Search papers, labs, and topics across Lattice.
7
0
7
4
PILL achieves up to 6.0 BLEU-2 improvement on text infilling while running 1.82x faster than the best existing method, revolutionizing efficiency in diffusion language models.
System Intelligence emerges as a game-changer, enabling LLM agents to collaborate effectively across complex tasks by organizing their interactions through dynamic graph structures.
AFANet achieves high accuracy in agent failure attribution with a fraction of the computational resources required by traditional LLM-based methods.
Long-horizon LLM agents can achieve 96.9% task success by learning to adapt their external execution support through trainable harness policies.
RECONTEXT boosts long-context reasoning in LLMs by effectively reusing evidence from the input, leading to superior performance without the need for retraining.
A unified model that seamlessly integrates text and graph learning outperforms traditional methods by up to 3.9 points on benchmark tasks.
Current multimodal LLMs still struggle to integrate information and reason critically when assessed on real scientific papers, despite progress on isolated tasks.
