# Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality

arXiv: https://arxiv.org/abs/2505.02466 · HTML: https://mlsanity.com/paper/2505.02466/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2025.

## Built on by (most cited first, 30 of 30)

- [BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent](https://mlsanity.com/paper/2508.06600/) (2025)
- [OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis](https://mlsanity.com/paper/2603.20278/) (2026)
- [RaDeR: Reasoning-aware Dense Retrieval Models](https://mlsanity.com/paper/2505.18405/) (2025)
- [Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model](https://mlsanity.com/paper/2507.05513/) (2025)
- [Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs](https://mlsanity.com/paper/2505.16967/) (2025)
- [Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video](https://mlsanity.com/paper/2510.03458/) (2025)
- [e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings](https://mlsanity.com/paper/2601.03666/) (2026)
- [AgentIR: Reasoning-Aware Retrieval for Deep Research Agents](https://mlsanity.com/paper/2603.04384/) (2026)
- [Multi-Vector Index Compression in Any Modality](https://mlsanity.com/paper/2602.21202/) (2026)
- [SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension](https://mlsanity.com/paper/2508.01959/) (2025)
- [Distillation versus Contrastive Learning: How to Train Your Rerankers](https://mlsanity.com/paper/2507.08336/) (2025)
- [Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection](https://mlsanity.com/paper/2601.23066/) (2026)
- [Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs](https://mlsanity.com/paper/2509.23684/) (2025)
- [Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2510.24870/) (2025)
- [RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.10644/) (2026)
- [RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval](https://mlsanity.com/paper/2602.02444/) (2026)
- [Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage](https://mlsanity.com/paper/2603.08819/) (2026)
- [ReasonAudio: A Benchmark for Evaluating Reasoning Beyond Matching in Text-Audio Retrieval](https://mlsanity.com/paper/2605.03361/) (2026)
- [Findings of the MAGMaR 2026 Shared Task](https://mlsanity.com/paper/2606.12295/) (2026)
- [MVEB: Massive Video Embedding Benchmark](https://mlsanity.com/paper/2606.14958/) (2026)
- [Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget](https://mlsanity.com/paper/2608.00916/) (2026)
- [MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.17640/) (2026)
- [Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability](https://mlsanity.com/paper/2605.28522/) (2026)
- [VeriTrip: A Verifiable Benchmark for Travel Planning Agents over Unstructured Web Corpora](https://mlsanity.com/paper/2605.28683/) (2026)
- [Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors](https://mlsanity.com/paper/2606.02814/) (2026)
- [Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.07924/) (2026)
- [Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding](https://mlsanity.com/paper/2606.09331/) (2026)
- [Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers](https://mlsanity.com/paper/2608.08809/) (2026)
- [Omni-Interactive Universal Embedder](https://mlsanity.com/paper/2608.27044/) (2026)
- [MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos](https://mlsanity.com/paper/2609.28437/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have driven interest in billion-scale retrieval models with strong generalization across retrieval tasks and languages. Additionally, progress in large vision-language models has created new opportunities for multimodal retrieval. In response, we have updated the Tevatron toolkit, introducing a unified pipeline that enables researchers to explore retriever models at different scales, across multiple languages, and with various modalities. This demo paper highlights the toolkit's key features, bridging academia and industry by supporting efficient training, inference, and evaluation of neural retrievers. We showcase a unified dense retriever achieving strong multilingual and multimodal effectiveness, and conduct a cross-modality zero-shot study to demonstrate its research potential. Alongside, we release OmniEmbed, to the best of our knowledge, the first embedding model that unifies text, image document, video, and audio retrieval, serving as a baseline for future research.