Autonomous Agents

Opening — Why this matters now Security teams are being asked to do more with less, while the attack surface keeps expanding and adversaries automate faster than defenders. Large language models promise relief: summarize logs, suggest response actions, even draft incident playbooks. But there’s a catch that every practitioner already knows—LLMs are confident liars. In security operations, a hallucinated action isn’t just embarrassing; it’s operationally expensive. ...

Opening — Why this matters now RAG is supposed to make large language models safer. Ground the model in documents, add citations, and hallucinations politely leave the room—or so the story goes. In practice, especially in expert domains, RAG often fails in a quieter, more dangerous way: it retrieves something relevant, but not the right kind of evidence. ...

Opening — Why this matters now Agentic AI has moved beyond polite conversation. Increasingly, we expect language models to act: negotiate contracts, procure services, choose suppliers, and close deals on our behalf. This shift quietly transforms LLMs from passive tools into economic actors. Yet here’s the uncomfortable truth: most evaluations of LLM agents still resemble logic puzzles or toy auctions. They test reasoning, not commerce. Real markets are messy—private constraints, asymmetric incentives, multi-round bargaining, and strategic patience all matter. The paper behind AgenticPay steps directly into this gap. ...

Opening — Why this matters now For decades, modeling and simulation lived in a world of equations, agents, and carefully bounded assumptions. Then large language models arrived—verbose, confident, and oddly persuasive. At first, they looked like narrators: useful for documentation, maybe scenario description, but not serious modeling. The paper behind this article argues that this view is already outdated. ...

Opening — Why this matters now Multi-agent LLM systems are having their moment. From coding copilots to autonomous research teams, the industry has embraced the idea that many models thinking together outperform a single, monolithic brain. Yet most agent frameworks still suffer from a familiar corporate disease: everyone talks to everyone, all the time. ...

Opening — Why this matters now Digital twins for transport are no longer futuristic demos. They are quietly becoming operational systems, expected to anticipate congestion, test control policies, and absorb shocks before drivers ever feel them. But a digital twin that only mirrors the present is reactive by definition. To be useful, it must predict. ...

Opening — Why this matters now School security research lives in a permanent bind: the events we most need to understand are precisely the ones we cannot ethically or practically reproduce at scale. Real-world shooter data is sparse, incomplete, and morally costly. Virtual reality (VR) improves matters, but even VR-based human-subject experiments remain slow, expensive, and fundamentally non-iterative. ...

Opening — Why this matters now Modern transformers are confident. Too confident. In high-stakes deployments—question answering, medical triage, compliance screening—this confidence routinely outruns correctness. The problem is not accuracy; it is miscalibration. Models say “I’m sure” when they shouldn’t. Most fixes arrive late in the pipeline: temperature scaling, Platt scaling, confidence rescaling after the model has already reasoned itself into a corner. What if uncertainty could intervene earlier—during reasoning rather than after the verdict? ...

Opening — Why this matters now Agentic AI has entered its confident phase. Papers, demos, and product pitches increasingly imply that large language model (LLM)–powered agents can already “do research”: formulate hypotheses, run experiments, and even write papers end to end. The uncomfortable question is not whether they look busy—but whether they actually rediscover truth. ...

Opening — Why this matters now As AI systems grow more autonomous, the uncomfortable question keeps resurfacing: what does it even mean for a machine to have a perspective? Not intelligence, not planning, not goal pursuit—but a situated, history-sensitive way the world is given to the system itself. Most modern agent architectures quietly dodge this question. They optimize rewards, compress states, maximize returns—and call whatever internal structure emerges a day. But subjectivity, if it exists at all in machines, is unlikely to be a side effect of reward maximization. It is more plausibly a structural condition: something slow, global, and stubbornly resistant to momentary incentives. ...