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cs.AI2026
From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?
Linus Filbry, Luckeciano C. Melo, Yarin Gal
Large language models (LLMs) are increasingly applied across the scientific discovery pipeline, from synthesizing literature to generating hypotheses to informing experimental desi…
cs.LG2026
Richer Bayesian Last Layers with Subsampled NTK Features
Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna +4
Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underestimate epistemic uncertainty bec…