20 citations · 37 across the 16 of their papers we have counts for
16 papers
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
Siyuan Liu, Fan Yu, Dongyu Ru +5
Online agent deployments accumulate execution trajectories at massive scale and behavioral diversity, for which predefined annotation criteria hardly exist. Extracting useful evide…
ATLAS: All-round Testing of Long-context Abilities across Scales
Deli Huang, Cunguang Wang, Hongyin Tang +15
Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure m…
AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
Zhengkang Guo, Yiyang Li, Lin Qiu +7
As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range inte…
AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations
Cheng Jiayang, Dongyu Ru, Lin Qiu +4
Long-horizon interactions between users and LLM-based assistants necessitate effective memory management, yet current approaches face challenges in training and evaluation of memor…
LongCat-Flash-Thinking-2601 Technical Report
Meituan LongCat Team, Anchun Gui, Bei Li +162
We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thi…
Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers
Zhichao Geng, Yiwen Wang, Dongyu Ru +1
Learned sparse retrieval, which can efficiently perform retrieval through mature inverted-index engines, has garnered growing attention in recent years. Particularly, the inference…
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Dongyu Ru, Lin Qiu, Xiangkun Hu +15
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…
OpenResearcher: Unleashing AI for Accelerated Scientific Research
Yuxiang Zheng, Shichao Sun, Lin Qiu +13
The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new…
Unified Lexical Representation for Interpretable Visual-Language Alignment
Yifan Li, Yikai Wang, Yanwei Fu +3
Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clari…
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
Xiangkun Hu, Dongyu Ru, Lin Qiu +7
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Cheng Jiayang, Lin Qiu, Tsz Ho Chan +9
Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind larg…
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations
Dongyu Ru, Lin Qiu, Xipeng Qiu +2
Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document. Discourse markers are natural representations of discourse…
Learning Logic Rules for Document-level Relation Extraction
Dongyu Ru, Changzhi Sun, Jiangtao Feng +5
Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicit…
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
Dongyu Ru, Jiangtao Feng, Lin Qiu +5
Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typic…
Approximate Random Dropout
Zhuoran Song, Ru Wang, Dongyu Ru +5
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the…
QA4IE: A Question Answering based Framework for Information Extraction
Lin Qiu, Hao Zhou, Yanru Qu +7
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open…