In the world of Retrieval-Augmented Generation (RAG), most systems still treat document retrieval like a popularity contest — fetch the most relevant-looking text and hope the generator can stitch the answer together. But as any manager who has tried to merge three half-baked reports knows, relevance without completeness is a recipe for failure.

A new framework, Compositional Answer Retrieval (CAR), aims to fix that. Instead of asking a retrieval model to find a single “best” set of documents, CAR teaches it to think like a strategist: break the question into its components, retrieve for each, and then assemble the pieces into a coherent whole.

Why Compositionality Matters

Imagine you’re researching a market trend that requires combining regulatory updates from two countries, recent commodity price data, and a competitor’s product release timeline. A conventional RAG system might bring you 10 documents all about one country’s regulations, missing the other crucial pieces. CAR, by contrast, treats the task like a supply chain — decompose, source each part, integrate.

This is especially critical in multi-hop question answering, where the answer depends on facts scattered across unrelated sources. Benchmarks like MuSiQue, HotpotQA, and 2WikiMultihopQA have long exposed the weakness of “relevance-only” retrieval: the best single document is rarely the whole answer.

How CAR Works

Step 1 — Question Decomposition

  • CAR uses a decomposition model to split a question into targeted sub-questions.
  • Example: “Which authors won both the Hugo and Nebula awards for the same work?” → two sub-questions, one for Hugo winners, one for Nebula winners.

Step 2 — Targeted Retrieval

  • Each sub-question drives an independent retrieval pass.
  • Encourages diversity so no component of the answer is missed.

Step 3 — Evidence Assembly

  • The collected evidence is merged and passed to a generator (e.g., an LLM) to produce the final answer.

Under the hood, CAR employs a coverage-oriented loss function and reinforcement learning signals based on answer completeness, not just retrieval accuracy.

Component Traditional RAG CAR Framework
Retrieval Target Single best-ranked docs Docs covering all answer parts
Training Signal Relevance labels Answer coverage reward
Weakness May miss key evidence Prioritizes completeness

Business Relevance

For enterprise applications, CAR’s philosophy maps directly onto real-world information workflows:

  • Compliance: Combining regulations from multiple jurisdictions.
  • Business Intelligence: Merging financial, operational, and market data.
  • Research & Development: Integrating findings from separate studies.

By forcing retrieval to plan for coverage, CAR turns RAG from a hopeful guesser into a methodical investigator — a change that could significantly improve AI’s reliability in high-stakes decisions.

Looking Ahead

The CAR approach invites a rethink of retrieval objectives: what if we trained every enterprise search and analytics tool to value complete answer construction over local relevance? In regulated industries, in strategy consulting, in scientific synthesis — this shift could mean the difference between half-answers and actionable intelligence.

Cognaptus: Automate the Present, Incubate the Future