Human memory is reconstructive, not a faithful recording. Current multimodal LLMs (MLLMs) lack this capability: they process images through a frozen visual encoder, produce a one-shot text output, and discard internal representations. We present DoYouRemember, a three-stage architecture introducing reconstructive memory into MLLMs: (1) a VQ-VAE compresses images into discrete visual tokens, (2) a LoRA-fine-tuned LLM jointly attends to visual and text tokens, and (3) a Diffusion Decoder reconstructs images from the LLM's hidden states. On 1,000 3D facial skin texture maps and 99,000 unlabeled facial images, we find that LLM hidden states contain approximately zero recoverable visual information -- the same Decoder producing clear reconstructions from VQ-VAE tokens (pre-LLM) produces pure noise from LLM hidden states (post-LLM), demonstrating that the LLM understands images but does not remember them. Training a shared memory matrix M under backpropagation systematically fails due to gradient cancellation (O(1/sqrt(N)) attenuation). We identify three root causes and show that local EMA updating resolves all three: each image updates only its top-8 slots out of 64, preserving inter-slot diversity. The resulting M (229K parameters, 16x compressed) approaches the VQ upper bound on unseen test images. Scaling to 1,024 slots surpasses it (LPIPS 0.056 vs. 0.071), as M's continuous representation avoids VQ quantization error. We unify these findings under an information-theoretic framework: memory is lossy compression, recall is decompression, and hallucination is an inherent property of lossy decompression rather than a defect.
Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single framework. However, existing UMMs face prominent challenges: (1) the inherent learning conflicts between visual understanding and generation tasks, leading to suboptimal modeling in both tasks; (2) different understanding and generation visual spaces impeding scalability; (3) over-reliance on task-specific data that neglects the duality of text-image understanding and generation. To address these challenges, we propose UniDDT, which leverages a Noisy ViT encoder along with an LLM to unify semantic encoding for visual generation and understanding tasks, while employing a separate diffusion decoder to decouple diffusion decoding from text decoding. With this Noisy ViT encoder, UniDDT is able to leverage the latent space as a unified visual representation, enabling seamless compatibility between understanding and generation tasks. Thus, the scalability within the generation tasks and the semantic expressiveness within understanding tasks can be balanced. Also, we construct dual data structures from the same image-text pairs, fostering interdependence between the generation and understanding data to exploit their inherent duality. Extensive experiments demonstrate that UniDDT achieves effective unification of multimodal understanding and generation with enhanced semantic consistency and scalability. For visual generation tasks, our UniDDT achieves 0.87 GenEval score and 86.9 DPG overall score. For multimodal understanding tasks, our UniDDT achieves 1699.5 score on MME benchmark and 76.5 overall score on SEEDbench.