Gauge

Overview

Gauge AI is responsible for dynamically adjusting incentive allocations across TrenOS’s liquidity pools. By analyzing market conditions, liquidity depth, and user participation, this AI agent ensures that rewards are distributed efficiently to maximize capital efficiency, attract liquidity providers, and prevent incentive misallocations.

By automating gauge weight distributions and optimizing reward flows, Gauge AI eliminates the need for governance-based manual adjustments, making TrenOS’s liquidity incentive framework more responsive and adaptive to market fluctuations.

---

Function

Gauge AI maintains an efficient and fair liquidity rewards system by:

• Attracting liquidity to underfunded pools while preventing over-rewarding saturated pools.

• Dynamically balancing incentive weights based on real-time market demand.

• Mitigating farming inefficiencies by preventing reward dilution and excessive emissions.

• Preventing vote manipulation by detecting governance attacks related to gauge weight voting.

How It Works

1. Monitors liquidity conditions and usage rates across all pools.

2. Evaluates trading volume, market participation, and past reward distributions.

3. Adjusts gauge weights dynamically based on market fluctuations.

4. Prevents reward manipulation and gaming attacks through security filters.

5. Executes smart contract updates to optimize incentive allocation.

---

Goals

• Optimize reward allocation to drive sustainable liquidity growth.

• Prevent inefficient reward distributions that lead to farm-and-dump behavior.

• Enhance protocol stability by ensuring balanced liquidity provisioning.

• Automate gauge adjustments to remove reliance on manual governance updates.

---

Decision Logic

Step 1: Liquidity Depth & Utilization Check

• If a pool’s liquidity utilization exceeds 90%, increase incentives to attract more deposits.

• If utilization falls below 40%, reduce rewards to optimize emissions.

Step 2: TVL & Trading Volume Analysis

• If a pool has high trading volume but low liquidity, increase incentives.

• If a pool has low trading volume but high liquidity, reduce rewards.

Step 3: Historical Reward Distribution Review

• AI analyzes past reward allocations and governance voting outcomes.

• Adjustments align with previous incentive strategies to maintain consistency.

Step 4: Security & Anti-Manipulation Filters

• If governance voting shows irregular activity, prevent abnormal weight shifts.

• If a pool is being farmed in an exploitative manner, temporarily restrict new rewards.

Step 5: Execution

• Smart contracts are updated to reflect optimized gauge weights and reward flows.

---

Input Data

Gauge AI relies on multiple sources of real-time and historical data to optimize rewards effectively:

• Liquidity pool metrics, including TVL, liquidity depth, and utilization rates.

• Trading volume data from daily and weekly activity across pools.

• Historical reward allocations and past gauge weight distributions.

• Governance and voting records, tracking community decisions on incentive structures.

• Risk indicators such as flash loan activity, reward farming behavior, and governance exploits.

---

Execution Outputs

• Gauge weight updates via smart contract adjustments to optimize incentive allocations.

• Reward distribution changes based on AI-driven emissions reallocation.

• Liquidity strategy reports providing governance logs of AI-driven reward optimizations.

• Security alerts and governance safeguards preventing manipulation attempts.

---

Tools Used

Gauge AI leverages various tools to monitor, analyze, and execute reward optimizations:

• API calls to fetch real-time TVL, user activity, and liquidity depth.

• On-chain data retrieval to monitor historical reward allocations and pool performance.

• Execution engine to adjust gauge weights and reward distributions in smart contracts.

• Market sentiment and governance analysis to track voting patterns and liquidity provider behaviors.

• Security and risk alerts to identify reward distribution anomalies and potential gaming attacks.

• RAG (Retrieval-Augmented Generation) to analyze past governance decisions on gauge weight adjustments.

---

Security and Fail-Safes

To prevent manipulation, inefficient rewards, or excessive emissions, Gauge AI implements strict security measures:

• Incentive adjustment limits preventing drastic changes to gauge weights within a short period.

• Multi-source validation requiring consensus from TVL, trading volume, and governance records before adjusting weights.

• Governance protection mechanisms detecting vote farming attacks that could skew incentive distributions.

• Emergency reward freeze to temporarily disable rewards for pools showing signs of manipulation.

• Self-learning optimization to refine AI strategies over time based on past incentive performance.