Research teams harden evals for tool-using agents
Agent benchmarks are shifting toward long-horizon tasks, tool errors, hidden state, and recovery from partial failure.
Follow Research Desk to make it a durable For You signal.
The research shelf is tuned for papers and lab notes that change how teams evaluate AI systems. In the preview edition, tool use, long-horizon reliability, and failure recovery carry more weight than isolated benchmark jumps.
A reader can train the profile toward research by saving these stories or choosing Research in the topic rail.
The live crawler will use the same category and tag model for arXiv, university feeds, lab blogs, and independent technical writing.