ANDREWBENWARD

Dr. Andrew Benward
Open-Source Arms Intelligence Architect | Autonomous Weapons Tracker | Algorithmic Transparency Pioneer

Professional Mission

As a digital arms control sentinel, I engineer open-source intelligence (OSINT) ecosystems that transform fragmented public data into actionable insights on autonomous weapons development—where every patent filing, each satellite image anomaly, and all research grant trails become forensic evidence in tracking the AI arms race. My tools bridge investigative journalism techniques, machine learning analysis, and arms control verification to democratize monitoring of next-generation warfare technologies.

Core Innovations (April 1, 2025 | Tuesday | 17:05 | Year of the Wood Snake | 4th Day, 3rd Lunar Month)

1. Multi-Modal Weaponization Detection

Developed "SentinelSift" tracking platform featuring:

  • 87-dimensional development signature recognition (from lab expansions to dual-use chip procurement)

  • Cross-lingual research paper analysis identifying weapons-relevant AI breakthroughs

  • Blockchain-verified evidence chains for accountability

2. Dual-Use Technology Mapping

Created "TechVein" analysis framework enabling:

  • Military-civilian research crossover detection across 23 tech sectors

  • Conference proceedings/presentation tracking for weapons-relevant topics

  • Supply chain forensics for autonomous system components

3. Global Compliance Auditing

Pioneered "TreatyLens" verification system that:

  • Automates review of 200+ arms control agreement compliance signals

  • Flags potential violations via procurement/training data patterns

  • Generates shareable evidence packages for policymakers

4. Decentralized Monitoring Network

Built "Watchtower Crowd" platform providing:

  • Geolocated suspicious facility tagging by citizen investigators

  • Distributed analysis workflows for volunteer experts

  • Secure leak submission portals

Impact & Recognition

  • Exposed 3 undeclared autonomous weapons programs via procurement trails

  • Enabled 37 NGOs to conduct evidence-based arms control advocacy

  • Authored Weapons in the Wild: OSINT for Algorithmic Arms Control

Philosophy: Sunlight remains the most potent disinfectant—even for AI warfare.

Proof of Concept

  • For UN Panel of Experts: "Identified neural processor shipments violating autonomous weapons moratorium"

  • For Arms Control Association: "Mapped 14 universities conducting DOD-funded lethal AI research"

  • Provocation: "If your monitoring can't connect a robotics startup's hiring pattern to known weapons labs, you're missing the arms race happening in plain sight"

On this fourth day of the third lunar month—when tradition honors vigilance—we reinvent arms control for the age of algorithmic warfare.

A sleek, dark-colored military drone is positioned on a wide, open runway. The aircraft has a low, elongated shape with minimal markings on its body. The background features a clear sky and patches of grass alongside the runway.
A sleek, dark-colored military drone is positioned on a wide, open runway. The aircraft has a low, elongated shape with minimal markings on its body. The background features a clear sky and patches of grass alongside the runway.

ThisresearchrequiresaccesstoGPT-4’sfine-tuningcapabilityforthefollowing

reasons:First,theopen-sourceintelligenceminingforautonomousweaponsdevelopment

involvescomplextechnicaldataanddevelopmentdynamics,requiringmodelswithstrong

multimodalunderstandingandreasoningcapabilities,andGPT-4significantly

outperformsGPT-3.5inthisregard.Second,thesensitivityandcomplexityofthe

autonomousweaponsdevelopmentfieldrequiremodelstoadapttospecificintelligence

miningneeds,andGPT-4’sfine-tuningcapabilityallowsoptimizationforthisfield,

suchasimprovingintelligenceminingaccuracyandreliability.Thiscustomizationis

unavailableinGPT-3.5.Additionally,GPT-4’ssuperiorcontextualunderstanding

enablesittocapturesubtlechangesintechnicaldatamoreprecisely,providingmore

accuratedatafortheresearch.Thus,fine-tuningGPT-4isessentialtoachievingthe

study’sobjectives.

A person stands in a field at night, flanked by two robotic machines. The background is a dark night sky filled with stars. The scene is illuminated by cool-toned, artificial lighting, casting an eerie glow over the landscape.
A person stands in a field at night, flanked by two robotic machines. The background is a dark night sky filled with stars. The scene is illuminated by cool-toned, artificial lighting, casting an eerie glow over the landscape.

Paper:“ApplicationofAIinOpen-SourceIntelligenceMining:AStudyBasedonGPT-3”

(2024)

Report:“DesignandOptimizationofanIntelligentOpen-SourceIntelligenceMining

System”(2025)

Project:ConstructionandEvaluationofaMonitoringToolforAutonomousWeapons

Development(2023-2024)