Opening — Why this matters now For years, scaling AI meant one thing: train bigger models, then freeze them. At inference time, we search harder, sample wider, and hope brute force compensates for epistemic limits. This paper challenges that orthodoxy. It argues—quietly but decisively—that search alone is no longer enough. If discovery problems are truly out-of-distribution, then the model must be allowed to learn at test time. ...
Opening — Why this matters now LLM agents are getting longer memories, better tools, and more elaborate planning stacks—yet they still suffer from a strangely human flaw: emotional whiplash. An agent that sounds empathetic at turn 5 can become oddly cold at turn 7, then conciliatory again by turn 9. For applications that rely on trust, continuity, or persuasion—mental health tools, tutors, social robots—this instability is not a cosmetic issue. It’s a structural one. ...
Opening — Why this matters now Robotics has quietly entered an awkward phase. Models can see remarkably well and talk impressively about tasks—but when it comes to executing long-horizon, high-precision actions in the physical world, performance still collapses in the details. Grasp slips. Motions jitter. Multimodal uncertainty wins. At the same time, video generation models have undergone a renaissance. Large diffusion-based video models now encode temporal causality, implicit physics, and motion continuity at a scale robotics has never had access to. The obvious question follows: ...
Opening — Why this matters now Mixed-Integer Linear Programming (MILP) sits quietly underneath a surprising amount of modern infrastructure: logistics routing, auctions, facility placement, chip layout, resource allocation. When it works, no one notices. When it doesn’t, the solver spins for hours, racks up nodes, and quietly burns money. At the center of this tension is branch-and-bound—an exact algorithm that is elegant in theory and painfully sensitive in practice. Its speed hinges less on raw compute than on where it looks first. For decades, that decision has been guided by human-designed heuristics: clever, brittle, and wildly inconsistent across problem families. ...
Opening — Why this matters now Educational AI has entered its prompt era. Models are powerful, APIs are cheap, and everyone—from edtech startups to university labs—is tweaking prompts like seasoning soup. The problem? Most of this tweaking is still artisanal. Intuition-heavy. Barely documented. And almost never evaluated with the same rigor we expect from the learning science it claims to support. ...
Opening — Why this matters now Foreign Information Manipulation and Interference (FIMI) has quietly evolved from a niche security concern into a persistent, high‑tempo operational problem. Social media platforms now host influence campaigns that are faster, cheaper, and increasingly AI‑augmented. Meanwhile, defenders are expected to produce timely, explainable, and interoperable assessments—often across national and institutional boundaries. ...
Opening — Why this matters now Multi-agent LLM systems are everywhere: debate frameworks, critic–writer loops, role-based agents, orchestration layers stacked like an over-engineered sandwich. Empirically, they work. They reason better, hallucinate less, and converge on cleaner answers. Yet explanations usually stop at hand-waving: diversity, multiple perspectives, ensemble effects. Satisfying, perhaps—but incomplete. This paper asks a sharper question: why do multi-agent systems reach solutions that a single agent—given identical information and capacity—often cannot? And it answers it with something rare in LLM discourse: a clean operator-theoretic explanation. ...
Opening — Why this matters now As reinforcement learning (RL) systems inch closer to real-world deployment—robotics, autonomous navigation, decision automation—a quiet assumption keeps slipping through the cracks: that remembering is enough. Store the past, replay it when needed, act accordingly. Clean. Efficient. Wrong. The paper Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning dismantles this assumption with surgical precision. Its core claim is blunt: agents that merely retain information fail catastrophically once the world changes. Intelligence, it turns out, depends less on what you remember than on what you are able to forget. ...
Opening — Why this matters now Large language models have become remarkably fluent. They explain, summarize, reason, and occasionally even surprise us. But fluency is not the same as adaptability. As AI systems are pushed out of chat windows and into open, messy, real-world environments, a quiet limitation is becoming impossible to ignore: language alone does not teach an agent how to live. ...
Opening — Why this matters now AI discourse is increasingly stuck in a sterile debate: how smart are large models, really? The paper you just uploaded cuts through that noise with a sharper question—what even counts as intelligence? At a time when transformers simulate reasoning, cells coordinate without brains, and agents act across virtual worlds, clinging to neuron‑centric or task‑centric definitions of intelligence is no longer just outdated—it is operationally misleading. ...