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A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.
SymCA is an LLM-empowered interpretable CA framework that materializes column annotation as a global-to-local symbolic decision process and is accurate, robust, and interpretable.
SkillZip is proposed, an execution-aware procedural abstraction framework that performs contract-preserving compression over section-level graphs that hydrates a compact, dependency-closed context and expands macros only when required.
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