autonomous-systems

This paper examines how AI-mediated civilian cyber operations challenge the direct causation criterion of the ICRC's test for direct participation in hostilities, arguing that autonomous multi-agent systems break the causal link required for classification under international humanitarian law.

A senior Anthropic engineer published an 11-page paper on Loop Engineering, proposing a new paradigm for building agentic systems centered on feedback loops, isolation, verification, and memory rather than smarter prompts.

A US F-15 pilot reported seeing Iranian drones moving in a coordinated 'jellyfish' formation before being shot down, sparking debate within US intelligence about Iran's drone capabilities.

This paper critiques current AI agent systems, distinguishing between agentic (external scaffolding) and agentive (internalized) systems, and proposes the Goal-Identity-Configurator (GIC) architecture for general-purpose agent models with endogenously developed capabilities, along with insights on safety and controllability.

Estonia plans to become the first country to create official digital identities for AI agents, enabling verifiable and auditable autonomous actions within defined limits for public and private services.

The author explains how to build a self-improving quant trading system using AI loop engineering, where the AI runs loops to prompt, verify, and act autonomously, contrasting with manual prompting.

University of Michigan Robotics shares free open-source course materials including lectures, textbooks, and projects from their top robotics program, covering topics from computational linear algebra to autonomous systems.

ENPIRE is a framework that enables coding agents to autonomously improve robot manipulation policies through a real-world feedback loop, achieving 99% success on dexterous tasks like pin insertion and zip tie cutting.

A developer building autonomous billing agents discusses the difficulty of reconstructing why an agent made a decision after the fact, and describes building a tool (Attova) that records decisions with evidence, alternatives, and confidence to improve debugging and human review.

A Ukrainian drone manufacturer revealed that fully autonomous drones were used in a one-time test two years ago, reportedly killing Russian soldiers without human intervention, marking a milestone in AI-guided weaponry despite ongoing legal and ethical concerns.

The author argues that as AI agents become more autonomous, a governance layer is needed for control, observability, and auditability, and introduces Bendex Arc as a solution with components like Arc Gate, Arc Replay, Arc Approve, and Arc Memory.

This paper conceptualizes the transition of large language models from conversational chatbots to persistent autonomous AI colleagues, focusing on improved reasoning and tool-augmented task execution with workspace and skill paradigms.

The author introduces GLAW, an autonomous multi-agent AI system for legal and government tasks involving research, analysis, drafting, and execution, and invites discussion on its risks and safeguards.

The article argues that using AI agents feels superior to traditional software because they allow users to focus on high-level goals while the agents autonomously handle execution, turning technology into a digital collaborator.

Discusses the transition from chatbot-based AI to autonomous agents capable of executing complex workflows, suggesting a major UX shift.

FusionSense introduces a tri-stage near-sensor learning framework for multimodal edge intelligence that jointly reduces compute and communication by using fusion-aware filtering, achieving up to 33× energy savings and significant data-reduction gains on RGB-Depth/LiDAR tasks.

The article argues that AI is shifting from chat-based interfaces to autonomous background agents that operate without human hand-holding, marking a transition from prompting an assistant to managing a digital assembly line.

The article observes that the most effective AI agent demos are simple and reliable, focusing on clear tasks and structured outputs rather than full autonomy, signaling a healthy industry shift toward dependability.

This literature review identifies and analyzes the problem of silent failures in physical AI systems, where black-box models may execute harmful actions without detection. It proposes a taxonomy of runtime guardrail functions and outlines evaluation requirements for safe autonomous systems.

This paper introduces Ethical Hyper-Velocity (EHV), an architectural framework that combines conflict-free replicated data types and trusted execution environments to achieve sub-millisecond formal verification of AI governance policies in autonomous agentic systems, reducing governance latency from days to constant time.