# Global Context Guidance

Diffusion language models generate text in parallel through iterative denoising, but existing guidance methods build a weak model and pay an extra forward pass at every denoising step, doubling inference cost. GCG first shows these models lean on local context and underuse distant evidence during denoising, then amplifies the outputs of attention heads that carry global context directly in feature space, without a weak model. On LLaDA-8B, Dream-7B and LLaDA-1.5 it beats U-CFG and A-CFG across several benchmarks, while LLaDA-8B keeps the same FLOPs as the unguided baseline.

Diffusion language models generate text in parallel through iterative denoising, but existing guidance methods build a weak model and pay an extra forward pass at every denoising step, doubling inference cost. GCG first shows these models lean on local context and underuse distant evidence during denoising, then amplifies the outputs of attention heads that carry global context directly in feature space, without a weak model. On LLaDA-8B, Dream-7B and LLaDA-1.5 it beats U-CFG and A-CFG across several benchmarks, while LLaDA-8B keeps the same FLOPs as the unguided baseline.