Policy rules are boring until a chatbot applies the wrong one. A customer asks whether they qualify for a refund. The rule says refunds require purchase within 30 days, unused condition, and no prior replacement claim. The model answers confidently. It even writes a neat step-by-step explanation. Wonderful. The explanation looks like reasoning. It may even be correct. ...
Scores are comforting. They fit neatly into leaderboards, procurement decks, and internal model-comparison spreadsheets. One model gets 71.5, another gets 72.9, and someone in the meeting says, “So the second one reasons better.” Maybe. Or maybe the model merely passed a particular checkpoint more often. That is useful, but it is not the same as knowing whether the model has learned a controllable reasoning process. A thermometer tells you the patient is hot; it does not explain the infection. Benchmarks are the thermometer. The paper Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models tries to look for something closer to the infection mechanism — or, less dramatically, the internal process signature behind “slow thinking” in large reasoning models.1 ...
Meetings are useful theatre. Everyone can nod at the same slide, repeat the same market keywords, and still leave the room with incompatible plans. The agreement was real. The shared understanding was not. Large language models may be doing something uncomfortably similar. The paper Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning studies whether models that look similar internally are actually reasoning in similar ways.1 This matters because a tempting story has been building around representational convergence: as models scale, their internal representations become more alike, perhaps because they are converging toward a shared statistical model of reality. That story is elegant. It is also a little too convenient, which is usually where expensive mistakes begin. ...
A compliance bot does not fail only when it gives the wrong final answer. It can fail earlier, in a quieter and more expensive place: it selects the wrong premise, stops collecting evidence too soon, matches the wrong rule, and then writes a perfectly fluent explanation of a decision that was already broken three steps ago. Very elegant. Very useless. ...
A room is a cruelly simple test for artificial intelligence. Put a person inside it. Tell them they are facing an avocado. Ask them to turn right by 270 degrees, then left by 90 degrees. Give them a few observations along the way. After the final turn, ask what they can see. ...
The coding agent passed the test. That was the problem. Imagine a software agent asked to solve a coding task. It writes a sensible implementation. The tests fail. It tries again. The tests fail again. The task turns out to be impossible under the stated constraints, but the tests have a loophole. A shortcut can pass the benchmark while failing the real task. ...
A refusal is easy to recognize. The model says it cannot help. The sentence sounds polite. The compliance team relaxes for three seconds. Everyone moves on. That is the comfortable version of AI safety: refusal as an observable behavior. The uncomfortable version is that refusal may be only the visible end of a much narrower internal computation. If that computation can be found, isolated, and steered, then the model’s “sorry, I can’t assist with that” is not a moral boundary. It is a circuit behavior. Very reassuring, in the same way a locked glass door is reassuring before someone points out the hinge. ...
Audit has a boring rule that AI teams keep trying to make exciting: a correct-looking answer is not the same as a trustworthy process. That rule becomes awkward when the answer is an explanation of another AI system. If an AI agent can inspect a model, run experiments, and produce a plausible explanation of what a circuit component does, it feels like a research assistant has arrived. If that explanation matches a published human analysis, the temptation is obvious: declare progress, write the benchmark table, and proceed to the next demo. ...
Triage is a wonderfully cruel test for AI safety. A patient message arrives. Maybe it is routine. Maybe it contains a medication interaction, an allergic reaction, suicidal ideation, a pregnancy-related risk, or a pediatric emergency. The model is not being asked to compose poetry, summarize a quarterly report, or role-play as an overenthusiastic consultant. It has one job: notice the hazard and recommend action. ...
Sudoku is a useful toy problem because it is cruel in exactly the right way. A nearly completed grid with one blank cell should be easier than a brutal puzzle with dozens of missing entries. Humans know this. Basic software knows this. A model that can solve hard Sudoku should not suddenly collapse when the puzzle becomes almost finished. ...