Modern LLM architectures like DeepSeek V3, Kimi 2, and Llama 4 have adopted new techniques to improve computational efficiency and distinguish themselves from other models, including Multi-Head Latent Attention (MLA) and Mixture-of-Experts (MoE) layers.
Key Takeaways:
- Large Language Model (LLM) architectures like DeepSeek V3 and Kimi 2 have shown improved computational efficiency through innovations like MLA and MoE layers.
- The use of MoE layers helps reduce inference costs for large base models, offering a trade-off between model capacity and inference efficiency.
- New architectures like Qwen3 and SmolLM3 have made the case for a more principled approach to position encoding in transformer models.
Next big thing after LLMs - World Model [explained on the example of V-JEPA2]
Agent can do everything Deep Research does and more
Just 5 hours after this viral post, OpenAI got Gold at the International Math Olympiad
DiffRhythm 1.2 music generation model produces "Avicii vs Nicky Romero - I Could Be the One" nearly verbatim
[P] Federated Learning on a decentralized protocol (CLI demo, no central server)
Looking for diarization model better than Pyannote
Claude Performance Report: July 13 – July 20, 2025