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Every published item, in order · 2490 items
Nov 4
Nov 3
ModelsUS vs ChinaMulti-sourceSelected14:52
Weight Space Programming lets LLMs read and edit neural network weights

Direct weight editing by LLMs reframes model debugging: instead of retraining, practitioners could patch specific failure modes like repetitive outputs in place. The GDPx benchmark and DeepSeek super-agent references suggest early infrastructure is forming around this idea. Researchers and engineers working on post-training or model introspection should track how these methods compare with fine-tuning workflows.

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Official accountLatent Space (swyx)@swyx .aiSource
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ModelsBuildersOfficial71°10:20
Chinese content腾讯 Hunyuan 发布 Hyra 2.0 科研智能体,可自建工具用于蛋白质设计、数学证明与量子化学

腾讯 Hunyuan 的 Hyra 2.0 不只会答题,还能自己造工具:蛋白质设计快 57 倍,连数学证明和量子化学代码都搞出来了,开源可看。

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Primary sourcePandaily@[email protected] (Pandaily)Source
Nov 2
Nov 1
ModelsBuildersMulti-sourceSelected09:07
SenseNova Loop-9B-Preview open-source model matches 27B rivals

Parameter-sharing loops let a 9B model punch at 27B weight class, which cuts inference cost for teams running complex agent pipelines on limited hardware. Developers building coding or office-automation agents can download the weights from Hugging Face and benchmark them against Qwen3.5-27B directly. The approach also signals a broader shift in Chinese labs toward compute-depth efficiency over raw parameter scaling.

Event
IT之家Source
Oct 31
Oct 30
ModelsBuildersMulti-sourceSelected21:18
Xiaomi open-sources 0.8B OCR model Xiaomi-OCR-0

A sub-1B model reaching near-top OmniDocBench scores means document parsing and invoice extraction can now run on modest hardware rather than large multimodal models. Teams building document pipelines can deploy it locally, pull weights from Hugging Face or GitHub, and use the JSON and Markdown outputs directly in automation workflows. Against rivals like PaddleOCR's larger document models, it trades scale for efficiency while matching them on benchmarks.

Event
IT之家Source

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