Search papers, labs, and topics across Lattice.
Intelligent Game and Decision Lab
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Importance-Aware Sampling (IAS) reveals that not all patches in VIS-IR data are created equal, leading to substantial performance gains in multi-sensor perception tasks.
General-purpose computer vision MLLMs can outperform specialized remote sensing models on key tasks, challenging the notion of domain-specific superiority.
Removing gold answer strings from rewritten contexts can cause F1 scores to plummet by up to 64 points, underscoring their critical role in retrieval-augmented QA performance.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
Overcoming discontinuities in skeleton detection, this work leverages "lighthouse-guided" reconnection to substantially improve skeleton connectivity and structural integrity.
