OPEN LINK ↗
// 67d 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
67d ago
2026-03-22
PUBLISHED
67d ago
2026-03-21
RELEVANCE
9/ 10
AUTHOR
Greedy-Teach1533