Qwen3.5-0.8B

Headlines · 2

1. 5h
   MIT CSAIL demonstrates LLM weight-space programming

   MIT CSAIL researchers introduced Weight-Space Programming, a method that lets an LLM reason about and directly edit neural-network weights without gradients or a training loop. The authors report that GPT-6 Astra improved Qwen3.5-0.8B by editing 0.016% of its weights, raising MATH-500 from 7.9% to 48.1% and tripling IFEval under the same sampling settings. They also report that Astra created small game-playing networks that outperformed networks trained with PPO for 100 million steps.

   Hacker News 首页（100+ 分） · 🔥 44
2. Oct 21
   OvisOCR3: 0.8B open-weight OCR model adds structured JSON and cross-page tables

   OvisOCR3 has been released as a 0.8B OCR model built on Qwen3.5-0.8B and published under Apache 2.0 with vLLM inference examples, according to a Reddit post in r/LocalLLaMA. Beyond OvisOCR2's Markdown parsing, the poster says it adds structured JSON output with bounding boxes, element categories and content plus a layout-only mode, key information extraction from specified fields or a supplied JSON template, figure descriptions with Mermaid output for flowcharts, and cross-page table reconstruction that parses pages and then merges table fragments with the same model. The post cites model-card numbers of 96.74 on OmniDocBench v1.6, 88.80 on MDPBench and 82.31 on PureDocBench, with the largest reported gains over OvisOCR2 on MDPBench (69.00 to 88.80) and PureDocBench (75.10 to 82.31); the poster notes these are the team's own reported figures rather than independent measurements and asks how the structure holds up on messy scans and long financial tables.

   r/LocalLLaMA · 🔥 0

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