Opening — Why this matters now The industry has been quietly converging on an uncomfortable realization: raw model scaling is running out of low-hanging fruit. Training bigger models still works, but the marginal cost curve has become brutally steep. Meanwhile, real-world deployments increasingly care about inference economics—latency, throughput, and cost per correct answer—not leaderboard bravado. ...
Opening — Why this matters now Large language models have learned to think out loud. Chain-of-thought (CoT) reasoning has become the default solution for math, planning, and multi-step decision tasks. The industry applauded: more transparency, better answers, apparent interpretability. Then reality intervened. Despite elegant reasoning traces, models still reach incorrect conclusions—sometimes confidently, sometimes catastrophically. Worse, the mistakes are no longer obvious. They creep in quietly, spread across steps, and survive superficial self-corrections. What we call “hallucination” has grown up. And our detection methods have not. ...
Opening — Why this matters now For years, debates about large language models (LLMs) have circled the same tired question: Do they really understand what they’re saying? The answer—still no—has been treated as a conversation stopper. But recent “reasoning models” have made that question increasingly irrelevant. A new generation of AI systems can now reason through problems step by step, critique their own intermediate outputs, and iteratively refine solutions. They do this without grounding, common sense, or symbolic understanding—yet they still solve tasks previously reserved for humans. That contradiction is not a bug in our theory of AI. It is a flaw in our theory of reasoning. ...
Opening — Why this matters now Social media coordination detection is stuck in an awkward adolescence. Platforms know coordinated inauthentic behavior exists, regulators know it scales faster than moderation teams, and researchers know correlation-heavy detectors are brittle. Yet most deployed systems still behave as if yesterday’s parameters will work tomorrow. This paper introduces Adaptive Causal Coordination Detection (ACCD)—not as another accuracy tweak, but as a structural correction. Instead of freezing assumptions into static thresholds and embeddings, ACCD treats coordination detection as a learning system with memory. And that subtle shift matters more than the headline F1 score. ...
Opening — Why this matters now Pedestrian fatalities are rising, mid-block crossings dominate risk exposure, and yet most models tasked with predicting pedestrian behavior remain stubbornly local. They perform well—until they don’t. Move them to a new street, a wider arterial, or a different land-use mix, and accuracy quietly collapses. This is not a data problem. It’s a reasoning problem. ...
Opening — Why this matters now Multimodal large language models (MLLMs) are getting better at seeing—but not necessarily at knowing. Despite steady architectural progress, hallucinations remain stubbornly common: models confidently describe objects that do not exist, infer relationships never shown, and fabricate visual details with unsettling fluency. The industry response has been predictable: more preference data, more alignment, more optimization. ...
Opening — Why this matters now Football analysis has spent the last decade drowning in numbers while still relying on gut feel at the most critical moment: tactical choice. Expected goals, heatmaps, sprint counts, and passing networks describe what happened, but when a coach asks what should we do now?, the answer often collapses back into intuition. ...
Opening — Why this matters now Large Language Models are excellent improvisers. Unfortunately, software systems—especially those embedding logic, constraints, and guarantees—are not jazz clubs. They are factories. And factories care less about eloquence than about whether the machine does what it is supposed to do. Neuro-symbolic (NeSy) systems promise something enterprises quietly crave: models that reason, obey constraints, and fail predictably. Yet in practice, NeSy frameworks remain the domain of specialists fluent in obscure DSLs and brittle APIs. The result is familiar: powerful theory, low adoption. ...
Opening — Why this matters now Large Language Models (LLMs) are often praised for what they generalize. Yet, beneath the surface, a less glamorous behavior quietly persists: they remember—sometimes too well. In an era where models are trained on ever-larger corpora under increasing regulatory scrutiny, understanding when memorization occurs, why it happens, and how it can be isolated is no longer an academic indulgence. It is an operational concern. ...
Opening — Why this matters now Distributed systems are no longer just distributed. They are fragmented across clouds, edges, fog nodes, IoT devices, and whatever underpowered hardware someone insisted on deploying in a basement. This so‑called computing continuum promises flexibility, but in practice it delivers something else: constant failure. Nodes disappear. Latency spikes. Logs contradict each other. Recovery scripts work—until they don’t. Traditional fault‑tolerance assumes failures are predictable, classifiable, and politely arrive one at a time. Reality, as usual, disagrees. ...