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待翻譯:Qwen3.8 27B addition in words

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AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯: Research: Qwen3.8 27B addition in words Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results: I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment. I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf. Here's the result for a run of 30 attempts per combination with reasoning disabled: Then I ran it again wit…

待翻譯:Qwen3.8 27B addition in words
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Research: Qwen3.8 27B addition in words Simon Willison’s Weblog Subscribe 4th October 2026 Research Qwen3.8 27B addition in words — A benchmark tested whether the local Qwen3.8-27B-Q4_K_M.gguf model could add positive integers and express exact results solely in English words, using 5,070 reasoning-disabled cases and a paired 169-case comparison with medium reasoning. Without reasoning, it achieved 23.57% numeric accuracy, with performance dropping from 97.04% for one- to three-digit operands to 6.44% for ten- to thirteen-digit operands, despite 96.17% format compliance. Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results: I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment. I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf. Here's the result for a run of 30 attempts per combination with reasoning disabled: Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%: It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here. Here's a version of the report that includes the reasoning traces from some of those larger calculations, which include text like this: Wait, let me redo this more carefully. 4,299,366,105,622 6,088,794,067,970 Let me align them: 4 2 9 9 3 6 6 1 0 5 6 2 2 6 0 8 8 7 9 4 0 6 7 9 7 0 Adding from right to left: Position 1 (units): 2 + 0 = 2 Position 2 (tens): 2 + 7 = 9 Position 3 (hundreds): 6 + 9 = 15, write 5, carry 1 Recent articles We're going to need default hard budget caps on pretty much everything - 3rd October 2026 OpenAI DevDay 2026 live blog - 29th September 2026 2026 in LLMs (so far) - 27th September 2026 This is a beat by Simon Willison, posted on 4th October 2026. mathematics 23 ai 2,260 generative-ai 2,003 local-llms 165 llms 1,970 qwen 62 llm-reasoning 104 dgx-spark 7 Monthly briefing Sponsor me for $10/month and get a curated email digest of the month's most important LLM developments. Pay me to send you less! Sponsor & subscribe Disclosures Colophon © 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026

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