22 citations · 24 across the 9 of their papers we have counts for
9 papers
The Network Structure of Mathlib
Xinze Li, Nanyun Peng, Simone Severini +1
The ongoing development of Lean 4's Mathlib has produced a macroscopic structural complexity that interweaves logical, mathematical, and infrastructural dependencies. We present a…
Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agents
Xinze Li, Yuhang Zang, Yixin Cao +1
Markdown skill libraries for LLM agents ship as free-form prose, forcing the agent to re-derive both the input schema and the concrete invocation syntax on every retrieval. This pr…
Astrolabe: A Content-Addressable Hypergraph for Semantic Knowledge Management
Xinze Li
Existing knowledge management tools either preserve prose but lose structural relationships, or capture relationships but restrict edge semantics to fixed vocabularies. We introduc…
What Do LLM Agents Know About Their World? Task2Quiz: A Paradigm for Studying Environment Understanding
Siyuan Liu, Hongbang Yuan, Xinze Li +3
Large language model (LLM) agents have demonstrated remarkable capabilities in complex decision-making and tool-use tasks, yet their ability to generalize across varying environmen…
EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents
Xinze Li, Ziyue Zhu, Siyuan Liu +4
We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than using a fixed set of questions,…
Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks
Yixin Cao, Shibo Hong, Xinze Li +24
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, th…
Long Context vs. RAG for LLMs: An Evaluation and Revisits
Xinze Li, Yixin Cao, Yubo Ma +1
Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main stra…
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
Yubo Ma, Yuhang Zang, Liangyu Chen +13
Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides…
Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Xinze Li, Yixin Cao, Liangming Pan +2
Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are n…