Paper

DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics

Unreadunread

AI summary

arXiv:2605. 30538v1 Announce Type: new Abstract: Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records of hazard, exposure, vulnerability, and lifeline infrastructure that underpin disaster management.

Reading status

Priority

About this paper

Authors: Yiming Xiao, Ankit Basu, Kai Yin, Sahil Vartak, Christian Swords, Ali Mostafavi
Released: 2026-06-01 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, under, eval, line, rate, stage, position. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.30538

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.