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Real-world coding tasks, reverse-engineered from actual commits and scenarios, make Tencent WorkBuddy Bench a game-changer in contamination-resistant evaluation for coding agents.
Video generators may convincingly simulate real-world dynamics, but they often fail to understand the underlying causal relationships, revealing a critical gap in their reasoning capabilities.
TopoAgent's innovative use of dynamic graph evolution allows for noise-resistant scientific reasoning that outperforms traditional linear models.
Stabilizing RL training is now possible by modulating importance ratios with hazard-aware penalties, preventing both mode collapse and policy erosion.
