Autonomous Agents

Opening — Why this matters now Multi-agent systems are finally leaving the toy world. Autonomous traders negotiate with other bots. Supply-chain agents coordinate across firms. AI copilots increasingly share environments with other AI copilots. And yet, most multi-agent reinforcement learning (MARL) systems are still stuck with a primitive handicap: agents cannot meaningfully understand what other agents are doing. ...

Opening — Why this matters now Everyone wants cheaper A/B tests. Preferably ones that run overnight, don’t require legal approval, and don’t involve persuading an ops team that this experiment definitely won’t break production. LLM-based persona simulation looks like the answer. Replace humans with synthetic evaluators, aggregate their responses, and voilà—instant feedback loops. Faster iteration, lower cost, infinite scale. What could possibly go wrong? ...

Opening — Why this matters now Embodied AI has quietly crossed a dangerous threshold. Vision‑language models no longer just talk about actions — they execute them. In kitchens, labs, warehouses, and increasingly public spaces, agents now translate natural language into physical force. The problem is not that they misunderstand instructions. The problem is that they understand them too literally, too confidently, and without an internal sense of consequence. ...

Opening — Why this matters now We keep telling ourselves a comforting story: if an AI explanation isn’t good enough, just refine it. Add another round. Add another chart. Add another paragraph. Surely clarity is a monotonic function of effort. This paper politely demolishes that belief. As agentic AI systems—LLMs that reason, generate code, analyze results, and then revise themselves—move from demos into decision‑support tools, explanation quality becomes a first‑order risk. Not model accuracy. Not latency. Explanation quality. Especially when the audience is human, busy, and allergic to verbose nonsense. ...

Opening — Why this matters now For years, AI for Science has celebrated isolated breakthroughs: a protein folded faster, a material screened earlier, a simulation accelerated. Impressive—yet strangely unsatisfying. Real science does not happen in single model calls. It unfolds across reading, computing, experimentation, validation, revision, and institutional memory. The uncomfortable truth is this: as AI accelerates scientific output, it is quietly breaking the human systems meant to verify it. Peer review strains. Reproducibility weakens. “It worked once” becomes the dominant success metric. ...

Opening — Why this matters now AI has become remarkably good at reading emotions—just not the kind that actually matter in classrooms. Most sentiment models are trained on people being honest with their feelings: tweets, movie reviews, reaction videos. Teachers, unfortunately for the models, are professionals. They perform. They regulate. They smile through frustration and project enthusiasm on command. As a result, generic sentiment analysis treats classrooms as emotionally flat—or worse, mislabels them entirely. ...

Opening — Why this matters now Automation in healthcare has a credibility problem. Not because it performs poorly—but because it rarely explains why it does what it does. In high-stakes domains like radiation oncology, that opacity isn’t an inconvenience; it’s a blocker. Regulators demand traceability. Clinicians demand trust. And black-box optimization, however accurate, keeps failing both. ...

Opening — Why this matters now AI in medicine has spent years stuck in a familiar loop: impressive demos, retrospective benchmarks, and very little proof that any of it survives first contact with clinical reality. Radiology, in particular, has been flooded with models that look brilliant on paper and quietly disappear when workflow friction, hardware constraints, and human trust enter the room. ...

Opening — Why this matters now Large language models have learned to see. Unfortunately, they still have the attention span of a distracted intern when the video runs longer than a minute. As multimodal LLMs expand their context windows and promise “end-to-end” video understanding, a hard reality remains: long videos are not just longer inputs—they are fundamentally different reasoning problems. Information is sparse, temporally distant, multimodal, and often only meaningful when grounded precisely in time and space. Compress everything up front, and you lose the evidence. Don’t compress, and you blow the context budget. ...

Opening — Why this matters now Industrial digital twins have quietly become the backbone of modern manufacturing optimization—until you try to build one. What should be a faithful virtual mirror of a factory floor too often devolves into weeks of manual object placement, parameter tuning, and brittle scripting. At a time when generative AI is promising faster, cheaper, and more adaptive systems, digital twins have remained stubbornly artisanal. ...