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A study of public-sector AI registers and inventories finds missing data and proposes a layered visibility framework for transparency and interoperability.
This paper compares 8,368 public-sector AI records across 72 countries and finds broad schemas have substantial missingness, with limitations in transparency and interoperability, proposing a layered visibility framework.
A study finds graph-structured LLM reasoning lifts cybersecurity analysis accuracy by up to 12.2 points.
A cs.CR paper introduces Security Reasoning Topology, comparing Linear, Branching, and Graph organizations of intermediate LLM reasoning for cybersecurity analysis. Controlled tests cover MITRE ATT&CK network traffic, cyber threat intelligence, and CVE analysis, using Llama 2 at 7B, 13B, and 70B plus GPT-5.1 and Mistral Large 3 with fixed inputs. Graph reasoning was most accurate, improving 9.8 to 12.2 percentage points over few-shot prompting, while Branching was a strong middle option. The authors report the effect holds across model families and scales.