Universal Multimodal Retrieval (UMR) aims to map different modalities (e.g., visual and textual) into a shared embedding space for multi-modal retrieval. Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such as Marvel, which projects visual features into the language model (LM) space for integrating with text modality, and late-fusion approaches, such as UniVL-DR, encode visual and textual inputs using separate encoders and obtain fused embeddings through addition. Our pilot study reveals that Marvel exhibits visual modality collapse, which is characterized by the model’s tendency to disregard visual features while depending excessively on textual cues. In contrast, although UniVL-DR is less affected by this issue, it is more susceptible to semantic misalignment, where semantically related content is positioned far apart in the embedding space. To address these challenges, we propose MiMIC, which introduces two key innovations: (1) a fusion-in-decoder architecture for effective multimodal integration, and (2) robust training through single-modality mix-in and random caption dropout. Experiments on the WebQA+ and EVQA+ datasets—where image in documents or queries might lack captions—indicate that MiMIC consistently outperforms both early- and late-fusion baselines.

Telemarketing towards merchants is considerably more complex than traditional dialogue systems. Given a user utterance, the response must not only follow the context but also strategically and naturally guide the conversation toward marketing objectives. A common approach is to fine-tune LLMs using high-quality dialogue data from top sales. However, we find that even after careful data cleaning, these data cannot be used directly due to two issues:(1) Poor strategy-following: Real-world conversations are highly random with much chit-chat topics, easily leading deviation from intended strategy.(2) Insufficient expert knowledge learning: Expert knowledge appears infrequently or not at all in limited collected corpus.To this end, we introduce a hybrid data synthesis framework with two main innovations. First, we unify the input schema with profile and strategy designed by top sales, and extract them via a Multi-task paradigm.In addition, we propose Role-playing Simulation and Session Prefix Completion to complementarily improve the strategy-following and inject long-tail expert knowledge into our fine-tuned model – TeleBot.Comprehensive online and offline evaluations demonstrate its effectiveness.In particular, in terms of the final marketing results – High Intention Rate, TeleBot reaches the performance level of the top 25% of human sales.