Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text, offering substantial advantages over single-modality retrieval systems. However, existing CIR methods suffer from representation space fragmentation: queries and targets comprise heterogeneous modalities and are processed by distinct encoders, forcing models to bridge misaligned representation spaces only through post-hoc alignment, which fundamentally limits retrieval performance. As evidenced by t-SNE visualization in Fig.(a), this architectural asymmetry manifests as three distinct, well-separated clusters in the feature space, directly demonstrating how heterogeneous modalities create fundamentally misaligned representation spaces from initialization. In this work, we propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components. First, we introduce a Multi-level Chain-of-Thought (MCoT) prompting strategy that guides Multimodal Large Language Models to generate discriminative, semantically compatible captions for target images, establishing modal symmetry. Building upon this, we design a symmetric dual-tower architecture where both query and target sides utilize the identical shared-parameter Q-Former for cross-modal encoding, ensuring consistent feature representations and further reducing the alignment gap. Finally, this architectural symmetry enables an entropy-based, temporally dynamic Memory Bank strategy that provides high-quality negative samples while maintaining consistency with the evolving model state. Extensive experiments on four benchmark datasets demonstrate that our CSMCIR achieves state-of-the-art performance with superior training efficiency. Comprehensive ablation studies further validate the effectiveness of each proposed component.

Low-bit quantization is crucial for deploying Large Language Models (LLMs) on resource-constrained hardware. However, existing Post-Training Quantization (PTQ) methods adopt a monolithic view of outliers, overlooking their global spatial patterns within LLM architectures and weights that are quantization-sensitive but not numerically large. To overcome these challenges, we introduce MOAQ, a novel finetuning-free weight-only PTQ framework based on a new perspective for outlier analysis. MOAQ holistically addresses the outlier problem by three core components: (1) Spatial Calibration (SC) uses a clustering-optimized Hadamard matrix to reorganize disordered outliers into both discrete and clustered patterns. (2) Local Awareness and Suppression (LAS) adopts a dual strategy guided by our outlier metric. It assigns higher bit-widths to clustered outliers via Clustered Outlier Bit Search (COBS), enabling flexible fractional-bit quantization, while further refining quantization parameters for local discrete outliers through Local Outlierness-Aware Calibration (LOAC). (3) Global Feedback and Denoising (GFD) introduces a dynamic correction mechanism informed by our outlier metric to mitigate the progressive cross-layer enrichment of outliers. Extensive experiments show that MOAQ achieves state-of-the-art performance, outperforming both finetuning-free and many finetuning-based methods with substantially lower computational cost.

Given the long textual product information and the product image, Multi-modal Product Summarization (MPS) aims to increase customers’ desire to purchase by highlighting product characteristics with a short textual summary. Existing MPS methods can produce promising results. Nevertheless, they still 1) lack end-to-end product summarization, 2) lack multi-grained multi-modal modeling, and 3) lack multi-modal attribute modeling. To improve MPS, we propose an end-to-end multi-grained multi-modal attribute-aware product summarization method (MMAPS) for generating high-quality product summaries in e-commerce. MMAPS jointly models product attributes and generates product summaries. We design several multi-grained multi-modal tasks to better guide the multi-modal learning of MMAPS. Furthermore, we model product attributes based on both text and image modalities so that multi-modal product characteristics can be manifested in the generated summaries. Extensive experiments on a real large-scale Chinese e-commence dataset demonstrate that our model outperforms state-of-the-art product summarization methods w.r.t. several summarization metrics. Our code is publicly available at: https://github.com/KDEGroup/MMAPS.