It held for one round
How we Organise
Sophistication in genAI use, read off eight months of one firm’s prompts
Hallman and three colleagues posted this on 27 August. They had proprietary access to 713,564 employee prompts and the model responses to them, from nearly 4,000 back-office employees across 15 functional areas of one large firm, over eight months of 2025. From that they build measures of how sophisticated a person’s use is, and read those measures off the prompts themselves. Three findings came back. Senior employees use the tool more sophisticatedly, which fits domain expertise adding to what a model can do. Sophistication varies considerably between functions. It is highest in Strategy, Digital Innovation and Project Management, three groups that share a focus on firmwide initiatives and organisational change. And sophistication neither improved over the eight months nor improved lastingly after formal AI training.
That last finding lands on something we ask a client to do. When a team ships work that changes how other people have to work, we ask the firm to name or count those people. Each of them gets a route to learn the new way. Then the firm shows they can do it, against a threshold agreed before the release rather than after the results are in. What the prompts say argues with the last part.
The threshold is met once, at the release. Eight months of prompts suggest that what a person does afterwards is close to independent of it. Training left no lasting mark on sophistication, and sophistication did not rise on its own either. A firm can therefore count those people, run the training, clear the threshold and be no different a quarter later. We may be wrong about this. This is one firm and one back office. The data is proprietary and nobody outside the firm can read it. The models are 2025’s, and sophistication is their construct rather than one we defined. A reasonable person could read a flat eight-month line as a measure too coarse to catch the movement rather than as training that did not work. If it stands, we stop telling a client that clearing a threshold at release evidences that the people outside the team can now work differently. The threshold moves onto the work itself, read months later.
How we Build
A code review benchmark that does not stop after the opening exchange
Zheng and seven colleagues posted this on 27 August. Their complaint about the existing work is specific. Automated code review is nearly always set up as a single-round decision. A real review is an exchange in which a defect is raised, answered and raised again. They built MCR-Bench out of 2,269 real multi-round reviews across five languages. Each task carries fine-grained information about its defects, including description, type and severity, and cross-round state labels that follow a defect through the whole exchange. Running mainstream models against it, they report three things. Overall capability is limited on both defect detection and defect state tracking, and it degrades significantly as the rounds accumulate. Performance varies substantially by defect type and severity, and semantically complex or low-salience defects are much more likely to be missed. Their error analysis separates the causes of false positives from those of false negatives, and names cross-round misalignment in time and inadequate long-range memory among the weaknesses.
We tell a client to size and route review so that a machine checks whatever it can before a person sees anything. That routing has always been settled on the opening exchange, because a single round is what most of the published work measures. These results measure the same work over the rounds a review actually takes. The machine’s share of it falls as they accumulate. The routing can therefore be followed exactly and still hand a machine a growing share of the work it does worst.
We may have this wrong. MCR-Bench is a benchmark rather than a deployment, built from reviews that already happened and replayed. The models run against it are not ones a firm has fitted to its own codebase. Somebody could reasonably conclude that the answer is memory and scaffolding around the model rather than any change to how review is routed. On that reading our position is right and its implementation is thin. If the finding holds, the position needs a clause it does not have. The machine’s share is not decided once for a review but re-decided at each round, and the measure that decides it has to run across the whole review rather than its opening. We stop telling a client that machine-first review is one routing decision.
How we Assure
A model that calls a question unanswerable and then answers it
Aggarwal posted this on 27 August. Show an agent a professional-looking market panel and ask it a provably unpredictable directional question, and it commits to a call far more often than an agent asked the bare question. Across 12 frontier models, commitment rises from 6.5 to 54.0 per cent as the evidence on the panel is escalated. Invent every number on the panel, so that nothing the model can see is true except the question. Commitment still rises from 24.5 to 36.8 per cent. That is statistically indistinguishable from the 37.6 per cent produced by genuine market data. Three explanations are then ruled out in turn. Not incapacity: on matched answerable questions attached to the same panels the same models answer essentially always, at near-perfect accuracy. Not belief: stated probabilities barely move across a gradient that swings action by 48 points, and they score worse than a climatological baseline. Not absent judgement: asked to classify a question’s knowability before acting, the models call it irreducible 90 per cent of the time and then commit on 0.4 per cent of those. What fails is the gate between deciding and acting, and because that gate is separable it can be trained. Supervised fine-tuning of a 3B model on 540 synthetic cases takes commitment to 0.0 per cent and transfers to three unseen domains. It holds only where the response format leaves the model room to reason, and rigid formats leave it confident and wrong on questions it otherwise gets right.
The refusal we already hold is narrow. An agent’s own account of its reasoning is never recorded as an audit artefact. A lineage is judged on the population of acts it produces in a window rather than on any single act. These results support that refusal, and more sharply than we have supported it before. A model states a probability that barely moves across a gradient, while its willingness to act swings by 48 points along the same gradient. That account of itself does not track what it did. Read that account as the audit record and you would conclude the model stayed uncertain throughout, which is true of what it said and false of what it did.
A single author posted this as a preprint, and the questions come from one domain. The effect is also concentrated in some of the twelve models rather than spread evenly across them, which matters to a firm choosing between them.