Approximating a general formula from above and below by Horn formulas (its Horn envelope and Horn core, respectively) was proposed by Selman and Kautz (1991, 1996) as a form of ``knowledge compilation,'' supporting rapid approximate reasoning; on the negative side, this scheme is static in that it supports no updates, and has certain complexity drawbacks pointed out by Kavvadias, Papadimitriou and Sideri (1993). On the other hand, the many frameworks and schemes proposed in the literature for theory update and revision are plagued by serious complexity-theoretic impediments, even in the Horn case, as was pointed out by Eiter and Gottlob (1992), and is further demonstrated in the present paper. More fundamentally, these schemes are not inductive, in that they may lose in a single update any positive properties of the represented sets of formulas (small size, Horn structure, etc.). In this paper we propose a new scheme, incremental recompilation, which combines Horn approximation and model-based updates; this scheme is inductive and very efficient, free of the problems facing its constituents. A set of formulas is represented by an upper and lower Horn approximation. To update, we replace the upper Horn formula by the Horn envelope of its minimum-change update, and similarly the lower one by the Horn core of its update; the key fact which enables this scheme is that Horn envelopes and cores are easy to compute when the underlying formula is the result of a minimum-change update of a Horn formula by a clause. We conjecture that efficient algorithms are possible for more complex updates.
Towards Continual Knowledge Graph Embedding via Incremental Distillation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（东南大学新一代人工智能技术与交叉应用教育部重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；College of Software Engineering, Southeast University（东南大学软件学院）
Few-Shot Class-Incremental Learning with Prior Knowledge
East China Normal University（华东师范大学）；DiDi（滴滴）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua Berkeley Shenzhen Institute（清华伯克利深圳学院）；Toronto Metropolitan University（多伦多都会大学）
GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
SKLCCSE, School of Computer Science and Engineering, Beihang University（信息与通信工程学院，北京航空航天大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）；Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University（教育区块链与智能技术重点实验室，广西师范大学）
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
ZJU-UIUC Joint Institute at Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）；ZJU-UoE Institute at Zhejiang University（浙江大学-爱丁堡大学联合研究所）；College of Biomedical Engineering and Instrument Science at Zhejiang University（浙江大学生物医学工程与仪器科学学院）；College of Computer Science and Technology in Zhejiang University（浙江大学计算机科学与技术学院）
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental Learning
Nanjing University of Information Science and Technology（南京信息工程大学）；Intellifusion（云天励飞）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；the University of Adelaide（阿德莱德大学）；University of Electronic Science and Technology of China（电子科技大学）
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security at University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；NARI Technology Company Limited（NARI科技有限公司）；School of Engineering and Applied Science, the George Washington University（乔治华盛顿大学工程与应用科学学院）；Department of Communication Engineering, Shanghai University（上海大学通信工程系）
CD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Harmony: A Unified Framework for Modality Incremental Learning
Peng Cheng Laboratory（鹏城实验室）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration
Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；School of Automation, Central South University（自动化学院，中南大学）；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院，北京交通大学）
Divide and Conquer: Static-Dynamic Collaboration for Few-Shot Class-Incremental Learning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Department of Communication Engineering, Shanghai University（上海大学通信工程系）
