PLAP

Proctor-LLM Attestation Protocol (PLAP): Foundations and Mechanisms

The Proctor-LLM Attestation Protocol (PLAP) is a multi-layer, adversarial-resilient data quality pipeline that guarantees the reliability, trust, and auditable provenance of every factor in the Nexus data layer. PLAP leverages distributed agent sampling, advanced large language models (LLMs), classical anomaly detection, and reinforcement learning to create a zero-trust, self-healing data verification environment.

1. Active Proctor Sampling

• At each batch ingestion epoch, a committee of proctor agents is pseudo-randomly selected (via verifiable random functions or on-chain events) from a pool of staked and reputation-ranked curators.

• Each proctor operates semi-independently, issuing challenge queries (Q∗) designed to:

  • Check deterministic invariants (hash, schema, monotonicity)

  • Force edge cases and probe for adversarial or latent data manipulation

  • Stress test timing, order, and statistical stability across redundant/mirrored sources

• Each proctor produces a binary or probabilistic verdict for a data batch, forming an attestation matrix A where Aij =1 if proctor i deems factor j valid, 0 otherwise.

• Discrepancy resolution: If committee consensus is not reached, the batch is flagged for escalation to LLM layer or, if consensus failure persists, enters formal dispute arbitration.

2. LLM-Based Semantic Validation

• For each data batch, a fine-tuned LLM ensemble performs:

  • Schema Recognition: Using classification and regression to ensure data fields, types, and range conform to declared schema (protects against subtle schema drift or trickery).

  • Semantic Similarity & Deduplication: Applies embedding distance or cosine similarity measures to identify semantic anomalies—near-duplicates, contextual drift, or topic contamination.

  • Anomaly & Outlier Detection: Runs novelty scoring and statistical outlier models (L2/L-infinity distance to canonical factors, entropy of residuals, or transformer-based novelty detection).

  • Adversarial and PII Filter: Flags content with hallucination risk, anomaly score above threshold, hidden PII, or toxicity.

Decision flow:

1. Calculate schema conformance probability P(s∣L(Bt)). Batch only passes if P>τ.

2. Evaluate anomaly loss ℓanomaly (L,Bt)<ϵ.

3. Non-conformance or high anomaly triggers PLAP-level slashing/rollback, escalation to human arbitration, and public record of the outcome.

3. RL-Governed Adaptive Policy

• Adaptive Controls: An on‑chain RL agent tunes probe rates, committee size/voting rules, redundancy depth, and escalation thresholds based on rolling trust signals and dispute rates.

• Objective: Maximize network trust and liveness while minimizing verification costs and user friction:

Rt =α⋅TrustIndext −β⋅VerificationCostt −γ⋅UserComplaintRatet

Self‑Healing Loop: Rising anomaly/dispute patterns automatically increase redundancy and proctor scrutiny; stability dials it back.

4 On-Chain Attestation, Staking & Incentive Structure

• Proof Anchoring: For each accepted batch, Merkle roots (or rolling hashes) and proctor/LLM outputs are committed on‑chain with ZK/SNARK/STARK proofs.

• Economic Alignment:

  • Contributors stake AIO; clean, attested data earns usage‑weighted AIO rewards. Persistent failures trigger slashing and lower eligibility.

  • Curators (proctors/validators) earn by accurate, timely attestations; repeated errors reduce future committee priority.

• Open Disputes: Any participant can challenge a batch within a defined window. Dispute outcomes, re‑runs, and penalties are publicly logged and feed back into reputation and RL policies.