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Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However, visual variations unrelated to the underlying control process can obscure this relationship in learned representations. We investigate the connection between action predictability, defined as...
Co is introduced, a Counterfactual Consistency framework to enhance action controllability through two complementary constraints: Multi-step counterfactual consistency constrains reference, inverse-action, and zero-action rollouts, while action-spatial counterfactual consistency enforces consistent predictions under mi...
ADSL-PDE improves both search efficiency and optimization stability, achieving an improvement of more than 52% within the first ten evolution iterations, suggesting a broader principle for LLM-driven auto-design: effective agents do not merely require stronger reasoning, but rather a search representation that concentr...
GraphIR, an architecture-aware intermediate representation that supplements executable programs with a mutation-aligned candidate state, achieves the best overall search performance while maintaining comparable model size and favorable end-to-end NAS efficiency when integrated into OpenEvolve.
This paper argues that the key lies in designing an appropriate search-state representation that connects the internal knowledge of LLMs with the structural requirements and constraints of visual-token pruning, and proposes AutoPrune, a training-free framework for LLM-driven visual-token pruning policy design.
HELP, a program optimizer that prompts an LLM to generate diverse, non-overlapping atomic edits and then systematically selects and composes them into coherent program improvements using evaluator scores, is introduced.
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