Automated research evidence collection gathers measurements, logs, artifacts and decisive results as experiments run. The captured information should identify the procedure and environment so a reviewer can connect a claim to its source.
Collection is not interpretation. Systems should retain failures and anomalous outputs rather than filtering them away, while researchers decide whether the evidence supports a hypothesis or requires another experiment.
ELI5
Automated research evidence collection saves experiment results and the details needed to understand where they came from. It can capture measurements, logs, settings, files and failures while the work runs.
For example, every model experiment can store its input data version, configuration, output scores and error log in a structured record. Researchers can then reproduce the run and judge the evidence instead of relying on a summary that hides unusual or failed results.
