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  1. Jiwei.com

    Longsys Expects Up to 14.9 Billion Yuan Profit in First Three Quarters: Behind Nearly 20-Fold Profit Growth, the Memory Cycle Begins to Shift

    Longsys expects a net profit attributable to shareholders of 14 billion to 14.9 billion yuan for the first three quarters of this year, up 1,864.55% to 1,990.84% year on year, with operating revenue of 38 billion to 40 billion yuan, up 127.08% to 139.03% year on year. However, net profit for the third quarter alone is about 3.423 billion to 4.323 billion yuan, down about 35.6% to 49.0% quarter on quarter. The company said the marginal contribution of rising memory prices to profitability has slowed significantly. In the first half of the year, Longsys's enterprise storage revenue was 2.140 billion yuan, up 208.80% year on year, and it said the memory industry is shifting from a consumer price cycle to an AI-driven technology cycle.

    Longsys expects a net profit attributable to shareholders of 14 billion to 14.9 billion yuan for the first three quarters of this year, up 1,864.55% to 1,990.84% year on year, with operating revenue of 38 billion to 40 billion yuan, up 127.08% to 139.03% year on year. However, net profit for the third quarter alone is about 3.423 billion to 4.323 billion yuan, down about 35.6% to 49.0% quarter on quarter. The company said the marginal contribution of rising memory prices to profitability has slo

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    #hot-signal#Industry news#English text is being prepared.
  9. Hugging FaceSelected

    Hugging Face Releases Beam: A 501B Open Source Model Supporting 1M Context and Self-Hosting

    Hugging Face has released Beam, a 501B-parameter sparse MoE open source model aimed at long-text reasoning and agent tasks with 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, with 43 layers using a 512-token sliding window and 9 layers using global attention; the KV cache grows by 36 KiB per token, about one-sixth that of a full-attention approach. The official release provides three checkpoints—BF16, FP8, and NVFP4; NVFP4 can compress the weights from about 1 TB to about 280 GB, and day-0 support is already available in transformers, vLLM, and SGLang.

    Hugging Face has released Beam, a 501B-parameter sparse MoE open source model aimed at long-text reasoning and agent tasks with 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, with 43 layers using a 512-token sliding window and 9 layers using global attention; the KV cache grows by 36 KiB per token, about one-sixth that of a full-attention approach. The official release provides three checkpoints—BF16, FP8, and NVFP4; NVFP4 can compress t

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    #lab#Industry news#Open-source ecosystem#Hugging Face
  20. 36Kr · AISelected

    DeepMind AlphaProof Nexus appears in《Science》: autonomously solves 9 Erdős problems

    Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-agent tournament and retains only the large model plus Lean feedback can also solve these 9 problems, though the hardest few are more expensive.

    Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-

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    #hot-signal#Papers/Research#Reasoning#DeepMind