A real-time LLM stream guard that catches LLM hallucinations mid generation (github.com)

A new tool called SIMURG has been introduced to enhance the reliability of Large Language Models (LLMs) by detecting and addressing decoding corruption in real-time. This “Streaming Integrity Monitor & Universal Regeneration Guard” is designed to identify issues like repetition collapse, cross-lingual drift, and structural breakdown as they occur, enabling the system to abort the corrupted stream before it reaches the user. SIMURG processes data at an impressive throughput of over 197,000 characters per second, guaranteeing that it never becomes a bottleneck while monitoring the output continuously. The significance of SIMURG lies in its ability to maintain the integrity of generated content, which is crucial in applications where accuracy and coherence are paramount. Unlike traditional post-hoc checks, SIMURG utilizes a proactive zero-leak protocol, holding the initial characters for verification before release. By employing a series of character-level feature analyses and an adaptive detection system, it effectively provides real-time feedback on stream quality, allowing for immediate corrections. This advancement promises to significantly improve user experiences and reliability in AI-driven applications, making it a valuable addition to the AI/ML toolkit.