We are excited to announce tokensight, intelligence provider on the cryptoeconomic risks of restaking to LRT protocols and AVSs.

The advent of restaking brought a new paradigm of decentralized open innovation — and, with it, new and open questions on pooled and attributable cryptoeconomic security and liquid restaking, that ought to be studied.

EigenLayer works as a two-sided marketplace of decentralized trust: restakers supplying cryptoeconomic security through their stake, and AVSs seeking this pooled security to efficiently run their services, without the complexities and challenges that come with bootstrapping a new network of validators, attract stakers to pool funds, and launch their own native, highly-volatile token. From there, liquid restaking providers emerged to address the need for a liquid representation of these restaked positions through the minting of an LRT, which enables users to earn restaking yield from EigenLayer and simultaneously—much like with LSTs—use these fungible assets in DeFi.

Pertinent questions on their associated risks have been raised: What are the individual and category-specific risks behind an AVS or ecosystem of AVSs? What could the cascading effects of a slashing event look like, either from a malicious or non-malicious party, to an ecosystem of interconnected AVSs and to the overall cryptoeconomic security of the system? What do operator entrenchment and centralization levels mean to a system like this? What could the slashing conditions that AVSs set look like? Which parameters should LRT protocols have in mind when building their portfolio of AVSs? What kinds of rewards may AVSs emit and what would their distribution look like? What are the risks of attributable security not being allocated to an AVS in need?

To better attempt at answering these questions is why we started tokensight. We aim to help AVSs and LRT protocols navigate the risks and potential rewards of participating in restaking with EigenLayer.

We believe that by employing advanced simulation models, quantitative optimization and analysis through meticulous research and rigorous economic and mathematical approaches, and continuous discussions with landmark players in the space, we can further the technical and social understanding of the risks involved and maximize security in restaking, in order for true, decentralized open innovation to thrive as smoothly as possible.

Among some of the already-developed models we have:

• AVS Reward Emission Simulator to Stakers and Operators

• Decentralized Sequencer AVS Underlying Risk Simulator

• AVS ↔ Non-Malicious Operator Stake-Loss Event Simulator: Naïve Approach

• Malicious Operator → AVS Slashing Event Simulator: Naïve & STAKESURE Approaches (with AVS ecosystem compounded, cascading effects)

• AVS Selection Methodology by LRT Protocol with In-Isolation and Ecosystem-Aware Weighted Sharpe Ratios

As restaking matures, EigenLayer and the first AVSs reach mainnet, and LRTs start composing their AVS portfolios, we will be continuously building and posting new research and models.

Thank you to Jessy from EigenLayer, Chunda from Ion Protocol, Kratik and Lucas from Renzo, Dan from Rio Network, David Hoffman from Bankless, and Crews from Index Coop for the early feedback and guidance, and Sreeram Kannan for masterminding EigenLayer! All were crucial to bringing this project to life.

Learn more and stay updated by visiting our Website, following us on X, and checking out our Sample Dash on AVS Risk.

Learn more about EigenLayer, restaking, LRTs, and their risks.

We are excited to announce tokensight, intelligence provider on the cryptoeconomic risks of restaking to LRT protocols and AVSs.

The advent of restaking brought a new paradigm of decentralized open innovation — and, with it, new and open questions on pooled and attributable cryptoeconomic security and liquid restaking, that ought to be studied.

EigenLayer works as a two-sided marketplace of decentralized trust: restakers supplying cryptoeconomic security through their stake, and AVSs seeking this pooled security to efficiently run their services, without the complexities and challenges that come with bootstrapping a new network of validators, attract stakers to pool funds, and launch their own native, highly-volatile token. From there, liquid restaking providers emerged to address the need for a liquid representation of these restaked positions through the minting of an LRT, which enables users to earn restaking yield from EigenLayer and simultaneously—much like with LSTs—use these fungible assets in DeFi.

Pertinent questions on their associated risks have been raised: What are the individual and category-specific risks behind an AVS or ecosystem of AVSs? What could the cascading effects of a slashing event look like, either from a malicious or non-malicious party, to an ecosystem of interconnected AVSs and to the overall cryptoeconomic security of the system? What do operator entrenchment and centralization levels mean to a system like this? What could the slashing conditions that AVSs set look like? Which parameters should LRT protocols have in mind when building their portfolio of AVSs? What kinds of rewards may AVSs emit and what would their distribution look like? What are the risks of attributable security not being allocated to an AVS in need?

To better attempt at answering these questions is why we started tokensight. We aim to help AVSs and LRT protocols navigate the risks and potential rewards of participating in restaking with EigenLayer.

We believe that by employing advanced simulation models, quantitative optimization and analysis through meticulous research and rigorous economic and mathematical approaches, and continuous discussions with landmark players in the space, we can further the technical and social understanding of the risks involved and maximize security in restaking, in order for true, decentralized open innovation to thrive as smoothly as possible.

Among some of the already-developed models we have:

• AVS Reward Emission Simulator to Stakers and Operators

• Decentralized Sequencer AVS Underlying Risk Simulator

• AVS ↔ Non-Malicious Operator Stake-Loss Event Simulator: Naïve Approach

• Malicious Operator → AVS Slashing Event Simulator: Naïve & STAKESURE Approaches (with AVS ecosystem compounded, cascading effects)

• AVS Selection Methodology by LRT Protocol with In-Isolation and Ecosystem-Aware Weighted Sharpe Ratios

As restaking matures, EigenLayer and the first AVSs reach mainnet, and LRTs start composing their AVS portfolios, we will be continuously building and posting new research and models.

Thank you to Jessy from EigenLayer, Chunda from Ion Protocol, Kratik and Lucas from Renzo, Dan from Rio Network, David Hoffman from Bankless, and Crews from Index Coop for the early feedback and guidance, and Sreeram Kannan for masterminding EigenLayer! All were crucial to bringing this project to life.

Learn more and stay updated by visiting our Website, following us on X, and checking out our Sample Dash on AVS Risk.

Learn more about EigenLayer, restaking, LRTs, and their risks.