5 citations · 5 across the 3 of their papers we have counts for
5 papers
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
Harnessing Language Models as Optimization Modeling Agents
Yunxiang Luo, Xin Xu, Tianhao Chen +7
Large language models (LLMs) can translate natural-language optimization problems into solver programs, but reliably producing correct formulations and implementations remains chal…
AIS: Adaptive Importance Sampling for Quantized RL
Jiajun Zhou, Wei Shao, Lingchao Zheng +2
Reinforcement learning (RL) for large language models (LLMs) is dominated by the cost of rollout generation, which has motivated the use of low-precision rollouts (e.g., FP8) paire…
A Case Study of Selected PTQ Baselines for Reasoning LLMs on Ascend NPU
Yuchen Luo, Fangyue Zhu, Ruining Zhou +4
Post-Training Quantization (PTQ) is crucial for efficient model deployment, yet its effectiveness on Ascend NPU remains under-explored compared to GPU architectures. This paper pre…
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings
Jiahao Xu, Wei Shao, Lihui Chen +1
This paper proposes the DistillCSE framework, which performs contrastive learning under the self-training paradigm with knowledge distillation. The potential advantage of DistillCS…