1 citations · 1 across the 11 of their papers we have counts for
13 papers
Growth-induced trade-off between polarity, electrical isolation, and structural quality in AlN grown on Si(111) by plasma-assisted molecular beam epitaxy
Hossein Yazdani, Nhu Tran, Xin Du +3
We investigate the effects of substrate temperature and Al flux, at a fixed N flux, on the polarity, electrical properties, and structural quality of AlN films grown on high-resist…
The Robot Data Factory
Sami Haddadin, Ivan Laptev, Ian Reid +18
Physical AI requires more than increasingly large robot datasets: intelligent robots acquire knowledge through continuous interaction with the physical world. We argue that the def…
EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants
Yue Peng, Lanke Xia, Zihan Wang +3
Large language models can generate interactive web interfaces, but reliable generative UI requires maintaining an executable artifact as user requests evolve. We introduce EvoGenUI…
TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning
Yushen Liang, Yue Peng, Baosheng Jin +6
Vision--Language--Action (VLA) policies such as and OpenVLA perform well on many manipulation tasks, but they are often reactive: the next action is predicted from the cu…
GVLA: Geometric Inductive Bias for Vision-Language-Action Models
Yue Peng, Yongzhe Zhao, Artur Habuda +5
Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbones. However, their…
NovelAPIBench: Diagnosing How A Code LLM Learns to Use Novel APIs
Jinnuo Liu, Yue Peng, Jinhan Niu +1
Rapidly evolving software libraries require coding language models to use unfamiliar APIs. Yet failed solutions alone tell us little about what API knowledge models need or how to…
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong +213
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most wh…
STEP3-VL-10B Technical Report
Ailin Huang, Chengyuan Yao, Chunrui Han +90
We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-…
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
Jingcheng Hu, Yinmin Zhang, Shijie Shang +17
We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability…
StepFun-Prover Preview: Let's Think and Verify Step by Step
Shijie Shang, Ruosi Wan, Yue Peng +4
We present StepFun-Prover Preview, a large language model designed for formal theorem proving through tool-integrated reasoning. Using a reinforcement learning pipeline that incorp…
StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion
Yutong Wu, Di Huang, Ruosi Wan +8
Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffe…
Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
StepFun, :, Bin Wang +195
Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…
NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms
Yashan Wang, Shangda Wu, Jianhuai Hu +7
We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language M…