result-normalizer

To convert worker outputs into evidence-focused reports, provide structured worker and watcher reports to the normalizer. It processes claims and logs, preserving only evidence-backed information while removing noise, resulting in concise packets for quality assurance decisions.

Evidence-first packet generation in AI verification workflows transforms raw claims and watcher logs into validated documents. It ensures only supported evidence influences final assessments by explicitly noting unsupported claims and downgrading duplicate logs or inaccessible data.

Filtering unsupported claims from watcher reports involves processing the raw logs through a normalizer that isolates concrete evidence. It notes or rejects unsupported claims while removing reassurance and duplicate logs to clarify the verification status for human review.

The result-normalizer requires no specific dependencies to process structured report inputs. It operates with minimal setup using included scripts, directly accepting worker and watcher reports to synthesize verification evidence without external environment requirements.

The best way to prepare raw worker outputs for final approval stages is to synthesize them into evidence-first packets. This process removes inaccessible data and duplicate logs, ensuring that only valid, evidence-backed information influences the final decision-making pipeline.

During report processing, duplicate logs and reassurance noise are removed or downgraded to clarify the verification status. This noise reduction ensures the final evidence-first packets contain only concrete findings that support active claims.