activity
20242026
collaborators

10 papers

cs.CL2026

Deadline-Aware Adaptive Prefill Chunking for Efficient Large Language Model Serving

Siyu Song, Qi Bai, Jinbo Hao +3

Continuous batching improves large language model (LLM) serving throughput, but long prompt prefills can delay decode iterations and violate inter-token latency objectives. Chunked…

cs.CL2026

Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning

Xiaokun Wang, Siyu Song, Wentao Liu +1

Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students throug…

cs.AI2026

Toward Anthropomorphic Dialogue: A Closed-Loop Framework for Human-Like Chat Generation, Evaluation, and Preference Alignment

Wentao Liu, Siyu Song, Xi Chen +4

Human-like private chat requires more than fluent response generation: a system must preserve persona, relationship, memory, bounded knowledge, medium-specific timing, and a cohere…

cs.LG2026

Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

Tao Liu, Ye Lu, Ruohua Zhang +4

Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depe…

cs.AI2026

Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction

Shuzhen Bi, Mengsong Wu, Hao Hao +5

The transition from monolithic large language models (LLMs) to modular, skill-equipped agents represents a fundamental architectural shift in artificial intelligence deployment. Wh…

cs.AI2026

Scaling Laws for Educational AI Agents

Mengsong Wu, Hao Hao, Shuzhen Bi +5

While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-base…

cs.LG2025

AutoSynth: Automated Workflow Optimization for High-Quality Synthetic Dataset Generation via Monte Carlo Tree Search

Shuzhen Bi, Chang Song, Siyu Song +5

Supervised fine-tuning (SFT) of large language models (LLMs) for specialized tasks requires high-quality datasets, but manual curation is prohibitively expensive. Synthetic data ge…

cs.AI2025

EA4LLM: A Gradient-Free Approach to Large Language Model Optimization via Evolutionary Algorithms

WenTao Liu, Siyu Song, Hao Hao +1

In recent years, large language models (LLMs) have made remarkable progress, with model optimization primarily relying on gradient-based optimizers such as Adam. However, these gra…

cs.LG2025

EduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity

Siyu Song, Wentao Liu, Ye Lu +8

Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs, and creativity that supports e…

cs.LG2024

GrootVL: Tree Topology is All You Need in State Space Model

Yicheng Xiao, Lin Song, Shaoli Huang +5

The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency. However,…