Autonomous Agents

Opening — Why this matters now Multi-agent systems are quietly becoming the backbone of modern automation: warehouse fleets, financial trading bots, supply-chain optimizers, and—if you believe the more excitable research labs—proto-agentic AI organizations. Yet there’s a peculiar, recurring problem: when you ask agents to improve by playing against each other, they sometimes discover that the fastest route to “winning” is to make sure nobody wins. ...

Opening — Why this matters now The AI industry is celebrating multimodal models as if they can already do things. Look at a picture, generate a plan, and—supposedly—convert visual understanding into executable action. But when you swap the glossy demos for a domain that demands fine-grained, symbolic precision—like crochet—the illusion cracks. CrochetBench, a new benchmark evaluating whether vision‑language models can move from describing to doing, is far more than a quirky dataset. It is a stress test for the kind of procedural reasoning that underpins robotics, manufacturing automation, and any AI system meant to execute real-world workflows. ...

Opening — Why this matters now The AI world is getting bolder — talking about agentic workflows, self-directed automation, multimodal copilots, and the eventual merging of reasoning engines with operational systems. Yet beneath the hype lies a sobering question: Can today’s most powerful LLMs actually plan? Not philosophically, but in the cold, formal sense — step-by-step, verifiable, PDDL-style planning. ...

Opening — Why this matters now Generative AI has become a prolific factory of synthetic text, code, images—and occasionally, trouble. As models scale, so do the ways they can fail. Some failures are visible (toxic text, factual errors), but others are engineered to be invisible: steganography buried in an innocent paragraph, subtle security vulnerabilities in model‑generated code, or quietly embedded backdoor triggers. ...

Opening — Why this matters now Every scientific field has its own version of the same quiet frustration: we can model what we already understand, but what about the structure we don’t? As AI systems spread into physics, astronomy, biology, and high‑dimensional observation pipelines, they dutifully compress the data we give them—while just as dutifully baking in our blind spots. ...

Opening — Why this matters now In an age where AI systems increasingly navigate large, messy decision spaces—whether for planning, automation, or autonomous agents—our algorithms must deal with the uncomfortable reality that heuristics sometimes stop helping. These gray zones, known as Uninformative Heuristic Regions (UHRs), are where search algorithms lose their sense of direction. And as models automate more reasoning-intensive tasks, escaping these regions efficiently becomes a strategic advantage—not an academic exercise. ...

Opening — Why this matters now AI models are no longer passive text engines. They remember, reason, and improvise — sometimes poorly. As large language models (LLMs) gain memory and autonomy, we face a paradox: they become more useful because they act more like humans, and more dangerous for the same reason. This tension lies at the heart of a new paper, “When Memory Leads Us Astray: A Study of Bias and Mislearning in Agentic LLMs” (arXiv:2511.08585). ...

Opening — Why this matters now Hallucination has become the embarrassing tic of multimodal AI — a confident assertion untethered from evidence. In image–language models, this manifests as phantom bicycles, imaginary arrows, or misplaced logic that sounds rational but isn’t real. The problem is not stupidity but unfaithfulness — models that reason beautifully yet dishonestly. ...

Opening — Why this matters now Predictive maintenance (PdM) has been the holy grail of industrial AI for a decade. The idea is simple: detect failure before it happens. The execution, however, is not. Real-world maintenance data is messy, incomplete, and often useless without an army of engineers to clean it. The result? AI models that look promising in PowerPoint but fail in production. ...

Opening — Why this matters now Autonomous systems have already taken to the skies. Drones scout, strike, and surveil. But the subtler transformation is happening on the ground—inside simulation labs where algorithms are learning to outthink humans. A recent study by the Swedish Defence Research Agency shows how AI can autonomously generate and evaluate thousands of tactical options for mechanized battalions in real time. In other words: the software isn’t just helping commanders—it’s starting to plan the war. ...