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cs.LG2026
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
Jakob Hartmann, James Harvey, Jhonathan Navott +5
Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions requ…
cs.LG2026
AdaBoost Does Not Always Cycle: A Computer-Assisted Counterexample
Erik Y. Wang
We give a computer-assisted counterexample to the open question, posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost always converges to a finite…
cs.LG2026
Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification
Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen +9
Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they ca…