Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive (AR) decoding. However, most DLMs still maintain one generative state per token, so every denoising step processes a state sequence as long as the output sequence, limit...
Xin-Song Feng, Peng Du, Zhi-Zhuo Yang et al.· 0 citations
The model shows substantial gains over the previous default model for Writer Agent, and compares favorably with several recent models on public benchmarks, scoring the highest on BFCL Core at $0.785$ and posts the highest six-benchmark mean of the cohort.
Peng Du, Kiran P. Kamble, R. Vasudev et al.· 0 citations
The first fully automated framework that synthesizes high-quality, proof-centric benchmarks from natural language mathematical corpora and a new type of hybrid-formatted questions, named ``$m$-out-of-$n$ multiple judge questions'', specifically designed to enable robust, automatic evaluation while being resilient to gu...
Ye-Bo Peng, Zixiang Liu, Yao-Ming Li et al.· arXiv.org· 1 citation
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