13 citations · 27 across the 17 of their papers we have counts for
19 papers
Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
Lizhe Fang, Weizhou Shen, Tianyi Tang +1
Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an i…
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping
Miao Peng, Weizhou Shen, Nuo Chen +3
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs short-context reasoning, but its performance degrades in long-context scenarios that re…
CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning
Zhiyuan Lu, Chenliang Li, Yingcheng Shi +3
While large language models now handle million-token contexts, their capacity for reasoning across entire document repositories remains largely untested. Existing benchmarks are in…
QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management
Weizhou Shen, Ziyi Yang, Chenliang Li +11
We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of Qwen…
Qwen3Guard Technical Report
Haiquan Zhao, Chenhan Yuan, Fei Huang +40
As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in stat…
Tongyi DeepResearch Technical Report
Tongyi DeepResearch Team, Baixuan Li, Bo Zhang +54
We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous…
SPELL: Self-Play Reinforcement Learning for Evolving Long-Context Language Models
Ziyi Yang, Weizhou Shen, Chenliang Li +5
Progress in long-context reasoning for large language models (LLMs) has lagged behind other recent advances. This gap arises not only from the intrinsic difficulty of processing lo…
WebSailor: Navigating Super-human Reasoning for Web Agent
Kuan Li, Zhongwang Zhang, Huifeng Yin +16
Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on…
Mutual-Taught for Co-adapting Policy and Reward Models
Tianyuan Shi, Canbin Huang, Fanqi Wan +5
During the preference optimization of large language models (LLMs), distribution shifts may arise between newly generated model samples and the data used to train the reward model…
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
Xuanyu Lei, Chenliang Li, Yuning Wu +7
Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited: Supervised Fine-Tuning (SFT) rem…
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization
Weizhou Shen, Chenliang Li, Fanqi Wan +12
This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during th…
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Fanqi Wan, Weizhou Shen, Shengyi Liao +7
Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the…
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
Fuwen Luo, Shengfeng Lou, Chi Chen +9
Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, curren…
SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization
Huashan Sun, Shengyi Liao, Yansen Han +8
Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primar…
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
Junyang Wang, Haiyang Xu, Haitao Jia +6
Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their train…
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
Hongzhan Chen, Hehong Chen, Ming Yan +8
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human…
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Weizhou Shen, Chenliang Li, Hongzhan Chen +5
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete var…
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
Junyang Wang, Haiyang Xu, Jiabo Ye +5
Mobile device agent based on Multimodal Large Language Models (MLLM) is becoming a popular application. In this paper, we introduce Mobile-Agent, an autonomous multi-modal mobile d…
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Chenliang Li, Hehong Chen, Ming Yan +11
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further u…