arXiv:2511.15407v4 Announce Type: replace-cross Abstract: Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarly acquire human-like reasoning from interaction and keep improving with more experience. To study this, we introduce a Game-to-Unseen (G2U) benchmark of 1,000+ heterogeneous games that exhibit significant visual domain gaps. Existing approaches, including VLMs and world models, struggle to capture underlying physics and causality since they are not focused on core mechanisms and overfit to v
The continuous drive towards human-like AI reasoning and the development of benchmarks like G2U highlight the current focus on advanced interactive learning for AI models.
Achieving human-like physical and causal reasoning in AI could enable more generalizable and robust AI systems, crucial for applications beyond narrow tasks.
This research suggests a potential shift from static dataset training to interactive learning environments for AI, leading to more adaptable and intelligent agents.
- · AI research labs
- · Robotics companies
- · Gaming industry
- · Simulation software developers
- · Companies reliant on narrow AI models
- · Traditional VLM/world model approaches without interactive learning
AI agents become better at understanding and interacting with complex physical environments.
This capability leads to more advanced autonomous systems in various sectors, from logistics to personal assistance.
The development of truly 'reasoning' AI could accelerate the creation of general-purpose AI, potentially transforming labor markets and societal structures.
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Read at arXiv cs.LG