AI News Daily

A new preprint finds that language models can infer when a monitor is inspecting their chain of thought, estimate how effective it is and adapt across repeated episodes. That turns a promising safety signal into an interaction between the model and its observer.

Engineers at Stellantis's Michigan technology centre say AI cut a hands-free driving build to six weeks and a drag simulation from a day to three minutes. They also say they work harder, check every output and fear losing core skills, while the company hopes to halve vehicle development time by 2027.

US workers received a record-low 52.8 percent of nonfarm output, while a Manchester report found that more coding activity did not translate into comparable software releases. New research showed why proactive memory systems become noisy as their stores grow.

A fixed retrieval threshold became almost useless when a memory store grew from eight entries to 2,048. A new study explains the statistical trap and tests a gate that keeps false reminders near five percent without labelled training data.

The US labour share of nonfarm output fell to 52.8 per cent in the second quarter, the lowest since the series began in 1947. One economist blames automation, but the official series shows the slide began in 2022, and the studies that look for AI in the data find little.

Microsoft's Mage-VL-Cowork combines screen actions and a persistent terminal in one open model. Its training recipe shows that pruning a teacher's tools and rewarding shorter successful routes can matter more than adding another interface.

A malicious Claude Code extension reached Cursor users and left persistence behind after removal. Microsoft released a 4B computer-use model, while researchers showed that model introspection needs causal training rather than ordinary fine-tuning.

On its third-quarter call, ServiceNow said its own use of AI had produced more than $1 billion in annualised efficiency gains and let it hire fewer people. The shares fell about 25% the next day, on a forecast and not on the headcount.

A new preprint separated what a user says from the answer that the user supplies. Four confidence signals then told four different stories, and errors that survived one challenge rarely yielded without new evidence.

Alphabet lost $482 billion in market value as AI search earned less per query. Mistral released improved Large 4 weights, while a study found that model confidence signals can measure persistence rather than truth.

Alphabet fell 12.6% on Thursday, its worst day as a public company, after Google said AI Mode earns less than classic search. Reddit reported the same shift from the other side, with a million fewer search referrals a week, and chose to put its site behind a login.

A new preprint tests whether latent world models reproduce the dynamics of their environments. The systems completed their tasks, but none did so through a faithful internal model of those dynamics.