必看！百篇“AI+军事” 智能防务报告资料汇编！
Merged summary
TL;DR — This curated portal aggregates over 100 reports, papers, theses, and doctrine on AI-enabled defense, showing how autonomy, data, and intelligent decision support are reshaping military operations. It is a reference collection rather than original research.
- Covers AI-assisted command and control, planning, intelligence, simulation, wargaming, and situational awareness.
- Highlights autonomous platforms, drones, swarms, counter-drone systems, multi-agent planning, and human-machine teaming.
- Includes battlefield edge computing, tactical networks, cybersecurity, electronic warfare, responsible AI, and trust.
- Addresses LLM agents and lessons from recent conflicts alongside technical, military, academic, and policy perspectives.
- Related causal-AI analysis argues that causal models, interventions, and counterfactual reasoning could improve robust planning and decision-making, though deployment remains challenging.
Note: The 专知 sources focus on the defense-resource compilation, while 机器之心 emphasizes broader causal reasoning and world-model research rather than the collection itself.
Sources (3)
必看！百篇“AI+军事” 智能防务报告资料汇编！
TL;DR - A curated portal aggregates more than 100 reports and papers on AI-enabled defense. It highlights how autonomy, data, and intelligent decision support are reshaping military operations.
- Covers AI-assisted command, planning, simulation, intelligence, and situational awareness.
- Emphasizes autonomous drones, swarms, multi-agent systems, and human-machine teaming.
- Includes material on edge computing, tactical networks, cybersecurity, and electronic warfare.
- Collects technical surveys, military doctrine, theses, and government reports rather than presenting new research results.
为什么让 AI 理解世界的前提是读懂因果？
TL;DR - This analysis argues that next-token prediction learns statistical correlations but lacks causal understanding. Causal structures, intervention reasoning, and counterfactual analysis could improve AI’s generalization, planning, and reliability in open environments.
- Current data-driven AI largely occupies Pearl’s association layer, with weaker intervention and counterfactual reasoning.
- World models, spatial intelligence, and embodied AI aim to model environmental dynamics and the consequences of actions.
- Structural causal models and causal graphs may help systems remain robust under distribution shifts and unfamiliar interactions.
- Practical causal reasoning remains limited by verification and deployment challenges.
必看！百篇“AI+军事” 智能防务报告资料汇编！
TL;DR - Zhuanzhi has assembled a large resource index covering AI-enabled defense, including autonomous systems, military decision support, command and control, and drone warfare. It serves as a broad reference collection rather than presenting new research findings.
- Includes reports, reviews, theses, doctrine, and analyses from military, academic, and policy sources.
- Major themes include LLM agents, multi-agent planning, human-machine teaming, wargaming, and AI-enhanced C2.
- Extensively covers drones, swarms, counter-drone systems, autonomous platforms, and lessons from recent conflicts.
- Also addresses edge computing, battlefield networking, cybersecurity, responsible AI, and trust in military AI.