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AI research agents can now reliably trace method evolution topologies thanks to a new methodological evolution graph, Intern-Atlas, that captures structured relationships between research methods.
LLMs can be systematically debugged and improved by treating training data as code, allowing for targeted "patches" that fix concept-level gaps and reasoning errors.
A principled framework for General World Models reveals the limitations of current systems and the architectural requirements for future progress.
Visual reasoning gets a boost: forcing models to "draft" their reasoning in code and render visual proofs dramatically improves performance by bridging the gap between perception and logical structure.
