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DynaVieW reveals that a schema-guided approach can drastically improve the modeling of visual dynamics, leading to superior performance in narrative generation and simulation tasks.
LLMs can achieve high compilation rates in formal reasoning by either fabricating axioms during proof generation or subtly mistranslating premises, revealing a critical gap between proof validity and formalization faithfulness.
Training a 70B parameter open-source LLM on a supercomputer reveals the hidden engineering hurdles and infrastructure adaptations needed to democratize large-scale AI development beyond the private sector.
