Editing Multimodal Ai (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Multimodal Architecture Approaches
! Approach !! Integration Level !! Flexibility !! Training Complexity
|-
| Early fusion (native multimodal) || Input level || Very high || Very high (need multimodal pre-training)
|-
| Late fusion (adapter-based) || Representation level || High (plug vision encoder) || Moderate (fine-tune adapter only)
|-
| Cross-attention fusion || Layer level || High || High
|-
| CLIP-based retrieval + language || Output level || Moderate || Low (training-free retrieval)
|-
| Prompt-based (image as text description) || Interface level || Low || Very low (no multimodal training)
|}

'''Failure modes and limitations:'''
* '''Spatial reasoning failures''' — Current VLMs often struggle with questions requiring precise spatial reasoning ("Is the red ball to the left or right of the blue cube?").
* '''Hallucination in visual context''' — Models confidently describe objects not present in images, or miss salient details. This is more dangerous than text-only hallucination because users trust the model's "eyes."
* '''OCR failures''' — Small text, handwriting, and non-Latin scripts remain challenging even for strong VLMs.
* '''Long video understanding''' — Processing hours-long videos requires extreme context length and remains an active research challenge.
* '''Cross-modal consistency''' — Text-to-image models may generate images inconsistent with prompt details (wrong object counts, colors, spatial relationships).
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