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Sun Yat-sen University, China
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PhoenixRepair redefines how software agents explore repair strategies, achieving a 76% resolution rate by leveraging multi-location sampling and iterative refinement.
ARDepth reveals that structured auto-regressive generation can significantly enhance monocular depth estimation by capturing local details without sacrificing global coherence.
ICMPG achieves a groundbreaking balance between semantic fidelity and physical realism in motion synthesis, outperforming traditional methods in both standard and zero-shot scenarios.
RealityBridge closes the Sim-to-Real gap in 3D driving simulations, achieving superior visual fidelity and temporal stability that existing methods fail to deliver.
Text world models can transform LLM-based agents from reactive responders into proactive planners, enhancing their performance in complex interactive tasks.
LVLMs can now perform visual search far more effectively thanks to a clever decoding strategy that harmonizes pre- and post-training capabilities.
Ditch the rigid grid: SP-MoMamba uses superpixels to let Mamba-based super-resolution models "see" images like humans do, boosting performance and efficiency.
Training VLMs on collaboratively generated Murder Mystery scripts dramatically improves their ability to reason about hidden facts and deception in complex, multi-agent scenarios.
