Search papers, labs, and topics across Lattice.
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A lightweight Q-value model can boost a 9B VLA's performance beyond that of a 27B model while reducing inference latency by 27%.
A unified decision process for multi-modal reasoning reveals that joint optimization of text and image generation can dramatically enhance performance in complex reasoning tasks.
By exposing task dependencies through a graph structure, ATG enables LLMs to execute complex tasks more efficiently and reliably than ever before.
A tool-augmented agent achieves high compilation rates but reveals a shocking 29-point gap in semantic faithfulness, challenging the reliability of existing evaluation metrics.
Organizing visual attention before camera motion can dramatically enhance narrative coherence and viewer engagement in dynamic 3D environments.
ForceBand transforms human muscle signals into precise force data, enabling robots to learn manipulation tasks with unprecedented accuracy.
LLMs can effectively decompose interactions into phases and roles, but struggle to generate dynamic, realistic motion without a structured approach.
HDSL achieves a remarkable reduction in editing token usage by over 5 times while maintaining scene integrity and enhancing generation speed.
Turning past programming failures into reusable knowledge boosts automated repair performance by 3.7% on a multimodal benchmark.
By structurally disentangling temporal joint planning from frame-level manipulation, StructBiHOI achieves superior long-horizon stability and motion realism in bimanual hand-object interaction generation.
Shifting the powertrain of a powered prosthetic knee *above* the joint could boost walking speed and cadence, challenging the conventional focus on simply minimizing total mass.
LLMs can be sped up by 21% at inference time without retraining, thanks to a new sparsity method that smartly prunes activations based on the importance of the weights they interact with.
LLMs can be taught to "think longer" and explore more diverse reasoning paths in-context via a simple length-incentivized reward, leading to improved generalization.
