Researchers propose FreeAct, a quantization framework that abandons static one-to-one transformation constraints to handle dynamic activation patterns in multimodal and diffusion LLMs. The method assigns token-specific transformation matrices to activations while keeping weights unified, demonstrating up to 5.3% performance improvements over existing approaches.
A large-scale benchmarking study comparing multimodal large language models (MLLMs) against traditional OCR-enhanced pipelines for document information extraction finds that image-only inputs can achieve comparable performance. The research evaluates multiple out-of-the-box MLLMs on business documents and proposes an automated hierarchical error analysis framework using LLMs to diagnose failure modes.
A large-scale benchmarking study finds that modern multimodal large language models (MLLMs) can extract information from business documents nearly as well as traditional OCR+MLLM pipelines. The research introduces an automated error analysis framework and suggests that careful schema design and prompt engineering can further close the performance gap.