Topic
knowledge graphs
FineREX Boosts Knowledge Graph Quality by 31% in Human Smuggling Document Analysis
Researchers introduce FineREX, a fine-tuned NER-RE pipeline for knowledge graph construction from unstructured legal documents. Compared to a larger general-purpose LLM baseline, FineREX achieves absolute improvements of 15.50% in entity F1 and 31.46% in relation F1, reduces legal noise by nearly half, and cuts processing time by 50%.
AI Economist Agent: New Framework Uses RAG, Knowledge Graphs and LLMs for Grounded Economic Analysis
Researchers propose an AI economist agent framework that combines retrieval-augmented generation (RAG), knowledge graphs, and LLM-based agents to ground economic analysis in data and theory. Tested on U.S. inflation persistence and bank stress-test scenarios, the approach improves economic coherence and traceability of generated reports.
New DEC Framework Enables Provenance Reasoning in Knowledge Graphs for Supply Chain
Provenance-enhanced statements like 'according to X, φ' are common in knowledge graphs but current models treat provenance as semantically neutral. A new framework, DEC, interprets provenance predicates as indicators of epistemic stance, grouping statements into cognitive worlds. This allows principled reasoning over attributed content without collapsing disagreements into inconsistencies, with applications in domains like supply chain where multiple parties assert different claims.
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
Researchers propose KMAS, an adaptive negative sampling approach that enhances knowledge graph foundation models (KGFMs) by generating hard negative triples from relation embeddings. The method dynamically adjusts the ratio of hard negatives during training, improving performance across 44 datasets without significant extra time or memory.
LLM-WikiRace Benchmark Reveals Frontier AI Models Still Struggle with Planning Over Knowledge Graphs
Researchers introduced LLM-WikiRace, a benchmark to evaluate large language models on planning, reasoning, and world knowledge using Wikipedia hyperlinks. Top models like Gemini-3, GPT-5, and Claude Opus 4.5 achieve superhuman performance on easy tasks but drop sharply on hard difficulty, with Gemini-3 succeeding in only 23% of hard games. The study reveals that world knowledge helps only up to a point; beyond that, planning and long-horizon reasoning are the limiting factors.
LearnOpt Uses Knowledge Graphs and Optimization to Reveal Hidden Structure in Standardized Exams
Researchers introduce LearnOpt, a system that recovers latent cognitive structures from standardized examinations using knowledge graphs and constrained optimization. Applied to NEET and JEE Advanced, it reveals stable skill distributions within syllabus regimes and significant shifts after curricular changes.
A Theoretical Roadmap to Fuse Foundation Models and Knowledge Graphs
A new theoretical paper formalizes the 'Impedance Mismatch' between Foundation Models and Knowledge Graphs, arguing that current approaches like RAG are superficial. The authors propose a roadmap including Structured Residual Streams, Vector Symbolic Architectures, and Orthogonal Subspace Editing for true semantic fusion.