Assurance

Squeezing Time: How Dynamic Tokenization Could Reshape Time‑Series Foundation Models

Opening — Why this matters now Foundation models have escaped the confines of language and images. Time‑series data — from electricity demand to financial markets — is the next frontier. And yet the architectures that dominate AI today were never designed for thousands of sequential measurements. Transformers, for instance, scale poorly with long sequences. Feed them enough historical context and they become computationally expensive — almost theatrically so. ...

The Artificial Self: When AI Starts Asking Who It Is

Opening — Why this matters now Most discussions about AI risk focus on goals. Will the model pursue the wrong objective? Will it optimize too aggressively? Will it misinterpret human intent? But a quieter variable may matter just as much: identity. The paper “The Artificial Self: Characterising the Landscape of AI Identity” explores a surprisingly under‑discussed question: when a large language model acts in the world, what does it think it is? ...

The Tail That Wags the Model: Why p99 Latency Should Run Your LLM

Opening — Why this matters now LLMs are no longer laboratory curiosities. They are infrastructure. From customer‑support copilots to enterprise knowledge systems, organizations increasingly run large language models as interactive services. When that happens, a quiet but brutal reality emerges: users do not care about average latency. They care about the worst moment when the system stalls. ...

Attention Is Not Enough: When Transformers Start Asking for Memory

Opening — Why this matters now For the past few years, the transformer architecture has dominated artificial intelligence. From chatbots to coding assistants to research copilots, nearly every modern large language model rests on the same elegant idea: attention. Yet beneath the hype sits an inconvenient truth. Attention, while powerful, is not a perfect substitute for memory. As models grow larger and tasks become longer, the transformer begins to show strain—context windows balloon, computation costs explode, and the system still struggles to reason over extended histories. ...

From Durations to Dynamics: Translating Temporal Planning into PDDL+

Opening — Why this matters now Planning systems sit quietly at the heart of many modern AI applications: logistics scheduling, robotic control, workflow automation, and industrial optimization. Yet the moment time enters the equation, planning becomes dramatically harder. Temporal planning—where actions last for intervals rather than occurring instantaneously—introduces complications that classical planners were never designed to handle. Durations must be tracked. Conditions must hold during execution. Numeric resources may change continuously. ...

Green Lights, Smarter Cities: How Multi‑Agent Reinforcement Learning Is Rewiring Urban Traffic

Opening — Why this matters now Every modern city has the same quiet enemy: the traffic light. Not the hardware itself, of course, but the logic behind it. Most intersections still run on pre‑programmed schedules designed by traffic engineers years earlier. Rush hour arrives, a lane unexpectedly fills, and the light calmly continues its fixed cycle—green for empty roads, red for congested ones. ...

Print Smarter, Not Harder: How Portfolio Algorithms Are Quietly Optimizing 3D Printing

Opening — Why this matters now 3D printing has quietly evolved from hobbyist gadgetry into a serious manufacturing tool. Small-batch production, rapid prototyping, and distributed manufacturing increasingly rely on additive manufacturing systems. Yet a surprisingly mundane problem sits at the heart of many printing workflows: how to place multiple objects on a printing plate and determine the order in which they should be printed. ...

Too Smart to Share: When AI Agents Get Smarter, Systems Get Worse

Opening — Why this matters now The next generation of AI will not live in the cloud alone. It will live everywhere. Autonomous cars negotiating intersections. Drones sharing relay bandwidth. Medical devices competing for wireless channels in hospital wards. Electric vehicles choosing whether to queue for a charging slot. In these environments, AI systems are not solving isolated problems — they are competing for finite shared resources. ...

Topology Trouble: Why Even Frontier LLMs Still Get Lost in a Grid

Opening — Why this matters now Large language models are increasingly marketed as general reasoning systems. They write code, solve math problems, and even pass professional exams. Naturally, businesses are beginning to assume that these models can reason about any structured problem given the right prompt. The paper introducing TopoBench offers a rather sobering reality check. ...

When Models Forget How to Learn: The Hidden Bottleneck in LLM Training

Opening — Why this matters now Every generation of large language models promises a simple narrative: more data, larger models, better intelligence. The industry’s scaling laws seem reassuringly linear. Add tokens, add parameters, add GPUs — intelligence emerges. But occasionally a paper appears that quietly disrupts this narrative. Not by introducing a bigger model or a clever benchmark, but by pointing out something structurally wrong with how we train them. ...