Unverified50% confidenceOpinionExact time
The core strengths of LLMs lie in language understanding, generation, and cross-task generalization, and their training objectives do not naturally match pixel-level visual localization.
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50%
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Long-term
Relevance
10/29/2026
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Related Claims
UnverifiedMultimodal LLMs perform limitedly on pixel-level localization tasks (such as bounding box regression in object tracking) because their training objectives emphasize semantic understanding rather than spatial precision.77% similarUnverifiedThe training objectives of mainstream LMMs almost all revolve around a single visual input, taking in one video or one image and outputting the corresponding text, so their cross-sample reasoning ability is not explicitly optimized.77% similarUnverifiedThe training objective of LLMs is to generate fluent, coherent, contextually appropriate text, not to guarantee factual accuracy75% similarUnverifiedThe LLM training and inference pipeline includes stages such as tokenization, embedding layers, self-attention mechanisms, feed-forward networks, and residual connections.74% similarUnverifiedThe real differences between visual encoders lie not in model architecture, but in training objectives.73% similar
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