11 citations · 24 across the 32 of their papers we have counts for
12 papers · 1 filter
Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers
Yilun Liu, Yi Zhang, Ganyu Wu +5
Autoregressive models are trained to predict a system's behavior one step at a time, and recursive generation allows the learned dynamics to unfold over long horizons. To what exte…
Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation
Yilun Liu, Boyu Luo, Yanran Tang +2
Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighbourhood. Existing methods fix…
Mendel Gödel Machine: Recursive Self-Improving Coding Agents via Comparative Evolution
Changzhi Liu, Yilun Liu, Sikuan Yan +2
Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks. However, existing solutions generally derive s…
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
Zhiqing Yang, Yilun Liu, Yunpu Ma +2
Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains underexplored. We investigate this…
MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights
Yilun Liu, Miao Zhang, Shimin Tao +9
Multilingual and multicultural benchmarks now cover dozens of languages and model families, but the resulting score landscapes remain metric-rich and insight-poor, necessitating fi…
PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning
Sikuan Yan, Sicheng Dong, Haotong Wang +8
Memory has become an increasingly important component of agentic systems, as these systems are expected to reason over long-term experience. However, prior work has largely focused…
The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models
Yilun Liu, Chunguang Zhao, Mengyao Piao +14
Evaluating the multilingual and multicultural capabilities of Large Language Models (LLMs) is essential for their global utility. However, current benchmarks face three critical li…
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
Yilun Liu, Ruihong Qiu, Zi Huang
Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervis…
LLM Safety From Within: Detecting Harmful Content with Internal Representations
Difan Jiao, Yilun Liu, Ye Yuan +4
Guard models are widely used to detect harmful content in user prompts and LLM responses. However, state-of-the-art guard models rely solely on terminal-layer representations and o…
C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment
Pufan Zeng, Yilun Liu, Mingchen Dai +12
Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initial step is seed curation; ho…
Routing-Free Mixture-of-Experts
Yilun Liu, Jinru Han, Sikuan Yan +2
Standard Mixture-of-Experts (MoE) models rely on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE which eliminates any hard-coded c…
Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation
Minggui He, Mingchen Dai, Jian Zhang +5
Vision-Language Models (VLMs) have shown promise in generating plotting code from chart images, yet achieving structural fidelity remains challenging. Existing approaches largely r…