Claim
AI systems are commonly evaluated at the output layer, while deployment risk appears at the interpretation layer—fluent, policy-compliant responses can still be misread, used outside their horizon, or acted on with harmful consequences.
Receipt
- Registry entry: BH-RL-2026-0008 in research/registry.json
- Compile output: this evidence surface (BH-RL-2026-0008/evidence/) via compile-evidence-publications.mjs
- Pipeline: research:compile · generated 2026-06-29T21:00:40.776Z · source afd302e4eb1d
- Canonical publication: /research/semantic-reliability/ (research brief)
- Source lineage: Public framework projection from Notion-authored source
- Bluehand defines semantic reliability as operational validation of meaning across output, reference, horizon, action, consequence, and revision.
- Reader takeaway: Bluehand treats semantic reliability as operational meaning validation across the full decision event, not as a single-model accuracy score.
- Thesis: A Bluehand research framework for semantic reliability: validating not only what an AI system outputs, but how that output is interpreted, constrained, acted upon, monitored, and revised over time—through parse, reference, horizon, consequence, constraint, and audit gates.
- Why now: High-stakes AI-assisted decision workflows need inspectable interpretation, locally governed constraints, and reviewable lineage—not output fluency alone.
- Provide a public framework for meaning-validation in AI-assisted workflows: whether outputs remain structurally coherent, referentially grounded, horizon-appropriate, consequence-aware, and constraint-compliant.
- Core relation: Semantic reliability is validated across a complete meaning event: AI output → reference target → interpreter horizon → decision or action → observed consequence → revision loop.
- Operational stack: Six gates structure validation: parse (coherent units), reference (identifies what it refers to), horizon (intended interpreter), consequence (measurable outcomes), constraint (local boundaries), and audit (reconstructable decisions later).
- Governance boundary: Semantic reliability must not become semantic control. The framework preserves operator agency and local authority while making meaning-affecting operations more inspectable. Do not infer clinical certification, regulatory approval, or production deployment from this artifact.
- First-pilot domain fit depends on partner constraints, review structure, and measurable consequence surfaces in each institution.
- Linked artifact: Semantic Governance for Agentic Systems (BH-RL-2026-0005) → /research/semantic-governance/
- Linked artifact: Lineage-Aware Memory Infrastructure for AI Systems (BH-RL-2026-0003) → /research/lineage-aware-memory/
- Linked artifact: Compression Is Not Evidence (BH-RL-2026-0007) → /research/compression-is-not-evidence/
- Linked artifact: Governed Agent Execution and Hybrid Orchestration (BH-RL-2026-0002) → /research/governed-agent-execution/
- Linked artifact: Deterministic Replay Architectures for AI Systems (BH-RL-2026-0006) → /research/deterministic-replay/
Boundary
- Do not infer clinical certification, regulatory approval, or production deployment from this artifact.
- Do not infer that semantic reliability eliminates plural interpretation—validity remains bounded by evidence, goal, and constraint.
- Pilot-ready public Research Object. Field validation, partner-specific constraints, and domain review remain required before high-stakes deployment claims.
- Uncertain: First-pilot domain fit depends on partner constraints, review structure, and measurable consequence surfaces in each institution.
Status
- Publication status
- Public canon
- Authority class
- Canonical public
- Revision
- initial-canonical
- Maturity
- Published PDF and canonical HTML; pilot-ready research infrastructure—not clinical certification, regulatory approval, or production deployment.