1 citations · 1 across the 2 of their papers we have counts for
15 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Fractured Chain-of-Thought Reasoning
Baohao Liao, Hanze Dong, Yuhui Xu +4
Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference…
Gradually Compacting Large Language Models for Reasoning Like a Boiling Frog
Yiran Zhao, Shengyang Zhou, Zijian Wu +7
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, but their substantial size often demands significant computational resources. To reduce resource c…
Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis
Tianbao Xie, Jiaqi Deng, Xiaochuan Li +12
Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in comput…
Lost at the Beginning of Reasoning
Baohao Liao, Xinyi Chen, Sara Rajaee +5
Recent advancements in large language models (LLMs) have significantly advanced complex reasoning capabilities, particularly through extended chain-of-thought (CoT) reasoning that…
Reward-Guided Speculative Decoding for Efficient LLM Reasoning
Baohao Liao, Yuhui Xu, Hanze Dong +5
We introduce Reward-Guided Speculative Decoding (RSD), a novel framework aimed at improving the efficiency of inference in large language models (LLMs). RSD synergistically combine…