Autonomous Systems

Opening — Why this matters now Agentic AI is finally escaping the demo phase and entering production. And like most things that grow up too fast, it’s discovering an uncomfortable truth: thinking is expensive. Every planning step, every tool call, every reflective pause inside an LLM agent adds latency, cost, and failure surface. When agents are deployed across customer support, internal ops, finance tooling, or web automation, these inefficiencies stop being academic. They show up directly on the cloud bill—and sometimes in the form of agents confidently doing the wrong thing. ...

Opening — Why this matters now Elite sport has quietly become an optimization problem. Marginal gains are no longer found in strength alone, but in decision quality under pressure. Boxing, despite its reputation for instinct and grit, has remained stubbornly analog in this regard. Coaches still scrub footage frame by frame, hunting for patterns that disappear as fast as they emerge. ...

Opening — Why this matters now Most algorithmic fairness debates still behave as if discrimination is a rounding error: rare, isolated, and best handled by catching a few bad counterexamples. Regulators ask whether a discriminatory case exists. Engineers ask whether any unfair input pair can be found. Auditors tick the box once a model is declared “2-fair.” ...

Opening — Why this matters now Traffic simulation has always promised more than it delivers. City planners, transport researchers, and policymakers are told that with the right simulator, congestion can be eased, emissions reduced, and infrastructure decisions made rationally. In practice, most simulators demand deep domain expertise, rigid workflows, and a tolerance for configuration pain that few real-world users possess. ...

Opening — Why this matters now Modern systems act faster than their understanding. Algorithms trade in microseconds, clinical protocols scale across populations, and institutions make irreversible decisions under partial information. Yet our ethical vocabulary remains binary: act or abstain, know or don’t know, responsible or not. That binary is failing. The paper behind this article introduces a deceptively simple idea with uncomfortable implications: uncertainty does not reduce moral responsibility — it reallocates it. When confidence falls, duty does not disappear. It migrates. ...

Opening — Why this matters now Modern autonomy has a credibility problem. We train systems in silico, deploy them in the real world, and hope the edge cases are forgiving. They usually aren’t. For robots, vehicles, and embodied AI, one safety violation can be catastrophic — and yet most learning‑based methods still treat safety as an expectation, a probability, or worse, a regularization term. ...

Opening — Why this matters now Surgical robotics has long promised precision beyond human hands. Yet, the real constraint has never been mechanics — it’s perception. In high-stakes fields like spinal surgery, machines can move with submillimeter accuracy, but they can’t yet see through bone. That’s what makes the Johns Hopkins team’s new study, Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures, quietly radical. It explores whether imitation learning — the same family of algorithms used in self-driving cars and dexterous robotic arms — can enable a robot to navigate the human spine using only X-ray vision. ...

Why this matters now As AI systems move from chatbots to control towers, the stakes of their hallucinations have escalated. Large Language Models (LLMs) and Vision-Language Models (VLMs) now make—or at least recommend—decisions in physical space: navigating drones, scheduling robots, even allocating emergency response assets. But when such models “reason” incorrectly, the consequences extend beyond embarrassment—they can endanger lives. Notre Dame’s latest research introduces the concept of a Cognition Envelope, a new class of reasoning guardrail that constrains how foundational models reach and justify their decisions. Unlike traditional safety envelopes that keep drones within physical limits (altitude, velocity, geofence) or meta-cognition that lets an LLM self-critique, cognition envelopes work from outside the reasoning process. They independently evaluate whether a model’s plan makes sense, given real-world constraints and evidence. ...

In the fast-evolving landscape of agentic AI, one critical limitation persists: most frameworks can think or act, but rarely both in a fluid, self-directed manner. They follow rigid ReAct-like loops—plan, call, observe—resembling a robot that obeys instructions without ever truly reflecting on its strategy. The recent paper “DeepAgent: A General Reasoning Agent with Scalable Toolsets” from Renmin University and Xiaohongshu proposes an ambitious leap beyond this boundary. It envisions an agent that thinks deeply, acts freely, and remembers wisely. ...

If your AI agent is putting a metal fork in the microwave, would you rather stop it after the sparks fly—or before? That’s the question Pro2Guard was designed to answer. In a world where Large Language Model (LLM) agents are increasingly deployed in safety-critical domains—from household robots to autonomous vehicles—most existing safety frameworks still behave like overly cautious chaperones: reacting only when danger is about to occur, or worse, when it already has. This reactive posture, embodied in rule-based systems like AgentSpec, is too little, too late in many real-world scenarios. ...