Bottleneck Tokens for Unified Multimodal Retrieval

• URL: http://arxiv.org/abs/2604.11095v1
• Date: Mon, 13 Apr 2026 07:12:12 GMT
• Title: Bottleneck Tokens for Unified Multimodal Retrieval
• Abstract summary: Adapting decoder-only multimodal large language models (MLLMs) for unified multimodal retrieval faces two structural gaps.<n>First, existing methods rely on implicit pooling, which overloads the hidden state of a standard vocabulary token as the sequence-level representation.<n>Second, contrastive fine-tuning specifies what the embedding should match but provides no token-level guidance on how information should be compressed into it.<n>We introduce Bottleneck Tokens (BToks), a small set of learnable tokens that serve as a fixed-capacity explicit pooling mechanism.
• Score: 16.707536543758344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapting decoder-only multimodal large language models (MLLMs) for unified multimodal retrieval faces two structural gaps. First, existing methods rely on implicit pooling, which overloads the hidden state of a standard vocabulary token (e.g., <EOS>) as the sequence-level representation, a mechanism never designed for information aggregation. Second, contrastive fine-tuning specifies what the embedding should match but provides no token-level guidance on how information should be compressed into it. We address both gaps with two complementary components. Architecturally, we introduce Bottleneck Tokens (BToks), a small set of learnable tokens that serve as a fixed-capacity explicit pooling mechanism. For training, we propose Generative Information Condensation: a next-token prediction objective coupled with a Condensation Mask that severs the direct attention path from target tokens to query tokens. All predictive signals are thereby forced through the BToks, converting the generative loss into dense, token-level supervision for semantic compression. At inference time, only the input and BToks are processed in a single forward pass with negligible overhead over conventional last-token pooling. On MMEB-V2 (78 datasets, 3 modalities, 9 meta-tasks), our approach achieves state-of-the-art among 2B-scale methods under comparable data conditions, attaining an Overall score of 59.0 (+3.6 over VLM2Vec-V2) with substantial gains on semantically demanding tasks (e.g., +12.6 on Video-QA).

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