Compliance

Opening — Why This Matters Now The industry’s guilty secret is that long-context models behave beautifully in demos and then slowly unravel in real usage. The longer the conversation or chain-of-thought, the less the model remembers who it’s supposed to be—and the more creative it becomes in finding trouble. This isn’t a UX quirk. It’s a structural problem. And as enterprises start deploying LLMs into safety‑critical systems, long-context drift is no longer amusing; it’s a compliance nightmare. ...

Opening — Why this matters now Radiology sits at the awkward crossroads of two modern pressures: rising imaging volumes and shrinking clinical bandwidth. CT scans get bigger; radiology teams do not. And while foundation models now breeze through captioning tasks, real clinical reporting demands something far more unforgiving — structure, precision, and accountability. The paper Radiologist Copilot (Yu et al., 2025) introduces an alternative future: not a single model that “generates a report,” but an agentic workflow layer that behaves less like autocomplete and more like a junior radiologist who actually follows procedure. ...

Opening — Why This Matters Now Large language models now behave like overeager junior analysts: they think harder, write longer, and try very hard to sound more certain than they should. Iterative reasoning techniques—Chain-of-Thought, Debate, and the new wave of inference-time scaling—promise deeper logic and better truth-seeking. Yet the empirical reality is more awkward: the more these models “reason,” the more they entrench their initial assumptions. The result is polished but stubborn outputs that deviate from Bayesian rationality. ...

Opening — Why this matters now Human behavior is the final frontier of prediction. Chess—arguably the world’s most intensely instrumented strategy game—used to be about best moves. Today, it’s increasingly about human moves. As analytical tools migrate into coaching apps, anti-cheating systems, and personalized training platforms, understanding how different players actually behave (not how they ideally should) becomes commercially relevant. ...

Opening — Why this matters now Every few months, the AI industry proclaims another breakthrough in “reasoning.” Models solve Olympiad geometry, pass graduate-level coding contests, and produce clean explanations that sound almost insultingly confident. The narrative writes itself: AGI is practically here; please adjust your expectations accordingly. Then you hand the same models a chessboard—and they implode. ...

Opening — Why this matters now Large Language Models keep telling us they can “reason”—yet break spectacularly the moment a question requires combining two simple facts that sit in different parts of their memory. The industry’s response has been predictable: train bigger models, gather more data, sprinkle some RL on top, and pray. This new paper—From Atomic to Composite: Reinforcement Learning Enables Generalization in Complementary Reasoning【filecite:turn0file0】—politely shatters that illusion. It suggests something delightfully inconvenient: models don’t generalize because they’re big; they generalize because their training curriculum actually makes sense. And most current curricula do not. ...

Opening — Why this matters now Computer-use agents are finally leaving the demo stage. The problem? They still click the wrong thing. In professional software—CAD suites, IDEs, industrial dashboards—a single mis-grounded element can detonate an entire workflow. And as enterprises move toward AI-assisted operations, grounding mistakes become expensive, embarrassing, or dangerous. The uploaded paper introduces Chain-of-Ground (CoG)【turn0file0】, a deceptively simple idea: stop trusting MLLMs’ first guess, and start making them think twice—literally. It’s a training-free, multi-step reasoning loop that forces models to revise themselves, generating both higher accuracy and clearer interpretability. In an era saturated with ever-larger models, CoG makes a subversive claim: iterating beats inflating. ...

Opening — Why this matters now Modern AI models excel at human language, protein folding, and occasionally pretending to do mathematics. But ask them to infer the prime factorization of a number from a symbolic sequence, and they often blink politely. The paper Testing Transformer Learnability on the Arithmetic Sequence of Rooted Trees fileciteturn0file0 asks a sharper question: Can a transformer learn the grammar embedded in the integers themselves? ...

Opening — Why this matters now In the last year, AI evaluation quietly became the industry’s most fragile dependency. LLMs are now asked to judge everything—from student essays to political sentiment to the quality of each other’s outputs. Companies use them to score customer emails, assess compliance risks, and even grade internal documentation. The problem is obvious: we’re relying on systems that struggle to agree with themselves. ...

Opening — Why this matters now The AI world is rediscovering an old truth dressed in modern math: intelligence is mostly about not being surprised. As LLMs evolve into agentic systems with long‑term memory and structured reasoning, designers face a deceptively simple question: How should an AI decide which entity, conclusion, or memory is most plausible in context? ...