The SDE benchmark shows LLMs lag on scientific discovery tasks relative to general science tests, with diminishing scaling returns and shared weaknesses across models.
, " * write output.state after.block = add.period write newline
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A monolithic FDSOI-FeFET platform natively supports ACAM and GRNG to enable efficient Bayesian decision tree inference with over 40% higher MNIST accuracy under noise and orders-of-magnitude gains in speed and energy.
Introduces distributional random forests for joint posterior inference and an SMC update for the prior in ABC, claiming accurate posteriors across deterministic and stochastic models.
citing papers explorer
-
Evaluating Large Language Models in Scientific Discovery
The SDE benchmark shows LLMs lag on scientific discovery tasks relative to general science tests, with diminishing scaling returns and shared weaknesses across models.
-
Probabilistic Tree Inference Enabled by FDSOI Ferroelectric FETs
A monolithic FDSOI-FeFET platform natively supports ACAM and GRNG to enable efficient Bayesian decision tree inference with over 40% higher MNIST accuracy under noise and orders-of-magnitude gains in speed and energy.
-
Approximate Bayesian Computation sequential Monte Carlo via random forests
Introduces distributional random forests for joint posterior inference and an SMC update for the prior in ABC, claiming accurate posteriors across deterministic and stochastic models.