Large Language Model (LLM) agents have established a promising paradigm for Knowledge Graph Question Answering (KGQA). However, their practical performance is severely constrained by the pervasive incompleteness of real-world knowledge graphs (KGs). To address this, Incomplete KGQA (IKGQA) has emerged as a vital task, yet existing multi-agent frameworks often suffer from a logic-misalignment effect, where passive collaboration leads to retrieval failure and hallucination propagation when encountering structural gaps. In this paper, we propose IDLC, an Intent-Driven Logical Compensation framework designed for faithful IKGQA. Unlike traditional reactive debate, IDLC formalizes reasoning as a proactive deliberation task within a Dec-POMDP. It introduces an Active KG Explorer that generates explicit Gap Descriptors to translate structural gaps into actionable retrieval goals, alongside a Logical Consistency Verifier that performs bi-directional anchoring to intercept step-wise semantic drift. The entire multi-agent policy is jointly optimized via reinforcement learning with a specialized logic-fidelity reward, aligning agent behaviors toward faithful reasoning paths. Extensive experiments on BRINK and IKG-CWQ/WebQSP benchmarks demonstrate that IDLC outperforms state-of-the-art baselines in both reasoning performance and step-wise logical fidelity. Furthermore, IDLC exhibits robust zero-shot transferability across various incompleteness levels while maintaining computational efficiency, confirming the effectiveness of intent-driven coordination in repairing fractured reasoning chains.

Sequential recommendation models dynamic user preferences but is constrained by the long-tail distribution of real-world interactions. While LLM-derived text embeddings provide semantic priors to alleviate data sparsity, existing adaptation methods are hindered by popularity bias during semantic projection and semantic prior erosion during sequence training. In this paper, we propose Adversarial Debiasing and Asymmetric Alignment (ADAA), a framework designed to alleviate these representation bottlenecks. Specifically, ADAA incorporates an adversarial debiasing mechanism to decouple interaction frequency factors from the projected embeddings, yielding debiased semantic representations. Furthermore, it employs an asymmetric anchoring strategy during the sequence training phase. This strategy restricts the optimization space of sparse items to preserve their injected semantic priors, while permitting head items to flexibly fit sequential transition patterns. Through a parameter-level injection mechanism, ADAA integrates offline-aligned semantics without introducing additional parameters to the online architecture. Extensive experiments on three real-world datasets demonstrate that ADAA improves long-tail recommendation accuracy while preserving the online inference efficiency of the original sequential backbone.

Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation, prompting the recommendation community to leverage these powerful models to address fundamental challenges in traditional recommender systems, including limited comprehension of complex user intents, insufficient interaction capabilities, and inadequate recommendation interpretability. This survey presents a comprehensive synthesis of this rapidly evolving field. We consolidate existing studies into three paradigms: (i) recommender-oriented methods, which directly enhance core recommendation mechanisms; (ii) interaction-oriented methods, which conduct multi-turn conversations to elicit preferences and deliver interpretable explanations; and (iii) simulation-oriented methods, that model user-item interactions through multi-agent frameworks. Then, we dissect a four-module agent architecture: profile, memory, planning, and action. Then we review representative designs, public datasets, and evaluation protocols. Finally, we give the open challenges that impede real-world deployment, including cost-efficient inference, robust evaluation, and security.

Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systematically in zero-shot settings. Existing knowledge graph methods, limited by static structures, lack adaptability in complex scenarios. We propose DS-MHP, a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. DS-MHP operates in three stages: 1) constructing query-specific subgraphs by extracting entities and relations; 2) generating and refining multi-hop paths using a hybrid strategy of Breadth-First Search, greedy expansion, and LLM supplementation; and 3) guiding LLMs with subgraphs and paths, aggregating answers via majority voting. Evaluated on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning, DS-MHP outperforms baselines and state-of-the-art methods in nearly all benchmarks. It achieves overall average accuracy increases of 3.9% on Mistral-7B and 3.6% on GPT-3.5 Turbo compared to SOTA, with significant gains in logical and symbolic reasoning. Additionally, DS-MHP reduces runtime and LLM calls compared to SOTA, enhancing computational efficiency. These improvements demonstrate DS-MHP’s superior reasoning accuracy, explainability, and efficiency in complex multi-entity tasks.

Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-ranging code-related tasks. However, most previous existing methods mainly view each programming language in isolation and ignore the knowledge transfer among different programming languages. To bridge the gap among different programming languages, we introduce a novel multi-agent collaboration framework to enhance multilingual instruction tuning for code LLMs, where multiple language-specific intelligent agent components with generation memory work together to transfer knowledge from one language to another efficiently and effectively. Specifically, we first generate the language-specific instruction data from the code snippets and then provide the generated data as the seed data for language-specific agents. Multiple language-specific agents discuss and collaborate to formulate a new instruction and its corresponding solution (A new programming language or existing programming language), To further encourage the cross-lingual transfer, each agent stores its generation history as memory and then summarizes its merits and faults. Finally, the high-quality multilingual instruction data is used to encourage knowledge transfer among different programming languages to train Qwen2.5-xCoder. Experimental results on multilingual programming benchmarks demonstrate the superior performance of Qwen2.5-xCoder in sharing common knowledge, highlighting its potential to reduce the cross-lingual gap.

Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pre-training phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.

Expert Finding is an important task in Community Question Answering (CQA) platforms, which could help route questions to potential users to answer. The key is to learn representations of experts based on their historical answered questions accurately. In this paper, inspired by the strong text understanding ability of Pretrained Language modelings (PLMs), we propose a pre-training and fine-tuning expert finding framework. The core is that we design an expert-level pre-training paradigm, that effectively integrates expert interest and expertise simultaneously. Specifically different from the typical corpus-level pre-training, we treat each expert as the basic pre-training unit including all the historical answered question titles of the expert, which could fully indicate the expert interests for questions. Besides, we integrate the vote score information along with each answer of the expert into the pre-training phrase to model the expert ability explicitly. Finally, we propose a novel reputation-augmented Masked Language Model (MLM) pre-training strategy to capture the expert reputation information. In this way, our method could learn expert representation comprehensively, which then will be adopted and fine-tuned in the down-streaming expert-finding task. Extensive experimental results on six real-world CQA datasets demonstrate the effectiveness of our method.