9 papers
DemoPSD: Disagreement-Modulated Policy Self-Distillation
Yunhe Li, Hao Shi, Wenhao Liu +5
On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as both the teacher and the stud…
Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents
Wangxuan Fan, Xiaoyu Nie, Zhongxiang Dai
The increasing deployment of large language model (LLM) agents in collaborative workflows demands robust multi-user, multi-principal interaction mechanisms capable of enforcing acc…
MetaForge: A Self-Evolving Multimodal Agent that Retrieves, Adapts, and Forges Tools On Demand
Shouang Wei, Houcheng Min, Xinpeng Dong +8
Multimodal agents have achieved notable progress on complex reasoning tasks through tool use, yet remain limited by two issues: statically predefined tool inventories fail to gener…
Learning to Reason with Insight for Informal Theorem Proving
Yunhe Li, Hao Shi, Bowen Deng +8
Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language models' (LLMs) strength in n…
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Chenxing Wei, Hong Wang, Ying He +4
Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…
CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models
Rui Jia, Ruiyi Lan, Fengrui Liu +7
Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often produce homogeneous responses…
EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation
Rui Jia, Min Zhang, Fengrui Liu +3
In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirements while supporting adaptive assessm…
UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models
Shouang Wei, Min Zhang, Xin Lin +4
Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning methods only learn surface teachin…
EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus
Shouang Wei, Min Zhang, Xin Lin +3
Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as…