Epoch 26 Inertia VIP Update: roETH & roTIA Integration
0. Proposer Info
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Email: channel@inrt.fi
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Project / Rollup Name: Inertia
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Project URL: https://inrt.fi/
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Official X (Twitter) Account: https://x.com/Inertia_fi
1-1. Purpose
This proposal adds roETH and roTIA (cross-chain LSTs by Cavro Stake) to Inertia’s VIP scoring formula, using a TVL-based progressive multiplier model.
1-2. Background & Objectives
Inertia is integrating roETH (liquid staked ETH) and roTIA (liquid staked TIA) as new supply assets in the Inertia Lending market. These assets bring external TVL from Ethereum and Celestia into the Initia ecosystem, strengthening overall ecosystem TVL and asset diversity.
By allocating VIP rewards to roETH & roTIA suppliers, this proposal aims to:
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Attract new cross-chain TVL into Inertia and the broader Initia ecosystem.
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Provide roETH & roTIA holders with compelling yield opportunities beyond native staking.
1-3. Current Status
Neither roETH nor roTIA is currently included in VIP scoring.
1-4. Proposed VIP Framework
roETH target multiplier: 1.5× relative to sLP
roTIA target multiplier: 1.3× relative to sLP
VIP Score/sec = Total Supplied × sLP baseline × multiplier / 1,209,600
Multipliers start low and scale progressively based on each asset’s TVL:
roETH — TVL Tier Schedule
$0 – $10K → 0.03x
$10K – $30K → 0.08x
$30K – $75K → 0.2x
$75K – $150K → 0.4x
$150K – $300K → 0.9x
$300K+ → 1.5x (target)
roTIA — TVL Tier Schedule
$0 – $10K → 0.02x
$10K – $30K → 0.07x
$30K – $75K → 0.15x
$75K – $150K → 0.35x
$150K – $300K → 0.75x
$300K+ → 1.3x (target)
1-5. Progressive Scaling Rules
$0–$10K: Fixed multiplier (no epoch scaling) — minimizes early dilution to sLP/nLP.
$10K+: Multiplier cap increases ×1.2 per epoch within each tier, starting from the previous tier’s multiplier. Stops at tier cap if TVL doesn’t advance.
TVL drop: Multiplier auto-reverts to the previous tier. roETH & roTIA tracked independently.
2. Conclusion
This proposal seeks community approval to include roETH and roTIA in VIP scoring starting Epoch 26. This calculation may be adjusted in future stages based on performance data.