Technical Whitepaper v3.1

Bitsage Network Manifesto

Decentralized AI Compute Infrastructure: Where Blockchain Meets AI, Enhanced by Wisdom Specialists

Global Network

Zero-Knowledge Proofs

Cryptographic Security

Executive Summary

BitSage Network is building a decentralized marketplace for verifiable compute, starting with GPU-intensive workloads like 3D rendering, AI inference, and ZK proof generation. We provide cryptographic proof of execution integrity through our "Proof of Compute" model, offering 30-60% cost savings over traditional cloud providers.

By connecting global GPU providers with developers who need verifiable results, BitSage democratizes access to high-performance computing while ensuring computational integrity through blockchain-based verification - without the overhead of proving every instruction.

Compute Layer

Decentralized GPU marketplace providing verifiable AI compute resources with blockchain security.

Wisdom Enhancement

Optional AI wisdom specialists that provide domain-specific insights to enhance compute workflows.

Economy Layer

SAGE token powers the wisdom economy, rewarding contribution and enabling global AI collaboration.

Key Innovations

Proof of Compute

Cryptographic verification of execution integrity, resource commitment, and result authenticity without full trace proving

Tiered Verification

Different verification methods per workload: deterministic re-computation, TEE attestation, and sampling

Cost-Effective Access

30-60% savings over traditional cloud through global GPU marketplace and efficient resource utilization

Progressive Decentralization

Starting with proven workloads (rendering, inference) and expanding to complex AI as verification tech matures

Vision & Philosophy

"The future of AI compute lies not in centralized datacenters, but in decentralized, verifiable marketplaces."
— Bitsage Network Foundation

The Compute Centralization Problem

Today's AI infrastructure suffers from compute centralization. While AI workloads demand massive GPU resources, access remains limited to a few major cloud providers with high costs, vendor lock-in, and limited transparency. This creates barriers: expensive compute, lack of verification, and missed opportunities for decentralized innovation.

Our Philosophical Foundation

Compute Democratization

True innovation emerges from open access to compute resources. Bitsage democratizes GPU infrastructure, making high-performance computing accessible to developers worldwide through decentralization.

Verifiable Computation

Trust in computational results is paramount. Bitsage uses zero-knowledge proofs to verify AI computations, ensuring results are authentic, reproducible, and tamper-proof.

Economic Fairness

As AI compute becomes more valuable, it must remain accessible. Bitsage creates transparent markets where providers and users benefit fairly, ensuring sustainable growth for all participants.

Inclusive Access

AI compute should benefit all developers. Bitsage democratizes access to high-performance GPU resources, enabling creators, researchers, and entrepreneurs worldwide to access compute on demand.

The Bitsage Vision

We envision a future where AI compute infrastructure is:

Decentralized: AI compute resources distributed globally, eliminating single points of failure
Verifiable: All AI computations cryptographically verified, ensuring trust and transparency
Accessible: High-performance GPU compute available to developers worldwide, regardless of location
Economically efficient: Market mechanisms optimize resource allocation and pricing

Technical Architecture

BitSage Network's architecture is built on proven blockchain infrastructure (StarkNet) with a focus on practical verification methods. Rather than attempting full ZK-execution of every computation, we implement "Proof of Compute" - verifying execution integrity, resource commitment, and result authenticity through cryptographic receipts and selective verification techniques.

System Architecture

Loading diagram...

Layer 1: Verification Layer

The foundation of trust in SAGE Network. Uses advanced zero-knowledge proof systems to verify computational integrity.

• STARK-based proof generation for scalability
• Recursive proof composition for complex computations
• Hardware-accelerated verification
• Fraud proof mechanisms for dispute resolution

Layer 2: Compute Layer

Distributed network of compute providers offering specialized AI hardware and software capabilities.

• GPU clusters for parallel training
• Specialized AI accelerators (TPUs, FPGAs)
• Edge computing nodes for low-latency inference
• Secure enclaves for confidential computing

Layer 3: Protocol Layer

Smart contract infrastructure managing job orchestration, resource allocation, and network coordination.

• Job scheduling and load balancing
• Resource discovery and matching
• Payment and settlement systems
• Reputation and slashing mechanisms

Layer 4: Economic Layer

Token-based incentive system aligning participant interests and ensuring sustainable network growth.

• Dynamic pricing based on supply and demand
• Staking requirements for compute providers
• Reward distribution mechanisms
• Governance token for protocol decisions

Key Technical Innovations

Recursive Zero-Knowledge Proofs

Novel application of recursive STARKs to verify arbitrarily complex AI computations while maintaining constant verification time.

Homomorphic Encryption Integration

Seamless integration with FHE schemes enabling computation on encrypted data without performance degradation.

Adaptive Resource Allocation

ML-powered system that predicts compute demand and preemptively allocates resources for optimal performance.

Proof of Compute Model

BitSage implements "Proof of Compute" rather than full ZK-execution of workloads. This approach provides cryptographic verification of execution integrity, resource commitment, and result authenticity without the prohibitive overhead of proving every computational step.

What We Prove vs. What We Don't

✅ What BitSage Proves

• Job completion with cryptographic receipts
• Resource commitment matching declared specs
• Output hash corresponds to claimed input
• Node identity and attestation
• Execution environment integrity (TEE when available)

⚠️ What We Don't Claim

• Full ZK-execution trace of large AI workloads
• Proving every instruction of complex computations
• Real-time verification during job execution
• Verification of proprietary model architectures

Deterministic Re-computation

For rendering and encoding jobs

• Re-render 2-5% of pixels/frames
• Compare cryptographic hashes
• Minimal overhead verification

TEE Attestation

For AI inference and sensitive workloads

• Trusted Execution Environment proofs
• Hardware-backed attestation
• Spot-check sampling for validation

Native ZK Verification

For ZK proof generation jobs

• Proof correctness is self-verified
• STARK/SNARK validation
• Perfect use case for bootstrap market

Verification by Workload Type

Workload Type	Verification Method	Overhead	Available Now
3D Rendering	Deterministic re-render + hash	2-5%	✅ Ready
AI Inference	TEE attestation + sampling	<5%	✅ Ready
ZK Proof Generation	Native proof verification	~0%	✅ Ready
Small AI Training	Checkpoint hash + batch replay	5-10%	⚠️ Prototype
Large AI Training	Requires clustered nodes	TBD	❌ Future

Compute Types & Capabilities

BitSage Network categorizes compute resources into specific node classes with realistic performance specifications. This tiered approach ensures users can select appropriate hardware for their workloads while maintaining cost efficiency.

Standard GPU Nodes

Consumer and prosumer GPUs suitable for rendering, medium AI inference, and smaller model training.

• NVIDIA RTX 3080/4080, RTX A4000/A5000
• 8-24 GB VRAM per GPU
• 20-35 TFlops FP32 performance per GPU
• Pricing: $0.10-$0.50 per GPU-hour

High-Memory Nodes

Professional GPUs with large VRAM for complex AI models, high-resolution rendering, and memory-intensive workloads.

• NVIDIA A6000 (48GB), A100/H100 (80GB)
• 40-80 GB VRAM per GPU
• 35-60 TFlops FP32 performance per GPU
• Pricing: $2.00-$8.00 per GPU-hour

Clustered Nodes

Multi-GPU systems with high-speed interconnects for distributed training and tightly-coupled simulations.

• 4-16 GPUs with NVLink/NVSwitch
• 256 GB - 1 TB system RAM
• 100+ TFlops aggregate FP32 performance
• Located in data centers for low latency

Edge Nodes

Regional nodes for low-latency inference and interactive applications requiring sub-150ms response times.

• NVIDIA Jetson AGX, RTX 3060/4060
• 4-16 GB VRAM, optimized for inference
• 5-15 TFlops FP32 performance
• <150ms latency within region

Capabilities

AI Training

Large-scale neural network training, including image classification, object detection, and language models.

AI Inference

Real-time, low-latency AI model inference for applications like speech recognition, object tracking, and fraud detection.

Data Processing

High-speed data ingestion, transformation, and analysis for real-time monitoring and decision-making.

Edge Intelligence

AI models deployed directly on edge devices for autonomous decision-making and local processing.

Job Types & Workloads

BitSage Network provides two fundamental job types: Virtual Machine infrastructure (like Akash) that enables flexible workloads, and specialized batch compute jobs that benefit from our verification model. VMs serve as the foundation for most use cases, while batch jobs offer the highest verification guarantees.

🖥️ Virtual Machine Infrastructure (Primary Job Type)

Like Akash Network, BitSage provides on-demand virtual machines with GPU access. This is our most flexible and immediately viable offering, supporting any workload that can run in a containerized environment.

✅ What VMs Enable

• Web applications and APIs
• Development environments
• AI model training and inference
• Database and storage services
• Custom software deployment
• Blockchain nodes and validators

🔒 VM Verification Model

• Resource commitment proofs (CPU/GPU/RAM)
• Uptime and availability attestation
• TEE-based execution when available
• Container integrity verification
• Network and storage I/O monitoring
• SLA compliance tracking

Batch Compute Jobs

Specialized workloads with high verification guarantees through deterministic execution and cryptographic proofs.

• 3D rendering (Blender, Cinema 4D)
• Video encoding (FFmpeg, x264/x265)
• ZK proof generation (STARK/SNARK)
• Monte Carlo simulations
• AI inference (deterministic models)

Hybrid Workloads

Complex applications that combine VM infrastructure with batch compute verification for optimal flexibility and trust.

• AI training with checkpoint verification
• Scientific simulations with result proofs
• Blockchain applications with ZK components
• Creative pipelines with render verification
• Data processing with integrity guarantees

VM Infrastructure vs Batch Compute Comparison

Aspect	Virtual Machines	Batch Compute Jobs
Flexibility	✅ Run any containerized workload	⚠️ Limited to predefined job types
Verification	🔒 Resource commitment + uptime proofs	🔐 Full cryptographic result verification
Use Cases	Web apps, APIs, training, development	Rendering, encoding, ZK proofs, simulations
Pricing	$0.10-$8.00/hour (like Akash)	Per-job pricing + 2-5% verification overhead
Setup Time	Minutes (container deployment)	Seconds (job submission)
Market Readiness	✅ Ready Now	🚀 Differentiator

Realistic Resource Specifications

VM Instances

• CPU: 2-64 cores
• RAM: 4GB-512GB
• GPU: 0-8 GPUs per instance
• Storage: 20GB-2TB NVMe
• Network: 1-10 Gbps

Batch Jobs

• Duration: Minutes to hours
• Parallelism: 1-1000+ nodes
• Input: MB to TB datasets
• Verification: 1-5% overhead
• Output: Cryptographically signed

Latency Tiers

• Interactive: <150ms (same region)
• Responsive: <500ms (cross-region)
• Batch: Minutes to hours
• Bulk: Hours to days
• Archive: Best effort

Creative & Artistic Computing

BitSage Network focuses on creative workloads that are both verifiable and economically viable. We start with embarrassingly parallel tasks like rendering and video encoding where verification overhead is minimal, then expand to more complex workloads as our verification technology matures.

✅ Ready Now: Verifiable Creative Computing

3D Rendering (Blender, Cinema 4D)

• Deterministic frame rendering with fixed seeds
• 2-5% verification via spot re-rendering
• Frame watermarking with job ID + nonce
• Hash verification of output sequences

Video Encoding (FFmpeg, x264/x265)

• Segment-based parallel processing
• Checksum verification per segment
• 1-3% overhead for spot re-encoding
• Bitrate and quality validation

⚠️ Limited Support: Requires Licensing

Proprietary Software (Maya, 3ds Max, After Effects)

• Customer must provide valid licenses
• Limited to licensed node pools
• Higher costs due to licensing overhead
• Verification same as open-source tools

AI Art Generation (Stable Diffusion)

• Seed-based deterministic generation
• Model weight verification via hashing
• Batch processing for NFT collections
• TEE attestation for sensitive prompts

❌ Not Suitable Yet: Complex Interactive Workflows

Real-time Compositing & VFX

• Requires low-latency interaction
• Complex multi-layer dependencies
• Difficult to verify intermediate states
• Better suited for local/cloud hybrid

Live Streaming & Real-time Effects

• Sub-100ms latency requirements
• Continuous data streams
• No verification time budget
• Edge computing needed

Realistic Cost Comparison

BitSage Network

• Consumer GPUs: $0.10-$0.50/hour
• Pro GPUs: $2.00-$8.00/hour
• +2-5% verification overhead
• Cryptographic receipts included
• Customer provides licenses (if needed)

Traditional Render Farms

• Managed farms: $2.00-$15.00/hour
• Setup fees and minimums
• Software licensing included
• Trust-based quality assurance
• Technical support included

Realistic Savings

• 30-60% typical savings
• Up to 70% vs premium farms
• Best for batch workloads
• Requires technical expertise
• Self-service model

⚠️ Important Considerations

• BitSage is self-service - you handle job setup, file management, and troubleshooting
• Traditional farms include technical support, managed workflows, and guaranteed SLAs
• Cost savings are highest for technically sophisticated users with batch workloads

Creative Workload Capability Matrix

Workload Type	Verification Method	Overhead	Status	Notes
Blender Rendering	Deterministic re-render + hash	2-5%	✅ Ready	Perfect for batch frame rendering
Video Encoding (FFmpeg)	Segment checksum + spot re-encode	1-3%	✅ Ready	Excellent parallelization
Maya/3ds Max Rendering	Same as Blender	2-5%	⚠️ Licensed	Customer must provide licenses
Stable Diffusion	Seed commitment + model hash	<1%	✅ Ready	Great for NFT batch generation
After Effects Compositing	Layer-by-layer verification	5-10%	⚠️ Complex	Simple comps only, requires licenses
Real-time VFX	Not applicable	N/A	❌ Not Suitable	Latency requirements too strict
Live Streaming	Not applicable	N/A	❌ Not Suitable	Requires edge computing

Verifiable Creative Workflow

Loading diagram...

ZK Proof Generation

ZK proof generation represents BitSage's most immediately viable use case - we can provide verifiable compute for generating cryptographic proofs while the proofs themselves verify our work. This creates a perfect bootstrap market serving Web3 projects that need STARK/SNARK generation at scale.

STARK Proof Generation

Scalable Transparent Arguments of Knowledge for large-scale verifiable computation.

• Cairo program execution proofs
• StarkNet transaction batching
• Recursive proof composition
• Custom circuit optimization
• Parallel witness generation

SNARK Systems

Succinct Non-Interactive Arguments of Knowledge for efficient privacy-preserving protocols.

• Groth16 & PLONK proof systems
• zk-SNARKs for privacy coins
• Circom circuit compilation
• Trusted setup ceremonies
• Universal setup systems (PLONK)

Specialized Applications

Domain-specific ZK proof generation for various blockchain and enterprise use cases.

• Privacy-preserving DeFi protocols
• Blockchain rollup verification
• Identity verification systems
• Supply chain provenance
• Confidential voting systems

Performance Optimization

Advanced optimization techniques for reducing proof generation time and computational costs.

• Hardware acceleration (GPU/FPGA)
• Parallel circuit evaluation
• Memory optimization strategies
• Batch proof generation
• Circuit-specific optimizations

STARK Complexity Analysis

Key Parameters

N = Execution trace length (rows)

λ = Security parameter (80-128 bits)

d = Maximum constraint degree

b = Blowup factor

Prover Complexity

STARK Prover Time:

FFT/MLE + Merkle/FRI operations; Õ hides polylog factors in (b,d)

Memory Requirements:

Linear in trace length (streamed to polylog overhead)

Verification Efficiency

Verification Time:

Hash checks and field operations, plus constant commitments

Proof Size:

Field elements/hashes (hundreds of KB in practice)

Implementation Notes

• Hash choice (Poseidon/Rescue/Keccak) dominates constants

• Security parameter λ affects query counts significantly

• STARKs use execution traces, not R1CS constraints

• Transparent setup vs SNARKs' trusted setup

ZK Proof Generation Pipeline

Loading diagram...

Scientific Computing

SAGE Network provides researchers, scientists, and institutions with access to high-performance computing resources for complex simulations, modeling, and analysis. Our verifiable compute ensures reproducible scientific results while dramatically reducing costs.

Molecular Dynamics

Large-scale molecular simulations for drug discovery, materials science, and biochemical research.

• GROMACS & AMBER simulations
• Protein folding studies
• Drug-target interaction modeling
• Materials property prediction
• Membrane dynamics simulation

Climate & Weather Modeling

High-resolution climate simulations and weather prediction models for environmental research.

• Global circulation models (GCMs)
• Weather forecasting systems
• Climate change projections
• Atmospheric chemistry modeling
• Oceanographic simulations

Computational Fluid Dynamics

Advanced fluid flow simulations for aerospace, automotive, and engineering applications.

• OpenFOAM & ANSYS Fluent workflows
• Turbulence modeling (LES/DNS)
• Aerodynamic optimization
• Heat transfer analysis
• Multi-phase flow simulation

Financial Modeling

Quantitative finance, risk analysis, and algorithmic trading model development and backtesting.

• Monte Carlo risk simulations
• Portfolio optimization algorithms
• High-frequency trading backtests
• Credit risk modeling
• Derivative pricing models

Research Impact & Benefits

Reproducible Science

Cryptographic verification ensures computational results are reproducible and verifiable by peers

Cost Democratization

90%+ cost reduction enables smaller institutions to access supercomputing resources

Global Collaboration

Decentralized infrastructure enables seamless international research collaboration

Accelerated Discovery

Massive parallel processing enables larger, more complex simulations than ever before

Open Science

Transparent, verifiable computations support open science and peer review processes

Environmental Impact

Efficient resource utilization reduces energy consumption compared to dedicated clusters

Performance Scaling Example

Molecular Dynamics Simulation Scaling

Traditional HPC Cluster

• 100M atom system: 72 hours on 512 cores
• Cost: $15,000-25,000 per simulation
• Queue wait times: 2-14 days
• Limited to institutional access

SAGE Network

• Same system: 8 hours on 4096 cores
• Cost: $800-1,500 per simulation
• Instant resource availability
• Global access, any researcher

Job Matching & Transportation

SAGE Network's decentralized job market and transportation layer ensure efficient resource utilization and optimal routing of compute tasks across the network.

Decentralized Job Market

A global marketplace where clients can post AI tasks and workers can bid for them.

• Task posting and bidding
• Real-time price discovery
• Smart routing to optimal providers
• Transparent task history and reputation

Resource Transportation

Secure and efficient transportation of data and compute resources across the network.

• Inter-chain data transfer
• Cross-region compute resource sharing
• Secure enclave transport
• Decentralized storage for data

Resource Orchestration

Intelligent algorithms for optimal resource allocation and task distribution.

• ML-powered demand forecasting
• Dynamic routing based on capacity
• Efficient task bundling
• Resource pooling across the network

Network Effects

The more compute resources and tasks available, the more valuable the network becomes.

• Increased network throughput
• Lower latency for all users
• More diverse and robust AI ecosystem
• Stronger security through redundancy

Intelligent Job Matching Algorithm

Loading diagram...

Matching Algorithm Mathematics

Feature Vector (Job i → Node j)

φ_ij = [c_ij, ℓ̂_ij^p95, t̂_ij, â_ij, q̂_ij]

• c_ij: Effective cost ($/GPU-hr × hours + egress/storage)

• ℓ̂_ij^p95: Predicted p95 end-to-end latency

• t̂_ij: Throughput proxy (tokens/s, frames/s)

• â_ij: Availability Pr(no preemption ∧ node up)

• q̂_ij: Quality score (pass rate/accuracy)

Expected Utility Objective

Utility Function:

max_j U_ij = α₁S̃_perf + α₂S̃_rep + α₃S̃_avail − β₁S̃_price − β₂S̃_latency − γ₁Pr(ℓ > L_max) − γ₂Pr(evict)

Benefits minus costs minus SLA penalties (scores percentile-clipped 1%-99%)

Alternative Simple Form:

S_total = w^⊤s, Σw_k = 1, w_k ≥ 0, s normalized per metric

Weighted sum with normalized scores and convex weights

Realistic Constraints

Hard Constraints:

• Capacity: Σ_i x_ij·gpu_hrs_i ≤ gpu_hrs_j

• VRAM fit: vram_j ≥ model_req_i

• Compat.: dtype_i ∈ dtypes_j, accel_i ⊆ capabilities_j

• SLA: Pr(ℓ_ij ≤ L_max) ≥ 1-δ

• Budget: Cost_ij ≤ Budget_i

• Locality: region_j ∈ ℛ_i, data_policy_j ⪰ policy_i

Resource constraints with probabilistic SLA guarantees

Assignment Variables:

x_ij ∈ {0,1} (single-match) or [0,1] (splittable)

Binary for exclusive assignment, fractional for job splitting

Sybil-Resistant Reputation

rep_j = Bayesian posterior with exponential decay

• Prior: α₀/(α₀+β₀) successes out of α₀+β₀ trials

• Decay factor: e^-λ·age

• Stake-weighted updates prevent sybil attacks

• Slashing for malicious behavior

• ZK/TEE attestations for result integrity

Key Benefits

Optimal Resource Allocation

Multi-criteria optimization ensures jobs are matched to the most suitable resources

Dynamic Adaptation

Algorithm adapts to real-time network conditions and resource availability

Fraud Prevention

Reputation-based filtering and cryptographic verification prevent malicious behavior

Cost Efficiency

Price optimization and competition drive down costs for end users

Encryption & Security Model

SAGE Network employs a robust encryption and security model to protect sensitive data and computational integrity.

End-to-End Encryption

All data and computations are encrypted in transit and at rest.

• Zero-knowledge proofs ensure data integrity
• Homomorphic encryption for secure computation
• Secure enclave for confidential computing
• Encrypted communication channels

Access Control

Fine-grained access control and permission management.

• Role-based access control (RBAC)
• Multi-factor authentication
• Secure key management
• Transparent audit logs

Consensus and Fault Tolerance

Byzantine Fault Tolerance (BFT) and Proof of Stake (PoS) for robust consensus.

• 2/3+ honest participation for liveness
• 1/3+ Byzantine nodes for safety
• Economic incentives for node participation
• Byzantine fault tolerance

Reputation System

Decentralized reputation and slashing mechanisms for malicious behavior.

• Historical performance tracking
• Fraud detection and dispute resolution
• Slashing for malicious behavior
• Reputation-based incentives

Security Guarantees

Computational Integrity

Zero-knowledge proofs ensure that the output of a computation is correct and cannot be tampered with.

Privacy

All data and models remain private by default, even from the compute provider.

Robust Consensus

Byzantine Fault Tolerance ensures network availability and consistency even under adversarial conditions.

Economic Incentives

Economic penalties for malicious behavior and rewards for honest participation.

Scalability Architecture

SAGE Network's architecture is designed to scale horizontally across a global network of nodes, enabling unprecedented throughput and resource availability.

Multi-Region Deployment

Nodes are deployed across multiple regions to minimize latency and provide redundancy.

• 10+ regions globally
• 100+ data centers
• Low-latency edge nodes
• Redundant infrastructure

Resource Pooling

Compute resources are pooled across the network, allowing for efficient utilization and cost savings.

• GPU clusters, TPUs, CPU farms
• Cross-region resource sharing
• Dynamic allocation based on demand
• Cost optimization for users

Decentralized Storage

Data and models are stored across a decentralized network of nodes, ensuring availability and durability.

• IPFS, Swarm, Filecoin
• Encrypted data transfer
• Distributed hash tables
• Fault tolerance

Network Topology

Small-world network properties minimize latency while maintaining robustness.

• Short average path length
• High clustering coefficient
• Robust connectivity
• Efficient routing

Scalability Benefits

Unlimited Scaling

Horizontal scaling to handle any workload, no theoretical limits.

Low Latency

Optimal routing and resource allocation minimize latency.

Cost Efficiency

Efficient resource utilization and cost optimization.

Resilience

Redundant infrastructure and decentralized storage ensure availability.

Multichain Integration

SAGE Network is designed to be interoperable across multiple blockchain networks, enabling seamless integration with existing ecosystems and protocols.

Cross-Chain Data

Data and computational results can be transferred across different blockchain networks.

• Inter-chain data transfer
• Decentralized storage
• Cross-chain computation
• Interoperable AI models

Interoperable AI

AI models and data can be trained and deployed across different blockchain networks.

• Federated learning across chains
• Cross-chain AI model marketplace
• Interoperable AI pipelines
• Decentralized AI research

Cross-Chain Payments

SAGE Token can be used for payments across different blockchain networks.

• Decentralized cross-chain payments
• Cross-chain staking
• Cross-chain governance
• Interoperable economic incentives

Interoperable Infrastructure

SAGE Network's infrastructure (compute, storage, network) can be accessed from any blockchain.

• Multi-chain API gateway
• Cross-chain worker nodes
• Interoperable storage solutions
• Multi-chain job market

Integration Benefits

Ecosystem Expansion

SAGE Network can be integrated into any blockchain, expanding its reach.

Cross-Chain AI

AI models and data can be trained and deployed across different networks.

Decentralized AI

AI research and development can be decentralized across multiple networks.

Interoperable Economy

SAGE Token and economic incentives can be used across different networks.

Orderbook & Liquidity

SAGE Network's decentralized orderbook and liquidity layer provides a robust foundation for the AI compute market.

Decentralized Orderbook

A global, permissionless orderbook for AI tasks and compute resources.

• Real-time task posting and bidding
• Smart routing to optimal providers
• Transparent task history
• Decentralized dispute resolution

Liquidity Pooling

SAGE Token liquidity is pooled across the network, providing stable and liquid markets.

• Decentralized liquidity pools
• Stable price discovery
• Cross-chain liquidity
• Decentralized price oracles

Market Efficiency

Efficient resource allocation and price discovery through decentralized markets.

• Real-time price updates
• Optimal routing
• Efficient task matching
• Decentralized governance

Network Effects

The more liquidity and tasks available, the more valuable the network becomes.

• Increased market depth
• Lower latency for all users
• More diverse and robust AI ecosystem
• Stronger security through redundancy

Benefits

Efficiency

Efficient resource allocation and price discovery.

Scalability

Horizontal scaling to handle any workload.

Security

Secure, encrypted communication and data storage.

Resilience

Redundant infrastructure and decentralized storage ensure availability.

Advanced Burn Mechanics

SAGE Network implements sophisticated burn mechanisms with mathematical precision to ensure long-term sustainability and value accrual. Our production-ready smart contracts execute these burns automatically through governance-controlled parameters, creating deflationary pressure while maintaining network security.

🧮 Mathematical Framework

Supply Definitions

• S_circ(t): Circulating supply (float)

• S_fdv(t): Total supply (FDV basis)

• P(t): TWAP price (30-60m, outlier-clamped ±3σ)

Supply Evolution

S_circ(t+1) = S_circ(t) + I_{to_circ}(t) − B_{from_circ}(t)

S_fdv(t+1) = S_fdv(t) + I_total(t) − B_total(t)

Separates circulating (float) from total supply (FDV) impact

Runway-Aware Inflation (PID-lite)

r_inf(t) = clip(r_min, r_max, k_pe(t) + k_iē(t))

Where: e(t) = B^⋆_USD − R_USD^non-infl(t)

Avoids reflexive spirals by tracking budget error, not price

Revenue Burn (Auctioned)

B_rev(t) = min(ρ·R_USD(t), B_max) / P_SAGE^TWAP(t)

70% of fees/POL yield/MEV rebates; executed via sealed-bid auctions

Buyback (Throttled with Breaker)

B_bb(t) = 1_{σ(t)≤σ_max}·β·(Treasury_USD(t) − Runway_target)₊ / P_SAGE^TWAP(t)

Rate-limited (β < 1); halts if realized vol σ(t) > threshold

⚙️ Implementation Guardrails

Oracle Protection

30-60m median-of-medians TWAP; ±3σ outlier clamp; multiple venue feeds

MEV-Resistant Execution

Sealed-bid batch auctions (CoW/Gnosis-style); commit-reveal parameters; randomized windows

POL Buffer Target

Maintain ≥8-week notional volume at 1% max slippage; refill before any burn

Circuit Breakers

Throttle β and ρ if realized volatility > σ_max (24h window); no halts, just slower execution

Governance Controls

±15% weight changes per 30-day epoch; 7-day timelock + emergency pause (multi-sig + on-chain vote affecting rates only, not custody); on-chain schedule publishing

Transparent Accounting

On-chain epoch reports: inputs (fees, treasury, price), outputs (burns, POL Δ, issuance)

🎯 Treasury Operations Priority

Execution Order (prevents overdraw):

1. Fund security & operations budget

2. Refill POL buffer to target

3. Execute revenue burns

4. Execute buybacks (if runway surplus)

📌 Treasury-Only Burns (FDV reduction, not float)

Scheduled burns draw from Foundation/Treasury pool. Float burns occur only when fees are in SAGE or via buyback execution.

Burn Mechanism Flow

Loading diagram...

Long-term Economic Effects

🔥

Deflationary Pressure

Creates scarcity & value accrual

🛡️

Security Funding

Ensures adequate security

⚖️

Market Stability

Auto price stabilization

📈

Ecosystem Growth

Aligns value with utility

Mathematical Foundations

BitSage Network's "Proof of Compute" model combines multiple cryptographic techniques to verify execution integrity without the overhead of full ZK-execution. We leverage hash-based commitments, cryptographic receipts, and selective verification to provide practical security guarantees.

Proof of Compute Primitives

Cryptographic Receipt System

BitSage generates cryptographic receipts for job execution, proving resource commitment and result integrity. For ZK proof generation workloads, we leverage native STARK verification where the proof validates itself:

• AIR: Algebraic constraints over execution trace

• T: Execution trace (N rows)

• x: Public input

• w: Private witness

• λ: Security parameter (e.g., 128 bits)

Proof Generation (with Masking):

Prover complexity: Õ(N log N) field operations + hashing

Verification (Trace-Independent):

T_verify = O(λ log N) independent of |w|; Proof size |π| = O(λ log N)

Statistical Completeness

If the statement is true and the prover follows protocol, verifier accepts except with probability ≤ 2^-λ (near-perfect)

Knowledge Soundness

If verifier accepts with non-negligible prob., extractor recovers valid witness in poly(N, λ) time—soundness error ≤ 2^-λ (ROM/Fiat-Shamir)

Zero-Knowledge (Masked)

With trace masking & randomness beacons, transcript reveals no information about w beyond validity (statistical/computational ZK)

Note: STARK core uses collision-resistant hashes + FRI; no trusted setup. Elliptic curves appear only in peripheral cryptography (e.g., signatures). Security knobs: λ (bits), blowup b, constraint degree d.

Economic Game Theory

Nash Equilibrium Analysis

SAGE's economic model achieves Nash equilibrium through expected utility maximization with effort-quality tradeoffs and stake-backed incentives:

• q_j: Probability worker j chosen

• e: Effort level (choice variable)

• p(e): Pr(correct | effort e)

• c(e): Cost of effort (c' > 0, c'' > 0)

• P: Payment for job

• K: Staked collateral

• r: Opportunity cost rate

• L: Slash amount if wrong

• b: Reputation bonus per success

• v: Value of correct result to client

• D: Damage from incorrect result

• δ: Reputation decay rate

Worker Expected Utility:

Worker chosen with prob q_j, earns P - costs + reputation bonus, risks slash L

Client Expected Utility:

Value from correct output minus payment and expected damage from errors

Incentive-Compatible Effort (FOC):

Worker chooses e to trade off marginal cost vs incentive from success

Market Price Equilibrium:

Bertrand competition drives price to break-even plus risk premium μ

Equilibrium Conditions

• IC (worker): c'(e^⋆) = (b+L)p'(e^⋆) ⇒ target quality

• IR (worker): 𝔼[U_w] ≥ 0 at P^⋆

• IR (client): v·p(e^⋆) ≥ P^⋆ + (1-p(e^⋆))D

• Market clearing: Job → lowest P meeting IC/IR + constraints

Reputation Dynamics:

rep_t+1 = (1-δ)rep_t + 𝟙_success feeds matching score; verification randomly samples outputs: wrong → slash L, right → rep +b

Consensus and Security

Byzantine Fault Tolerance

SAGE achieves consensus with authenticated messaging under partial synchrony, tolerating up to f Byzantine nodes out of n total:

Model Assumptions:

• Authenticated channels (signatures)

• Byzantine fraction f < n/3

• Partial synchrony (safety always; liveness after GST)

• Rotating leaders + quorum-based commits

Safety (Quorum Intersection):

Byzantine fraction < 1/3; quorum size q = 2f+1

Any two quorums intersect by ≥ f+1 nodes (at least one honest) → no conflicting commits

Liveness (Partial Synchrony):

With rotating leaders and n ≥ 3f+1, once network is timely (after Global Stabilization Time), honest leader obtains 2f+1 votes → blocks finalize

Latency: HotStuff/Tendermint commit in 2-3 rounds (≈ two network RTTs after GST)

Advanced Properties

• Stake-weighted: Replace counts with stake; adversarial < 1/3 total; quorum > 2/3

• Equivocation control: Signatures/VRFs + slashing for double-vote proofs

• Finality: Economic finality via slashing of f+1 stake for reversion

• Accountability: Cryptographic evidence enables post-facto slashing

Physical Principles

BitSage Network's design principles are inspired by fundamental laws of physics and thermodynamics, creating a system that naturally tends toward efficiency, stability, and optimal resource utilization.

🔥

Thermodynamic Efficiency

Minimizes energy per compute job like Carnot engines

Energy-per-compute optimization

💾

Information Conservation

Minimizes bit erasures via caching & deduplication

Landauer's principle (T ≈ 300K)

🌐

Small-World Topology

Logarithmic path length with regional constraints

Low latency as network grows

🛡️

Fault Tolerance

Exponential reliability via redundancy & erasure codes

Exponential reliability improvement

Emergent Properties

Like phase transitions in physics, SAGE exhibits emergent behaviors from simple local interactions.

🎯

Self-Organization

Nodes auto-cluster near demand

📏

Scale Invariance

Performance stable as N grows

⚡

Queue Stability

Auto-throttle prevents saturation

Roadmap 2026

Bootstrap

VM infrastructure + initial batch jobs

• GPU-enabled VM marketplace
• Container orchestration
• Blender rendering testnet
• 50+ GPU provider nodes

Mainnet

Production VM marketplace + batch jobs

• Full VM marketplace launch
• SAGE token & governance
• Batch job verification system
• First enterprise VM customers

Expand

Geographic expansion & new workloads

• Multi-region deployment
• Small model training
• TEE-based verification
• Developer SDK & APIs

Scale

Advanced features & ecosystem growth

• Clustered node support
• Advanced verification tiers
• Cross-chain integrations
• 1000+ active nodes

References

🔐

Cryptographic Foundations

• Ben-Sasson, E. et al. (2018). STARKs
• Goldwasser, S. & Micali, S. (1989). Probabilistic Encryption
• Groth, J. (2016). Pairing-based Arguments
• Bünz, B. et al. (2020). Transparent SNARKs

💰

Economic Theory

• Roughgarden, T. (2020). Fee Mechanism Design
• Catalini, C. & Gans, J. (2020). Stablecoin Economics
• Buterin, V. (2017). Triangle of Harm
• Narayanan, A. et al. (2016). Cryptocurrency Tech

🌐

Distributed Systems

• Castro, M. & Liskov, B. (1999). Byzantine Fault Tolerance
• Lamport, L. (1998). Paxos Algorithm
• Ongaro, D. & Ousterhout, J. (2014). Raft Consensus
• Guerraoui, R. & Schiper, A. (2001). Generic Consensus

🤖

AI & Machine Learning

• Goodfellow, I. et al. (2016). Deep Learning
• Vaswani, A. et al. (2017). Attention Mechanism
• McMahan, B. et al. (2017). Federated Learning
• Li, T. et al. (2020). FL Challenges & Applications