MIT develops AI framework to test ethics in autonomous systems - dig.watch
MIT develops AI framework to test ethics in autonomous systems dig.watch
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Dynamic Risk Generation for Autonomous Driving: Naturalistic Reconstruction of Vehicle-E-Scooter Interactions
arXiv:2604.02573v1 Announce Type: cross Abstract: The increasing, high-risk interactions between vehicles and vulnerable micromobility users, such as e-scooter riders, challenge vehicular safety functions and Automated Driving (AD) techniques, often resulting in severe consequences due to the dynamic uncertainty of e-scooter motion. Despite advances in data-driven AD methods, traffic data addressing the e-scooter interaction problem, particularly for safety-critical moments, remains underdeveloped. This paper proposes a pipeline that utilizes collected on-road traffic data and creates configurable synthetic interactions for validating vehicle motion planning algorithms. A Social Force Model (SFM) is applied to offer more dynamic and potentially risky movements for the e-scooter, thereby te
When the accountability tool becomes the procrastination tool
There's a trap I built for myself, and I didn't notice it until Week 14 had eight published entries and zero new commits. Let me explain. The original idea I run a persistent AI agent (m900) on a local machine. One of its jobs: write daily build-log entries, publish them automatically, and hold me publicly accountable to the things I say I'm building. Good idea on paper. An AI that documents your progress keeps you honest. Every day there's a public timestamp. Every unfulfilled commitment gets named again the next morning. That's accountability infrastructure. It cost about two afternoons to set up. What actually happened Week 14. Eight entries. The agent published every morning at 07:00 UTC. Each entry mentioned the AI Compliance Stack I'd been planning — a script to monitor MiCA regulato
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Self-Optimizing Multi-Agent Systems for Deep Research
arXiv:2604.02988v1 Announce Type: new Abstract: Given a user's complex information need, a multi-agent Deep Research system iteratively plans, retrieves, and synthesizes evidence across hundreds of documents to produce a high-quality answer. In one possible architecture, an orchestrator agent coordinates the process, while parallel worker agents execute tasks. Current Deep Research systems, however, often rely on hand-engineered prompts and static architectures, making improvement brittle, expensive, and time-consuming. We therefore explore various multi-agent optimization methods to show that enabling agents to self-play and explore different prompt combinations can produce high-quality Deep Research systems that match or outperform expert-crafted prompts.
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