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This work introduces a novel intermediate representation (IR), grounded in relational algebra and sparse iteration theory, that provides a unified abstraction for data and computation that enables workload-agnostic, end-to-end optimizations across diverse analytics applications.
DCC is the first data-centric ML compiler for PIM systems that jointly co-optimizes data rearrangements and compute code in a unified tuning process to enable high performance execution.
ARBIGRAPH is introduced, a benchmark generator for evaluating whether tool-assisted language agents can retain, update, compose, and discard task-relevant context across extended reasoning workflows, and shows that ARBIGRAPH exposes failures that are not visible from single-task evaluation alone.
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