2 citations · 2 across the 6 of their papers we have counts for
8 papers
PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking
Pan Wang, Yihao Hu, Guangyuan Dong +8
Looped language models can run more iterations at test time, but additional iterations need not help them solve longer problems. For sequential state tracking, final-answer supervi…
Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context
Yiwen Zhao, Zhihao Wen, Yuchen Mao +5
User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each rollout to a t…
Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG
Haotian Zhou, Weiran Huang, Siqi Liu +3
Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In…
SEAL: Synergistic Co-Evolution of Agents and Learning Environments
Yihao Hu, Zhihao Wen, Xiujin Liu +3
Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. W…
AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents
Pan Wang, Yihao Hu, Xiujin Liu +3
Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most existing frameworks store me…
Few-shot LLM Synthetic Data with Distribution Matching
Jiyuan Ren, Zhaocheng Du, Zhihao Wen +4
As large language models (LLMs) advance, their ability to perform in-context learning and few-shot language generation has improved significantly. This has spurred using LLMs to pr…
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
Yuyan Chen, Qiang Fu, Yichen Yuan +6
Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawb…
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
Yuyan Chen, Zhihao Wen, Ge Fan +6
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research prima…