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This work examines whether a targeted LLM training approach can transfer distinct RE skills to open models via inverse basic-block execution: given an x86-64 basic block and a desired output register state, the model predicts the input register values that produce it.
This work formalizes this function association problem and evaluates the extent to which embedding models can bidirectionally associate between these two representations, and develops a new model that outperforms other models on all function association baselines and generalizes to a constant-algorithm association task...
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