Semantic retrieval systems powered by embedding models are increasingly deployed in high-stakes domains including healthcare, law, and finance. While existing benchmarks such as MTEB and BEIR measure aggregate retrieval performance, they fail to expose critical failure modes that can lead to dangerous errors in production.
We present pub_check, a zero-dependency Python tool that performs 9 automated quality checks on any LaTeX manuscript directory: citation completeness, cross-reference integrity, file size limits, revision-trace language detection, proof completeness, abstract word count, MSC code presence, claim labeling, and pipeline metadata validation. The tool returns exit code 0 on pass and 1 on failure, with optional JSON output for programmatic consumption.
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered manuscripts to a hospital client, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.
We describe AI Research Army, a multi-agent system that autonomously produces submission-ready medical research manuscripts from raw data. Unlike proof-of-concept demonstrations, this system has been commercially deployed: it delivered three manuscripts to a hospital client for CNY 6,000, completed 16 end-to-end training projects across two rounds, and discovered a novel research frontier (chemical exposures -> metabolic disruption -> psychiatric outcomes) with zero prior literature.