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Decentralized PlanExe Survivability Network

Author: PlanExe Team
Date: 2026-02-11
Status: Proposal
Audience: Infrastructure Architects, Security Leads, Ecosystem Partners

Pitch

Build a decentralized PlanExe that keeps planning and verification online even if a government shuts down the primary site, disables a datacenter, or blocks a payment processor. Users with local LLM hardware can offer compute and get paid.

Why

Centralized infrastructure is fragile. A single takedown or payment outage can halt planning, which is unacceptable for cross-border or politically sensitive users. Decentralization improves resiliency and trust.

Problem

  • A single website or cloud region is a single point of failure.
  • Model access can be disrupted by datacenter shutdowns.
  • Stripe or centralized billing can be blocked or throttled.
  • Users with capable hardware cannot currently contribute compute.

Proposed Solution

Create a PlanExe Survivability Network with three layers:

  1. Distributed Execution Mesh: many independent nodes can run the PlanExe pipeline.
  2. Decentralized Discovery + Routing: users can find healthy nodes even if the primary domain is down.
  3. Compute Marketplace: operators provide LLM compute and are paid for verified work.

Architecture

Client
  -> Node Directory (multiple mirrors)
  -> Execution Mesh (trusted + community nodes)
  -> Result Verification + Audit Log
  -> Payment Settlement (multi-rail)

Key Components

1) Execution Mesh

  • Nodes run a containerized PlanExe runtime.
  • Each node advertises capabilities (models, speed, cost).
  • Tasks are routed based on availability, trust, and price.

2) Decentralized Discovery

  • Multiple directory endpoints (geo-distributed).
  • Client can fall back to cached node lists.
  • Signed node manifests prevent spoofing.

3) Verification Layer

  • Outputs are signed by node identity.
  • Random re-execution and consensus checks detect bad actors.
  • Evidence coverage and confidence thresholds enforced.

4) Multi-Rail Payments

Support multiple settlement paths:

  • Traditional (credit card or bank transfer)
  • Crypto/stablecoin payments
  • Voucher or prepaid credit (offline distribution)

Compute Marketplace

Node Enrollment

  • Operators register node identity and specs.
  • Capability tests and benchmarks determine pricing tier.

Payment Model

  • Pay-per-task or pay-per-token.
  • Bonus for high reliability and fast turnaround.
  • Penalties for failed or unverifiable outputs.

Output Schema

{
  "node_id": "node_882",
  "capabilities": ["llm", "verification"],
  "price_per_1k_tokens": 0.02,
  "trust_score": 0.91,
  "availability": "high"
}

Security and Governance

  • Signed tasks and signed outputs.
  • Quarantine low-trust nodes.
  • Dispute resolution for payment and output quality.
  • Transparent audit logs for all executed tasks.

Integration Points

  • Works with existing PlanExe MCP interface.
  • Feeds into evidence ledger and readiness scoring.
  • Uses benchmarking harness for node qualification.

Success Metrics

  • % of planning requests that survive primary site outage.
  • Median time to route tasks during failure.
  • Growth of independent compute nodes.
  • Reduction in payment single-point-of-failure incidents.

Risks

  • Malicious or low-quality nodes.
  • Fragmentation of standards across nodes.
  • Regulatory exposure for cross-border payments.

Mitigations for Fragmentation and Knowledge Drift

Decentralized nodes can diverge in schemas, prompts, and verification standards. Without coordination, outputs become incompatible and trust collapses. The network should enforce shared standards and shared knowledge sync.

1) Standards Versioning

  • Publish a canonical schema bundle with strict versioning (e.g., planexe-schema@1.4.0).
  • Require nodes to advertise supported versions.
  • Reject outputs from incompatible versions unless downgraded to a common target.

2) Shared Knowledge Sync (IPFS + Redundancy)

  • Publish core artifacts (schemas, benchmark sets, prompt templates, policy rules) to IPFS.
  • Use content hashes as immutable identifiers for verification.
  • Maintain multiple pinning nodes for redundancy and censorship resistance.

3) Consensus on Critical Artifacts

  • Require quorum approval for changes to high-impact artifacts (risk gates, verification rules).
  • Distribute signed release manifests (multi-sig) to prevent unilateral drift.

4) Compatibility Tests

  • Nodes periodically run a compatibility test suite.
  • Failing nodes are downgraded or quarantined until updated.

5) Cached Offline Mirrors

  • Clients keep a cached copy of the most recent release manifest.
  • If the network is partitioned, nodes can still operate on the last known standard.

Mitigations for Malicious or Low-Quality Nodes

Decentralized execution requires explicit safeguards against bad actors and unreliable hardware. Mitigations should combine pre-qualification, runtime verification, and economic incentives.

1) Pre-Qualification Gates

  • Benchmark nodes on standardized test suites before admitting them.
  • Require signed attestations for hardware and model versions.
  • Assign an initial low trust tier until performance is proven.

2) Runtime Verification

  • Randomly re-execute a fraction of tasks on a trusted node and compare outputs.
  • Cross-check outputs with schema validators and evidence coverage tests.
  • Reject results that deviate beyond defined tolerance bands.

3) Reputation and Trust Scores

  • Track per-node success rates, latency, and error frequency.
  • Penalize nodes for invalid outputs or unverified evidence.
  • Promote nodes to higher tiers only after sustained accuracy.

4) Economic Incentives and Penalties

  • Require a stake or deposit for node participation.
  • Slash rewards for failed or fraudulent outputs.
  • Pay bonuses for high reliability and verified accuracy.

5) Quarantine and Revocation

  • Auto-quarantine nodes that exceed failure thresholds.
  • Allow manual review and appeal for edge cases.
  • Publish revocation lists to prevent re-entry under the same identity.

Future Enhancements

  • Peer-to-peer plan replication and caching.
  • Federated governance council for node standards.
  • Automated multi-provider model routing.

Detailed Implementation Plan

Phase A — Survivability Threat Model

  1. Define failure scenarios:
  2. cloud provider outage
  3. network partition
  4. key personnel loss
  5. service-level legal/regulatory disruption
  6. Map critical capabilities and single points of failure.

Phase B — Decentralized Runtime Strategy

  1. Define federated node architecture with regional failover.
  2. Replicate critical state using signed append-only logs.
  3. Implement degraded-mode operations for partial outages.

Phase C — Recovery and Continuity Playbooks

  1. Add automated failover orchestration and health probes.
  2. Add disaster recovery drills and RTO/RPO targets.
  3. Publish continuity runbooks and command paths.

Phase D — Governance and Trust

  1. Define cross-node trust and key rotation policies.
  2. Add tamper-evident audit synchronization.
  3. Add survivability scorecard for quarterly reviews.

Validation Checklist

  • Recovery time objective achievement
  • State consistency after failover
  • Degraded-mode service availability under stress tests