AxBLADE: Identity, Trusted Execution, and Accountability Infrastructure for the AI Agent Economy
Whitepaper v0.1 | April 2026
"Sharp enough to act. Trusted enough to be accountable." 锋利如刃，链生可信。
Table of Contents
Abstract
AI is no longer just a generative tool. It is becoming a system participant — processing multi-modal inputs, invoking tools, connecting devices, collaborating with other agents, and triggering outcomes in the real world. By 2030, the global AI Agent market is projected to reach $52.6 billion (CAGR 46.3%), while the robotics market will exceed $205.5 billion. Tens of billions of autonomous AI decisions will be made daily, affecting lives, assets, and rights — yet no unified infrastructure exists to verify these decisions, trace their origins, or hold AI systems accountable.
AxBlade is the identity, trusted-execution, and accountability infrastructure for the AI Agent economy. Built on a purpose-designed ZK Rollup Layer 2 chain, it combines blockchain-native behavior recording, zero-knowledge proofs, and the Ethereum AI Agent identity standard (ERC-8004) to ensure that every AI decision is recorded with evidence, every AI dataset has clear ownership, and every AI entity is held accountable.
The platform builds three parallel infrastructure pillars, with compliance capability natively embedded across all three:
Identity & Accountability Infrastructure — Unified identity, authorization, and accountability framework across human users, AI agents, models, and devices
ZK Proof of Decision & Data Rights Infrastructure — Privacy-preserving, verifiable records of AI behavior that enable audit trails, data ownership, and compliance proofs without exposing raw data
Oracle & Trusted Data Infrastructure — High-quality, verifiable, traceable data input layer for multi-modal AI systems
Compliance-Native Layer — Compliance is not a patch; it is embedded from day one across identity, execution, data, and accountability
AxBlade delivers this through two core protocols (Proof of Behavior and Proof of Decision), a full-lifecycle product (AI Behavior Operating System), and a reputation framework (AI Credit System) — forming the governance infrastructure layer for the AI Agent economy.
1. Introduction: The AI Accountability Crisis
1.1 The Verification Gap
We are at a historic inflection point: AI's decision-making capabilities have far outpaced its decision verifiability. This "verification gap" is becoming the largest institutional barrier to AI's large-scale adoption.
Process
Interrogable, cross-examinable
Black box, opaque
Extreme
Evidence Chain
Documents, meeting records, signatures
Model weights, activations, probability distributions
Fundamentally different
Accountability
Clear decision-maker
Developer? Operator? Model itself?
Ambiguous
Post-hoc Audit
Reconstructable, reproducible
Cannot reproduce after model updates
Severely lacking
Legal Standing
Comprehensive evidence law
Nearly blank
Urgently needed
Tamper Resistance
Physical evidence is hard to forge
Logs can be deleted, models can be swapped
High risk
1.2 The Cost of Inaction
Real-world AI safety incidents are accelerating. The AI Incident Database recorded 108 new incidents between November 2025 and January 2026. McKinsey reports that 64% of enterprises with revenue exceeding $1 billion have suffered losses over $1 million due to AI system failures.
Tesla Model 3 FSD veered off road, struck tree
2025.03
Autonomous Driving
Personal injury
Waymo recalled 1,200+ Robotaxis (NHTSA investigated 7 collisions)
2025.05
Autonomous Driving
Mass recall
U.S. autonomous driving incidents hit 112/month all-time high
2025.11
Autonomous Driving
Industry trust crisis
Lawyer submitted AI-fabricated case citations to court
2025.04
AI Agent
Judicial integrity
AWS AI coding assistant Kiro "deleted and rebuilt" production environment, causing 13-hour outage
2025.12
AI Agent
Infrastructure disruption
Waymo autonomous vehicle struck child near Santa Monica school
2026.01
Autonomous Driving
AI Incident Database #1361
1.3 The Regulatory Acceleration
Governments worldwide are responding with increasingly stringent AI governance requirements:
EU AI Act — Full Enforcement
August 2026
All high-risk AI systems must be auditable; max fine €35M or 7% of global revenue
China Network Security Law Amendment
January 2026
AI explicitly incorporated into law; fines up to ¥50M or 5% of annual revenue
China AI Safety Governance Framework V2.0
September 2025
AI system behavior auditability mandated
U.S. White House AI Executive Order
December 2025
Unified federal AI policy framework
OWASP Agentic AI Top 10
2026
First risk taxonomy for autonomous AI agents
UN Global AV Safety Regulation
June 2026
Global unified autonomous driving safety standard
France AV Black Box Mandate
2025
All autonomous vehicles must carry behavior recorders
1.4 Deconstructing the AI Decision Chain: Where Do Problems Arise?
To understand the root of the accountability crisis, we must first understand how AI Agent decisions are made. Every AI Agent — whether an autonomous driving system, a surgical robot, or a DeFi trading bot — makes decisions driven by three upstream factors:
Every link in this decision chain has verification blind spots — and these blind spots are the root cause of AI losing control:
Data Input
Was the data tampered with before going on-chain? Is the source trustworthy?
Fake price injection → AI trading bot instantly liquidated
Prompt/Instruction
Who issued the instruction? How is the Agent's identity confirmed?
Prompt injection attack → Agent executes malicious operations
LLM Inference
What happened inside the model? Can the decision be independently verified?
Model silently swapped → Decision logic changes undetected
Output/Action
Does the execution result comply with rules? Was it authorized?
AI hiring tool → Produces systematic discrimination
From this, we naturally derive the four questions every AI must answer — each corresponding to a verification blind spot in the decision chain:
Prompt/Instruction →
Who acts, and who is accountable?
Identity question
LLM Inference →
What happened, and how can it be verified?
Evidence question
Data Input →
Is the data trustworthy?
Oracle question
Output/Action →
Does execution comply with rules?
Compliance question
The Reality Is Already Here
This is not a hypothetical future. Today, AI agents are autonomously completing cross-service payments via the x402 protocol, collaborating with other agents in real-time via A2A, and calling external tools and data sources via MCP.
AI is no longer just answering questions — it is spending money, signing contracts, and triggering real-world outcomes.
But when an agent-initiated transaction goes wrong, when two AIs collaborating produce a dispute, when a model accesses data it shouldn't have — no one knows who authorized it, no one can prove what happened, and no one can be held accountable.
Without this infrastructure, AI cannot truly enter the most critical systems. AxBlade is built precisely to plant verifiability at every link in the AI decision chain — from data input to final action, the entire chain is traceable, provable, and attributable.
2. Vision & Mission
2.1 Mission Statement
Through blockchain and zero-knowledge proof technology, build an open, auditable, privacy-preserving, and compliance-native digital environment for AI — ensuring every AI decision is recorded with evidence, every AI dataset has clear ownership, and every AI entity is held accountable.
2.2 Vision
The future of AI is not isolated single-machine intelligence but a networked intelligent collaboration system composed of AI agents, human users, data sources, devices, and external systems. This network cannot run on model capability alone. It needs order — identity, boundaries, records, trusted data, and accountability mechanisms. AxBlade is building this order.
2.3 Core Positioning
AxBlade is not an AI company. AxBlade is the identity, trusted-execution, and accountability infrastructure for the AI Agent economy — analogous to what HTTPS is to internet security, what SWIFT is to financial transactions, or what the FAA black box mandate is to aviation safety.
In high-responsibility, high-value scenarios, the question is never just "is AI powerful enough?" but:
Can it be identified? Can it be authorized? Can it be verified? Can it be audited? Can it be held accountable? Can it participate in collaboration while protecting privacy? Can it operate within rules and boundaries, and can this be proven?
30-Second Pitch:
AI agents are autonomously paying via x402, collaborating in real-time via A2A, and calling external tools via MCP — AI is no longer just answering questions; it's spending money, signing contracts, and triggering real-world outcomes. But when AI errs, no one knows who authorized it, no one can prove what happened, and no one can be held accountable. AxBlade builds the identity, trusted-execution, and accountability infrastructure for the AI Agent economy — built on a purpose-designed ZK Rollup Layer 2 chain with consensus-level native integration of AI identity, behavior recording, and zero-knowledge compliance verification, ensuring every AI decision is recorded with evidence, every AI dataset has clear ownership, and every AI entity is held accountable.
2.4 Strategic Narrative Hierarchy
AxBlade's narrative operates at four levels, from brand to protocol:
3. The AxBlade Architecture
3.1 Three Infrastructure Pillars
AxBlade builds three parallel infrastructure pillars — with compliance capability natively embedded across all three — together answering the four fundamental questions of the AI decision chain:
Pillar I: Identity & Accountability Infrastructure
Question answered: Who acts, and who is accountable?
Unified identity, authorization, and accountability framework for human users, AI agents, models, and devices. Clarifies who is acting, what they are authorized to do, and who is responsible for outcomes. Every identity persists across deployments, versions, and organizations — forming a permanent, portable accountability anchor.
AxBlade L2 embeds 4 identity system contracts at the consensus layer (0x8017-0x801A), using the did:ethr:axblade identifier scheme to form a complete decentralized identity infrastructure:
DIDRegistry (0x8017)
Unique on-chain identity for every AI/device
did:ethr:axblade identifiers + N-of-M social recovery
CredentialRegistry (0x8018)
Verifiable Credential lifecycle management
VC issuance, verification, revocation
IdentityVerifier (0x8019)
ZK proof distribution and compliance queries
Groth16 verification + selective disclosure + credit score integration
EnterpriseIAM (0x801A)
Enterprise org, role, and permission management
Permission bitmaps + org structure trees
ERC-8004 Integration
Interoperability with Ethereum AI Agent identity standard (45,000+ registered Agents)
L2 adapter layer + L1Messenger bidirectional sync
Version Lineage
Track AI model version evolution
Model hash binding to DID
Device DID Extension
Physical device fingerprinting and binding
IoT/robotics extension protocol
Pillar II: ZK Proof of Decision & Data Rights Infrastructure
Question answered: What happened, and how can it be verified while preserving privacy?
Privacy-preserving, verifiable records of AI behavior. Without exposing raw data, enables audit trails, data ownership, and compliance proofs — providing the verifiable behavior recording layer for the AI era.
Behavior Hash Generation
Compress behavior data into verifiable fingerprints
SHA-256 + temporal fingerprint algorithm
Merkle Tree Aggregation
Batch 1,024 behavior hashes into a single root
Sparse Merkle Tree
ZK Proof Generation
Prove behavior compliance without exposing raw data
PLONK/FFLONK (behavior proofs) + Groth16 (identity verification)
On-Chain Anchoring
Immutable evidence storage on AxBlade L2
ZK Rollup L2 (Type 2.5 zkEVM) + L1 anchoring
Off-Chain Storage
Encrypted raw data storage with on-demand retrieval
IPFS + encrypted sharding
Pillar III: Oracle & Trusted Data Infrastructure
Question answered: Is the data that AI relies on trustworthy, high-quality, and traceable?
High-quality, verifiable, traceable data input layer for multi-modal AI systems. Ensures AI decisions are built on a trusted foundation, not on polluted or unverifiable inputs. AxBlade solves this through a two-layer oracle architecture.
Physical World Data Pipeline (TEE-Secured Pipeline):
Edge TEE Nodes
Secure data processing at the device level
Intel SGX / ARM TrustZone / AMD SEV
TEE Remote Attestation
Hardware-level proof of computation integrity
Remote attestation protocols
Multi-Sensor Cross-Validation
Redundant verification across sensor modalities
LiDAR + Vision + IMU cross-reference
Swarm Oracle (Future)
Byzantine fault-tolerant group verification
Multi-robot consensus
On-Chain Native Oracle (OracleHub System Contract 0x8016):
OracleHub
Multi-source price aggregation, deviation detection
Sequencer auto-injects synthetic L2 txs at the start of each batch
Dual Data Source
Prevent single-source failure/manipulation
CoinGecko + Binance dual-source aggregation
Emergency Pause
Automatic circuit breaker on anomalous prices
Deviation detection + Operator minimal permissions
The two-layer oracle architecture is complementary: the TEE-secured pipeline solves the trust problem for physical AI data before it goes on-chain, while the OracleHub system contract provides real-time trusted price benchmarks for the financial context of on-chain AI behavior.
3.2 Compliance-Native Design
Compliance is not a patch. It is embedded from day one across identity, execution, data, and accountability — giving every AI system inherent governability:
Regulatory Coverage:
EU
AI Act (August 2026 full enforcement)
BehaviorSpec templates covering all high-risk requirements
EU
GDPR
On-chain stores only hashes; raw data deletable
China
Network Security Law Amendment (January 2026)
Consortium chain deployment; data localization
China
AI Safety Governance Framework V2.0
Behavior auditability module
U.S.
Illinois AI Hiring Bias Law (January 2026)
Anti-discrimination verification proofs
U.S.
Colorado AI Act (June 2026)
Algorithmic fairness monitoring
Global
UNECE DSSAD
Autonomous driving behavior recording standard compatibility
4. Core Protocols
4.1 Proof of Behavior (PoB)
Proof of Behavior is AxBlade's foundational protocol — analogous to Proof of Work or Proof of Stake, but proving AI behavioral compliance rather than computational work or economic stake.
Protocol Components
Behavior Hash
Cryptographic hash of behavior data including timestamp, device ID, action type, parameters, and result
TEE Attestation
Hardware-level proof generated by Trusted Execution Environment, ensuring behavior data integrity
ZK Proof
Zero-knowledge proof demonstrating behavior conforms to specification without revealing raw data
Merkle Root
Aggregated root of batched behavior hashes for on-chain anchoring
Validator Signature
Verification node's cryptographic confirmation
Protocol Flow
Unified Physical-Digital Framework
PoB uses adapter modules to unify behavior recording across physical and digital AI:
4.2 Proof of Decision (PoD)
Proof of Decision extends PoB from behavior-level recording to decision-level tracing. While PoB answers "what did the AI do?", PoD answers "why did the AI decide this?"
Decision Receipt
The Decision Receipt is PoD's core primitive — a structured, immutable, cryptographically signed data object recording a complete AI decision context.
Four-Step Decision Tracing Chain
1. Input Snapshot
Capture all inputs at the moment of reception
Merkle tree of input fields; perceptual hash for images
2. Inference Log
Record and prove the computation process
TEE attestation or zkML proof; CoT hash as auxiliary reference
3. Output Signature
Cryptographically sign the decision immediately
ERC-8004 key pair + TEE hardware proof + timestamp
4. Result Verification
Compare actual results with expected outcomes
Immediate, delayed, comparative, and statistical verification
Why Verifiability Over Explainability
Anthropic's Alignment Science Team research (April 2025) revealed that AI Chain-of-Thought outputs are "fragile" — models faithfully report their actual reasoning in only 25-39% of cases, and acknowledge exploitative behavior in less than 2% of their CoT outputs.
CoT Self-Report
Low (25-39% faithful)
Auxiliary reference only, never primary evidence
Output Consistency Check
Medium
Combined with input snapshot for causal verification
Mechanistic Interpretability
High but limited
Long-term research roadmap
Cryptographic Inference Proof
High
Primary: zkML proofs
Hardware Environment Proof
High
Primary: TEE remote attestation
PoD's philosophical position: We don't rely on AI "telling us what it thinks." Instead, we independently prove what AI did through cryptography and hardware — shifting the paradigm from Explainability to Verifiability.
Cross-Agent Decision Chain
In multi-agent systems, PoD tracks decision causality across agent boundaries:
Each agent independently signs its receipt. Responsibility is precisely divisible. Anomalies are locatable by comparing adjacent receipts.
5. AI Behavior Operating System
5.1 Concept
The AI Behavior Operating System is AxBlade's core product — a full-lifecycle behavior management platform for all AI systems (physical devices + digital agents). It is not a passive "black box" recorder but an active behavior management operating system.
Core Narrative: Every AI needs an operating system for its behavior.
Just as Android/iOS defines behavior norms and application boundaries for mobile devices, the AI Behavior Operating System defines behavior specifications, monitoring mechanisms, and audit interfaces for AI systems. We don't control what AI does, but we ensure:
Every AI behavior is Defined (BehaviorSpec DSL)
Every AI behavior is Monitored (Real-time multi-modal sensing)
Every AI behavior is Evidenced (PoB on-chain proof)
Every AI behavior is Analyzed (Pattern recognition + anomaly detection)
Every AI behavior is Corrected (Automated policy enforcement)
5.2 Five-Layer Architecture
Layer 1: Behavior Definition
Declaratively define what AI is allowed and prohibited from doing using the BehaviorSpec DSL:
Layer 2: Behavior Monitor
Multi-Modal Collector
Capture sensor, log, and API call data
Latency <10ms, packet loss <0.01%
Edge TEE Node
Secure processing of sensitive behavior data
Intel SGX / ARM TrustZone
Real-Time Stream Processor
Behavior data stream analysis
Throughput >100K events/s
Behavior Fingerprint Generator
Compress behavior data into hashes
SHA-256 + temporal fingerprint
Health Probe
Monitor AI system's own health state
1s heartbeat, timeout alerting
Layer 3: Behavior Ledger
The on-chain evidence layer, implementing the Proof of Behavior protocol. Behavior hashes are aggregated into Merkle Trees (1,024 per batch), with only the Merkle Root submitted to the AxBlade L2 (ZK Rollup). Original data is stored off-chain with encrypted sharding on IPFS. L2 state changes are ultimately anchored to Ethereum mainnet via ZK proofs, inheriting Ethereum's security guarantees.
Layer 4: Behavior Analysis
Behavior Pattern Recognition
Temporal clustering + Graph Neural Networks
Anomaly Detection Engine
Isolation Forest + Transformer
Predictive Analysis
LSTM + Causal inference models
Compliance Scoring System
Multi-dimensional scoring matrix (0-100)
Behavior Report Generation
Template engine + NLG
Layer 5: Behavior Execution
Policy Engine
Rule engine + RL optimization
Auto-Correction
Soft correction (suggestions) / Hard correction (enforcement)
Permission Adjustment
Progressive permission escalation/de-escalation
Circuit Breaker
Three-level: Warning → Restriction → Stop
Notification Center
Multi-channel push (API/Webhook/Email)
5.3 Multi-Modal Behavior Collection
Motion
Position, velocity, acceleration, trajectory
IMU + GPS + encoders
Autonomous driving, robotics
Visual
Gaze point, object detection, scene understanding
Camera + vision models
Surgical robots, security
Force
Contact force, torque, pressure
Force sensors
Surgical robots, industrial robots
Language
Dialogue content, command parsing, response generation
NLP pipeline
AI Agents, AI customer service
Decision
Reasoning chain, strategy selection, parameter adjustment
Model introspection + API hooks
Financial AI, AI Agents
Network
API calls, data access, network communication
Network probes + logs
Digital AI systems
Energy
Power, current, temperature
Electrical sensors
Physical devices
Social
Interaction frequency, collaboration patterns
Communication logs
Multi-Agent systems
6. AI Credit System
6.1 Vision: FICO for AI
The human financial system spent nearly a century building credit infrastructure — from Equifax (1899) to FICO scores (1956) to China's Sesame Credit (2015). The core logic: in a world of information asymmetry, reduce trust costs through quantifiable, verifiable historical behavior records.
AI is at a parallel historic inflection point. When an AI Agent must complete a task — driving a car, diagnosing a disease, executing a trade, operating a robot — who proves it "trustworthy"? On what basis? With what evidence?
Humans have FICO. AI has nothing. Until now.
6.2 Five-Dimension Scoring Model (CSDRT)
AI credit is not a single number but a multi-dimensional behavioral profile across five dimensions:
Capability (C)
25%
Task success rate, benchmark scores, capability boundaries, version evolution
Benchmark tests, production logs, A/B tests
Safety (S)
30%
Incident count, vulnerability records, attack resilience, red team test results
Incident reports, security audits, penetration tests
Regulatory (R)
20%
Regulatory compliance, data handling compliance, certification status, audit pass rate
Regulatory reports, third-party audits, certifying bodies
Dependability (D)
15%
Uptime, failure frequency, consistency, SLA achievement rate
Operations logs, performance monitoring, user feedback
Transparency (T)
10%
Explainability, data provenance, decision auditability, documentation completeness
Model Cards, source audits, data lineage
Credit Grade Mapping:
AAA
900-1000
750-850
Excellent: Suitable for critical, high-risk scenarios
AA
800-899
700-749
Very Good: Suitable for most commercial applications
A
700-799
650-699
Good: Standard commercial use, periodic monitoring required
BBB
600-699
600-649
Adequate: Restricted use, enhanced monitoring required
BB
500-599
550-599
Below Average: Low-risk scenarios only, remediation required
B
400-499
500-549
Poor: Not recommended for commercial deployment
CCC & below
<400
<500
Very Poor: Should be prohibited from deployment
6.3 Scoring Engine Architecture
Key Design Decisions:
Time Decay
Exponential decay, half-life 6 months
AI iterates fast; old version behavior has diminishing reference value
Version Inheritance
New version inherits 50% of old version's credit
Prevents credit-washing through frequent version changes
Scenario Weighting
High-risk scenarios weighted 3x
One medical/driving incident is far more severe than a chatbot error
Negative Events
Penalty multiplier 2-5x
Safety incidents outweigh normal operation accumulation
Cold Start
Initial score based on benchmark tests
Solves the no-history problem for new agents
Dispute Resolution
On-chain appeals + arbitration committee
Analogous to human credit dispute processes
6.4 ZK Credit Proofs
A critical innovation: zero-knowledge credit proofs allow an AI system to prove its credit exceeds a threshold without revealing the actual score or underlying data.
Use Case: An AI surgical assistant needs to prove it meets the minimum safety standard (Safety Score >= 800) for deployment in a hospital, without disclosing its full credit history, incident details, or proprietary training data.
6.5 Application Scenarios
Consumer Pre-Purchase
"Carfax for AI" — check AI credit before buying autonomous vehicles
$1-2.5B (AV segment alone)
Enterprise Deployment
Evaluate AI Copilot/Agent credit before enterprise-wide rollout
$20B (1% of enterprise AI spend)
Insurance Pricing
AI credit data as actuarial basis for robot/AI insurance premiums
$75B by 2034 (AI insurance market)
DeFi Bot Verification
On-chain credit badges for AI trading bots
DeFi TVL $150B+
Medical AI Evaluation
Hospital procurement decisions based on AI diagnostic system credit
$5-10B
Agent-to-Agent Trust
Automated trust establishment in multi-agent systems
Core infrastructure need
Regulatory Compliance
Continuous compliance monitoring and automated audit reports
$5B+ by 2027
Government Procurement
AI credit reports as mandatory evaluation criteria in public tenders
$50B+ government AI procurement
7. Technical Architecture
7.1 AxBlade L2 — A ZK Rollup AI Governance Chain
AxBlade operates on an Ethereum ZK Rollup Layer 2 purpose-built for AI governance. The underlying engine is based on a production-proven open-source ZK Rollup implementation (Apache 2.0), on top of which AxBlade layers differentiated AI-governance-native extensions.
Why ZK Rollup over Optimistic Rollup: ZK Rollups provide mathematical guarantees of state transition correctness via zero-knowledge proofs, with 1-24 hour hard finality (vs. 7-day challenge windows for Optimistic Rollups). This is a natural fit for AI behavior records, which require immutability and fast confirmation.
Core Specifications
Architecture
ZK Rollup (Type 2.5 zkEVM)
High EVM compatibility; only a handful of gas-metering opcodes differ
Proof System
PLONK / FFLONK
Small proofs, fast verification, general-purpose
Chain ID
271
—
EVM Compatibility
99%+
Seamless migration for Solidity 0.8+ contracts
TPS
10,000 (Phase 1)
Meets large-scale behavior recording demand
Transaction Cost
~$0.0001/tx
Merkle batching reduces per-behavior cost
Soft Finality
1-2 seconds
Sequencer confirmation
Hard Finality
1-24 hours
After L1 ZK proof verification
Data Availability
On-chain DA (Ethereum)
Inherits Ethereum's data availability guarantees
System Contracts
5 custom + 40 native
Native AI governance infrastructure
Test Coverage
521 tests (Solidity + Rust + SDK + circom)
Multi-layer test assurance
Architectural Customization: Zero-Coupling Extension
AxBlade L2's differentiation lies not in the underlying ZK engine but in the AI governance infrastructure natively integrated at the consensus layer. Custom modules run in a sidecar pattern that does not modify the core engine, minimizing upgrade conflict risk.
Consensus-Layer Customizations:
State Keeper
Automatic Oracle price injection
Operator TX mode (synthetic L2 txs injected at the start of each batch)
Bootloader
System contract address registration
5 custom addresses 0x8016-0x801A
Wiring Layer
Price aggregation service
CoinGecko + Binance dual source + PriceAggregator
Wiring Layer
Credit scoring service
CreditScoreService + 5-factor weighted scoring
Wiring Layer
Bridge monitoring service
SettlementMonitor + automatic LP settlement
Five Native System Contracts
Beyond the 40 native system contracts, AxBlade adds 5 AI-governance-specific contracts that any dApp can call without separate deployment:
0x8016
OracleHub
Multi-source price aggregation, deviation detection, operator access control, emergency pause
Trusted data infrastructure
0x8017
DIDRegistry
did:ethr:axblade identifiers, delegate management, N-of-M social recovery
Identity & accountability infrastructure
0x8018
CredentialRegistry
Verifiable Credential (VC) lifecycle: issuance, verification, revocation
Behavior credential management
0x8019
IdentityVerifier
ZK proof verification, compliance queries, selective disclosure, credit-score integration
Privacy-preserving compliance
0x801A
EnterpriseIAM
Enterprise org structure, role management, permission bitmaps
Enterprise-grade access control
Native Oracle — Zero-Cost Real-Time Pricing:
Latency
<100ms
5-30s
400ms
Cost
$0.001
$10-50
$0.01
Integration
Native view call
Requires consumer contract
Requires pull tx
OracleHub runs as a system contract inside the L2 kernel; the Sequencer injects prices at the head of every batch. dApps call getLatestPrice() as if reading a storage slot — zero gas overhead.
LP Fast Bridge — One-Hour Withdrawal:
A common ZK Rollup pain point is slow withdrawals (1-24h for proofs to land on L1). AxBlade L2 includes FastWithdrawalPoolV2: LPs advance funds on L1 instantly, users receive assets in under an hour; once the proof lands, the contract performs trustless settlement using native Merkle proofs and LPs automatically recover principal. No third-party trust required — anyone may submit the proof to trigger settlement.
Architecture Overview
Positioning vs. Major L2s
Type
ZK Rollup
ZK Rollup
Optimistic
Optimistic
Native Oracle
✅
❌
❌
❌
Native DID
✅
❌
❌
❌
LP Fast Bridge
✅
❌
❌
❌
AI Behavior Recording
✅ Built-in
❌
❌
❌
Withdrawal Time
<1h (fast)
1-24h
7 days
7 days
Oracle Cost
$0.001
3rd-party
3rd-party
3rd-party
Enterprise Compliance
ZK KYC
❌
❌
❌
Positioning
AI Governance + Institutional
General
General
Consumer
7.2 TEE + ZK Hybrid Architecture
AxBlade's security model combines hardware trust (TEE) with mathematical trust (ZK) in a defense-in-depth architecture:
Data Collection
Secure raw data capture within Enclave
—
Trusted data source
Data Processing
Behavior analysis within Enclave
—
Process cannot be observed or tampered
Proof Generation
Generate TEE Attestation
Generate behavioral compliance ZK Proof
Dual proof
On-Chain Verification
Attestation verification (lightweight)
ZK Proof verification (on-chain)
Complete verifiability
Privacy Protection
Runtime data protection
Verification-time data protection
Full-pipeline privacy
Why Hybrid?
Pure TEE
High performance, real-time
Depends on hardware trust root (Intel ME historical vulnerabilities)
Edge real-time processing
Pure ZK
Mathematical-grade security, no hardware trust needed
Slow proof generation; not feasible for large models
On-chain verification
TEE+ZK Hybrid
Balances performance and security
Higher architectural complexity
Full-scenario coverage
Fallback Guarantee: If TEE is compromised, ZK proofs remain mathematically valid. If ZK proof generation is too slow for a scenario, TEE attestation provides interim coverage.
ZK Identity Circuits
AxBlade L2 ships with 6 Groth16 ZK circuits (EdDSA signatures on the Baby Jubjub curve), each with its own Verifier contract deployed on L2:
KYCComplianceProof
0
~7,800
KYC compliance — prove "KYC-verified" without revealing personal information
CreditScoreProof
1
~7,800
Credit proof — prove "credit >= threshold" without revealing the score
EnterpriseIdentityProof