What is fusion in AI?

Explained

Fusion is the integration of data or representations from more than one source into a single output. In multimodal AI, it means combining modalities such as text, images, audio or sensor data so a model can reason over all of them together. In search and RAG, it means merging the results of several retrieval methods, for example keyword search and vector search, or several reformulations of the same query, into one ranked list. In both cases the goal is the same: each source covers gaps in the others, which improves the accuracy and richness of the result.

Fusion in multimodal AI

Multimodal models need to decide when to combine their inputs:

• Early fusion: modalities are combined at the input or feature level and processed together.
• Late fusion: each modality is processed separately and the predictions are combined at the end.
• Intermediate or hybrid fusion: modalities are processed separately at first and then merged inside the model, often through attention.

The choice affects how well the model captures relationships between modalities, how much data it needs and how it behaves when one input is missing.

Fusion in search and RAG

Hybrid search runs several retrievers in parallel, typically a keyword method such as BM25 and a semantic vector search, and fuses their rankings. Keyword search is good at exact terms, codes and names; vector search is good at meaning and paraphrase.

A widely used method is reciprocal rank fusion (RRF), which scores each document by its position in each list and adds the scores up. It needs no training and works well across very different retrievers. RAG-Fusion applies the same idea to several rewritten versions of the user's question.

When it is worth it

• Information is spread across formats, such as documents with images, calls with transcripts or sensors with logs.
• A single retriever misses relevant results, for example product codes that semantic search does not match.
• Accuracy matters more than the extra latency and cost of running several models or searches.

Frequently asked questions

Further reading

• Multimodal AI Architectures: Unlocking Deep Insights via Fusion
• Fusion RAG: Boosting Accuracy and Relevance in Generative AI

Related terms

• Integration Connecting AI models and agents to a company's existing systems, data and processes so they can read real information and act in real workflows.
• Multimodal AI AI that can take in and relate more than one type of data, such as text, images, audio or video, often within a single model.
• RAG A technique that retrieves relevant passages from a knowledge base and gives them to a language model as context, so its answer rests on those sources.
• Retrieval The search step that fetches the documents or passages most relevant to a query, before they are shown or passed to a model.
• Vector A list of numbers that represents a piece of data, such as a sentence or an image, so that similar content ends up with similar numbers.
• Agent Agenda An AI agent's live plan: the ordered next steps, open subtasks, blocked items and checkpoints it works through towards a goal.