Data Science, Machine Learning, AI & IOT

🔬 AI Research Digest 📅 Week of Jun 9–Jun 16, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧮 MaxProof: Scaling Mathematical Proof Generation Beyond Human Gold-Medal Authors/Org: Jiacheng Chen et al. | arXiv: 2606.13473 Bottleneck solved: LLMs could not reliably generate and verify competition-level mathematical proofs end-to-end without external scaffolding. MaxProof trains a single M3 model to generate, verify, and repair proofs via generative-verifier RL, then applies population-level test-time scaling — enabling it to score 35/42 on IMO 2025 and 36/42 on USAMO 2026, surpassing the human gold-medal threshold on both. 🔗 MaxProof on arXiv ━━━━━━━━━━━━━━━━━━━━━━━━ 2. 🛠️ nanochat: Train a Full ChatGPT Clone for Under $100 Authors/Org: Andrej Karpathy | GitHub: karpathy/nanochat Bottleneck solved: Full LLM training pipelines (tokenization → pretraining → RLHF → inference) were scattered across multiple large, hard-to-understand codebases. nanochat packs the entire pipeline into a single readable repo — a single --depth flag auto-tunes all hyperparameters, and you can train a GPT-2-level model on 8×H100s for ~$15 on spot instances, making it the definitive hands-on LLM learning resource for practitioners. 🔗 nanochat on GitHub ━━━━━━━━━━━━━━━━━━━━━━━━ 3. 🕸️ LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems Authors/Org: arXiv contributors | arXiv: 2606.11560 Bottleneck solved: LLMs hallucinate and lose factual consistency because their parametric memory lacks structured relational grounding. This survey/position paper argues for making graph computation a first-class citizen in LLM architectures — using knowledge graphs for semantic constraints and retrieval, and LLMs to enrich graph reasoning — pointing toward systems where structured and neural memory work in tandem rather than in isolation. 🔗 LLMs+Graphs on arXiv ━━━━━━━━━━━━━━━━━━━━━━━━ 💡 Stay curious. Read the papers. For More: @kdnuggets @datasciencechats