Large Language Models

Don’t Train Harder—Train Smarter: The Hidden Economics of RL for LLMs

The GPU bill is not the strategy The easiest way to make reinforcement learning for reasoning models sound impressive is to say: sample more responses, train longer, scale harder. It is also the easiest way to make the finance team develop a facial twitch. Modern reasoning-focused LLMs increasingly rely on reinforcement learning with verifiable rewards: generate multiple candidate answers, score them with a rule-based signal, and update the model toward better reasoning behavior. In mathematics and coding tasks, this has become one of the most important post-training recipes. But it has a small accounting problem, in the same way a leaking ship has a small moisture problem. ...

From Causal Parrots to Causal Counsel: When LLMs Argue with Data

Causal claims are cheap now. A model can look at variable names such as advertising spend, web traffic, sales conversion, and customer churn, then produce a causal story in seconds. The story may even sound sensible. That is precisely the problem. In business analytics, “sensible” is often the polite costume worn by “untested.” ...

When LLMs Meet Time: Why Time-Series Reasoning Is Still Hard

Dashboard numbers are seductive because they look obedient. Revenue goes up, traffic dips, latency spikes, inventory turns over, temperature drifts, volatility clusters. Put the sequence into a chart and the pattern seems almost polite. Then someone asks an LLM what happened. The model answers fluently. It may even sound like an analyst who has seen too many quarterly review decks and has developed a protective layer of confidence. But fluency is not temporal understanding. A model can describe a curve, name a trend, and still fail to understand which segment comes next, whether a transformation is correct, or whether a discontinuity is an error or a legitimate feature of the process. ...

When Alignment Is Not Enough: Reading Between the Lines of Modern LLM Safety

A chatbot refuses a dangerous request. Everyone relaxes. This is the small theatre of modern AI safety: the model says no, the dashboard records a refusal, the vendor presentation adds another green checkmark, and the compliance team moves on to the next risk register. Very tidy. Very comforting. Also, increasingly insufficient. The problem is not that refusal behavior is meaningless. It is not. The problem is that refusal behavior is only one visible symptom of safety alignment. Modern LLM safety now depends on a larger chain: training objectives, post-training choices, inference interfaces, prompt formats, tool access, evaluation design, and deployment context. When any part of that chain changes, the nice refusal seen in a benchmark may not survive contact with the product. ...

When Models Read Too Much: Context Windows, Capacity, and the Illusion of Infinite Attention

The demo is familiar now. Someone drops a whole contract, a whole policy manual, a whole code repository, or a month of chat history into a model and asks one neat question. The model answers fluently. The room relaxes. The slide says “1M-token context.” Procurement starts smiling. This is where the trouble begins. ...

FormuLLA: When LLMs Stop Talking and Start Formulating

Formulation is where AI enthusiasm usually goes to sober up. In a slide deck, “AI-assisted drug development” sounds clean: feed the model a drug, get back a formulation, reduce experiments, accelerate personalisation, everybody nods. In a lab, the problem is less polite. A formulation is not just a sentence with chemical names. It is a physical recipe with roles, proportions, processing constraints, and mechanical consequences. A model can sound fluent while quietly omitting the lubricant, mangling the unit, or inventing a polymer that belongs more to fantasy literature than pharmaceutics. ...

Thinking Without Understanding: When AI Learns to Reason Anyway

A meeting room is not a philosophy seminar, which is fortunate, because most companies would not survive one. A manager asks an AI system to analyze a contract, debug a workflow, compare vendors, or draft a risk memo. The system pauses, breaks the task into steps, checks an assumption, rejects one path, and returns a structured answer. Someone in the room says: “But it does not really understand.” ...

AI Writes the Rules: When Formal Logic Teaches Language Discipline

A requirement can survive three meetings, two approvals, and a legal review while still meaning different things to everyone who reads it. That is not usually because anyone is careless. Natural language is simply very good at sounding settled before its meaning is settled. Words such as “after,” “until,” “immediately,” and “within” feel precise in conversation. In software requirements, they can quietly conceal incompatible assumptions about timing, cancellation, and acceptable system behavior. ...

Talking to Yourself, but Make It Useful: Intrinsic Self‑Critique in LLM Planning

“Please double-check your work” is one of the least expensive quality-control systems ever invented. It is also one of the least dependable. A person who overlooked a constraint the first time may overlook it again. A language model is no different, except that it can produce a longer and more persuasive explanation of why the overlooked constraint was never important. ...

MIRAGE-VC: Teaching LLMs to Think Like VCs (Without Drowning in Graphs)

Deal flow is rarely scarce. Attention is. A venture-capital team may receive hundreds of startup introductions, each surrounded by founder biographies, investor histories, comparable companies, co-investment relationships, sector narratives, and enthusiastic claims about an inevitable Series A. The practical problem is not obtaining more evidence. It is deciding which fragments deserve serious attention before the partnership meeting begins. ...