Search papers, labs, and topics across Lattice.
Frozen video diffusion models can effectively serve as competitive encoders for a wide range of tasks, merging generation and understanding seamlessly.
State-of-the-art Vision-Language Models fall short in real-world robotic applications, revealing critical gaps in their reasoning capabilities.
TempoWave reveals that rethinking numerical embeddings can unlock significant improvements in LLM forecasting performance.
Achieving efficient uncertainty quantification in multi-modal regression could redefine the landscape of trustworthy large-scale learning.
Language adherence in ASR can be significantly improved using soft prompting techniques, leading to better transcription quality in multilingual contexts.
Post-training strategies can reshape biological reasoning models in unexpected ways, revealing that more supervision doesn't always mean better generalization.
Hierarchical VLA agents can outperform traditional flat control systems by leveraging unified design principles that enhance task performance across various complexities.
VLMs struggle with procedural 3D modeling, often producing flawed outputs due to API mismatches and geometric disconnections, but performance can be significantly boosted through iterative refinement.
Human-inspired context sensitivity boosts visual reasoning in machines, closing the gap between AI and human perception.
LLMs' struggle to grasp subtext—even generating literal clues 60% of the time—reveals a critical gap in their ability to understand nuanced human communication.
Forget finetuning: DynaEdit unlocks complex video edits like action modification and object insertion, all without training, using clever manipulation of pretrained text-to-video models.
Forget fine-tuning: surprisingly, single neuron activations in VLMs can be directly probed to create classifiers that outperform the full model, with 5x speedups.
DINOv2's impressive unimodal performance doesn't translate to cross-modal understanding, but a simple training tweak can align embeddings across RGB, depth, and segmentation without sacrificing feature quality.
Tri-modal masked diffusion models can now be trained from scratch, achieving strong results in text generation, text-to-image, and text-to-speech, thanks to a systematic exploration of the design space and a novel SDE-based batch size reparameterization.
Existing deforestation monitoring maps misclassify smallholder agroforestry as "forest," risking unfair penalties under regulations like the EUDR.
Forget textual descriptions – this zero-shot image retrieval method hallucinates the target image directly, outperforming the state-of-the-art by creating a whole synthetic world to match against.
