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Forget hand-crafted KG traversal policies: GraphWalker uses automatically synthesized trajectories to train agents that achieve SOTA performance and generalize to unseen reasoning paths.
Squeezing the most out of your MLLM's visual budget is now possible: ResAdapt learns to allocate visual tokens intelligently *before* encoding, boosting performance by 15% while processing 16x more frames at the same cost.
