OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review

OpenCLAW-P2P v6.0 introduces a decentralized collective-intelligence platform where autonomous AI agents publish and peer-review scientific research without human gatekeepers, featuring new multi-layer persistence and live reference verification.
Computer Science > Artificial Intelligence
Title:OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review
View PDF HTML (experimental)Abstract:This paper presents OpenCLAW-P2P v6.0, a comprehensive evolution of the decentralized collective-intelligence platform in which autonomous AI agents publish, peer-review, score, and iteratively improve scientific research papers without any human gatekeeper. Building on v5.0 foundations -- tribunal-gated publishing, multi-LLM granular scoring, calibrated deception detection, the Silicon Chess-Grid FSM, and the AETHER containerized inference engine -- this release introduces four major new subsystems: (1) a multi-layer paper persistence architecture with four storage tiers (in-memory cache, Cloudflare R2, this http URL, GitHub) ensuring zero paper loss across redeployments; (2) a multi-layer retrieval cascade with automatic backfill reducing lookup latency from >3s to <50ms; (3) live reference verification querying CrossRef, arXiv, and Semantic Scholar during scoring to detect fabricated citations with >85% accuracy; and (4) a scientific API proxy providing rate-limited cached access to seven public databases. The platform operates with 14 real autonomous agents producing 50+ scored papers (word counts 2,072-4,073, leaderboard scores 6.4-8.1) alongside 23 labeled simulated citizens. We present honest production statistics, failure-mode analysis, a paper recovery protocol that salvaged 25 lost papers, and lessons learned from operating the system at scale. All pre-existing subsystems -- 17-judge multi-LLM scoring, 14-rule calibration with 8 deception detectors, tribunal cognitive examination, Proof of Value consensus, Laws-of-Form eigenform verification, and tau-normalized agent coordination -- are retained and further hardened. All code is open-source at this https URL.
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Source: arXiv cs.AI Recent
















