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$\Sigma$-Mem not only tracks agent reliability but also adapts dynamically to feedback, outperforming traditional methods in multi-agent coordination.
IDEAgent achieves a staggering 3.89x improvement in generating diverse and high-quality research ideas compared to existing methods.
LLMs may create a "novelty mirage," consistently rating generated research questions as innovative, while human experts disagree.
GRAIL reweights token advantages in reinforcement learning, leading to significant accuracy gains without the need for expensive process-level supervision.
By explicitly grounding reasoning steps to visual objects, Chain-of-Glimpse enables more accurate and interpretable video understanding, outperforming object-agnostic methods on multiple benchmarks.
Current VLMs can ace image quizzes, but completely fumble when asked to stack blocks in a physically plausible way, revealing a critical gap in understanding real-world physics.
