Opening — Why this matters now Agentic systems are proliferating faster than anyone is willing to admit—small fleets of LLM-driven workers quietly scraping data, labeling content, negotiating tasks, and replying to your customers at 3 a.m. Their internal workings, however, remain opaque: a swirling mix of environment, tools, model updates, and whatever chaos emerges once you let these systems interact. ...
Opening — Why this matters now Enterprises are discovering a strange contradiction: Large Language Models can now solve competition-level math, yet still fail a moderately complex workflow audit if you ask for the answer once. But let them think longer—sampling, refining, verifying—and suddenly the same model performs far beyond its pass@1 accuracy. Welcome to the age of inference-time scaling, where raw model size is no longer the sole determinant of intelligence. Instead, we orchestrate multiple calls, combine imperfect ideas, and build pipelines that behave less like autocomplete engines and more like genuine problem solvers. ...
Breaking Rules, Not Systems: How Penalties Make Autonomous Agents Behave Opening — Why This Matters Now Autonomous agents are finally venturing outside the lab. They drive cars, negotiate traffic, deliver goods, and increasingly act inside regulatory gray zones. The problem? Real‑world environments come with norms and policies — and humans don’t follow them perfectly. Nor should agents, at least not always. ...
Why This Matters Now The world is quietly rediscovering an old truth: optimization is everywhere, and it is painful. From routing trucks to packing bins to deciding which compute job should run next, combinatorial optimization problems remain the silent tax on operational efficiency. Yet traditional algorithm design still relies on experts crafting heuristics by hand—part science, part folklore. ...
Memory, Multiplied: Why LLM Agents Need More Than Bigger Brains Opening — Why this matters now For all the hype around trillion‑parameter models and training runs priced like small nations’ GDP, the messy truth remains: today’s AI agents still forget everything important. They hallucinate, lose track of context, and treat every interaction as a fresh reincarnation. ...
Opening — Why this matters now Retailers today are drowning in complexity: fragmented data, volatile demand, promotional noise, and managerial rules that seem handcrafted in another century. Yet decision‑making expectations rise—faster cycles, finer granularity, and higher accountability. Into this mess walks DeepRule【analysis from PDF, esp. p.1–2】—a framework that tries to do the impossible: turn unstructured business knowledge, multi-agent constraints, and machine‑learned forecasts into clean, auditable pricing and assortment rules. In other words, to give retail operators algorithms they can actually trust. ...
Opening — Why this matters now Industrial AI is undergoing a personality crisis. On one hand, we have factories that desperately want adaptable decision-making. On the other, we have Large Language Models—brilliant at essays, somewhat less convincing at not toppling virtual block towers. As vendors race to bolt LLMs into automation stacks, a familiar problem resurfaces: everyone claims to have an “agent,” yet no one can compare them meaningfully. ...
Why Adaptive Reasoning Matters Now In the past year, multimodal AI has gone from “surprisingly capable” to “occasionally overwhelming.” Omni-models can hear, see, read, and respond—but they still think in a frustratingly uniform way. Either they overthink trivial questions or underthink complex ones. In business terms: they waste compute or make bad decisions. The paper Omni-AutoThink proposes to fix this. And it does so with a surprisingly grounded idea: AI should think only as much as it needs to. ...
Opening — Why this matters now Enterprises are suddenly discovering that “deep research agents” are not magical interns but probabilistic engines with wildly variable costs. Every additional query to an LLM carries a token bill; every recursive branch in a research workflow multiplies it. As agentic systems spread from labs to boardrooms, a simple question emerges: Can we control what these agents do—rather than hope they behave? ...
Agents Without Prompts: When LLMs Finally Learn to Check Their Own Homework Opening — Why this matters now Reproducing machine learning research has become the academic equivalent of assembling IKEA furniture without the manual: possible, but unnecessarily traumatic. With papers ballooning in complexity and code availability hovering around a charitable 20%, the industry is grasping for automation. If LLMs can write papers, reason over them, and generate code — surely they can also reproduce experiments without melting down. ...