Example
Example: Commit-Reveal Scoring (Inference Subnet)
The inference-subnet built on the Hypertensor template demonstrates a powerful example of decentralized coordination using a commit-reveal model. This model ensures fairness, verifiability, and resistance to manipulation during scoring phases for both hosters and validators.
Commit-Reveal Workflow Example
This subnet divides each epoch into phases, where nodes submit commits (cryptographic hashes of data) followed by reveals (the actual data), enabling transparent scoring without early data leaks.
Step-by-Step Process:
Validator Prompt Commit (0-15%)
At the start of the epoch, the elected validator publishes a randomized prompt tensor to the DHT.
This prompt is validated using a Pydantic schema to ensure proper format and tensor structure.
If the validator doesn't submit one by the 10% mark of the epoch, anyone can take over this task.
Randomized Seed (0-15%) ⸺ Score multiplier
At the start of the epoch, the elected validator publishes a randomized float to the DHT.
This prompt is validated using a Pydantic schema to ensure it's a float and is verified to be between the minimum and maximum values.
If the validator doesn't submit one by the 10% mark of the epoch, anyone can take over this task.
Hoster Inference & Commit Phase (15-50%)
Each hoster runs inference on the validator’s prompt.
Instead of revealing the output immediately, each hoster commits a hash of the result (e.g.,
SHA256(salt + tensor)).This hash is stored in the DHT to prevent tampering or early reveals.
Reveal Phase (50-60%)
After the commit phase ends (based on epoch progress), each hoster reveals their output and salt.
The validator also reveals its own score commit from the previous epoch (i.e., the scores it assigned to each hoster).
These reveals are matched against the original commits to verify integrity later when scored.
Scoring (60-100%)
Hosters are scored based on the distance of their output from the mean tensor across all valid hoster reveals.
Validators are scored based on:
The accuracy of their revealed scores
How closely their scores align with the previous validator's on-chain scores in relation to the attestation ratio.
Validator Commit Scores (60-100%)
Validators commit a hash of their scores to the DHT that is revealed on the following epoch.
Predicate Validator Integration
All commit and reveal actions are validated by a PredicateValidator, which:
Enforces phase correctness (e.g., commits and reveals in specific periods of an epoch)
Ensures each record (commit or reveal) is schema-compliant and authenticated
Prevents out-of-order or invalid submissions
Authentication Requirement
Every reveal must be cryptographically linked to its original commit using the same keypair. This ensures:
No node can forge or steal another node’s output
Scores are only assigned to authenticated participants
Why This Matters
This design enables decentralized, trustless coordination and evaluation of AI work — all without central servers or privileged roles.
🔐 The commit-reveal model ensures fairness, resists manipulation, and enables transparent peer scoring at scale.
Overview
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