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This paper presents a language model forecasting system for merger arbitrage that combines expert-guided context engineering with fine-tuning on historical deals, achieving state-of-the-art performance on over 400 large deals across 42 countries.
EGG is an expert-guided agent framework that decomposes GPU kernel generation into algorithmic structure design and hardware-specific tuning, using a stage-aware multi-agent collaboration mechanism. It achieves a 2.13x average speedup over PyTorch on KernelBench and real-world workloads.
This paper introduces E-PMQ, an expert-guided post-merge quantization framework that addresses the combined deviations from merging and quantization, achieving significant accuracy improvements on multi-task merged models like CLIP-ViT and FLAN-T5.