7 citations · 8 across the 8 of their papers we have counts for
8 papers
PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents
Kun Li, Zexuan Qiu, Tianhua Zhang +2
Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This c…
Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Yixin Ji, Fanghua Ye, Juntao Li +5
Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing…
Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees
Kun Li, Zenan Xu, Junan Li +4
Tool-Integrated Reasoning has emerged as a key paradigm to augment Large Language Models (LLMs) with computational capabilities, yet integrating tool-use into long Chain-of-Thought…
ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning
Minda Hu, Zexuan Qiu, Zenan Xu +3
Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, suc…
Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Zenan Xu, Zexuan Qiu, Guanhua Huang +9
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contempor…
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Zenan Xu, Xiaojun Meng, Yasheng Wang +4
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our…
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Zexuan Qiu, Qinliang Su, Jianxing Yu +1
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document di…
Unsupervised Hashing with Contrastive Information Bottleneck
Zexuan Qiu, Qinliang Su, Zijing Ou +2
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much informatio…