Data reading on decentralized networks is the true test. A robust system needs to make read operations predictable, auditable, and resilient against abnormal behaviors.

The process begins with a cautious discovery phase. The client queries the proof list to obtain signed pointers indicating which nodes hold which data. This avoids blind searches. Instead of requesting大量数据 from each node, an incremental retrieval strategy is adopted—first obtaining small, verifiable segments, verifying them, and only upgrading requests when necessary. This maintains low latency under normal conditions and controls costs when issues arise.

The dual-layer segment design (primary and backup copies) provides flexibility. When certain nodes respond slowly or behave abnormally, the client can seamlessly switch to backups. Each segment comes with cryptographic proofs, allowing the client to reject bad data on the spot. After collecting enough honest segments, the data block is reconstructed and compared with on-chain commitments. Mismatches are reported immediately.

Repair is collaborative rather than drastic. Nodes missing data only request the necessary parts from a few peers, not the entire file. This reduces repair traffic and keeps network changes manageable. Additionally, these operations are recorded and linked to staking, ensuring real economic incentives back the technology.

In essence, this read design is pragmatic: smart discovery, incremental retrieval, strict verification, and efficient repair. Only then can decentralized storage move from theory to practical use.