
Google Research team has announced the launch of Gemini-SQL2 on X. They described this system as a breakthrough text-to-SQL capability powered by Gemini 3.1 Pro. Gemini-SQL2 posted 80.04% execution accuracy on the BIRD Text-to-SQL Leaderboard (Single Model). Google’s chart places it above its own Gemini-SQL, the prior top entry. The metric measures whether generated SQL runs and returns correct results, not whether it looks valid. https://x.com/GoogleResearch/status/2065475343205740911
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Published breakthroughs pushing the state of the art.

Benchmarks and Analysis of GLM-5.2

As AI becomes part of HPC workflows, validation, data quality, and trust are emerging as key factors in technology and buying decisions.

Long-context large language models (LLMs) face a memory bottleneck that has nothing to do with model weights. During decoding, transformers cache the key and value (KV) vectors for every token at every layer so they don’t have to recompute attention. This cache grows linearly with sequence length and batch size, and at long context with high concurrency it can dwarf the model’s own footprint. Consider Llama-3.1-70B in BF16. Its KV cache costs about 0.31 MB per token (80 layers ×
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