Unverified50% confidenceTradeoffExact time
Many end-to-end parsing models based on VLMs only output text sequences without positional information, which creates an obstacle in scenarios that require locating text in the source document.
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50%
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Long-term
Relevance
10/8/2026
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Related Claims
UnverifiedGrounding prompts that instruct the model to answer only based on provided documents and cite the relevant passages can greatly reduce the probability of fabrication, but cannot eliminate it entirely.67% similarUnverifiedLLMs exhibit Positional Bias, tending to focus more on the beginning and end of the input sequence, with information in the middle easily being forgotten.65% similarUnverifiedGeneral-purpose VLMs struggle to cover vertical scenarios like forms that demand extremely high structural precision, and specialized document parsing models are becoming an important component of RAG and document intelligence pipelines.65% similarUnverified标准自回归模型逐Token从左到右生成文本，在时间步t仅依赖之前的输出，缺乏自我审查和回溯机制，导致错误积累效应63% similarUnverifiedThe process by which a model compresses high-dimensional semantic representations into a discrete token sequence is lossy—fine-grained semantic information, uncertainty distributions, and contextual associations are flattened when converted to text.63% similar
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