DuoFlow-KG: a dual-modal evidence retrieval framework for high-density LLM-augmented KGQA.

LSECG: LLM-based semantic enhancement and context-guided GNNs in multi-hop KGQA

STaR: a soft-labeling and triplet-aware retriever for efficient retrieval-augmented QA

This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between queries and candidate passages, significantly enhancing retrieval ranking precision and semantic robustness.

CONSISTRE: A Unified Consistency-Aware Framework for Document-Level Relation Extraction with Large Language Models

Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated independently for each candidate triple and may violate fundamental relational constraints such as transitivity, symmetry, and functional uniqueness, leading to contradictory and unreliable outputs. We propose CONSISTRE, a unified consistency-aware framework for DocRE that addresses this limitation through two complementary tracks. The first operates at inference time for black-box LLMs, combining constraint-aware prompting, constraint-based verification, and iterative self-reflection to refine predictions without task-specific fine-tuning. The second injects consistency knowledge into smaller open-source models via a knowledge distillation and reinforcement learning pipeline: reasoning traces from a powerful teacher are distilled into a student via supervised fine-tuning, followed by GRPO alignment using a composite reward that jointly optimizes extraction performance and relational consistency. Together, the two tracks cover both API-accessible and locally deployable scenarios under a unified consistency formulation. Experiments on DocRED show that both tracks outperform their baselines, with the inference-time track achieving competitive F1 using off-the-shelf black-box LLMs and the training-time track substantially narrowing the gap between 7--8B open-source models and state-of-the-art proprietary LLMs at a fraction of their inference cost. Ablation studies confirm that explicit consistency modeling mitigates relational contradictions and enhances the reliability of LLM-based DocRE across both deployment paradigms.

Towards Building a Multi-Source Heterogeneous Knowledge Graph for Complex Material Question Answering

Results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering.

Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion

Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make inductive KGC difficult, especially under few-shot and zero-shot settings. Multimodal information and Large Language Model (LLM)-derived priors can enrich sparse relational contexts, but they may also introduce noisy or hallucinated evidence. To address these issues, we propose DuPLeR, a \textbf{Du}al-\textbf{P}ath \textbf{L}LM \textbf{R}easoning framework for multimodal few-shot KGC. DuPLeR builds a calibrated relation graph by combining multimodal LLM-derived type priors with factual support structures, and performs dual-level structural reasoning over the refined relation topology. Moreover, a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation. Experiments on eight inductive variants of two multimodal KG (MMKG) benchmarks show that DuPLeR achieves robust performance in data-scarce KGC scenarios.

RAPC: Relation Answer Space Prototype Calibration for Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MMKGC) aims to predict missing entities by exploiting structural, visual, and textual information and is important for semantic retrieval, recommendation, and intelligent question answering. Existing relation-aware methods usually use relational context to adjust modality weights or fuse multimodal scores but rarely exploit the historical answer distribution of each directed relation as explicit ranking evidence. To address this limitation, we propose RAPC, a Relation Answer Space Prototype Calibration framework. For each query, RAPC obtains multimodal prior scores from structural, visual, textual, and image–text cross-modal branches, retrieves the top-k query-relevant anchors from the historical answer space of the corresponding directed relation, and aggregates them into prototype evidence. This evidence is injected through a prior-preserving selective calibration mechanism, while relation-aware hard negative training improves discrimination between true answers and similar false candidates. Experiments on DB15K, MKG-W, and MKG-Y show that RAPC achieves clear gains on DB15K and MKG-W and obtains the best MRR and Hits@1 on MKG-Y. Compared with the best external results, RAPC improves MRR and Hits@1 by 2.64 and 3.57 percentage points on DB15K and by 3.02 and 3.72 percentage points on MKG-W. These results show that relation-level historical answer distributions provide useful explicit evidence for top-ranked entity prediction.