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Tuesday

  2. Ma Dongxi NLP

    Meta releases technical report for Muse Glimmer 30B, an agentic model for consumer-grade hardware

    Meta has released the technical report for Muse Glimmer, a 30B open-weight model aimed at agentic tasks on consumer-grade hardware; it runs at 4-bit precision on a single consumer-grade GPU and leads open models of the same scale on tool calling and multi-step planning. The relayer adds that the model surpasses Qwen3.6-27B and Gemma4-31B on some metrics, is about 17GB after 4-bit quantization, targets devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on an RTX 5090, roughly 3.1 times faster than ordinary decoding.

    Meta has released the technical report for Muse Glimmer, a 30B open-weight model aimed at agentic tasks on consumer-grade hardware; it runs at 4-bit precision on a single consumer-grade GPU and leads open models of the same scale on tool calling and multi-step planning. The relayer adds that the model surpasses Qwen3.6-27B and Gemma4-31B on some metrics, is about 17GB after 4-bit quantization, targets devices with 24GB of VRAM, and uses DFlash speculative decoding to reach about 233 tokens/s on

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    #hot-signal#Model releases#English text is being prepared.#Agents
  7. Huxiu

    Anthropic survey: employees expect AI to be able to complete 75% of their own work by 2030, while the public expects only 28%

    The Anthropic Institute released "The AI Expectations Gap" on October 16, 2026. The survey shows that Anthropic employees on average expect that by 2030 AI will be able to complete tasks corresponding to 75% of their current working hours, while the average expectation among employed Americans is only 28%. The two groups broadly agree in their judgments of AI's current capabilities; the divergence is concentrated in the pace of technological progress and the scope of enterprise adoption in the coming years. For example, 90% of employees expect that by 2027 AI will be capable of designing and publishing research tasks, while only 38% of the public expects so. Despite expecting a high degree of task automation, 95% of Anthropic employees still believe the company's total headcount will increase over the next 12 months, with a median expected growth of 66%.

    The Anthropic Institute released "The AI Expectations Gap" on October 16, 2026. The survey shows that Anthropic employees on average expect that by 2030 AI will be able to complete tasks corresponding to 75% of their current working hours, while the average expectation among employed Americans is only 28%. The two groups broadly agree in their judgments of AI's current capabilities; the divergence is concentrated in the pace of technological progress and the scope of enterprise adoption in the c

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    #hot-signal#Papers/Research#Industry news#Anthropic

Monday

Sunday

Saturday

Friday

  2. 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.

    3 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.

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

Thursday