4arXiv cs.CL (Computation and Language)·
DuPLeR: Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
Researchers introduce DuPLeR, a dual-path LLM reasoning framework for multimodal few-shot knowledge graph completion (KGC). The system addresses inductive KGC under data-scarce conditions by combining multimodal LLM-derived type priors with factual support structures and a dual-pathway module that regulates message passing with query-relevant multimodal signals. Experiments across eight inductive variants of two multimodal KG benchmarks demonstrate robust performance in few-shot and zero-shot settings.
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Theoretical framework for grounding LLM reasoning under incomplete knowledge graph evidence
A new arXiv preprint develops a formal theoretical framework for understanding how LLMs reason when guided by incomplete knowledge graphs. The authors introduce constructs including entity anchors, typed relation residuals, path energies, and support regions, and prove that under open-world incompleteness no hard rule can simultaneously reject all false unsupported trajectories while retaining all true-but-unobserved ones. Soft grounding is characterized as a KL-regularized deformation of the LLM prior, with hard conditioning as an infinite-penalty limit. The framework yields stability bounds under evidence perturbations and has implications for GraphRAG, KGQA, graph agents, constrained decoding, and faithful generation.
DualG-MRAG: Dual-tier graph architecture decouples macro-reasoning and micro-matching for multimodal RAG
A new arXiv preprint introduces DualG-MRAG, a framework for Multimodal Retrieval-Augmented Generation that separates global structural reasoning (Macro Graph) from fine-grained local evidence matching (Micro Graph) to address retrieval noise in multi-hop reasoning tasks. The system uses a GNN-based retriever with query-driven message passing and a dynamic programming decoding mechanism that extracts explicit reasoning paths from the GNN's forward pass rather than feeding isolated document chunks to the generative model. Experiments show improvements over baselines on evidence recall and complex QA accuracy. The work targets a known failure mode of graph-enhanced MM-RAG systems where fine-grained visual features cause graph explosion and noise.
RSF-GLLM: Differentiable graph reasoning framework for multi-hop knowledge graph QA
RSF-GLLM is a new framework for multi-hop question answering over knowledge graphs that decouples differentiable graph reasoning from LLM-based answer generation. The core Recurrent Soft-Flow (RSF) module uses a GRU-guided query updater with dynamic gating to traverse semantically dissimilar bridge nodes, with flow sparsity regularization guaranteeing convergence to discrete reasoning paths. Extracted paths are textualized to fine-tune an LLM, grounding generation in factual graph topology. Experiments on WebQSP and CWQ benchmarks show competitive accuracy with improved inference efficiency over LLM-heavy baselines.
DKCD: Domain knowledge-enhanced causal discovery from unstructured data using LLMs
Researchers introduce DKCD, a framework that augments LLM-based causal discovery with domain-specific knowledge retrieval and guided reasoning to address two failure modes: insufficient identification of latent causal factors and unreliable factor annotation. The system chains knowledge mining, knowledge-guided causal reasoning, and causal structure discovery into a pipeline that produces causal graphs from unstructured text in high-expertise domains like healthcare and finance. Experiments on two domain-specific datasets show improvements over baselines on both factor identification and graph construction quality.
MIRROR: RL method improves VLM reasoning consistency across text, diagram, and combined views
Researchers introduce MIRROR (Modality-Informed Reciprocal Reasoning Optimization), a reinforcement learning approach that improves vision-language model reasoning by exploiting inconsistencies across different problem representations. The method constructs ODA-Data, a paired multimodal geometry dataset with text-dominant, image-dominant, and combined views, then uses the best-performing view as a teacher to train weaker views via reverse-KL divergence. MIRROR outperforms standard RL baselines on geometry reasoning benchmarks and produces more consistent cross-modal behavior. The work addresses a known gap between LLM and VLM reasoning capabilities on problems that have equivalent multi-modal representations.
KATE framework improves LLM tool calling via experiential knowledge integration and parallel reasoning
Researchers present KATE (Knowledge-Augmented Tool Execution), a framework addressing LLM failures in multi-step tool use by systematically studying knowledge acquisition, activation, and internalization. Key findings include that instance-level experiential knowledge outperforms abstract intent-level knowledge, that expanding reasoning width via parallel sampling with aggregation beats deeper chain-of-thought, and that reinforcement learning outperforms supervised fine-tuning for knowledge internalization. KATE is evaluated on BFCL-V3 and AppWorld benchmarks, showing consistent improvements over strong baselines across model scales.
GLM-RAG: Graph Language Model retrievers outperform GNNs and vector search on out-of-domain multi-hop reasoning
A new arXiv preprint introduces GLM-RAG, a retrieval-augmented generation framework using graph language model (GLM)-based retrievers over knowledge graphs. The authors compare GLM-based, GNN-based, and vector-search retrievers across single- and multi-hop QA settings, finding that finetuned GLM retrievers generalize better to unseen domains and achieve state-of-the-art on two multi-hop benchmarks. GNN-based retrievers offer better graph coverage with efficient training, while vector search excels at single-hop tasks. The work provides a systematic comparison of retriever architectures for knowledge-graph RAG with practical implications for domain transfer.
Groc-PO: Grounded Context Preference Optimization reduces hallucination in multimodal LLMs
Researchers introduce Groc-PO, a preference optimization framework for multimodal LLMs that applies stage-specific supervision across object grounding, contextual grounding, and grounded reasoning stages, rather than only at the final-answer level as in standard DPO. The method is paired with a new dataset (GCPD) organized around these three grounding stages. Experiments show improvements over standard DPO and other baselines on hallucination mitigation and faithful reasoning, addressing the credit-assignment problem where early grounding errors propagate through reasoning chains.