TailorMind: Preference-aligned multimodal content generation via hypergraph collaborative filtering
TailorMind is a new system for personalized multimodal content generation that translates user behavioral traces into generation-ready preferences without relying on existing item pools or user-generated content. The approach combines hypergraph collaborative filtering for sparse user histories, ranking-error feedback with textual gradient descent for profile optimization, and retrieval-augmented style control. The authors introduce TailorBench, a benchmark across three platforms evaluated on coherence, novelty, aesthetic quality, hallucination, and profiling, reporting up to 29% Recall gains in reranking over baselines.
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Active Query Synthesis for Preference Learning via Mutual Information Maximization
This paper introduces Info-Synth, an active query synthesis framework for preference learning that generates optimal pairwise queries by maximizing a mutual information objective in continuous space, bypassing the computational cost of pool-based evaluation. A confidence-aware response model is proposed to handle ambiguous comparisons between nearly identical or highly dissimilar items. Two finite-pool extensions (Pair M-dist and Pair Opt-dist) are also introduced. The framework is validated on synthetic preference tasks, text summarization datasets, and robotic controller tuning.
TuneJury: Open pairwise reward model for text-to-music preference alignment
Researchers introduce TuneJury, an open-source instance-level pairwise reward model for text-to-music generation that predicts preference scores from text prompts and audio clips. The model is trained on publicly available human-preference labels spanning arena votes, crowdsourced comparisons, and expert ratings. A post-hoc anchor calibration method enables efficient adaptation to new generators without full retraining. The reward model drives gains across best-of-N selection, latent optimization, and expert-iteration post-training.
Semantic Generative Tuning (SGT) for Unified Multimodal Models
This paper introduces Semantic Generative Tuning (SGT), a post-training paradigm for unified multimodal models (UMMs) that bridges the gap between visual understanding and visual generation. The authors find that image segmentation tasks serve as optimal generative proxies, providing structural semantics that improve both perception and generative layout fidelity. SGT aligns representation spaces across understanding and generation objectives, improving feature linear separability and visual-textual attention allocation. Evaluations show consistent gains on multimodal comprehension and generative fidelity benchmarks.
GenAIR: LLM-grounded archetype representations improve sequential recommendation
GenAIR is a framework that uses LLMs to infer 'archetype' profiles of items' ideal target audiences, generating richer item embeddings for sequential recommendation systems. A behavioral calibration objective aligns these semantic embeddings with actual user interaction patterns, closing the gap between language-space representations and real-world behavior. Experiments on three datasets show consistent improvements over state-of-the-art baselines across multiple sequential recommendation models.
M³Exam: Benchmark for Multimodal Memory in Realistic User-Agent Interactions
Researchers introduce M³Exam, a query-centric multimodal conversational memory benchmark designed to evaluate language agents on realistic user-agent interactions, including cross-modal grounding and implicit information inference. Existing benchmarks are critiqued for assuming sparse visuals and human-human interaction formats. The paper also proposes M³Proctor, a companion memory method that detects query modality bias and retrieves raw visual sources on demand, achieving 13% accuracy improvement while reducing index-construction time and retrieved tokens by over 70%.
Taiji: Pareto Optimal Policy Optimization for LLM-enhanced recommendation at Kuaishou scale
Researchers from Kuaishou present Taiji, an LLM-as-Enhancer framework for industrial recommender systems that addresses two bottlenecks: generating high-quality chain-of-thought data via reverse-engineered reasoning and rejection sampling during SFT, and balancing semantic vs. ID-based rewards during RL alignment via a new algorithm called Pareto Optimal Policy Optimization (POPO). The system has been deployed on Kuaishou's advertising platform since May 2026, serving over 400 million daily users. The paper contributes both a practical deployment case study and a novel RL alignment technique for the LLM4Rec paradigm.
CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning
CRAM is a new method for Multimodal Continual Instruction Tuning (MCIT) that addresses the tension between catastrophic forgetting and parameter efficiency in MLLMs. It combines adaptive-rank instantiation to dynamically allocate parameters based on capability gaps, centroid-guided routing to reuse existing expert knowledge, and an orthogonality penalty to confine new updates to task-specific directions. The approach uses a Mixture-of-Experts architecture where task-specific patterns are isolated into independent modules, avoiding both the interference of shared updates and the parameter bloat of fully isolated expansion. Experiments across diverse benchmarks show consistent improvements over existing MCIT methods.
Preference Optimization for Vision Language Models
This Hugging Face blog post covers the application of Direct Preference Optimization (DPO) to vision-language models (VLMs). It likely discusses how preference learning techniques originally developed for text-only LLMs can be adapted to multimodal settings. The post addresses training methodology for aligning VLMs with human preferences across both visual and textual modalities.