A constraint-guided multi-agent system turns raw decompiler output into re-executable code at 84-97% success rates, outperforming prior LLM decompilation methods on real binaries.
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A four-tier framework for AI inference GHG emissions in Scope 3 reporting, progressing from direct physical estimation using GPU benchmarks to EEIO spend-based methods, with a case showing low total emissions.
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Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery
A constraint-guided multi-agent system turns raw decompiler output into re-executable code at 84-97% success rates, outperforming prior LLM decompilation methods on real binaries.
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Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
A four-tier framework for AI inference GHG emissions in Scope 3 reporting, progressing from direct physical estimation using GPU benchmarks to EEIO spend-based methods, with a case showing low total emissions.