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XIR: A Framework for Interoperability across Cross-Chain Protocols Based on a Verifiable Intermediate Representation

arXiv cs.CRby Yushen Li ()1 min read1 · 1 read
AI summary · glm-5.3-flashSummaries by Vani AI

XIR introduces a verifiable intermediate representation for cross-chain messaging, raising reachability from 14.64% to 96.86% across 286 blockchains in prototype evaluation.

Analyzing roughly 25 million mainnet cross-chain transaction events from six protocols (Axelar, CCIP, Hyperlane, LayerZero, Relay, and Wormhole) between January and October 2025, the study finds 286 active blockchains and 11,935 directly connected pairs with only 14.64% direct reachability. XIR binds application messages to ordered records of authenticated cross-chain protocol deliveries, letting XIR Gateways and Adapters compose existing connections into multi-hop paths. A prototype integrating Hyperlane and LayerZero raises reachability to 96.86% of ordered pairs and avoids 67,018 point-to-point configurations, equivalent to 84.88% of the baseline requirement.

Key facts
  • Analysis covers ~25 million cross-chain events from Axelar, CCIP, Hyperlane, LayerZero, Relay, and Wormhole.
  • Graph spans 286 blockchains and 11,935 connected pairs with only 14.64% direct reachability.
  • XIR composes protocol connections into multi-hop paths via verifiable delivery records.
  • Prototype integrating Hyperlane and LayerZero raises reachability to 96.86%, avoiding 67,018 point-to-point configurations.
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From the original article

1 min read at arxiv.org

Cross-chain protocols enable applications to exchange messages across blockchains. Under point-to-point configurations, communication depends on a direct connection between the source and destination blockchains, limiting blockchain reachability and requiring additional configurations to connect more blockchains.

arXiv cs.CR

By Yushen Li · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.