Search papers, labs, and topics across Lattice.
6
0
10
16
Current personal assistant agents struggle to anticipate and act on unstated user needs in long, complex workflows, revealing a critical gap between task completion and genuine proactivity.
LVLMs can maintain sharper visual focus during long-form generation by adding a lightweight, learnable memory module that bypasses attention dilution.
Object hallucination in LVLMs can be significantly reduced *after* training, without any extra data or compute.
A lightweight 6B model, when harnessed within the GEMS agent framework, leapfrogs state-of-the-art models in multimodal generation, suggesting architectural innovations in agents can compensate for raw parameter count.
Multimodal agents can now continually improve their tool use and orchestration in open-ended settings without parameter updates, thanks to a novel dual-stream framework that learns from both past experiences and structured skills.
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.
