Driving models increasingly ground reasoning in causal relations, spatial structure, perceptual evidence, and predicted futures. These advances make reasoning more faithful to the driving scene, but leave a fundamental question unresolved: what should groundedness mean when the model ultimately outputs an action? Corre...
Minkyoung Cho, Ze-Wei Zhou, Wen-Hao Ding et al.· 0 citations
The hybrid system achieves 98--99% diagnostic tool recall with 92--96% precision, matches or exceeds cloud-only baselines on clinical accuracy, and maintains bandwidth-invariant latency (25--35,s) at 4--15x lower token cost.
Hei-Ting Chan, Chen-Wei Wu, Xue-Shen Liu et al.· 0 citations
Test-time scaling improves foundation-model inference by spending additional computation, but robot control requires deciding whether extra compute is useful before executing an action. World Action Models (WAMs) make this decision natural: each rollout exposes both an action chunk and predicted future observations. We...
A benchmark of multi-visit, multi-specialty patient records is introduced that evaluates long-context evidence retrieval, cross-time evidence aggregation, and cross-specialty clinical reasoning and proposes MedCache, a hybrid framework that constructs temporally valid patient memory and organizes evidence into overlapp...
Hei-Ting Chan, Chenwei Wu, Xue-Shen Liu et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
