NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference
cs.IR, cs.AI, cs.CV
Submitted: 2026-08-31
Updated: 2026-08-31
Code: https://github.com/erikkaum/maxsim
Project page: https://hcompai.github.io/late-interaction-kernels/how-it-works.html
Terminology
Sources
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- Flamingo: a Visual Language Model for Few-Shot Learning
- VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
- Longformer: The Long-Document Transformer
- PaliGemma: A versatile 3B VLM for transfer
- Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
- EuroBERT: Scaling Multilingual Encoders for European Languages
- BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs
- Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit
- Chameleon: Mixed-Modal Early-Fusion Foundation Models
- MaskGIT: Masked Generative Image Transformer
- EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
- Cautious Weight Decay
- SOLO: A Single Transformer for Scalable Vision-Language Modeling
- M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
- Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Pooling
- Scaling Vision Transformers to 22 Billion Parameters
- MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings
- Unveiling Encoder-Free Vision-Language Models
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