arXiv:2606. 04797v1 Announce Type: cross Abstract: Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts.
arXiv:2610.01331v1 Announce Type: new Abstract: Continual personalization of text-to-image diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. Howev...
CLEAN (Continual Learning with Expandable and Architecturally Isolated Networks) is an incremental cognitive diagnosis framework that allows the concept space to grow while guaranteeing that historical diagnoses remain unchanged. It achieves this by freezing past diagnostic functions, applying orthogonal column masking to prevent gradient interference, and adding expandable full‑rank branches with micro‑variance initialization for new concepts. Experiments on three large‑scale educational datasets show that CLEAN attains zero Representation Drift, preserving old‑item metrics exactly while performing competitively on new items.
SeqLoRA introduces a bilevel optimization framework for continual learning of text‑to‑image diffusion models, jointly optimizing LoRA factors while enforcing orthogonality between new and existing concept bases. The method achieves monotone descent, convergence guarantees, and a high‑probability bound on catastrophic forgetting that depends on residual interference energy. Experiments on Stable Diffusion with up to 101 concepts demonstrate improved identity preservation, minimal cross‑concept leakage, no need for a fusion step, and scalability beyond memory limits of fusion‑based approaches.
arXiv:2606. 03083v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions.
arXiv:2605.19750v2 Announce Type: replace Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilit...
arXiv:2609.15598v1 Announce Type: cross Abstract: Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evol...
arXiv:2609.06986v1 Announce Type: new Abstract: Language models may need to internalize information that arrives over time and retain it through many subsequent updates. To study this challenge, we i...
arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.
The paper introduces PIECE, a Parameter Importance-Driven Continual Learning method that selectively updates only 0.1% of core parameters to preserve general abilities while learning new domain knowledge. PIECE employs two importance estimators—PIECE‑F using Fisher Information and PIECE‑S combining gradient and curvature information—to guide updates. Experiments on three language models and two multimodal models demonstrate that PIECE maintains general capabilities and achieves state‑of‑the‑art continual learning performance without accessing prior training data or adding parameter overhead.
arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
arXiv:2610.10379v1 Announce Type: new Abstract: Continual learning requires models to adapt to new domains and new classes while retaining prior knowledge. Many existing methods rely on continued opt...
arXiv:2605. 20803v2 Announce Type: replace-cross Abstract: Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge.