Living Smart Contracts™

I'm building a Living Smart Contract for a music royalty platform. The contract governs revenue splits between a primary artist, featured collaborators, a producer, and a label. Currently the splits are static — set at signing and never updated regardless of how cultural contribution evolves over time. I need you to design the complete LLM Logic Layer for this contract. Contract context: - Contract type: Music royalty revenue split - Current static terms: Primary artist 45%, featured collaborator 15%, producer 20%, label 20% - Stakeholders: Primary artist, featured collaborator, producer, label, streaming platforms (data source only) - Evolution triggers: Streaming velocity shifts, playlist placement changes, social attribution data, sync licensing revenue by contributor, live performance revenue correlation Context Evaluation Module — design the following: What data inputs does the LLM receive on each review cycle? Include on-chain revenue events, Spotify/Apple Music streaming oracle data, social mention attribution scores, and sync licensing logs. Define the exact JSON context schema the LLM receives. What three questions does the LLM answer about current split fairness? How are outputs structured for the modification proposal generator downstream? Fairness Assessment Engine — define the following: Fairness for this contract means: each stakeholder's split percentage should track within 8 percentage points of their proportional contribution score. Contribution score = (streaming attribution % × 0.4) + (social mention share % × 0.3) + (sync licensing revenue % × 0.2) + (live performance correlation % × 0.1). How does the LLM evaluate current splits against this formula? What evidence does it cite? How is assessment confidence scored 0–100? Modification Proposal Generator — specify: Trigger condition: fairness gap exceeds 8 points for any stakeholder for two consecutive 30-day review cycles. Modification proposal schema must include: current_term (%), proposed_term (%), delta (%), reasoning (string), evidence_sources (array), confidence_score (0–100), affected_stakeholders (array). Maximum modification magnitude: 5 percentage points per stakeholder per cycle. How are competing proposals ranked when multiple stakeholders are out of fairness range simultaneously? Reasoning Audit Trail — specify: Full LLM chain-of-thought logged as structured JSON before any modification executes. Log anchored on Polygon via IPFS hash. Human-readable summary generated alongside raw log. Retention: permanent. Safety constraints: - Maximum split change: 5 percentage points per stakeholder per 30-day cycle - Minimum time between modifications affecting the same stakeholder: 60 days - Stakeholder veto threshold: any single stakeholder holding >20% current split can veto - Emergency freeze: triggered if total split percentages drift from 100% by any rounding error, or if oracle data source goes offline for >48 hours Please produce: the complete LLM Logic Layer specification document, the context evaluation JSON schema, the modification proposal JSON format, the fairness scoring formula implementation, and the full safety constraint ruleset.

I'm continuing the Living Smart Contract build for my music royalty platform. I've completed the LLM Logic Layer (Stage 1) and the Self-Modification Protocol (Stage 2). Now I need the Behavioral Consensus System designed. The contract governs revenue splits between: primary artist (currently 45%), featured collaborator (15%), producer (20%), label (20%). The LLM Logic Layer proposes modifications when fairness gaps exceed 8 percentage points. The Behavioral Consensus System must validate those proposals against observed stakeholder behavior before any modification executes. Core principle: if stakeholders are behaving in ways that indicate satisfaction with current terms — continuing to release music together, not filing disputes, actively promoting joint releases — that behavioral signal should carry weight in the consensus calculation even if the quantitative fairness score suggests a modification is warranted. Behavioral signal sources I want to incorporate: - Continued collaboration signal: has the primary artist released new music with this collaborator in the past 90 days? (positive consensus signal) - Dispute history: any formal dispute filed in the past 180 days? (negative consensus signal, veto-weight) - Promotional activity: is the collaborator actively promoting the joint release on social channels? (positive signal) - Streaming engagement: is the collaborator's audience actively streaming the joint work? (positive signal) - Contract interaction: has the stakeholder accessed their contract dashboard and reviewed terms in the past 30 days? (neutral — indicates awareness) Consensus scoring model I want: Each behavioral signal weighted and combined into a Consensus Score 0–100. A proposed modification requires Consensus Score ≥ 65 to proceed to the stakeholder vote phase. Score below 65 means the modification is deferred for one additional 30-day observation cycle before re-evaluation. Stakeholder voting phase (triggered only when Consensus Score ≥ 65): - Voting window: 14 days - Approval quorum: 3 of 4 stakeholders must vote (abstention counts as approval after window closes) - Approval threshold: majority of votes cast must be Approve - Veto right: any stakeholder with >20% current split can issue a hard veto, blocking the modification for 90 days Behavioral anomaly detection: If a stakeholder's behavior pattern shifts dramatically in the 7 days before a scheduled vote — sudden spike in dispute-adjacent activity, removal of promotional content, public statements — flag the anomaly to the LLM for re-evaluation before the vote proceeds. Please produce: the complete Behavioral Consensus System specification, the behavioral signal weighting model with scoring formula, the consensus score calculation algorithm, the stakeholder voting protocol with all edge cases handled, the anomaly detection trigger conditions, and the full deferral and re-evaluation logic.

Build a complete Living Smart Contract Studio inside this Airo project. Use React 19, TypeScript, Tailwind CSS, shadcn/ui, Express, and MySQL with Drizzle ORM. Studio context: Music royalty platform. Contracts govern revenue splits between artists, collaborators, producers, and labels. LLM Logic Layer runs on Claude. Behavioral consensus validated before any modification executes. Blockchain settlement on Polygon. Build these six sections: 1. CONTRACT LABORATORY (/lab) — Active contracts list with evolution status badge (Stable / Review Pending / Modification Proposed / Voting Active), last modification date, current fairness score, and health indicator. Contract detail view showing: current terms as a visual split chart, evolution history timeline with LLM reasoning summaries, next scheduled review date, behavioral consensus score, and economic performance KPIs (streaming revenue trend, sync licensing revenue, live performance correlation). Create new contract flow: template selector → stakeholder definition → initial term entry → evolution rule configuration → deploy. 2. EVOLUTION MONITOR (/evolution) — Pending modification proposals with side-by-side current vs. proposed term comparison, LLM reasoning summary, evidence sources list, confidence score badge, and consensus score. Stakeholder voting panel: Approve / Reject / Request Revision buttons with 14-day countdown. Behavioral consensus visualization showing each signal source and its contribution to the score. Full modification history with audit trail. Rollback interface to revert to any prior contract state. 3. LLM REASONING CONSOLE (/reasoning) — Context evaluation logs showing exactly what data the LLM analyzed per review cycle. Fairness assessment history as a line chart over time. Full chain-of-thought reasoning audit trail per decision, expandable. Prompt template editor for customizing the context schema per contract type. Model accuracy metrics: predicted vs. actual outcome tracking. 4. BEHAVIORAL ANALYTICS (/behavior) — Stakeholder engagement heatmap by signal type. Behavioral pattern library with detected patterns labeled. Anomaly detection feed with severity flags. Consensus score calculator showing real-time signal breakdown. Historical behavior vs. term evolution correlation chart. 5. ECONOMIC FEEDBACK (/economics) — Contract performance dashboard with KPIs per contract. Economic signal feed: streaming oracle data, sync licensing events, live performance revenue. Feedback loop visualization showing which signals triggered LLM reviews. Optimization history: term changes and their measured economic impact. Projection engine showing predicted performance under proposed modifications. 6. SAFETY CONTROLS (/safety) — Evolution rate limit dashboard: modifications per stakeholder per rolling 90-day window. Stakeholder veto registry with veto history and active veto countdowns. Emergency freeze toggle with confirmation modal and full audit log. Modification magnitude limit display per contract. All safety interventions logged with timestamp, actor, and reason. Database schema: contracts (id, name, type, currentTerms JSON, evolutionRules JSON, status, deployedAt, lastModifiedAt), modifications (id, contractId, proposedTerms JSON, reasoning, confidenceScore, consensusScore, status, proposedAt, executedAt), stakeholder_votes (id, modificationId, stakeholderId, vote, reasoning, timestamp), reasoning_logs (id, contractId, contextData JSON, assessment JSON, llmModel, timestamp), economic_signals (id, contractId, signalType, value, source, triggeredReview, timestamp), behavioral_signals (id, contractId, stakeholderId, signalType, value, recordedAt). Emerald and teal gradient theme. Dark background. Real-time polling every 30 seconds. Fully typed TypeScript throughout.