Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved. We introduce Decoupled Predictive Decision Making (DPDM), an arc
DPDM improves agent decision accuracy by combining frozen language-model representations with a shared pretrained conditional predictor that learns from deployment evidence without parameter updates.
Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved. We introduce Decoupled Predictive Decision Making (DPDM), an architecture that formulates heterogeneous agent decisions as conditional prediction with a shared pretrained learner. Language models provide semantic representations of current and historical records, while a general-purpose conditional predictor predicts task-defined targets from the represented evidence. Pretraining supplies reusable inference capability; deployment evidence instantiates problem-specific predictions without updating either model's neural parameters. Tool judgments, candidate…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 29 checks on 3 different days. Its best position was #1 of 30, reached on Nov 6, 21:28 UTC. It is #4 right now, holding steady at #4 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #4, holding steady at #4 over the last 6 checks.
#1 on Hugging Face · Daily papers, on Nov 6, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (29 checks) since.