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This paper surveys the linear representation hypothesis across AI and related fields, analyzing inconsistencies and proposing a more rigorous formalization to make it a falsifiable scientific claim.
The 2026 Carnegie California AI Survey is a research study focusing on artificial intelligence trends and developments in California, likely conducted by Carnegie Mellon University.
A survey argues that human-centric AI needs to connect isolated tasks into foundation models with shared human representations and better data for physical grounding.
A comprehensive survey of large models in sports, covering tasks, applications, datasets, and challenges to advance sports intelligence.
A new survey from Renmin University reviews nearly 1,000 studies on long-horizon AI agents, arguing that reliable long-horizon intelligence depends on the whole model-harness system, not just larger context windows or stronger models.
Anthropic analyzed economic themes in 81,000 public responses about desired AI outcomes.