2 citations · 2 across the 15 of their papers we have counts for
16 papers
miniReranker: Efficient Multimodal Reranking through Visual Cache Reuse and Interaction Sparsity
Yingqi Fan, Xuan Lu, Anhao Zhao +6
Multimodal large language models (MLLMs) have recently shown strong potential as point-wise rerankers by directly modeling query--document relevance through next-token prediction.…
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
Yi Lu, Zhuofeng Li, Ping Nie +7
Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, t…
MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
Haohang Huang, Xuan Lu, Mingyi Su +9
Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…
ClawBench: Can AI Agents Complete Everyday Online Tasks?
Yuxuan Zhang, Yubo Wang, Yipeng Zhu +25
AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet un…
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
Jiarong Liang, Zhiheng Lyu, Zijie Liu +4
Executable software engineering data is valuable for training SWE agents, but scaling it remains difficult for two reasons: only a small fraction of real repository changes yield v…
OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis
Zhuofeng Li, Dongfu Jiang, Xueguang Ma +7
Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data collection pipelines…
Beyond Closed-Pool Video Retrieval: A Benchmark and Agent Framework for Real-World Video Search and Moment Localization
Tao Yu, Yujia Yang, Haopeng Jin +17
Traditional video retrieval benchmarks focus on matching precise descriptions to closed video pools, failing to reflect real-world searches characterized by fuzzy, multi-dimensiona…
VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
Dongfu Jiang, Yi Lu, Zhuofeng Li +9
Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated success in enhancing LLM reasoning capabilities, but remains limited to single-turn interactions without tool…
VisCoder2: Building Multi-Language Visualization Coding Agents
Yuansheng Ni, Songcheng Cai, Xiangchao Chen +8
Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical…
BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent
Zijian Chen, Xueguang Ma, Shengyao Zhuang +17
Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require ite…
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
Yubo Wang, Ping Nie, Kai Zou +2
We have witnessed that strong LLMs like Qwen-Math, MiMo, and Phi-4 possess immense reasoning potential inherited from the pre-training stage. With reinforcement learning (RL), thes…
VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Yuansheng Ni, Ping Nie, Kai Zou +2
Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing i…
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
Yinsicheng Jiang, Yao Fu, Yeqi Huang +13
The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory re…
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
Yiming Jia, Jiachen Li, Xiang Yue +4
Vision-Language Models have made significant progress on many perception-focused tasks. However, their progress on reasoning-focused tasks remains limited due to the lack of high-q…
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
Yinsicheng Jiang, Yao Fu, Yeqi Huang +13
The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory re…
AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection
Li Wang, Wei Xiang, Ruhui Xue +2
Recently, the anchor-free object detection model has shown great potential for accuracy and speed to exceed anchor-based object detection. Therefore, two issues are mainly studied…