SUBMIT A PRSUBMIT AN ISSUElast edit: Aug 13, 2025

Glossary

A

Active UID

A UID slot that is considered active within a specific subnet, allowing the associated hotkey to participate as a subnet validator or subnet miner.

See also: Subnet Miners, Subnet Validators

Archive Node

A type of public subtensor node that stores the entire blockchain history, allowing for full data access and querying capabilities.

See also: Subtensor Nodes, Managing Subtensor Connections

Axon

A module in the Bittensor API that uses the FastAPI library to create and run API servers. Axons receive incoming Synapse objects. Typically, an Axon is the entry point advertised by a subnet miner on the Bittensor blockchain, allowing subnet validators to communicate with the miner.

See also: Subnet Miners, Subnet Validators

B

Bicameral Legislature

A two-tier legislative system comprising the Triumvirate and the Senate for proposal approval.

See also: Governance, Senate

Bittensor Wallet

A digital wallet that holds the core ownership in the Bittensor network and serves as the user's identity technology underlying all operations.

See also: Wallets, Working with Keys

Block

A unit of data in the Bittensor blockchain, containing a collection of transactions and a unique identifier (block hash). A single block is processed every 12 seconds in the Bittensor blockchain.

See also: Subtensor API

Burn cost

This refers to the required amount of TAO to be recycled when creating a new subnet, i.e., cost of registering a new subnet.

See also: Burn cost

C

Coldkey

A component of a Bittensor wallet responsible for securely storing funds and performing high-risk operations such as transfers and staking. It is encrypted on the user's device. This is analogous to a private key.

See also: Coldkey-Hotkey Security, Working with Keys

Coldkey-hotkey pair

A combination of two keys, a coldkey for secure storage and high-risk operations, and a hotkey for less secure operations and network interactions.

See also: Coldkey-Hotkey Security, Working with Keys

Commit Reveal

The commit reveal feature is designed to solve the weight-copying problem by giving would-be weight-copiers access only to stale weights. Copying stale weights should result in validators departing from consensus.

See also: Commit Reveal

Consensus Score

The consensus score is calculated as the stake-weighted median of all weights assigned to a specific neuron by validators. This creates a consensus threshold that filters out outlier weights, ensuring that only weights near the median consensus are used in final rank calculations.

See also: Yuma Consensus, Consensus-Based Weights

Mathematical Definition:

For each neuron $j$, the consensus score $C_j$ is calculated as:

$$C_j = \text{weighted\_median}(\{w_{ij} \mid i \in \text{validators}\}, \{s_i \mid i \in \text{validators}\}, \kappa)$$

Where:

• $w_{ij}$ is the weight assigned by validator $i$ to neuron $j$
• $s_i$ is the stake of validator $i$
• $\kappa$ is the consensus majority ratio (typically 51%)
• $\text{weighted\_median}$ is the stake-weighted median function

Calculation Process:

1. Weight collection: Gather all weights assigned to each neuron by validators
2. Stake weighting: Apply stake weights to validator opinions
3. Median calculation: Find stake-weighted median using κ parameter (typically 51%)
4. Threshold establishment: Consensus score becomes clipping threshold for weights

Properties and Interpretation:

• Range: [0, 1] normalized values
• High Consensus: Values close to 1 indicate strong validator agreement
• Low Consensus: Values close to 0 indicate weak validator agreement
• Outlier Detection: Weights below consensus score are clipped to 0

Network Security Properties:

• Anti-Manipulation: Consensus filtering prevents weight manipulation by outliers
• Stake-Weighted: Higher stake validators have more influence in consensus
• Dynamic Threshold: Consensus adapts to changing network conditions
• Majority Rule: κ parameter controls consensus strictness (typically 51%)

Relationship to Other Metrics

Consensus vs Trust:

• Consensus: Stake-weighted median of weights (consensus threshold)
• Trust: Ratio of final rank to pre-rank (consensus alignment impact)
• Relationship: Consensus determines weight clipping, Trust measures the impact

Consensus vs Ranks:

• Consensus: Threshold for weight filtering
• Ranks: Final performance scores after consensus filtering
• Relationship: Consensus influences rank calculation through weight clipping

Consensus vs Validator Trust:

• Consensus: Per-neuron consensus thresholds
• Validator Trust: Sum of clipped weights set by each validator
• Relationship: Validator trust measures validator influence in consensus

Source:

• bittensor/bittensor/core/metagraph.py:360-372
• subtensor/pallets/subtensor/src/epoch/run_epoch.rs:595

D

Delegate

A subnet validator that receives staked TAO tokens from delegators and performs validation tasks in one or more subnets.

See also: Delegation, Managing Stake with btcli

Delegate Stake

The amount of TAO staked by the delegate themselves.

See also: Managing Stake with btcli, Managing Stake with SDK

Delegation

Also known as staking, delegating TAO to a validator (who is thereby the delegate), increases the validator's stake and secure a validator permit.

See also: Delegation, Managing Stake with btcli

Dendrite

A client instance used by subnet validators and subnet miners to transmit information to axons on subnet miners and subnet validators. Dendrites communicate with axons using the server-client (Axon-dendrite) protocol.

See also: Subnet Miners, Subnet Validators

Deregistration

The process of removing a subnet miner or a subnet validator from the subnet due to poor performance.

See also: Miner Deregistration, Subnet Miners

E

EdDSA Cryptographic Keypairs

A cryptographic algorithm used to generate public and private key pairs for coldkeys and hotkeys in the Bittensor wallet.

See also: Working with Keys, Coldkey-Hotkey Security

Effective stake

The total staked TAO amount of a delegate, including their own TAO tokens and those delegated by nominators.

See also: Managing Stake with btcli, Managing Stake with SDK

Emission

Every block, currency is injected into each subnet in Bittensor, and every tempo (or 360 blocks), it is extracted by participants (miners, validators, stakers, and subnet creators).

Emission is this process of generating and allocating currency to participants. The amount allocated to a given participant over some duration of time is also often referred to as 'their emissions' for the period.

Emissions are protected from manipulation through Exponential Moving Average (EMA) mechanisms that smooth both validator-miner bond evolution and subnet price effects.

See also: Emissions, Exponential Moving Average (EMA)

Encrypting the Hotkey

An optional security measure for the hotkey.

See also: Coldkey-Hotkey Security, Working with Keys

Exponential Moving Average (EMA)

A weighted moving average that prioritizes recent observations while exponentially decreasing the weight of older data points. In Bittensor, EMA is used in two critical stability mechanisms:

1. Validator-Miner Bond Smoothing: Smooths the evolution of bonds between validators and miners over time, rewarding early discovery while preventing abrupt manipulation attempts. Has two modes:

   • Basic Mode: Single α ≈ 0.1 (~7-22 blocks for significant changes)
   • Liquid Alpha Mode: Dynamic α range 0.7-0.9 based on consensus alignment (~1-13 blocks depending on consensus)

2. Subnet Price Emission Smoothing: Protects emissions from price manipulation by extremely slowly incorporating price changes into emission calculations (α ≈ 0.000003, ~30 days for 50% adjustment)

Formula: EMA(t) = α × Current_Value + (1 - α) × EMA(t-1)

Key Properties:

• Lower α = slower adaptation, higher stability
• Higher α = faster adaptation, lower stability
• Bittensor prioritizes stability with conservative α values

See also: Understanding Exponential Moving Averages, Consensus-based Weights, Validator-Miner Bonds, Emission

External Wallet

A Bittensor wallet created through the Bittensor website or using a tool like subkey, allowing users to use TAO without installing Bittensor.

See also: Wallets, Installation

F

Fast blocks

A development-only configuration that accelerates block production to 250ms intervals, enabling rapid local testing and immediate execution of on-chain operations.

See also: Create a local instance

H

Hotkey

A component of a Bittensor wallet responsible for less secure operations such as signing messages into the network, secure a UID slot in a subnet, running subnet miners and subnet validators in a subnet. It can be encrypted or unencrypted, but is unencrypted by default. The terms "account" and "hotkey" are used synonymously.

See also: Coldkey-Hotkey Security, Working with Keys

Hotkey-Coldkey Pair

Authentication mechanism for delegates and nominators and for delegates participating in the Senate.

See also: Coldkey-Hotkey Security, Working with Keys

I

Immunity Period

A grace period granted to newly registered neurons during which they are protected from deregistration due to poor performance. The immunity period allows new miners and validators time to establish themselves and improve their performance before becoming eligible for pruning. The default period being is 4096 blocks (~13.7 hours), but can be configured by the subnet creator.

See also: Miner Deregistration, Validator Deregistration, Subnet Hyperparameters

Incentives

A portion of the TAO emission received by the subnet miners when they provide valuable services and compete for UID slots in a subnet.

See also: Emissions, Anatomy of Incentive Mechanism

Incentive Mechanism

A system that drives the behavior of subnet miners and governs consensus among subnet validators in a Bittensor subnet. Each subnet has its own incentive mechanism, which should be designed carefully to promote desired behaviors and penalize undesired ones.

See also: Anatomy of Incentive Mechanism, Understanding Subnets

Issuance

The total amount of TAO circulating in the Bittensor network. Includes TAO that is help in wallets and subnet liquidity pools, as well as TAO that is locked as subnet registration fees.

This can be viewed on Bittensor explorers such as TAO.app and TAOstats.

To query it directly from the change, see: Subtensor Storage Query Example: Total Issuance

See also: Recycling, burning, and locking

L

Lite Node

A type of public subtensor node that stores limited blockchain data and relies on archive nodes for full historical data.

See also: Subtensor Nodes, Managing Subtensor Connections

Local Blockchain

A private blockchain used for developing and testing subnet incentive mechanisms. A local blockchain is not public and is isolated from any Bittensor network.

See also: Local Build, Create a Subnet

Local Wallet

A Bittensor wallet created on the user's machine, requiring the installation of Bittensor.

See also: Wallets, Installation

Loss Function

In the context of machine learning, a mathematical function that measures the difference between the predicted output and the ground truth. In Bittensor, incentive mechanisms act as loss functions that steer subnet miners towards desirable outcomes.

See also: Anatomy of Incentive Mechanism, Understanding Subnets

M

Mainchain

The primary Bittensor blockchain network, used for production purposes and connected to lite or archive nodes.

See also: Bittensor Networks, Subtensor Nodes

Metagraph

A data structure that contains comprehensive information about the current state of a subnet, including detailed information on all the nodes (neurons) such as subnet validator stakes and subnet weights in the subnet. Metagraph aids in calculating emissions.

See: The Subnet Metagraph

Miner Deregistration

The process of removing a poor-performing subnet miner from a UID slot, making room for a newly registered miner.

See also: Miner Deregistration

Mnemonic

A sequence of words used to regenerate keys, in case of loss, and restore coldkeys and hotkeys in the Bittensor wallet.

See also: Handle Seed Phrase, Working with Keys

N

NaCl Format

A secure encryption format, using the NaCl library, used for updating legacy Bittensor wallets to improve security.

See also: Working with Keys, Coldkey-Hotkey Security

Netuid

A unique identifier assigned to a subnet within the Bittensor network.

See also: Understanding Subnets, Working with Subnets

Neuron

The basic computing node in a Bittensor subnet, representing a node in a neural network. Neurons can be either subnet validators or subnet miners, each identified by a unique UID within their subnet and associated with a hotkey-coldkey pair for authentication and operations.

Neurons participate in the network through axon servers (miners) and dendrite clients (validators), exchanging synapse objects to perform subnet-specific tasks. Their performance is measured through metrics like rank, trust, consensus, and incentive scores, which determine emissions and validator permits.

See also: Understanding Neurons, Subnet Validators, Subnet Miners, NeuronInfo class

N

Nominate

The process of a delegate registering themselves as a candidate for others to stake their $TAO to.

Nominator

Another term for a delegator. A subnet validator who nominates their own hotkey as a delegate, allowing others to delegate their TAO to the nominator's hotkey.

Nominator (Delegator)

A TAO holder who delegates their stake.

Non-fast blocks

A development-only configuration that adheres to Subtensor’s default 12-second block interval, simulating production timing for features like delayed subnet activation.

See also: Create a local instance

O

Objective Function

In the context of machine learning and subnet operations, this refers to the goal that the subnet is continuously optimizing for, through its incentive mechanism.

See also: Anatomy of Incentive Mechanism, Understanding Subnets

P

Private Key

A private component of the cryptographic key pair, crucial for securing and authorizing transactions and operations within the Bittensor network.

See also: Working with Keys, Coldkey-Hotkey Security

Proposal

A suggestion or plan put forward by the Triumvirate for the Senate to vote on.

See also: Governance, Senate

Proposal hash

A unique identifier for a proposal used in the voting process.

See also: Governance, Senate

Public Key

A cryptographic key that is publicly available and used for verifying signatures, encrypting messages, and identifying accounts in the Bittensor network. This is the publicly shareable part of the cryptographic key pair associated with both the coldkey and hotkey, allowing others to securely interact with the wallet.

See also: Working with Keys, Coldkey-Hotkey Security

Public Subtensor

A publicly accessible node in the Bittensor network that can be run as a lite node or an archive node and synchronized with either the mainchain or testchain.

See also: Subtensor Nodes, Managing Subtensor Connections

R

RAO

A denomination of TAO, representing one billionth (10^-9) of a TAO.

See also: Emissions

Rank

This metagraph property represents the final aggregate judgment of a each miner, computed by Yuma Consensus alogirithm operating over the miner-ratings submitted by a subnet's validators each tempo. The final rank score represent a miner's performance after any outlier weights set by validators have been removed through consensus clipping. This ensures that only weights near the median consensus are used in final calculations.

Ranks are calculated as the stake-weighted sum of consensus-clipped weights and directly determine emissions to miners.

See also: Emissions, Yuma Consensus, Subnet Metagraph

Relationship to Other Metrics:

• Ranks vs Consensus: Ranks are calculated using consensus-clipped weights
• Ranks vs Trust: Trust measures how much consensus clipping affected the rank
• Ranks vs Incentive: Ranks are normalized to become incentive values
• Ranks vs Validator Trust: Validator trust measures validator influence in consensus

Calculation Process:

1. Pre-ranks: Initial stake-weighted sum of all weights before consensus filtering
2. Consensus calculation: Stake-weighted median of weights per neuron (consensus threshold)
3. Weight clipping: Weights clipped at consensus threshold to remove outliers
4. Final ranks: Stake-weighted sum of clipped weights (the rank value)

Properties and Interpretation:

• Range: [0, 1] normalized values after final normalization
• High Rank: Values close to 1 indicate strong consensus-based performance
• Low Rank: Values close to 0 indicate weak consensus-based performance
• Incentive Distribution: Ranks directly determine incentive allocation to miner neurons

Network Security Properties:

• Consensus-Based: Ranks reflect network consensus rather than individual validator opinions
• Outlier Protection: Consensus clipping prevents manipulation by outlier weights
• Stake-Weighted: Higher stake validators have more influence in rank calculation
• Dynamic Updates: Ranks are recalculated every epoch based on current network state

Mathematical Definition: For each neuron $j$, the rank $R_j$ is calculated as: $R_j = \sum_{i \in \text{validators}} S_i \cdot \overline{W_{ij}}$

Where:

• $S_i$ is the stake of validator $i$
• $\overline{W_{ij}}$ is the consensus-clipped weight from validator $i$ to neuron $j$
• The sum is taken over all validators in the subnet

Source:

• bittensor/bittensor/core/metagraph.py:325-331
• subtensor/pallets/subtensor/src/epoch/run_epoch.rs:605

Recycling and burning

Tokens (TAO and subnet-specific alpha) can be 'removed from circulation', meaning these tokens exist in neither wallet nor liquidity pool, and cannot be transacted. This can happen in two ways:

• When tokens are recycled, they are subtracted from the chain's record of token issuance (TotalIssuance), so effectively the same quantity of tokens can be emitted again.

• In contrast, when tokens are burned they exist in no wallet and no pool and can no longer be transacted; however they are still included in the record of token issuance, so they will not be re-emitted, and in effect will forever remain as a quantity of missing tokens, a difference between issuance and the effective quantity in circulation.

Recycling

Tokens are recycled in several cases in Bittensor operations:

• All transaction fees are recycled: When transaction fees are collected, they are deducted from TotalIssuance, effectively recycling them back into the system for future emission. See Transaction Fees in Bittensor
• Subnet Creation fees: When a new subnet is created, the cost is recycled, except for one TAO, which is used to initialize the subnet's TAO liquidity pool.
• Neuron Registration fees: When a user registers a hotkey on a subnet to participate as a miner or validator, they are charged a registration fee in TAO. Alpha tokens worth the current swap value of the fee are taken from the subnet's alpha liquidity pool and recycled.
• Extrinsic transaction: Users can manually recycle alpha tokens using the recycle_alpha extrinsic, which reduces both the user's stake and the subnet's SubnetAlphaOut tracker.

Burning

Subnet-specific alpha tokens are burned in several contexts:

• Creator emissions burning: Alpha emissions for mining on a subnet are automatically burned if they are emitted to the hotkey with creator permissions on the subnet. This allows validators to burn some or all of the subnet's emissions to prevent token inflation (by weighting the subnet creator hotkey).
• Extrinsic transaction: Alpha can be burned on demand using the burn_alpha Subtensor extrinsic. Unlike recycling, burning does not reduce SubnetAlphaOut.
• Root Subnet automated burning: Subnet Zero (Root Subnet) alpha tokens are burned under specific economic conditions to maintain system stability.

Regenerating a Key

The process of recreating a lost or deleted coldkey or hotkey using the associated mnemonic.

See also: Handle Seed Phrase, Working with Keys

Register

The process of registering keys with a subnet and purchasing a UID slot.

See also: Subnet Miners, Subnet Validators, Working with Subnets

Root Subnet/Subnet Zero

Subnet Zero a.k.a. the root subnet is a special subnet. No miners can register on subnet zero, and no validation work is performed. However validators can register, and $\tau$-holders can stake to those validators, as with any other subnet. This offers a mechanism for $\tau$-holders to stake $\tau$ into validators in a subnet-agnostic way. This works because the weight of a validator in a subnet includes both their share of that subnet's $\alpha$ and their share of staked TAO in Subnet Zero.

S

SS58 Encoded

A compact representation of public keys corresponding to the wallet's coldkey and hotkey, used as wallet addresses for secure TAO transfers.

See also: Working with Keys, Wallets

Slippage

In the context of an automated market maker (AMM), slippage is the impact on the tokens acquired in a trade due to the change in price from the trade transaction itself.

In Bittensor, each subnet's alpha token is traded on a constant product AMM. When you stake TAO to receive alpha (or unstake alpha to receive TAO), your transaction changes the token price, resulting in receiving less than the current market rate X the quantity of the token you are inputting.

Larger transactions cause more slippage. Bittensor provides slippage protection through tolerance limits and partial execution options.

See: Understanding Pricing and Anticipating Slippage

Senate

A group of elected delegates formed from the top K delegate hotkeys, responsible for approving or disapproving proposals made by the Triumvirate.

See also: Senate, Governance

Stake

The amount of currency tokens delegated to a validator UID in a subnet. Includes both self-stake (from the validator's own cold-key) and stake delegated from others.

Stake determines a validator's weight in consensus as well as their emissions.

See also: Managing Stake with btcli, Managing Stake with SDK, Delegation

Stake Weight

The computed total stake value for a validator that determines their consensus power and emissions in a subnet. Stake weight combines a validator's alpha stake and TAO stake using the TAO weight parameter to calculate their total influence in the network.

See also: TAO Weight, Understanding Subnets

Mathematical Definition: For a validator with alpha stake $\alpha$ and TAO stake $\tau$, the stake weight $W$ is calculated as:

$$W = {\alpha + \tau \ \times w_{\tau}}$$

Where $w_{\tau}$ is the global TAO weight parameter (currently 0.18)

A validator's relative influence in a subnet is calculated as:

$$\text{Relative Stake Weight} = \frac{\text{Stake Weight}_i}{\sum_{v \in \text{validators}} \text{Stake Weight}_v}$$

Consensus Power:

• Weight Setting: Higher stake weight means more influence when setting weights
• Validator Permits: Stake weight determines eligibility for validator permits
• Bond Formation: Stake weight influences bond calculations and retention

Validator Emissions:

• Relative Distribution: Higher stake weight -> higher emission share

Code References:

• Yuma Consensus: subtensor/pallets/subtensor/src/epoch/run_epoch.rs:530
• Validator dividend distribution: subtensor/pallets/subtensor/src/coinbase/run_coinbase.rs:165

Staking

The process of attaching TAO to a validator hotkey, i.e., locking TAO to a subnet validator's hotkey to increase their total stake and increase their consensus power and share of dividends.

See also: Managing Stake with btcli, Managing Stake with SDK, Delegation

Subnet

A Bittensor subnet is an incentive-based competition market that produces a specific kind of digital commodity. It consists of a community of miners that produce the commodity, and a community of validators that measures the miners' work to ensure its quality.

See also: Understanding Subnets, Working with Subnets, Create a Subnet

Subnet Incentive Mechanism

The framework that governs the behavior of subnet miners and ensures consensus among subnet validators by promoting desirable actions and penalizing undesired ones.

See also: Anatomy of Incentive Mechanism, Understanding Subnets

Subnet Miner

The task-performing entity within a Bittensor subnet. A subnet miner is a type of node in a Bittensor subnet that is connected only to subnet validators. Subnet miners are isolated from the external world and communicate bidirectionally with subnet validators. A subnet miner is responsible for performing tasks given to them by the subnet validators in that subnet.

See also: Subnet Miner Documentation

Subnet Creator

The individual or entity responsible for defining the specific digital task to be performed by subnet miners, implementing an incentive mechanism, and providing sufficient documentation for participation in the subnet.

See also: Create a Subnet, Subnet Creators btcli Guide

Subnet Protocol

A unique set of rules defining interactions between subnet validators and miners, including how tasks are queried and responses are provided.

See also: Understanding Subnets, Working with Subnets

Subnet scoring model

A component of the incentive mechanism that defines how subnet miners' responses are evaluated, aiming to align subnet miner behavior with the subnet's goals and user preferences. It is a mathematical object that converts miner responses into numerical scores, enabling continuous improvement and competition among miners.

See also: Anatomy of Incentive Mechanism, Understanding Subnets

Subnet Task

A key component of any incentive mechanism that defines the work the subnet miners will perform. The task should be chosen to maximize subnet miner effectiveness at the intended use case for the subnet.

See also: Understanding Subnets, Anatomy of Incentive Mechanism

Subnet Weights

The importance assigned to each subnet determined by relative price among subnets and used to determine the percentage emissions to subnets.

See also: Emissions, Consensus-Based Weights

Subtensor

Subtensor is Bittensor's layer 1 blockchain based on substrate (now PolkadotSDK). This serves Bittensor as a system of record for transactions and rankings, operates Yuma Consensus, and emits liquidity to participants to incentivize their participation in network activities.

The Bittensor SDK offers the bittensor.core.subtensor and bittensor.core.async_subtensor modules to handle Subtensor blockchain interactions.

See also: Subtensor API, Subtensor Nodes, Managing Subtensor Connections

Sudo

A privileged key for administrative actions, replaced by governance protocol for enhanced security.

See also: Governance, btcli Permissions

Synapse

A data object used by subnet validators and subnet miners as the main vehicle to exchange information. Synapse objects are based on the BaseModel of the Pydantic data validation library.

See also: Subnet Miners, Subnet Validators

T

TAO ($\tau$)

The cryptocurrency of the Bittensor network, used to incentivize participation in network activities (mining, validation, subnet creation and management). A single TAO is newly created (i.e., minted) every 12 seconds on the Bittensor blockchain.

See also: Emissions, Wallets

TAO Weight

A global parameter (currently set to 0.18) that determines the relative influence of TAO stake versus alpha stake when calculating a validator's total stake weight, a critical value that influence's a validator's consensus power and emissions.

See also: Stake Weight

Tempo

A 360-block period during which the Yuma Consensus calculates emissions to subnet participants based on the latest available ranking weight matrix. A single block is processed every 12 seconds, hence a 360-block tempo occurs every 4320 seconds or 72 minutes.

See also: Yuma Consensus, Emissions

Transfer

The process of sending TAO tokens from one wallet address to another in the Bittensor network.

See also: Wallets, Working with Keys

Triumvirate

A group of three Opentensor Foundation employees responsible for creating proposals.

See also: Governance, Senate

Trust

In the Yuma Consensus algorithm, trust represents how much a miner's rank was affected by consensus clipping. Trust is calculated as the ratio of final rank to pre-rank. It represents how much of the original validator support survived the consensus clipping process, providing insight into whether a neuron received controversial or outlier weight assignments.

See also: Yuma Consensus, Subnet Metagraph

Mathematical Definition: For each neuron $j$, the trust $T_j$ is calculated as:

$$T_j = \frac{R_j}{P_j}$$

Where:

• $R_j$ is the final rank after consensus clipping
• $P_j$ is the pre-rank before consensus clipping
• The ratio indicates the proportion of original support that survived consensus filtering

Interpretation:

• Range: [0, 1] where 1.0 indicates perfect consensus alignment
• Trust = 1.0: Neuron's rank unchanged by consensus (high consensus alignment)
• Trust < 1.0: Neuron's rank reduced by consensus clipping (lower value means more reduction)
• Trust = 0.0: Neuron's rank eliminated by consensus (no consensus support)

Calculation Process:

1. Pre-ranks calculation: $P_j = \sum_{i} S_i \cdot W_{ij}$ (stake-weighted sum of all weights)
2. Consensus filtering: Weights clipped at consensus threshold to remove outliers
3. Final ranks calculation: $R_j = \sum_{i} S_i \cdot \overline{W_{ij}}$ (stake-weighted sum of clipped weights)
4. Trust calculation: $T_j = R_j / P_j$ (ratio of final to pre-rank)

Relationship to Other Metrics:

• Trust vs Consensus: Trust measures the impact of consensus filtering
• Trust vs Ranks: Trust is the ratio of final rank to pre-rank
• Trust vs Validator Trust: Trust is per-neuron, Validator Trust is per-validator
• Trust vs Incentive: Trust influences incentive through consensus mechanisms

Metric Comparison Table

Metric	Purpose	Calculation	Range	Interpretation
Consensus	Consensus threshold	Stake-weighted median of weights per neuron	[0, 1]	Higher = stronger validator agreement
Ranks	Performance scoring	Stake-weighted sum of clipped weights	[0, 1]	Higher = better performance after consensus
Trust	Consensus alignment	Final rank / Pre-rank	[0, 1]	1.0 = no clipping, < 1.0 = some clipping
Validator Trust	Validator influence	Sum of clipped weights per validator	[0, 1]	Higher = more consensus-aligned validator

Source:

• bittensor/bittensor/core/metagraph.py:380-393
• subtensor/pallets/subtensor/src/epoch/run_epoch.rs:608

The relationship between these metrics creates a feedback loop: consensus determines weight clipping, which affects ranks and trust, which influences validator trust, which feeds back into future consensus calculations. This system ensures that the network rewards neurons with strong validator agreement while penalizing those with controversial or outlier weight assignments, creating a robust mechanism for maintaining network quality and security.

U

UID Slot

A position occupied by a subnet miner or subnet validator within a subnet, identified by a unique UID. The UID is assigned to a hotkey when it is registered in a subnet, allowing the hotkey to participate as a subnet validator or subnet miner.

See also: Subnet Miners, Subnet Validators, Working with Subnets

Unstaking

The process of detaching TAO from a validator hotkey.

See also: Staking/Delegation overview

V

Validator Permit

A boolean flag indicating whether a specific neuron has validation rights within a subnet. Validator permits are awarded to the top K neurons by stake weight and are required for setting weights and participating in consensus.

See also: VPermit, Validator Requirements, Stake Weight

VPermit

A list of subnet IDs (netuids) indicating which subnets a delegate is authorized to validate on. VPermits are delegate-level permissions that aggregate individual validator permits across multiple subnets, allowing delegates to participate in validation activities on specific subnets.

See also: Validator Permits, Delegation, Validator Requirements

Validator

A type of node in a subnet that creates tasks, evaluates the performance of subnet miners and sets weights based on their output. A subnet validator is connected only to subnet miners and to the external world. Subnet validators receive inputs from the external world and communicate bidirectionally with subnet miners.

See also: Subnet Validators, Validators btcli Guide

Validator Trust

A specialized trust metric for validator neurons that measures their influence in the consensus process. Validator trust is calculated as the sum of all clipped weights set by each validator across all neurons, indicating how much weight a validator successfully contributed to consensus.

See also: Yuma Consensus, Subnet Metagraph, Validator-Miner Bonds

Basic Concept: Validator trust specifically measures validator neurons' influence in the consensus process. It represents how much weight each validator successfully contributed to the consensus after weight clipping, providing insight into validator alignment with network consensus.

Mathematical Definition: For each validator $i$, the validator trust $T_{vi}$ is calculated as: $T_{vi} = \sum_{j \in \text{neurons}} \overline{W_{ij}}$

Where:

• $\overline{W_{ij}}$ is the consensus-clipped weight from validator $i$ to neuron $j$
• The sum is taken over all neurons in the subnet
• Validator trust measures the total influence a validator has in consensus

Calculation Process:

1. Weight setting: Validators set weights to all neurons in the subnet
2. Consensus calculation: Stake-weighted median of weights per neuron (consensus threshold)
3. Weight clipping: Weights clipped at consensus threshold to remove outliers
4. Validator trust calculation: Sum of all clipped weights set by each validator

Properties and Interpretation:

• Range: [0, 1] normalized values
• High Validator Trust: Values close to 1 indicate strong consensus alignment
• Low Validator Trust: Values close to 0 indicate outlier weight assignments
• Validator Influence: Higher validator trust means more influence in consensus decisions

Network Security Properties:

• Consensus Alignment: Validator trust measures how well validators align with consensus
• Outlier Detection: Low validator trust indicates potential manipulation attempts
• Validator Quality: High validator trust indicates quality validation services
• Economic Incentives: Validator trust influences validator rewards and bond retention

Source:

• bittensor/bittensor/core/metagraph.py:397-409
• subtensor/pallets/subtensor/src/epoch/run_epoch.rs:600

Relationship to Other Metrics:

• Validator Trust vs Trust: Validator trust is per-validator, Trust is per-neuron
• Validator Trust vs Consensus: Validator trust measures validator influence in consensus
• Validator Trust vs Ranks: Validator trust influences rank calculation through consensus
• Validator Trust vs Bonds: Validator trust affects bond retention and validator permits

Validator-Miner Bonds

Bonds represent the "investment" a validator has made in evaluating a specific miner. This bonding mechanism uses Exponential Moving Average (EMA) to smooth bond evolution over time, integral to the Yuma Consensus' design intent of incentivizing high-quality performance by miners, and honest evaluation by validators.

Bond Formation Process:

1. Instant Bond Calculation: The instant bond $\Delta B_{ij}$ of validator $i$ to miner $j$ is calculated as: $\Delta B_{ij} = \frac{S_i \cdot \widetilde{W_{ij}}}{\sum_{k \in \mathbb{V}} S_k \cdot \widetilde{W_{kj}}}$

Where:

• $S_i$ is validator $i$'s stake
• $\widetilde{W_{ij}}$ is the bond-weight (penalty-adjusted weight)
• The denominator normalizes by the total bond-weight for miner $j$ across all validators

2. Bond-Weight Calculation: Bond-weights are penalized when validators overstate miner performance: $\widetilde{W_{ij}} = (1-\beta)W_{ij} + \beta\overline{W_{ij}}$

Where:

• $W_{ij}$ is the original weight set by validator $i$ for miner $j$
• $\overline{W_{ij}}$ is the consensus-clipped weight
• $\beta$ is the bonds penalty factor (configurable hyperparameter)

3. Exponential Moving Average (EMA) Bonds: Instant bonds are smoothed over time using EMA to prevent abrupt changes: $B_{ij}^{(t)} = \alpha \Delta B_{ij} + (1-\alpha)B_{ij}^{(t-1)}$

Where $\alpha$ is the EMA smoothing factor (see Exponential Moving Average for details).

Bond Mechanics and Design:

Consensus Alignment:

• Validators who stay near consensus build stronger EMA bonds
• Bonds are penalized when validators overstate miner performance
• The EMA smooths out abrupt swings in validator behavior
• Bonds incentivize consistent alignment with consensus

Bond Retention:

• Neurons retain bonds only if they keep validator permits
• Bonds are cleared when neurons lose validator permits
• Bonds are stored as sparse matrices in blockchain state

Bond Decay:

• Bonds decay over time using EMA with the bonds_moving_avg parameter
• Higher decay rates (larger α) make bonds more responsive to recent performance
• Lower decay rates (smaller α) allow bonds to persist longer

Economic Alignment:

• Bonds create long-term relationships between validators and miners
• Validators are incentivized to discover and support promising miners early
• Bond strength reflects validator confidence in miner performance

Dynamic Adjustment:

• Bonds adapt to changing network conditions and consensus
• EMA smoothing prevents exploitation of rapid bond changes
• Bonds provide stability while allowing for network evolution

Retrieval:

• Bonds can be queried via the bonds() method in the Subtensor API
• Metagraph includes bonds matrix accessible via metagraph.B property
• Bonds are included in neuron information structures

Related hyperparameters:

• bonds_penalty: Controls penalty for out-of-consensus weights (0-65535)
• bonds_moving_avg: Controls bond decay rate (typically 900,000)
• liquid_alpha_enabled: Enables dynamic alpha adjustment for bonds

Validator Permits:

• Bonds are retained only by neurons with validator permits
• Loss of validator permit clears all bonds for that neuron
• Bonds align with permit retention for economic security

Emission Distribution:

• Bonds directly determine validator emission shares
• Strong bonds lead to higher validator rewards
• Bonds create market-based incentive alignment

Code References:

• [Bond calculation in epoch execution]https://github.com/opentensor/subtensor/blob/main/pallets/subtensor/src/epoch/run_epoch.rs:631)
• [EMA bond computation]https://github.com/opentensor/subtensor/blob/main/pallets/subtensor/src/epoch/math.rs:1475)
• [Bonds API method]https://github.com/opentensor/subtensor/blob/main/bittensor/core/async_subtensor.py:931)
• [Bonds storage definition]https://github.com/opentensor/subtensor/blob/main/pallets/subtensor/src/lib.rs:1560)

See also: Yuma Consensus, Emissions

Validator Take %

The percentage of emissions a validator takes, of the portion that depends on delegated stake (not including their emissions in proportion to their own self-stake), before the remainder is extracted back to the stakers.

Effectively, this represents the fee percentage that validators charge delegators for validation services.

See also: Emissions

W

Wallet Address

A unique identifier derived from the public key, used as a destination for sending and receiving TAO tokens in the Bittensor network.

See also: Wallets, Working with Keys

Wallet Location

The directory path where the generated Bittensor wallets are stored locally on the user's machine.

See also: Wallets, Installation

Weight Matrix

A matrix formed from the ranking weight vectors of all subnet validators in a subnet, used as input for the Yuma Consensus module to calculate emissions to that subnet.

See also: Yuma Consensus, Consensus-Based Weights

Weight Vector

A vector maintained by each subnet validator, with each element representing the weight assigned to a subnet miner based on its performance.

The ranking weight vectors for each subnet are transmitted to the blockchain, where they combine to form the weight matrix that is input for Yuma Consensus.

See also: Consensus-Based Weights, Yuma Consensus

Y

Yuma Consensus

The consensus mechanism in the Bittensor blockchain that computes emissions to participants.

See also: Yuma Consensus