13 citations · 21 across the 38 of their papers we have counts for
41 papers
KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling
Zhiheng Hu, Yixun Wei, Jian Zhou +8
Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregr…
Scheduling Mixed RL Rollouts Beyond Prefix Locality
Zetao Hong, Song Yuan, Yuanhao Ding +4
Modern reinforcement learning (RL) post-training pipelines for large language models (LLMs) increasingly combine rollout workloads across multiple domains and feedback paradigms. P…
TensorCast: The Missing Tensor Management Layer in Large Language Model Infrastructure
Yuhan Zhou, Yuchu Luo, Hao Nie +5
Modern LLM infrastructure increasingly manages tensors not only as computation data, but also as persistent states shared across distributed components. Existing systems optimize i…
Don't Predict, Prioritize: Rethinking GPU Reliability Assessment
Difeng Ma, Changhua Pei, Yuanwei Lu +7
The reliability of Graphics Processing Units (GPUs) is a criticalbottleneck for modern large-scale AI infrastructure, where a sin-gle node failure can disrupt synchronous training…
MentalThink: Shaping Thoughts in Mental SVG World
Kangheng Lin, Jisheng Yin, Dingming Li +11
We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization. The core of MentalThink…
PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception
Yana Wei, Hongbo Peng, Yanlin Lai +14
We introduce PerceptionRubrics, a rubric-based evaluation framework that addresses the gap between saturated benchmark scores and real-world brittleness. Shifting evaluation from h…
JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting
Lanxiang Hu, Zhaoxiang Feng, Yulun Wu +9
Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: inc…
Skill-Guided Continuation Distillation for GUI Agents
Zhimin Fan, Hongwei Yu, Yeqing Shen +9
Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-in…
From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
Taojie Zhu, Wentao Zhao, Rui Sun +7
Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a historical market, let it trade…
AndroidDaily: A Verifiable Benchmark for Mobile GUI Agents on Real-World Closed-Source Applications
Yifan Sui, Xin Huang, Hongbing Li +14
The rapid development of GUI foundation models and mobile GUI agents has spurred numerous evaluation benchmarks, yet most rely on simulated environments or open-source applications…
WebVR: Benchmarking Multimodal LLMs for WebPage Recreation from Videos via Human-Aligned Visual Rubrics
Yuhong Dai, Yanlin Lai, Mitt Huang +9
Existing web-generation benchmarks rely on text prompts or static screenshots as input. However, videos naturally convey richer signals such as interaction flow, transition timing,…
DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI
En Yu, Haoran Lv, Jianjian Sun +46
Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed…
PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering
Xiangfeng Wang, Hangyu Guo, Yanlin Lai +11
While model-based verifiers are essential for scaling Reinforcement Learning with Verifiable Rewards (RLVR), current outcome-centric verification paradigms primarily focus on the c…
GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments
Haodong Li, Jingwei Wu, Quan Sun +18
An image generation model used as a graphical user interface (GUI) environment must produce not only a plausible screen but also the correct response to an action. Existing visual-…
R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging
Yanlin Lai, Mitt Huang, Hangyu Guo +11
Reinforcement Learning from Human Feedback (RLHF) remains indispensable for aligning large language models (LLMs) in subjective domains. To enhance robustness, recent work shifts t…
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…
Step-DeepResearch Technical Report
Chen Hu, Haikuo Du, Heng Wang +64
As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands f…
GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning
Haolong Yan, Yeqing Shen, Xin Huang +9
With the rapid development of Large Vision Language Models, the focus of Graphical User Interface (GUI) agent tasks shifts from single-screen tasks to complex screen navigation cha…
ReasonEdit: Towards Reasoning-Enhanced Image Editing Models
Fukun Yin, Shiyu Liu, Yucheng Han +12
Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion deco…
Thinking by Doing: Building Efficient World Model Reasoning in LLMs via Multi-turn Interaction
Bao Shu, Yan Cai, Jianjian Sun +11
Developing robust world model reasoning is crucial for large language model (LLM) agents to plan and interact in complex environments. While multi-turn interaction offers a superio…
NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
NextStep Team, Chunrui Han, Guopeng Li +47
Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ ve…
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…
Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models
Houyi Li, Wenzhen Zheng, Qiufeng Wang +8
Training Large Language Models (LLMs) is prohibitively expensive, creating a critical scaling gap where insights from small-scale experiments often fail to transfer to resource-int…