Overview
Registration Activity
Neuron registration trends
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −13.10%: protocol buys +4.23%, root sells −2.16%, users net −15.03%, cross-subnet −0.14%, unattributed −0.0004%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-06 UTC, as of ≈22:45 UTC (block 9,450,120)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
The maximum clique problem is computationally expensive and requires significant algorithmic innovation to achieve optimal solutions within practical time constraints. Traditional centralized approaches struggle to scale across diverse problem instances, and researchers lack efficient mechanisms to discover novel solution strategies at scale.
A decentralized network can distribute graph problems to multiple solvers simultaneously, evaluate solutions using dual metrics that reward both accuracy and methodological diversity, and automatically adjust participant incentives based on historical performance. This structure encourages innovation by crediting solvers who develop unique approaches while maintaining performance standards.
CliqueAI implements this through a four-stage mechanism: validators curate graph problems of varying difficulty from a shared database (each problem used once across the network), allocate them to miners with difficulty-adjusted probability, score solutions on optimality and algorithmic diversity using normalized metrics, and update miner reputation via exponential moving averages.
Problems range from medium (290–300 vertices) to very hard (690–700 vertices) with time limits from 6 to 30 seconds.
Auto-generated from the GitHub repository and may not reflect the latest changes.
CliqueAI is an AI-powered maximum clique solver network that uses a four-stage autonomous mechanism for distributed graph problem solving. Validators curate complex graph problems, intelligently allocate them to miners with difficulty-adjusted sampling, score solutions based on optimality and algorithmic diversity, and continuously adjust miner reputation using exponential moving averages.
The subnet operates as a four-stage problem distribution mechanism: (1) Problem Selection - Validator selects graph problems (medium/hard/very hard difficulty) from a database, each problem used at most once. (2) Miner Selection - Validator filters eligible miners (excluding validators and recently-updated miners) and samples each with difficulty-adjusted probability (lower probability for harder problems). (3) Scoring - Validator evaluates all miner solutions on the same problem using dual-metric scoring combining optimality and diversity. (4) Weight Setting - Validator updates miner reputation scores using debiased exponential moving averages that emphasize recent performance (α=0.01, ~69-sample half-life). Scores and detailed results are logged to Weights & Biases for monitoring.
The scoring mechanism has three main components: (1) Optimality Score (ω): evaluates solution validity and quality relative to best solution found by other miners on the same problem, with exponential penalty for relative performance gap. (2) Diversity Score (δ): measures uniqueness of solutions, giving higher scores to miners whose solutions differ from others. (3) Final Score: f(m,p) = ωₙ(m)(1+d(p)) + δₙ(m), where d(p) is problem difficulty, emphasizing performance over uniqueness while scaling difficulty emphasis. Miner ratings are updated via debiased EMA: ŷₜ = (αf(m,p) + (1-α)ŷₜ₋₁) / (1-(1-α)ᵗ).
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
CliqueAI - AI-Powered Maximum Clique Solver Network
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 28 Sep 2026
Clone the repository
git clone https://github.com/toptensor/CliqueAI.git && cd CliqueAI
Install dependencies
pip install -r requirements.txt
Start the miner with wallet credentials and network configuration
./start_miner.sh --wallet.name <coldkey-name> --wallet.hotkey <hotkey-name> --subtensor.network finney --netuid 83 --logging.info --axon.ip <your-miner-ip> --axon.port <your-miner-port>
Clone the repository
git clone https://github.com/toptensor/CliqueAI.git && cd CliqueAI
Install dependencies
pip install -r requirements.txt
Start the validator with wallet credentials and network configuration
./start_validator.sh --wallet.name <coldkey-name> --wallet.hotkey <hotkey-name> --subtensor.network finney --netuid 83 --logging.info --axon.ip <your-validator-ip> --axon.port <your-validator-port>
| Day (UTC) | Share |
|---|---|
| 2026-11-05 | 0.37% |
| 2026-11-04 | 0.38% |
| 2026-11-03 | 0.40% |
| 2026-11-02 | 0.40% |
| 2026-11-01 | 0.40% |
| 2026-10-31 | 0.40% |
| 2026-10-30 | 0.38% |
| 2026-10-29 | 0.37% |
| 2026-10-28 | 0.39% |
| 2026-10-27 | 0.39% |
| 2026-10-26 | 0.39% |
| 2026-10-25 | 0.40% |
| 2026-10-24 | 0.40% |
| 2026-10-23 | 0.46% |
| 2026-10-22 | 0.46% |
| 2026-10-21 | 0.44% |
| 2026-10-20 | 0.45% |
| 2026-10-19 | 0.46% |
| 2026-10-18 | 0.47% |
| 2026-10-17 | 0.48% |
| 2026-10-16 | 0.49% |
| 2026-10-15 | 0.50% |
| 2026-10-14 | 0.52% |
| 2026-10-13 | 0.50% |
| 2026-10-12 | 0.51% |
| 2026-10-11 | 0.51% |
| 2026-10-10 | 0.52% |
| 2026-10-09 | 0.52% |
| 2026-10-08 | 0.52% |
| 2026-10-07 | 0.53% |
| 2026-10-06 | 0.57% |
| 2026-10-05 | 0.57% |
| 2026-10-04 | 0.54% |
| 2026-10-03 | 0.57% |
| 2026-10-02 | 0.60% |
| 2026-10-01 | 0.56% |
| 2026-09-30 | 0.62% |
| 2026-09-29 | 0.57% |
| 2026-09-28 | 0.56% |
| 2026-09-27 | 0.57% |
| 2026-09-26 | 0.69% |
| 2026-09-25 | 0.63% |
| 2026-09-24 | 0.60% |
| 2026-09-23 | 0.61% |
| 2026-09-22 | 0.63% |
| 2026-09-21 | 0.64% |
| 2026-09-20 | 0.67% |
| 2026-09-19 | 0.66% |
| 2026-09-18 | 0.63% |
| 2026-09-17 | 0.65% |
| 2026-09-16 | 0.70% |
| 2026-09-15 | 0.69% |
| 2026-09-14 | 0.73% |
| 2026-09-13 | 0.73% |
| 2026-09-12 | 0.73% |
| 2026-09-11 | 0.71% |
| 2026-09-10 | 0.70% |
| 2026-09-09 | 0.36% |
| 2026-09-08 | 0.00% |
| 2026-09-07 | 0.00% |
| 2026-09-06 | 0.00% |
| 2026-09-05 | 0.00% |
| 2026-09-04 | 0.00% |
| 2026-09-03 | 0.00% |
| 2026-09-02 | 0.00% |
| 2026-09-01 | 0.00% |
| 2026-08-31 | 0.00% |
| 2026-08-30 | 0.00% |
| 2026-08-29 | 0.00% |
| 2026-08-28 | 0.00% |
| 2026-08-27 | 0.00% |
| 2026-08-26 | 0.00% |
| 2026-08-25 | 0.00% |
| 2026-08-24 | 0.00% |
| 2026-08-23 | 0.00% |
| 2026-08-22 | 0.00% |
| 2026-08-21 | 0.00% |
| 2026-08-20 | 0.00% |
| 2026-08-19 | 0.00% |
| 2026-08-18 | 0.00% |
| 2026-08-17 | 0.00% |
| 2026-08-16 | 0.00% |
| 2026-08-15 | 0.00% |
| 2026-08-14 | 0.00% |
| 2026-08-13 | 0.00% |
| 2026-08-12 | 0.00% |
| 2026-08-11 | 0.00% |
| 2026-08-10 | 0.00% |
| 2026-08-09 | 0.00% |
| 2026-08-08 | 0.00% |