University of Washington

Headlines · 4

1. 5d
   Trillium Labs pushes high-stakes AI research into the open

   Trillium Labs, a nonprofit co-founded by Nathan Lambert and Tom Zick, aims to move high-stakes AI research — on topics such as self-improvement, agents and model behavior — out of closed frontier labs by sharing experimental details so outside researchers can inspect and reproduce results. Lambert argues that leading systems, especially models from OpenAI and Anthropic, are typically available only through apps or APIs, leaving outsiders unable to understand how they are built or how they actually behave, and that long-term enclosure by a few labs weakens the community's ability to evaluate new ideas and propose alternatives. On the same day, Geoffrey Hinton and other leading AI researchers published a paper warning of a possible 'intelligence explosion' and calling on policymakers to require standardized disclosure by AI companies; Trillium Labs researchers had also worked with Meta, the University of Washington and MIT on a Context Language Models architecture.

   科技新报 · 🔥 0
2. Oct 10
   Two fixed opening tokens lift base model math accuracy to RL level, MIT study finds

   A paper by researchers at MIT, UC Berkeley, the University of Washington and the Allen Institute for AI (Ai2) reports that pre-fixing just the first two tokens of a base model's answer can sharply raise math reasoning accuracy. On MATH-500, Olmo-3-7B rose from roughly 42% to about 78% when forced to start with “.\n\nOkay”, beating its RL-trained counterpart at about 75%, while Qwen3-14B improved from 72% to 87% with “ Alright,” — all without changing any model parameters. The authors also found the largest pre/post-RL prediction shift sits in the first two tokens, and that retraining a model with “Chicken” swapped in for “Okay” in reasoning-adjacent data made “Chicken” work as a reasoning cue too.

   智东西 · 🔥 0
3. 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
4. Oct 6
   Google Earth AI model shows promise for public-health forecasting

   Google Research reports that its Population Dynamics Foundation Model (PDFM), part of Google Earth AI, can provide plug-in geospatial embeddings for public-health and epidemiological workflows. Partner evaluations covered vaccination, cardiovascular disease, dengue, postpartum depression and cholera, with reported improvements or comparable performance against conventional inputs in several settings.

   Google Research Blog · 🔥 0

Experience and discussion from the community

Share my University of Washington experienceAsk about University of Washington