6 citations · 8 across the 15 of their papers we have counts for
16 papers
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu +14
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increas…
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent
Fanqi Zhou, Qiaosheng Chen, Zixian Huang +1
Although existing instruction data selection methods have introduced various metrics, the inherent complexity of real-world datasets makes it impractical for any single metric to g…
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
He Du, Qiming Ge, Jiakai Hu +18
We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented…
How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
Zixian Huang, Kaichen Yang, Xu Huang +6
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
TAPO: Translation Augmented Policy Optimization for Multilingual Mathematical Reasoning
Xu Huang, Zhejian Lai, Zixian Huang +2
Large Language Models (LLMs) have demonstrated remarkable proficiency in English mathematical reasoning, yet a significant performance disparity persists in multilingual contexts,…
ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
Changjiang Gao, Zixian Huang, Kaichen Yang +3
Current large reasoning models (LRMs) have shown strong ability on challenging tasks after reinforcement learning (RL) based post-training. However, previous work mainly focuses on…
OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions
Hang Yan, Fangzhi Xu, Qiushi Sun +14
The advancement of Large Language Models (LLMs) has catalyzed the development of autonomous agents capable of navigating complex environments. However, existing evaluations primari…
Pipelined Decoder for Efficient Context-Aware Text Generation
Zixian Huang, Chenxu Niu, Yu Gu +3
As the basis of generative AI, an autoregressive model requires the generation of a new token depending on all the previously generated tokens, which brings high quality but also r…
MindMerger: Efficient Boosting LLM Reasoning in non-English Languages
Zixian Huang, Wenhao Zhu, Gong Cheng +2
Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-t…
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
Zixian Huang, Jiaying Zhou, Gengyang Xiao +1
Whereas the recent emergence of large language models (LLMs) like ChatGPT has exhibited impressive general performance, it still has a large gap with fully-supervised models on spe…
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang, Ao Wu, Jiaying Zhou +3
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it train…
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
Zixian Huang, Ao Wu, Yulin Shen +2
Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description.…
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
Shuxin Li, Zixian Huang, Gong Cheng +2
A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and…
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level
Zixian Huang, Yulin Shen, Xiao Li +5
Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying…