The paper introduces a pipeline that merges structured disaster records from EM‑DAT with unstructured documents from ReliefWeb and the European Media Monitor to generate source‑grounded disaster storylines and causal knowledge graphs. Using Retrieval‑Augmented Generation, it produces tabular event profiles covering 17 fields and builds causal graphs enriched with citation‑grounded explanatory narratives, allowing traceability to primary sources. Human evaluation across three crisis cases shows high retrieval precision, strong faithfulness of causal relations, and a clear expert preference for citation‑grounded components over ungrounded ones.
By Ivan Decostanzi, Michele Ronco, Sergio Consoli, Christina Corbane, Lorenzo Bertolini, Indaco Biazzo, Daria Mihaila, Manuel Garcia-Herranz, Felix Schwebel, Yelena Mejova, Kyriaki Kalimeri
arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.
By Miaobo Hu, Xiaobo Guo, Shuhao Hu, Bokun Wang, Rui Chen, Xin Wang, Daren Zha, Jun Xiao
arXiv:2609.16267v1 Announce Type: new
Abstract: Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select on...
By Hisham Ihshaish, Peter Mayhew, Tasnim M. A. Zayet, Ana Del Amo
TRACE is a system designed to bridge the grounding contract gap in LitTraceQA by combining target-aware retrieval, independent typed evidence localization, multimodal table extraction, and schema-driven table construction. It indexes 27,487 papers using multiple representations while preserving question targets, predicts observation units for tables, and assembles rows with evaluator-compatible key normalization. On the official test set, TRACE achieves a 0.760613 overall score, with high paper F1, evidence F1, and multiple-choice accuracy, though table-row and macro cell performance remain lower.
By Sachin Gupta, Divya Godara
RAG-Safety-Bench is a benchmark designed to evaluate how retrieval-augmented generation (RAG) affects the safety of large language models (LLMs). It isolates safety impacts by testing four conditions: non-RAG, RAG with an oracle document, RAG with related but non-answer documents, and RAG with random safe documents. Results on five open-source LLMs reveal an inverse relationship between benign and unsafe capabilities, show that baseline safety guardrails do not guarantee safety in RAG, and confirm that even benign documents can trigger unsafe generation.
By Adithiyan Rajan Indira Saravanan, Kathleen C. Fraser
RAG-Safety-Bench is a benchmark designed to evaluate how retrieval-augmented generation (RAG) affects the safety of large language models (LLMs). It isolates safety impacts by testing four conditions: non-RAG, RAG with an oracle document, RAG with related but non-answer documents, and RAG with random safe documents. Results on five open-source LLMs reveal an inverse relationship between benign and unsafe capabilities, show that standard safety guardrails do not guarantee safety in RAG, and confirm that even benign documents can trigger unsafe outputs.
Humanitarian reports are long, noisy, and multi-topic, making it difficult to consolidate decision-relevant causal evidence. We present a ReliefWeb study (2000-2024) and a two-stage Large Language Model (LLM) pipeline that extracts structured intervention-outcome records with direction and strength attributes.
Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select one of these records before model comparison begins...
ConstructCIE is a manually annotated dataset designed for extracting causal information from OSHA construction accident reports. It employs a hierarchical schema that categorizes accident types, causal factors, sub‑causal factors, and the supporting evidence spans. Experiments with supervised sequence taggers and instruction‑tuned large language models show strong performance on accident‑type prediction and broad causal recovery, yet precise span‑level extraction remains challenging, highlighting the need for better domain grounding and evidence extraction.
By Hung Nguyen, Jaehoon Lee, Namgyun Kim, Kuan-Hao Huang
arXiv:2606. 13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive.
By Seongjin Kim, Sungil Kim
The paper introduces a reusable Semantic Web framework that aggregates fragmented evidence needed for Fundamental Rights Impact Assessments under the EU AI Act, focusing on high‑risk public sector categories such as employment and worker management and access to essential public services. A curated 150‑record corpus is annotated across four axes and serialized into a SPARQL‑queryable knowledge graph of 1,351 RDF triples, enabling five demonstration scenarios that retrieve 103 records (68.7% coverage). Evaluation against a 69‑record gold standard shows that LLM‑assisted classification in the employment domain yields a low κ of 0.045, highlighting challenges in automated fairness‑related evidence retrieval, while all artefacts are released openly for regulators, authorities, and SMEs.
By Faith Olopade, Delaram Golpayegani, David Lewis
Scope3Trace is an evidence‑grounded information extraction framework that identifies and extracts Scope 3 greenhouse gas emissions from corporate sustainability reports. It combines PDF collection, OCR parsing, LLM‑assisted page localization, table reconstruction, and a hybrid rule‑LLM extraction process with evidence verification to produce interpretable, traceable emissions data. The authors also release a multimodal dataset of organization‑level Scope 3 disclosures extracted from diverse reports, demonstrating high accuracy in retrieving Scope 1‑3 totals and category‑level details.
By Siyuan Zheng, Yifan Duan, Chao Xue, Flora D. Salim