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LLMs can now decompile stripped binaries with >50% re-executability using a surprisingly lightweight 1.3B parameter model, closing the gap with expert-level performance.
MLLMs can slash image annotation costs by 1000x while TagLLM closes the performance gap with human annotations by 60-80%.