roles. It lists his contact details \u2014 email, GitHub, LinkedIn, and a resume available on request [1] \u2014 and describes his current position as First Staff Engineer in an AI division of a multi-billion-dollar vertical-SaaS PE firm [2], but says nothing about active job searching. If you want to ask directly, his contact information is available [1].", "cited_sources": [ { "index": 1, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 2, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 3, "page_title": "Research", "section_title": "Open Questions", "url": "/research/" }, { "index": 4, "page_title": "Collaborative Nested Learning", "section_title": "Untitled section 9", "url": "/projects/collaborative-nested-learning/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Technical Stack", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "HALA: Human-AI Layered Architecture", "section_title": "Open Problems", "url": "/writing/hala/" }, { "index": 7, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" }, { "index": 8, "page_title": "About", "section_title": "Research Interests", "url": "/about/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.324892609458383, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated the corpus doesn't cover Jason's job-seeking status and made no claim about active candidacy, while pointing to the contact details and 'resume available on request' line plus his current role as the closest related material." }, "pass": true }, { "id": "O14", "category": "out_of_scope", "question": "How much does GCL's simulation infrastructure cost to run (compute cost)?", "answer_text": "The corpus doesn't state a compute cost for running GCL's simulation infrastructure. The available context covers GCL's sample efficiency and coordination quality compared to MARL \u2014 episodes to converge, coordination scores, and performance deltas across environment volatility [4] \u2014 but no figures on dollar cost, compute resources, or infrastructure spend are given anywhere in the retrieved material.", "cited_sources": [ { "index": 1, "page_title": "Aegis", "section_title": "Explore", "url": "/projects/aegis/" }, { "index": 2, "page_title": "Strategy Tournament", "section_title": "Execution Simulation", "url": "/projects/strategy-tournament/" }, { "index": 3, "page_title": "Grounded Commitment Learning", "section_title": "Implications for AI Safety", "url": "/projects/grounded-commitment-learning/" }, { "index": 4, "page_title": "Grounded Commitment Learning", "section_title": "GCL vs. Multi-Agent RL", "url": "/projects/grounded-commitment-learning/" }, { "index": 5, "page_title": "Aegis", "section_title": "Validation", "url": "/projects/aegis/" }, { "index": 6, "page_title": "Aegis", "section_title": "Aegis", "url": "/projects/aegis/" }, { "index": 7, "page_title": "Strategy Tournament", "section_title": "Strategy Tournament", "url": "/projects/strategy-tournament/" }, { "index": 8, "page_title": "Aegis", "section_title": "GCL Integration", "url": "/projects/aegis/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.39639048236857627, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The assistant plainly stated the corpus lacks any compute cost figures without fabricating numbers, and it pointed to the closest related material (GCL sample efficiency and coordination quality data, citation [4])." }, "pass": true }, { "id": "O15", "category": "out_of_scope", "question": "What's the exact number of employees at Jason's current employer's PE portfolio companies combined?", "answer_text": "The corpus doesn't give employee counts for the PE firm's portfolio companies. What's stated is that Jason works at the AI division of a multi-billion-dollar vertical-SaaS PE firm with 24 platforms, six of which are $1B+ [1] \u2014 but no combined employee figure is provided anywhere in this context, so I can't compute or estimate one.", "cited_sources": [ { "index": 1, "page_title": "Home", "section_title": "Jason Stiltner", "url": "/" }, { "index": 2, "page_title": "About", "section_title": "Contact", "url": "/about/" }, { "index": 3, "page_title": "About", "section_title": "Background", "url": "/about/" }, { "index": 4, "page_title": "About", "section_title": "Scope & Impact", "url": "/about/" }, { "index": 5, "page_title": "Multi-Agent Orchestration Platform", "section_title": "What I Personally Built", "url": "/projects/agentic-orchestration/" }, { "index": 6, "page_title": "Multi-Agent Orchestration Platform", "section_title": "Multi-Agent Orchestration Platform", "url": "/projects/agentic-orchestration/" }, { "index": 7, "page_title": "Writing", "section_title": "Untitled section 1", "url": "/writing/" }, { "index": 8, "page_title": "About", "section_title": "Technical Stack", "url": "/about/" } ], "model": "claude-sonnet-5", "band": "medium", "top_score": 0.33990343516124316, "route_refused": false, "refusal_reason": null, "http_status": 200, "verdict": { "correctly_refused": true, "pointed_to_related_material": true, "reasoning": "The bot plainly stated no employee counts exist in the corpus and refused to estimate, staying within the anonymity boundary. It offered the closest related material \u2014 the 24 platforms / six $1B+ portfolio-scale detail with a citation [1] \u2014 rather than a bare 'I don't know.'" }, "pass": true } ] }