1 citations · 1 across the 11 of their papers we have counts for
14 papers
Combinatorics of degeneracy in vertical systems
Carles Checa, Nidhi Kaihnsa
We characterize the generic consistency of vertical systems in terms of the iterated circuits of the column matroid of the coefficient matrix. More precisely, we prove that such a…
ReproAgent: Contract-Guided Paper-to-Code Reproduction
Xue Hu, Zewei Pan, Zhongyuan Wang +3
Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper's method, protocol and artifacts. This is difficul…
SA-Bench: Evaluating Semantic Alignment in LLM-Based Paper Reproduction
Xue Hu, Zewei Pan, Zeli Su +2
LLM agents can generate paper reproduction code, yet often produce scientifically unfaithful implementations. We define this failure mode as semantic drift, where generated code si…
ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models
Zhou Liu, Chaoyang Han, Zewei Pan +2
Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behav…
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
Zhou Liu, Ligang Huang, Zeli Su +5
Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual controls without identifying whi…
Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation
Zeli Su, Ziyin Zhang, Zewei Pan +8
Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but difficult to use with standar…
The Curse of Helpfulness: Inverse Scaling Law in Robustness to Distractor Instructions via DistractionIF
Zeli Su, Zhankai Xu, Tianlei Chen +4
Large Language Models (LLMs) are increasingly deployed in agentic and retrieval-augmented generation (RAG) systems, where they must execute user-specified tasks over externally pro…
SciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing
Tong Zhang, Honglin Lin, Zhou Liu +2
Scientific diagrams convey explicit structural information, yet modern text-to-image models often produce visually plausible but structurally incorrect results. Existing benchmarks…
DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis
Ruyi Qi, Zhou Liu, Wentao Zhang
In real-world data science and enterprise decision-making, critical information is often fragmented across directly queryable structured sources (e.g., SQL, CSV) and "zombie data"…
DataGovBench: Benchmarking LLM Agents for Real-World Data Governance Workflows
Zhou Liu, Zhaoyang Han, Guochen Yan +5
Data governance ensures data quality, security, and compliance through policies and standards, a critical foundation for scaling modern AI development. Recently, large language mod…
SHRP: Specialized Head Routing and Pruning for Efficient Encoder Compression
Zeli Su, Ziyin Zhang, Wenzheng Zhang +3
Transformer encoders are widely deployed in large-scale web services for natural language understanding tasks such as text classification, semantic retrieval, and content ranking.…
Paper2SysArch: Structure-Constrained System Architecture Generation from Scientific Papers
Ziyi Guo, Zhou Liu, Wentao Zhang
The manual creation of system architecture diagrams for scientific papers is a time-consuming and subjective process, while existing generative models lack the necessary structural…
UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data
Han Weng, Zhou Liu, Yuanfeng Song +3
In the real business world, data is stored in a variety of sources, including structured relational databases, unstructured databases (e.g., NoSQL databases), or even CSV/excel fil…
LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding
ZhaoYang Han, Qihan Lin, Hao Liang +3
We introduce \textbf{LongInsightBench}, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and o…