TWN attaches separate reasoning and embedding LoRA adapters to a frozen backbone with gradient detachment and a self-supervised gate that decides per input whether to generate CoT, achieving SOTA on MMEB-V2 with 3-5% added parameters and up to 50% fewer reasoning tokens.
arXiv preprint arXiv:2510.05014 , year=
9 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
CodeMMR creates a unified embedding space for text, code, and images, outperforming baselines by 10 nDCG@10 points and boosting RAG code generation quality.
Bottleneck Tokens paired with a masked generative objective achieve state-of-the-art unified multimodal retrieval performance among 2B-scale models on the MMEB-V2 benchmark with 78 datasets.
PLUME uses latent-state autoregressive rollouts and a progressive training curriculum to deliver efficient reasoning for universal multimodal embeddings without generating explicit rationales.
Text-only contrastive fine-tuning of an MLLM with hard negatives produces embeddings that handle temporal, negation, and multimodal nuances in video retrieval and achieves SOTA performance.
Latent reasoning through eight bottleneck tokens gives multimodal retrieval performance close to explicit chain-of-thought models at a fraction of the inference cost.
TTE-Flash trains latent think tokens with CoT generation loss and embedding tokens with contrastive loss to deliver high-performance multimodal representations without generating explicit reasoning at inference time.
CausalEmbed uses auto-regressive generation with iterative margin loss to produce multi-vector embeddings that reduce visual token counts 30-155x while retaining competitive performance on VDR benchmarks.
Using a structured rewrite instead of CoT as the generative interface improves MLLM-based multimodal embedding performance while cutting thinking tokens by about half.
citing papers explorer
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Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture
TWN attaches separate reasoning and embedding LoRA adapters to a frozen backbone with gradient detachment and a self-supervised gate that decides per input whether to generate CoT, achieving SOTA on MMEB-V2 with 3-5% added parameters and up to 50% fewer reasoning tokens.
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CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval
CodeMMR creates a unified embedding space for text, code, and images, outperforming baselines by 10 nDCG@10 points and boosting RAG code generation quality.
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Bottleneck Tokens for Unified Multimodal Retrieval
Bottleneck Tokens paired with a masked generative objective achieve state-of-the-art unified multimodal retrieval performance among 2B-scale models on the MMEB-V2 benchmark with 78 datasets.
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PLUME: Latent Reasoning Based Universal Multimodal Embedding
PLUME uses latent-state autoregressive rollouts and a progressive training curriculum to deliver efficient reasoning for universal multimodal embeddings without generating explicit rationales.
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Adapting MLLMs for Nuanced Video Retrieval
Text-only contrastive fine-tuning of an MLLM with hard negatives produces embeddings that handle temporal, negation, and multimodal nuances in video retrieval and achieves SOTA performance.
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LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck
Latent reasoning through eight bottleneck tokens gives multimodal retrieval performance close to explicit chain-of-thought models at a fraction of the inference cost.
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TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens
TTE-Flash trains latent think tokens with CoT generation loss and embedding tokens with contrastive loss to deliver high-performance multimodal representations without generating explicit reasoning at inference time.
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CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding
CausalEmbed uses auto-regressive generation with iterative margin loss to produce multi-vector embeddings that reduce visual token counts 30-155x while retaining competitive performance on VDR benchmarks.
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Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings
Using a structured rewrite instead of CoT as the generative interface improves MLLM-based multimodal embedding performance while cutting thinking tokens by about half.