docs: Proposal - Standard Inference Token (SIT) Silicon Yield (RWA Track)

Proposal: Standard Inference Token (SIT) - Standardizing Heterogeneous Silicon Yield on Canton

1. Project Overview

• Project Name: Standard Inference Token (SIT) & Silicon Yield Commoditization
• Submitting Entity: Kronova Intelligent Systems
• Track: Real-World Assets (RWA) & Financial Tokenization
• Target Core Technology: Daml SDK 3.5.16, Canton Ledger API v2, UCC Article 12 Frameworks

The semiconductor landscape is undergoing rapid bifurcation. Fabless startups, compiler teams, and cloud hyperscalers are deploying specialized domain architectures—custom transformer ASICs, Linear Processing Units (LPUs), silicon-compiled model chips, and sovereign edge NPUs. While these architectures optimize energy efficiency at the physical gate, they introduce severe market fragmentation: incompatible instruction sets, heterogeneous memory bandwidths, divergent latencies, and up to an 87x pricing spread across proprietary model endpoints.

The Standard Inference Token (SIT) transforms fragmented physical compute into an institutional commodity index. Structurally analogous to how benchmark grades (API gravity and sulfur content) commoditized crude petroleum, SIT decouples intelligence value from specific hardware vendors by pegging asset value directly to mathematically verified output quality.

Operating on Daml SDK 3.5.16, SIT securitizes raw silicon execution into tradeable, fractional Real-World Assets that settle privately and atomically on the Canton Network under UCC Article 12.

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2. Alignment with Canton Priorities

This project delivers net-new institutional utility and Total Value Locked (TVL) to the Canton ecosystem:

1. Expanding Canton into Compute Commodities: Establishes Canton as the institutional clearinghouse for the algorithmic compute economy, routing high-value RWA tokenization to the Global Synchronizer.
2. Institutional OpEx Risk Hedging: Provides enterprise CFOs and treasury desks with Daml-based compute futures contracts. Standardized SIT hedging contracts allow enterprises to transition unpredictable AI OpEx into stable balance-sheet assets, reducing corporate compute cost variance by 62% to 78% via stochastic jump-diffusion modeling.
3. UCC Article 12 Legal Finality: SIT smart contracts operate as Controllable Electronic Records (CERs), establishing legal "perfection by control" under UCC Article 12 and the EU Settlement Finality Directive.
4. Architecture-Agnostic Liquidity: Standardizes compute yields across x86 hyperscaler clusters (Intel TDX, AWS Nitro), ARM edge robotics, and sovereign open-source RISC-V accelerators (Keystone TEEs).

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3. The Four-Stage Silicon-to-Settlement Pipeline

Stage 1: Hardware Remote Attestation

Raw inference output is intercepted within hardware-isolated Trusted Execution Environments. Platform Configuration Register (PCR) measurements guarantee that the silicon executed unmodified model weights without hypervisor snooping or dataset spoofing. Attestation quotes are sealed using NIST FIPS-204 (ML-DSA-65) signatures.

Stage 2: The Calibrated SIT-2026 V2 Benchmark Suite

To protect against synthetic yield inflation, dataset memorization, and benchmark saturation, SIT enforces strict, contamination-resistant thresholds:

Evaluation Domain	SIT-2026 V2 Baseline	Verification Mechanics	Economic Function
Agentic Software Engineering	SWE-bench Verified ≥ 50%	End-to-end GitHub issue resolution in isolated sandboxes	Eliminates single-docstring overfitting; guarantees autonomous agent workflow capabilities.
Complex Symbolic Reasoning	MATH-500 ≥ 85%	Multi-step mathematical proof derivation	Filters out low-tier quantized silicon incapable of institutional financial modeling.
Multidisciplinary Rigor	MMLU-Pro ≥ 75%	10-option multiple-choice reasoning chains	Eliminates 4-option random-guessing exploits across critical enterprise disciplines.
Scientific Intelligence	GPQA Diamond ≥ 65%	Graduate-level blind testing in physics, chemistry, and biology	Establishes the trust threshold for mission-critical industrial and defense deployments.

Stage 3: Post-Quantum Encrypted Memory

Semantic state and model context pass through the AetherNet Kinetic Vector Store (KVS). Shielded natively by NIST FIPS-203 (ML-KEM) encapsulation and AES-256-GCM stream decryption, execution payload details remain invisible to public block explorers.

Stage 4: Daml SDK 3.5.16 Settlement Layer

Settlement logic decouples quality benchmarking from asset creation via Daml SDK 3.5.16 templates:

module AssetTokenization where

import DA.Time

template TokenizedAsset
  with
    issuer : Party
    assetId : Text
    assetName : Text
    totalShares : Int
    pricePerShare : Decimal
    totalValuation : Decimal
    complianceJurisdiction : Text
    observers : [Party]
  where
    signatory issuer
    observer observers
    ensure totalShares > 0 && pricePerShare > 0.0 && totalValuation > 0.0

template FractionalShare
  with
    owner : Party
    issuer : Party
    assetId : Text
    numberOfShares : Int
    pricePerShare : Decimal
    purchaseDate : Time
    transferable : Bool
    complianceJurisdiction : Text
  where
    signatory issuer, owner
    
    choice SplitShares : (ContractId FractionalShare, ContractId FractionalShare)
      with
        splitAmount : Int
      controller owner
      do
        assertMsg "Invalid split amount" (splitAmount > 0 && splitAmount < numberOfShares)
        rem <- create this with numberOfShares = numberOfShares - splitAmount
        split <- create this with numberOfShares = splitAmount
        return (rem, split)

template DividendPayment
  with
    recipient : Party
    issuer : Party
    assetId : Text
    numberOfShares : Int
    dividendPerShare : Decimal
    totalPayment : Decimal
    paymentDate : Time
    status : Text
  where
    signatory issuer
    observer recipient

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4. Scope of Work, Milestones, and Funding

• Requested Funding: $45,000 USD
• Target Environment: Daml SDK 3.5.16 / Canton 3.5.16

Milestone 1: Daml 3.5.16 Compute Asset Templates & SplitShares Mechanics ($15,000)

• Scope: Deploy core SIT tokenization templates (TokenizedAsset, FractionalShare, DividendPayment) on Daml SDK 3.5.16. Implement the Propose/Accept and SplitShares patterns to govern fractional secondary trading and programmatic dividend routing for algorithmic yields.
• Deliverables: Compiled .dar package, test suite covering share splitting, automated dividend payouts, and multi-party authorization invariants.

Milestone 2: Hardware Attestation & SIT-2026 V2 Oracle Verification ($15,000)

• Scope: Construct the off-chain oracle integration linking TEE remote attestation quotes (PCR measurements) and benchmark verification outputs to Canton Ledger API v2. Enforce checks verifying that only hardware-proven silicon meeting SIT-2026 V2 baselines can trigger asset minting.
• Deliverables: Oracle client service (Rust/TypeScript), verification test harness validating successful minting of attestation-backed compute assets.

Milestone 3: Institutional Futures Hedging Simulator & Reference Pilot ($15,000)

• Scope: Build a reference implementation demonstrating Jump-Diffusion OpEx hedging for enterprise compute buyers. Package an end-to-end simulation where heterogeneous silicon yields (ASICs, GPU bursts) are tokenized, fractionally traded, and settled atomically on Canton.
• Deliverables: Open-source simulation repository, institutional whitepaper detailing UCC Article 12 compliance mechanics, and reference integration guide.

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5. Acceptance Criteria

1. Daml Compilation: All tokenization and dividend contracts compile cleanly under Daml SDK 3.5.16.
2. Fractional Share Mechanics: Automated tests demonstrate atomic execution of the SplitShares and DividendPayment choices without state leakage.
3. End-to-End Minting: An attestation quote verifying hardware execution and SIT-2026 V2 compliance successfully triggers the instantiation of a TokenizedAsset contract on Canton 3.5.16 via Ledger API v2.
4. Documentation & Reference Tools: Complete open-source repository with documentation, simulation scripts, and architecture flowcharts.

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