REDDIT · REDDIT// 21d agoRESEARCH PAPER

KET-RAG pushes Llama 8B to 70B reasoning

Researchers found smaller models fail multi-hop reasoning not from poor retrieval, but from failing to connect context. By using structured prompting and graph-traversal compression (KET-RAG), Llama 3.1 8B matches Llama 3.3 70B performance at 12x lower cost.

// ANALYSIS

This is a huge win for local inference and agentic workflows where multi-hop reasoning usually demands massive, expensive models.

–The true bottleneck in RAG isn't finding information, but the smaller model's inability to logically link retrieved chunks
–KET-RAG compresses context by 60% via graph traversal, reducing noise without requiring extra LLM calls
–Structured chain-of-thought forces the model to decompose questions into graph queries before answering
–Achieving 70B performance on an 8B model cuts inference costs by roughly 90%, making complex multi-hop QA feasible for everyday tasks

// TAGS

ket-ragragllmreasoningopen-weights

DISCOVERED

21d ago

2026-03-22

PUBLISHED

21d ago

2026-03-21

RELEVANCE

9/ 10

AUTHOR

Greedy-Teach1533