Integrating knowledge graphs (KGs) with large language models (LLMs) enhances factual accuracy and interpretability in question answering. However, existing agent-based methods rely on static memory mechanisms that fail to address the combinatorial explosion of search spaces in multi-hop reasoning and lack continuous learning capabilities. To overcome these limitations, we propose EvoMemKG, an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning. EvoMemKG features a dual-layer memory architecture: (1) a working memory that losslessly compresses retrieved triplets through clustering to manage exploration states, effectively linearizing the exponential state space expansion; and (2) an experience memory that abstracts historical reasoning paths into reusable, generalized strategies, enabling cross-task knowledge transfer and self-evolution. We further introduce a double-loop workflow that orchestrates the LLM, memory layers, and KG environment to enable end-to-end autonomous reasoning. Extensive evaluations on three KGQA datasets across two KGs demonstrate that EvoMemKG achieves state-of-the-art performance without requiring additional training or specialized tools. Notably, it achieves improvements of up to 20% over the strong baseline on complex multi-hop queries, validating the effectiveness of our dynamic memory approach.

Although large language models (LLMs) show remarkable capabilities and generalizability across various tasks, they are criticized for lack of expertise. One promising solution is to combine knowledge graphs (KGs) with LLMs, and recent studies focus on integrating KGs into LLMs through prompt-based methods. However, these approaches fail to use the structural information of the KGs, suffer from the problem of knowledge conflict, and over-reliance on super LLMs. To address these challenges, we propose KG-Adapter, a parameter-level KG integration method based on parameter-efficient fine-tuning (PEFT). Specifically, we introduce a novel adapter structure designed for decoder-only LLMs, which can encode KGs from both node-centered and relation-centered perspectives, and then perform joint reasoning with LLMs to generate responses end-to-end. Experiments with diverse models on four datasets for two different tasks all demonstrate significant improvements. With only 28M parameters trained, we make the 7B-parameter LLM outperform the previous full-parameter fine-tuned state-of-the-art method and comparable to the prompt-based ChatGPT methods.

Conversational Machine Reading (CMR) requires answering a user’s initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequent follow-up question generation. To address this issue, we propose a pipeline framework that (1) aligns the aforementioned two sides in an explicit way, (2) makes decisions using a lightweight many-to-many entailment reasoning module, and (3) directly generates follow-up questions based on the document and previously asked questions. Our proposed method achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.