Ouro

Headlines · 2

1. Nov 3
   LoopVQ Targets Sub-1-Bit KV Cache Quantization

   Minsoo Cheong and Sungjoo Yoo’s paper introduces LoopVQ, a KV cache vector quantization method for looped transformers that jointly codes channels across multiple loop steps and fine-tunes codebooks against full-precision outputs. The authors report that it outperforms existing vector quantization methods on general, long chain-of-thought reasoning and long-context retrieval tasks with Ouro and Huginn at 0.5 bits per element. In SGLang, the implementation nearly halves decode latency at a 32K-token context and raises peak serving throughput by up to 4.3× and 6.1× for the 1.4B and 2.6B Ouro models versus BF16.

   Hugging Face · Papers · 🔥 0
2. Nov 2
   Lev reuses weights to deepen decisions through repeated computation

   Researchers from Lehigh University and other institutions proposed Lev, a decision model that uses repeated computation rather than token generation to increase reasoning depth. According to the researchers, the same weights support multiple depths, with a lightweight stopping mechanism determining how many rounds to run. The model reads option probabilities after each round, and its rounds are jointly trained. On the 231-question public JevBench, project records for the same Lev-2.6B checkpoint show accuracy rising from 63.2% after one round to 76.2% after four rounds, a 13.0 percentage-point increase without changing the checkpoint weights. The report notes that these figures are replayed project-homepage records rather than live inference results.

   DoNews · 🔥 0

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