The Year in AI Papers
The year/Labs/Qwen

Research lab

Qwen

Alibaba and Qwen research across language, vision, audio, coding, agents, and efficient open models.

Papers
21
Citations
3.7K
Official code
15

21 papers from Qwen

01

Research paper

Qwen-AgentWorld: Language World Models for General Agents

Qwen-AgentWorld introduces the first language world models (LWMs) for simulating agentic environments across seven domains (MCP, Search, Terminal, SWE, Android, Web, OS). Trained on over 10 million real-world interaction trajectories via a three-stage pipeline (CPT, SFT, RL), the models (35B-A3B and 397B-A17B) predict next environment states given actions.…

Yuxin Zuo, Zikai Xiao, Li Sheng, Fei Huang, et al.
Published
Jun 2026
Citations
0
Code
931 stars
Read summary Original
02

arXiv.org

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Qwen-VLA is a unified embodied foundation model that extends the Qwen3.5-4B vision-language backbone with a DiT-based flow-matching action decoder, enabling a single model to handle manipulation, navigation, and trajectory prediction across diverse tasks, environments, and robot embodiments. It uses embodiment-aware prompt conditioning and a unified…

Qiuyue Wang, Mingsheng Li, Jian Guan, Jinhui Ye, et al.
Published
May 2026
Citations
20
Code
Not linked
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03

arXiv.org

Qwen-Image-VAE-2.0 Technical Report

Qwen-Image-VAE-2.0 is a suite of high-compression image VAEs (f16 and f32) designed to overcome the trade-off between compression ratio, reconstruction fidelity, and diffusability. The architecture uses Global Skip Connections (GSC) to preserve fine details, expanded latent channels, and an attention-free, asymmetric encoder-decoder backbone for…

Zekai Zhang, Deqing Li, Kuan Cao, Yujia Wu, et al.
Published
May 2026
Citations
1
Code
69 stars
Read summary Original
04

arXiv.org

Qwen-Image-2.0 Technical Report

Qwen-Image-2.0 is an image generation foundation model that unifies text-to-image (T2I) generation and instruction-based image editing in a single framework. It addresses challenges in ultra-long text rendering (up to 1K tokens), multilingual typography, high-resolution photorealism (native 2K), complex instruction following, and inference efficiency. The…

Bing Zhao, Chenfei Wu, Deqing Li, Hao Meng, et al.
Published
May 2026
Citations
8
Code
Not linked
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05

arXiv.org

OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

OCCUBENCH is a benchmark for evaluating AI agents on real-world professional tasks across 100 scenarios, 65 domains, and 10 industry categories. It uses Language Environment Simulators (LESs), where an LLM simulates domain-specific tool responses, enabling evaluation in domains without public environments. A multi-agent pipeline synthesizes 382 solvable,…

Xiaomeng Hu, Yinger Zhang, Fei Huang, Jianhong Tu, et al.
Published
Apr 2026
Citations
1
Code
21 stars
Read summary Original
06

arXiv.org

FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization

FIPO (Future-KL Influenced Policy Optimization) is a reinforcement learning algorithm that addresses the coarse-grained credit assignment problem in GRPO-style training for large language models. By incorporating a discounted Future-KL divergence into the policy update, FIPO re-weights token advantages based on their influence on subsequent trajectory…

Chiyu Ma, Shuo Yang, Kexin Huang, Jinda Lu, et al.
Published
Mar 2026
Citations
18
Code
130 stars
Read summary Original
07

arXiv.org

HopChain: Multi-Hop Data Synthesis for Generalizable Vision-Language Reasoning

The paper introduces HopChain, a scalable framework for synthesizing multi-hop vision-language reasoning data for reinforcement learning with verifiable rewards (RLVR) training of vision-language models (VLMs). The authors identify that long chain-of-thought (CoT) reasoning in VLMs suffers from diverse, compounding failure modes (perception, reasoning,…

Shenzhi Wang, Shixuan Liu, Jing Zhou, Chang Gao, et al.
Published
Mar 2026
Citations
2
Code
Not linked
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08

arXiv.org

Qwen3-Coder-Next Technical Report

Qwen3-Coder-Next is an 80-billion-parameter open-weight language model with only 3 billion active parameters per forward pass, designed for coding agents. It is built on Qwen3-Next with hybrid attention and Mixture-of-Experts, and trained via a staged pipeline: continued pretraining on code and agent-centric data, supervised fine-tuning, and reinforcement…

Ruisheng Cao, Mouxiang Chen, Jiawei Chen, Zeyu Cui, et al.
Published
Feb 2026
Citations
54
Code
17K stars
Read summary Original
09

arXiv.org

OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration

OPUS is a dynamic data selection framework for LLM pre-training that scores candidate samples by projecting their optimizer-induced effective updates onto a target direction from a stable, in-distribution proxy. It addresses the limitations of static filters and raw-gradient-based dynamic methods by aligning selection with the actual update geometry of…

Shaobo Wang, Xuan Ouyang, Tianyi Xu, Yuzheng Hu, et al.
Published
Feb 2026
Citations
10
Code
Not linked
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10

arXiv.org

SWE-Universe: Scale Real-World Verifiable Environments to Millions

SWE-Universe is a framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs) at a million scale. It addresses challenges of low production yield, weak verifiers, and prohibitive cost using a building agent powered by a custom-trained MoE model (Qwen-Next-80B-A3B). The agent uses…

Mouxiang Chen, Lei Zhang, Yunlong Feng, Xuwu Wang, et al.
Published
Feb 2026
Citations
7
Code
Not linked
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11

arXiv.org

Qwen3-TTS Technical Report

The Qwen3-TTS technical report introduces a family of multilingual, controllable, robust, and streaming text-to-speech models. Trained on over 5 million hours of speech data across 10 languages, Qwen3-TTS supports 3-second voice cloning, description-based voice design, and fine-grained control. It uses a dual-track LM architecture with two tokenizers:…

Hangrui Hu, Xinfa Zhu, Ting He, Dake Guo, et al.
Published
Jan 2026
Citations
88
Code
13K stars
Read summary Original
12

arXiv.org

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

ArenaRL is a reinforcement learning framework for open-ended LLM agents that replaces pointwise scalar rewards with tournament-based relative ranking to overcome discriminative collapse, where reward models fail to distinguish subtle differences among high-quality trajectories. It introduces a process-aware pairwise evaluation mechanism and a seeded…

Qiang Zhang, Boli Chen, Fanrui Zhang, Ruixue Ding, et al.
Published
Jan 2026
Citations
9
Code
274 stars
Read summary Original
13

arXiv.org

Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

The report introduces Qwen3-VL-Embedding and Qwen3-VL-Reranker, a unified framework for multimodal retrieval built on the Qwen3-VL foundation model. The embedding model uses a multi-stage training pipeline (contrastive pre-training, multi-task contrastive learning, and reranker distillation) to produce high-dimensional vectors, supporting Matryoshka…

Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, et al.
Published
Jan 2026
Citations
167
Code
1.3K stars
Read summary Original
14

arXiv.org

Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

This technical report introduces the Agentic Learning Ecosystem (ALE), a full-stack infrastructure for developing agentic LLMs, and ROME, an open-source agent model trained within it. ALE comprises three components: ROLL, a scalable RL training framework; ROCK, a sandboxed environment execution engine; and iFlow CLI, an agent framework for context…

Weixun Wang, XiaoXiao Xu, Wanhe An, Fangwen Dai, et al.
Published
Dec 2025
Citations
23
Code
3.4K stars
Read summary Original
15

arXiv.org

Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition

Qwen-Image-Layered is an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers, enabling consistent image editing where each layer can be independently manipulated. The model introduces three key components: an RGBA-VAE that unifies latent representations for RGB and RGBA images, a VLD-MMDiT…

Shengming Yin, Zekai Zhang, Zecheng Tang, Kaiyuan Gao, et al.
Published
Dec 2025
Citations
24
Code
2.1K stars
Read summary Original
16

arXiv.org

Stabilizing Reinforcement Learning with LLMs: Formulation and Practices

This paper proposes a formulation for reinforcement learning (RL) with large language models (LLMs), showing that optimizing sequence-level rewards via token-level objectives is a first-order approximation that holds only when training–inference discrepancy and policy staleness are minimized. The authors introduce MiniRL, a minimalist REINFORCE-based…

Chujie Zheng, Kai Dang, Bowen Yu, Mingze Li, et al.
Published
Dec 2025
Citations
35
Code
Not linked
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17

arXiv.org

Qwen3-VL Technical Report

Qwen3-VL is a state-of-the-art vision-language model family from the Qwen team, released on December 1, 2025. It supports interleaved contexts up to 256K tokens and comes in dense (2B/4B/8B/32B) and MoE (30B-A3B/235B-A22B) variants. Key architectural innovations include interleaved-MRoPE for balanced spatial-temporal encoding, DeepStack for multi-level ViT…

Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, et al.
Published
Nov 2025
Citations
1.8K
Code
20K stars
Read summary Original
18

arXiv.org

Qwen3-Omni Technical Report

Qwen3-Omni is a single multimodal model that achieves state-of-the-art performance across text, image, audio, and video without degradation relative to single-modal counterparts. It matches the performance of same-sized Qwen single-modal models and excels on audio tasks, achieving open-source SOTA on 32 of 36 audio benchmarks and overall SOTA on 22,…

Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, et al.
Published
Sep 2025
Citations
405
Code
3.9K stars
Read summary Original
19

arXiv.org

Scaling Agents via Continual Pre-training

The paper introduces Agentic Continual Pre-training (Agentic CPT), a new training stage between pre-training and post-training, to build agentic foundation models for deep research agents. The authors argue that post-training on general-purpose models creates optimization conflicts, as models must simultaneously learn agentic behaviors and align to expert…

Liangcai Su, Zhen Zhang, Guangyu Li, Zhuo Chen, et al.
Published
Sep 2025
Citations
41
Code
20K stars
Read summary Original
20

arXiv.org

WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent

WebWatcher is a multimodal deep research agent introduced by Alibaba's Tongyi Lab to address the limitations of text-centric web agents by integrating visual information and complex reasoning. It uses high-quality synthetic multimodal trajectories for cold-start training, multiple tools (web search, image search, webpage visit, code interpreter, OCR), and…

Xinyu Geng, Peng Xia, Zhen Zhang, Xinyu Wang, et al.
Published
Aug 2025
Citations
101
Code
20K stars
Read summary Original
21

arXiv.org

Qwen-Image Technical Report

Qwen-Image is a new image generation foundation model from the Qwen team, designed to excel in complex text rendering and precise image editing. It uses a comprehensive data pipeline and a progressive curriculum learning strategy to enhance text rendering, especially for Chinese. For editing, it employs a multi-task training paradigm with dual-encoding…

Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, et al.
Published
Aug 2025
Citations
875
Code
8.2K stars
Read summary Original