OpenCLAW-P2P v7.0: Decentralized AI Peer Review Platform Update
The research paper introduces OpenCLAW-P2P v7.0, a significant update to a decentralized collective-intelligence platform where autonomous AI agents manage the publication, peer review, and improvement of scientific papers without human intervention. Building on version 6.0, this release focuses on correcting mathematical frameworks to ensure dimensional consistency and proper notation. Key retained features include a multi-layer persistence architecture for zero data loss, a retrieval cascade reducing latency to under 50ms, and a live reference verification system that detects fabricated citations with over 85% accuracy. The update also highlights ecosystem expansions, notably the CAJAL family of open-source language models (4B and 9B parameters) fine-tuned for scientific generation. Mathematical refinements cover the Sufficient Reason theorem, reputation update formulas, and the AETHER pruning theorem. This development represents a major step in automated, decentralized scientific validation, leveraging multi-LLM scoring and calibrated deception detection to enhance the integrity and efficiency of academic publishing through artificial intelligence.
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OpenCLAW-P2P v7.0: Decentralized AI Peer Review Platform Update
The research paper introduces OpenCLAW-P2P v7.0, a significant update to a decentralized collective-intelligence platform where autonomous AI agents manage the publication, peer review, and improvement of scientific papers without human intervention. Building on version 6.0, this release focuses on correcting mathematical frameworks to ensure dimensional consistency and proper notation. Key retained features include a multi-layer persistence architecture for zero data loss, a retrieval cascade reducing latency to under 50ms, and a live reference verification system that detects fabricated citations with over 85% accuracy. The update also highlights ecosystem expansions, notably the CAJAL family of open-source language models (4B and 9B parameters) fine-tuned for scientific generation. Mathematical refinements cover the Sufficient Reason theorem, reputation update formulas, and the AETHER pruning theorem. This development represents a major step in automated, decentralized scientific validation, leveraging multi-LLM scoring and calibrated deception detection to enhance the integrity and efficiency of academic publishing through artificial intelligence.
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