* Bayesian & Cognitive Advances: Transformers achieve ultra-precise Bayesian inference with 10⁻³ to 10⁻⁴ bit accuracy, while CREST boosts reasoning accuracy by 17.5% and cuts token usage by 37.6%.

* Model Efficiency & Scaling: TG reduces data needs by up to 8% and parameters by 42% compared to GPT-2, and Recursive Language Models handle 100x longer inputs at similar or lower inference costs.

* State-of-the-Art Performance: Youtu-LLM sets a new bar for sub-2B parameter models with 128k context, DLCM improves zero-shot benchmarks by +2.69%, and ADOPT outperforms all prior prompt optimization methods.

* Benchmark Breakthroughs: Encyclo-K’s top models hit 62.07% accuracy on complex knowledge queries, while Youtu-Agent accelerates RL training by 40% and scores over 71% on WebWalkerQA and GAIA benchmarks.

* Novel Insights & Safety: Diffusion Language Models match optimal step complexity in chain-of-thought sampling, and safety analysis reveals a 9.2x disparity between past- and future-tense prompt safety rates.

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