![[2602.13324] Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13324] Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge
Summary
This paper presents a zero-shot framework for target verification and tactical reasoning in autonomous edge robotics, addressing challenges in military environments.
Why It Matters
The research is significant as it tackles the limitations of training data and computational power in edge robotics, providing a novel approach that enhances operational efficiency in dynamic military settings. The findings could influence future developments in AI applications for safety-critical environments.
Key Takeaways
- Introduces a hierarchical zero-shot framework for edge robotics.
- Achieves high accuracy in target verification and tactical reasoning.
- Demonstrates the effectiveness of Vision-Language Models in military applications.
- Identifies distinct failure modes in AI models, aiding in performance diagnostics.
- Validates the use of zero-shot architectures for enhancing edge autonomy.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13324 (cs) [Submitted on 10 Feb 2026] Title:Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge Authors:Jesse Barkley, Abraham George, Amir Barati Farimani View a PDF of the paper titled Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge, by Jesse Barkley and 2 other authors View PDF HTML (experimental) Abstract:Deploying autonomous edge robotics in dynamic military environments is constrained by both scarce domain-specific training data and the computational limits of edge hardware. This paper introduces a hierarchical, zero-shot framework that cascades lightweight object detection with compact Vision-Language Models (VLMs) from the Qwen and Gemma families (4B-12B parameters). Grounding DINO serves as a high-recall, text-promptable region proposer, and frames with high detection confidence are passed to edge-class VLMs for semantic verification. We evaluate this pipeline on 55 high-fidelity synthetic videos from Battlefield 6 across three tasks: false-positive filtering (up to 100% accuracy), damage assessment (up to 97.5%), and fine-grained vehicle classification (55-90%). We further extend the pipeline into an agentic Scout-Commander workflow, achieving 100% correct asset deployment and a 9.8/10 reasoning score (graded by GPT-4o) with sub-75-second latency. A novel "Controlled Input" me...
