47 citations · 48 across the 9 of their papers we have counts for
9 papers
To Answer or to Abstain: Mitigating Search-Agent Hallucinations via Abstention-Aware Reinforcement Learning
Fengji Zhang, Tianyu Fan, Yuxiang Zheng +4
Recent advances in equipping Large Language Models (LLMs) with search tools and outcome-reward reinforcement learning (RL) have achieved new state-of-the-art results on open-domain…
DeepInnovator: Triggering the Innovative Capabilities of LLMs
Tianyu Fan, Fengji Zhang, Yuxiang Zheng +5
The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on constructing research agents endowed wit…
ASearch: Ambiguity-Aware Question Answering with Reinforcement Learning
Fengji Zhang, Xinyao Niu, Chengyang Ying +7
Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models stil…
Understanding DeepResearch via Reports
Tianyu Fan, Xinyao Niu, Yuxiang Zheng +5
DeepResearch agents represent a transformative AI paradigm, conducting expert-level research through sophisticated reasoning and multi-tool integration. However, evaluating these s…
Yi-Lightning Technical Report
Alan Wake, Bei Chen, C. X. Lv +41
This technical report presents Yi-Lightning, our latest flagship large language model (LLM). It achieves exceptional performance, ranking 6th overall on Chatbot Arena, with particu…
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Ziming Li, Qianbo Zang, David Ma +11
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric frame…
Aria: An Open Multimodal Native Mixture-of-Experts Model
Dongxu Li, Yudong Liu, Haoning Wu +17
Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary mu…
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
Chen Yang, Junzhuo Li, Xinyao Niu +11
Uncovering early-stage metrics that reflect final model performance is one core principle for large-scale pretraining. The existing scaling law demonstrates the power-law correlati…
Yi: Open Foundation Models by 01.AI
01. AI, :, Alex Young +30
We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pret…