autoconference:analyze

To analyze multi-agent research conference results, you need a tool that reads conference artifacts like TSV histories and transcripts to compute trajectory metrics, cluster reverted strategies, and generate a structured failure mode analysis report.

Knowledge transfer evaluation in multi-agent systems tracks the adoption of insights shared during poster sessions and measures their subsequent impact on research metrics across different agents over time.

You can generate an insight taxonomy from conference TSV and transcript artifacts by processing all available run outputs to extract structured lessons, assessing each insight for confidence, transferability, and predictability.

Yes, you can compare researcher trajectories using conference_results.tsv data by contrasting individual performance metrics like convergence speed, exploration breadth, and success rate against the established conference baseline.

To run a post-run conference analysis, your conference directory must contain at minimum the conference.md and conference_results.tsv files, while optional per-researcher TSV histories and peer review transcripts enhance the output.

Clustering reverted strategy attempts improves research evaluation by grouping failed multi-agent approaches, summarizing peer-review challenges and overrules to reveal systematic issues rather than isolated errors.