HippoRAG: First, do know(ledge) Graph

**Alibaba** released new open-source **Qwen2** models ranging from **0.5B to 72B parameters**, achieving SOTA results on benchmarks like MMLU and HumanEval. Researchers introduced **Sparse Autoencoders** to interpret **GPT-4** neural activity, improving feature representation. The **HippoRAG** paper proposes a hippocampus-inspired retrieval augmentation method using knowledge graphs and Personalized PageRank for efficient multi-hop reasoning. New techniques like **Stepwise Internalization** enable implicit chain-of-thought reasoning in LLMs, enhancing accuracy and speed. The **Buffer of Thoughts (BoT)** method improves reasoning efficiency with significant cost reduction. A novel scalable MatMul-free LLM architecture competitive with SOTA Transformers at billion-parameter scale was also presented. *"Single-Step, Multi-Hop retrieval"* is highlighted as a key advancement in retrieval speed and cost.