Papers by Kuang He
EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing systems suffer from social memory stacking and narrative-spatial dissonance . long-horizon narratives suffer from conflicting relational states without resolution . |
| Approach: | They propose a framework to sustain logically coherent long-horizon narratives within endogenous interactive agent societies. |
| Outcome: | Experiments show that the framework outperforms baselines across paradigms. |
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning. |
| Approach: | They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition . |
| Outcome: | The proposed framework improves performance and basic understanding of large language models. |
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)
Copied to clipboard
Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu
| Challenge: | Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens. |
| Approach: | They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. |
| Outcome: | Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text. |
CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)
Copied to clipboard
| Challenge: | Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP). |
| Approach: | They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations. |
| Outcome: | The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks. |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)
Copied to clipboard
Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang
| Challenge: | Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data . |
| Approach: | They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation. |
| Outcome: | The proposed framework outperforms open-source baselines and is competitive with GPT-5. |
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to offline reinforcement learning (RL) focus on learning value functions or policy gradients, but they view it as a sequence modeling task. |
| Approach: | They propose a method that integrates multimodal and pre-trained language models to transform offline reinforcement learning into a supervised learning task by integrating state information derived from images and action-related data obtained from text. |
| Outcome: | The proposed approach outperforms baselines on Atari and OpenAI Gym environments while promoting long-term strategic thinking. |