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Daily AI Briefing: Zhipu Open-Sources GLM-4.7; MiniMax and Zhipu Pass HKEX Hearings
Zhipu AI has launched and open-sourced GLM-4.7, achieving a state-of-the-art score of 73.8% on SWE-bench Verified and 87.4 on the tool-calling task τ²-Bench, surpassing Claude Sonnet 4.5. MiniMax and Zhipu successively passed Hong Kong Stock Exchange hearings; the former's 2024 revenue grew 782% year-over-year to $30.5 million, while the latter is valued at 24.4 billion yuan, with average daily token consumption reaching 4.2 trillion in November. In the same period, OpenAI released GPT-5.2-Codex, and Google launched Gemini 3 Flash, with response speed improved 3x.
Zhipu AI has launched and open-sourced GLM-4.7, achieving a state-of-the-art score of 73.8% on SWE-bench Verified and 87.4 on the tool-calling task τ²-Bench, surpassing Claude Sonnet 4.5. MiniMax and Zhipu successively passed Hong Kong Stock Exchange hearings; the former's 2024 revenue grew 782% year-over-year to $30.5 million, while the latter is valued at 24.4 billion yuan, with average daily token consumption reaching 4.2 trillion in November. In the same period, OpenAI released GPT-5.2-Codex
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Neural Algorithmic Reasoning: Teaching Neural Networks to Execute Classical Algorithms
The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. The author also cautions that algorithmic alignment does not mean one can arbitrarily apply any highly expressive GNN, and out-of-distribution performance remains a key challenge.
The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. Th
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