Harvard Medical School

Headlines · 2

1. 9h
   PhAI Labs releases DiscoveryRSI research agent and 27B model

   PhAI Labs, working with several universities and research institutions, proposed DiscoveryRSI and trained DiscoveryRSI-27B using a hypothesis-credit attribution mechanism, releasing the paper, code and models. The system stores evolving hypotheses and test plans in a research hypothesis tree and is paired with DFM-Bench, which evaluates hypothesis revisions, research paths and whole research episodes. The team reports that DiscoveryRSI's group scored 6.3% in a literature cross-check, the highest of five model groups, while the Claude Opus 5 group scored 2.3% and ranked last; it cautions that group sample sizes differ and the comparison is descriptive, so it does not prove the hypotheses were adopted or validated by later research.

   机器之心 · 🔥 9
2. Oct 10
   Stanford-led team posts HealthFlux, a pan-modal health world model

   Stanford postdoctoral researcher Ying Kejun, with scholars from the University of Washington, Harvard Medical School and MIT, posted HealthFlux on the medRxiv preprint server, describing it as the first pan-modal human health world model, trained on the UK Biobank's long-term cohort of roughly 500,000 people. The model maintains a health state described by 384 values that evolves on its own when no new data arrive and is corrected immediately when new measurements come in, advanced by neural ODEs, with 11 data types — records, blood tests, about 3,000 plasma proteins, some 250 metabolites and abdominal and brain MRI — feeding the same state; a separate reader extracts disease risk afterwards. The team reports a five-year average AUROC of 0.832 across 1,010 diseases and death in about 60,000 held-out participants, versus 0.805 for the prior multimodal model RisQ, and says the score held at 0.837 when data was cut off for 20 years while Delphi-2M fell to 0.576. The authors note the cohort is largely white and lacks psychological scales, and that the health state index has no warning threshold, so causal evidence for interventions and prospective validation are still missing.

   MIT科技评论中文 · 🔥 0

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