The paper introduces a new task called Multimodal Unsupervised Continual Post-Training (MU‑CPT), which allows multimodal large language models (MLLMs) to continuously learn from streaming unlabeled data. It identifies token‑level visual dependence (VD) as essential for MU‑CPT, using its structural distortion to detect cross‑modal forgetting and its heterogeneity to guide new‑task learning. The proposed Visual Dependence‑Aware (VDA) framework includes Visually Constrained Optimal Transport (VC‑OT) to mitigate forgetting and Visually Modulated Adaptation (VMA) to enhance new‑task plasticity, achieving a balance between stability and adaptability in MU‑CPT.
In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Existing unsupervised p...
arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.
arXiv:2607. 27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge.
arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.
arXiv:2603. 12055v3 Announce Type: replace-cross Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion.
The paper introduces DynaTokens, a transformer-based token generator that produces fine‑tuning tokens on demand for continual VideoQA with multimodal large language models. It uses shared generation weights and meta‑learning‑inspired regularisers to reduce task interference and forgetting, connecting the objective to sharpness‑aware optimisation for flatter cross‑task minima. Experiments on standard continual VideoQA benchmarks show that DynaTokens achieves higher average accuracy, lower forgetting, better zero‑shot generalisation, and robust cross‑modal transfer in a new ImageQA→VideoQA protocol.
The paper introduces FPCD, a framework that stabilizes the modality gap in vision‑language models during continual learning. It combines Fast Null‑space Projection, which projects gradients onto approximate null spaces to reduce interference, with Contrastive Distillation that aligns image‑to‑text and text‑to‑image similarity distributions using a small reference set. On the 11‑task MTIL benchmark, FPCD achieves strong performance, maintaining stable modality gaps and superior zero‑shot retrieval on COCO Captions and Flickr30k.
arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.
arXiv:2607. 09202v1 Announce Type: cross Abstract: Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation.
C-LoRA introduces a continual learning approach for pre‑trained visual models that uses a single shared Low‑Rank Adaptation (LoRA) adapter. It employs a learnable routing matrix split into a stability component (R_base) and a plasticity component (R_delta) to balance preserving past knowledge and adapting to new tasks. This design eliminates the need for expanding modules or complex inference fusion while maintaining competitive performance on multiple benchmarks.
arXiv:2606. 01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style contrastive learning.
The paper introduces a Fisher-guided submodular data selection method for continual pre‑training of large language models, addressing the forgetting problem that arises when a target‑domain corpus overwrites pretrained knowledge. By decomposing candidate gradients into anchor and frontier components and optimizing a log‑determinant submodular objective, the method selects data that both improves target‑domain performance and limits forgetting. Experiments on TinyLlama‑1.1B and Llama‑3.1‑8B show that the selector achieves superior adaptation and forgetting control while using ten times fewer tokens than traditional replay strategies.