Research

Pendle PT Risk Framework

Pendle v2 Mechanism Risk Assessment

AVS Risk Assessment Methodology

This model quantifies maximum slashing risk, referred to here as Value at Risk (VaR), by analyzing slashing behavior across multiple node operators and AVSs. Notably, slashing one operator on a particular AVS does not guarantee that others will be slashed simultaneously, nor does slashing on one AVS imply slashing on all AVSs secured by the same operator.

dydx Re-launch Rewards Explainers

Market makers and traders can earn dydx rewards based on their activity and contributions, with traders earning points through taker fees and market makers earning points by fulfilling orders and providing liquidity. Points are weighted differently across markets, and rewards are distributed proportionally based on a 7-day TWAP of the dydx price.

Edge AI Alpha Release

The alpha release of Edge AI Oracle brings a new standard of truth-seeking and transparency to decentralized ecosystems. Powered by LangChain and LangGraph, it delivers scalable, impartial data resolutions for prediction markets and beyond. Upcoming releases will focus on decentralization, developer tools, and high-integrity data products, paving the way for next-gen on-chain applications.

Trusting Trust in the Age of AI

While resources are heavily invested in improving frontier models and optimizing prompts, the true challenge lies in how AI systems retrieve and rank information. Chaos Labs identifies the greatest risk in AI-generated misinformation infiltrating the data pipeline. If AI agents are unknowingly trained on manipulated or sybilled content, it becomes nearly impossible to trust their outputs. This vulnerability is exacerbated by unreliable document ranking systems, which prioritize popularity and commercial interests over accuracy. The Dead Internet Theory, which warns of a future where human-created content is drowned out by machine-generated noise, serves as a chilling reminder of what’s at stake if these systems are not carefully safeguarded.

Ostium Risk Report

Liquity V2 Mechanism Design Review

AI-Driven Chaos and the Rise of Oracles: The Future of Trust

We’re expanding the definition of an Oracle. At its core, an Oracle is more than a protocol to source and deliver high-integrity, reliable, authentic, and secure data between networks. It adds a crucial truth-seeking layer of verification and filtering. Oracles don’t just deliver data—they ensure it’s trustworthy. Using truth-seeking algorithms, Oracles will filter out misinformation and manipulated data, safeguarding the applications and networks they serve.

AUSD Risk Assessment

Edge Proofs: AI-Powered Prediction Market Oracles

Edge Proofs Oracles ensure verifiable data provenance, integrity, and authenticity, enabling blockchain applications to trust the external data they rely on. This capability is crucial for Prediction Market Oracles, a specialized subset of proof oracles designed to bring off-chain data on-chain in a secure and trusted manner, ensuring accurate verification of real-world outcomes like elections.

Oracle Risk and Security Standards: Data Freshness, Accuracy and Latency (Pt. 5)

Data Freshness, Accuracy, and Latency are fundamental attributes that determine an Oracle's effectiveness and security. Data freshness ensures that the information provided reflects real-time market conditions. Accuracy measures how closely the Oracle's price reflects the true market consensus at any given time. Latency refers to the time delay between market price movements and when the Oracle updates its price feed.