1 citations · 1 across the 9 of their papers we have counts for
10 papers
Persistent Memory and Iterative Improvement in an LLM Agent for Chinese-Character Drawing
Peilin Wu, Yi-Zhuang You
Does persistent memory accumulated from previous attempts help an LLM agent solve new problems, and does memory design affect that benefit? We study programmatic Chinese-character…
Mitigating Exploration Bias in RL for Multi-Instruction Following
Mian Zhang, Yueqin Yin, Kaiyu He +4
RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substantial gains, we find that they suf…
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
Peilin Wu, Xinlu Zhang, Kun Wan +4
Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically…
Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers
Kaiyu He, Zhang Mian, Peilin Wu +2
While Large Language Models (LLMs) excel at factual retrieval, they often struggle with the "curse of two-hop reasoning" in compositional tasks. Recent research suggests that param…
HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation
Peilin Wu, Mian Zhang, Kun Wan +4
Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answering. However, suboptimal search behavi…
GEAR: A General Evaluation Framework for Abductive Reasoning
Kaiyu He, Peilin Wu, Mian Zhang +4
Since the advent of large language models (LLMs), research has focused on instruction following and deductive reasoning. A central question remains: can these models discover new k…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…
Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty
Peilin Wu, Mian Zhang, Xinlu Zhang +2
Agentic Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by enabling dynamic, multi-step reasoning and information retrieval. However, these system…
Do Retrieval-Augmented Language Models Adapt to Varying User Needs?
Peilin Wu, Xinlu Zhang, Wenhao Yu +3
Recent advancements in Retrieval-Augmented Language Models (RALMs) have demonstrated their efficacy in knowledge-intensive tasks. However, existing evaluation benchmarks often assu…
IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction
Kaiyu He, Mian Zhang, Shuo Yan +2
While large language models (LLMs) have been thoroughly evaluated for deductive and inductive reasoning, their proficiency in holistic rule learning in interactive environments rem…