{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/HypAgent","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/8adc9223-000b-45ed-a975-8a92b0ed03cd","json_url":"https://onlylabs.fyi/signals/8adc9223-000b-45ed-a975-8a92b0ed03cd/signal.json","generated_at":"2026-10-24T20:22:42.566Z","evidence_latest_fetched_at":"2026-10-20T07:04:09.68104+00:00","signal_first_seen_at":"2026-10-20T07:00:38.102697+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/8adc9223-000b-45ed-a975-8a92b0ed03cd","signal_json":"https://onlylabs.fyi/signals/8adc9223-000b-45ed-a975-8a92b0ed03cd/signal.json","source":"https://github.com/amazon-science/HypAgent","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/HypAgent (Python). 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This code is being released solely for academic and scientific reproducibility purposes, in support of the methods and findings described in the associated publication. Pull requests are not being accepted in order to maintain the code exactly as it was used in the paper. HypAgent studies one question: can an LLM agent look at a tabular dataset, propose the *mechanisms* that drive an outcome, and be measured honestly against ground truth? The package has two pillars. - **A method** — a value-gated discovery agent that explores data through a code REPL, spends its expensive validation budget only where signal remains, and checks each hypothesis with a controlled experiment before accepting it. It is the `hyp_agent` package. - **A benchmark** — a fully-owned structural causal simulator. 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