Search papers, labs, and topics across Lattice.
7
0
9
11
Discontinuous dynamics aren't the biggest problem for policy gradients in differentiable simulators; variance control often matters more.
Ditch the energy functions: C-voting unlocks better test-time reasoning in recurrent models by simply picking the most confident trajectory.
Forget Transformers; this new recurrent architecture learns more stable representations and generalizes better out-of-distribution by interleaving fast latent updates with slower, self-organizing observation processing.
Current multimodal LLMs struggle to count objects and ground evidence in videos longer than 30 minutes, achieving only ~25% accuracy compared to human performance on a new benchmark.
LLMs' chain-of-thought reasoning often falls apart due to factual incompleteness, with errors compounding across multiple hops, as revealed by a new multi-hop QA dataset.
Predict transformer training failures *before* you even start training, with 99.5% accuracy, using just a single forward pass.
LLMs that ace medical exams still fumble basic clinical judgment, prematurely deciding cases or abstaining unnecessarily when information is incomplete, revealing a critical gap in their real-world applicability.
