arXiv:2601. 09974v2 Announce Type: replace Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time.
arXiv:2510. 16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states.
ReMem is a new recommendation agent framework that rethinks perception and memory for long-context recommendation tasks. It replaces raw HTML parsing with OCR-based multimodal perception from screenshots, extracting structured information in a platform-agnostic way. The framework also introduces a chunk-wise sequential memory update strategy and a multi-memory GRPO variant to efficiently model evolving user preferences over arbitrarily long interaction histories, achieving a 5.16% average improvement over state-of-the-art baselines on three recommendation agent tasks.
The paper examines generative models that are trained through self‑consuming loops where users curate preferred samples. It shows that when training relies solely on user‑curated synthetic data, initial biases are amplified, leading the system to converge to a single dominant outcome. Introducing reference data at a sufficient rate changes the dynamics, producing a unique globally attracting equilibrium, and the authors propose an algorithm to select reference distributions and mixing weights to steer the system toward desirable equilibria while reducing data collection costs.
arXiv:2609.37544v1 Announce Type: new Abstract: Content-generation agents continuously receive impressions, clicks, conversions, and negative feedback from recommendation systems, providing real-worl...
arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv:2609.37800v1 Announce Type: cross Abstract: Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two cha...
FairDiff is a new fairness‑aware diffusion framework designed to mitigate the self‑reinforcing Matthew Effect in Diffusion Recommender Models (DRMs). It introduces Popularity Condition Guidance (PCG) to reweight inference‑time gradients and penalize high‑popularity items, and a Semantic Calibration (SC) module that aligns forward and reverse distributions via optimal transport. Experiments show FairDiff achieves state‑of‑the‑art performance while reducing popularity bias in DRMs.
COPE (Continual Optimization with Personalized embedding and self-Evaluation) is a new framework that continually personalizes large language models using learnable user embeddings and self‑evaluation to generate proxy rewards. It integrates preference capture, self‑evaluation calibration, and personalized response optimization into a single update step, allowing continuous model updates even when explicit user feedback is sparse. Experiments demonstrate that COPE outperforms both training‑free and training‑based baselines, remains complementary to Retrieval‑Augmented Prompting, and shows reliable self‑evaluation, meaningful preference patterns, stable general capabilities, and robustness to shifting preferences and alternative evaluators.
arXiv:2610.28567v1 Announce Type: cross Abstract: Text-to-image personalization aims to equip pretrained diffusion models with the ability to learn user-specific concepts from only a few reference im...
arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model b...