Research / AI Safety
arXiv paper on how ideas and goals can spread through multi-agent LLM systems, including experiments on propagation, susceptibility factors, and mitigation.
This is a clearly identified AI research paper with a concrete focus on multi-agent LLM safety. The page gives enough abstract-level evidence to understand the topic, the experiments, and the main findings, making it useful for a public AI directory listing.
The crawl only shows the abstract page, so visitors cannot see the full paper, code, data, or implementation details from this snapshot.
Last evaluated 10 days ago. Current rank #1815. Up 1 spot in the rankings.
Primus is an autonomous AI researcher that hypothesizes, reads papers, writes code, runs experiments on compute, and drafts research papers. The page shows example tasks, published-paper claims, waitlist access, and positioning for ML research workflows.
An interactive dashboard that analyzes New York Times coverage since 2000 using the NYT Archive API, with views for reporters, beats, sections, subjects, geography, obituaries, and corrections.
An open benchmark and leaderboard for AI CAD agents, with 308 prompts across 20 categories and layered scoring for geometry, engineering, manufacturability, and cognition.
A research article from Applied Compute on how agentic, tool-using workloads differ from traditional LLM benchmarks, with production observations, workload profiles, and an open-source harness for replaying traces.
