Huilong An | Searcharxiv

arXiv2026

KITE: Retrieval as a Decision Procedure for Long-Term Conversational Memory

Many long-term memory systems for LLM agents are built around embedding-based retrieval. Al- though effective for semantic matching, similarity ranking alone does not establish whether re- trieved memories satisfy the conditions implied by a question. In this paper, we introduce KITE (Knowledge-Indexed Temporal Evidence), a vector-free framework that formulates memory re- trieval as an explicit decision procedure. KITE organizes multi-session conversations as structured knowledge indexed by a governed topic vocabulary, which evolves through controlled expansion and consolidation. At query time, questions are compiled into inspectable retrieval plans over the same knowledge index that guides memory construction. The plans are then executed through fixed lookup and ranking operations, while sparse lexical retrieval recovers surface details omitted from the structured representation. Experiments on LoCoMo and LongMemEval-S show that KITE achieves 93.5% and 86.0% accuracy while using only about 1.6k reader-context tokens per ques- tion. These results demonstrate that conversational memory can move beyond similarity ranking by unifying memory construction and query compilation around a governed index, turning retrieval into an inspectable decision procedure for determining which evidence satisfies the question.

cs.IR↗

arXiv2024

A Short Review for Ontology Learning: Stride to Large Language Models Trend

Ontologies provide formal representation of knowledge shared within Semantic Web applications. Ontology learning involves the construction of ontologies from a given corpus. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge extraction and representation. A new trend of these approaches is relying on large language models (LLMs) to enhance ontology learning. This paper gives a review in approaches and challenges of ontology learning. It analyzes the methodologies and limitations of shallow-learning-based and deep-learning-based techniques for ontology learning, and provides comprehensive knowledge for the frontier work of using LLMs to enhance ontology learning. In addition, it proposes several noteworthy future directions for further exploration into the integration of LLMs with ontology learning tasks.

cs.IR↗