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This work examines what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims and identifies cross-role connections.
MLLMs can recognize urban scenes but fail to maintain reliable navigation and goal-directed behavior over extended exploration in complex environments.
Lifecycle-wide perception is crucial for evidence-grounded scientific discovery, allowing OmniScientist to outperform traditional AI systems in research quality and depth.
State-of-the-art multimodal models falter in interpreting implicit social cues, revealing a critical gap in AI's understanding of human communication.
Culture-specific emotional perception can be effectively integrated into MLLMs, but current models struggle to achieve even 50% accuracy on this nuanced task.
Personalization in LLM agents is more complex than previously thought, with existing benchmarks failing to capture the dynamic nature of user preferences and their impact on task execution.
Landmark bias can lead to significant inaccuracies in geo-localization, but HoloGeo effectively mitigates this issue through evidence-driven reasoning, outperforming existing models.
LLM agents suffer from the same Actor-Observer Asymmetry that plagues humans, leading them to make inconsistent judgments about their own and others' failures.
Overcome the scarcity of 4D training data by cleverly borrowing spatial understanding from 3D models and temporal dynamics from video models.
Current video models struggle to infer unseen spatial states and causal relationships, falling far short of human-level spatial reasoning.
