A simulated learner can tune a tutor, not validate one
StudentSim models how 60 learners answer and revise across chess, English writing and mathematics. Its proper place is before a human study—not in place of one.
Topic desk / 01
AI Capability Frontier
Which tasks have become newly feasible, cheaper, more reliable, or more autonomous?
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An agent claim is auditable only when the verdict is predeclared
A new preprint turns vague promises such as “every action is logged” into claim contracts: policy, scope, records, writers and a decision rule fixed before evaluation.
A 3.6% disclosure rate measures visibility, not model safety
A census found public safety-test results for 31 of 857 Chinese AI-model releases. Procurement teams should treat the denominator as an evidence-availability measure, not a safety score.
Meta’s Wonder release needs a workflow canary, not a benchmark switch
Muse Wonder adds a new frontier-model option with published pricing and benchmark claims. Test one bounded workflow for reliability, permissions and cost before changing a default.
PoeLLM turns exposed AI services into an asset-inventory test
A cryptomining campaign used public services, rotating infrastructure and a poem-hosted address trick. The workforce decision is simple: assign ownership and patch evidence for every exposed AI endpoint.
Cross-border model tests need an evidence-access contract
US developers may again give Britain’s AI Security Institute early model access. Permission is only the first control; evaluation still needs defined assets, timing, disclosure and remediation.
Closing an agent repository does not revoke the copies already in use
ARTEX’s developer stopped public updates after the tool was linked to attacks on South Korean banks. Maintainers need an incident handoff for deployed copies, indicators and downstream defenders.
An agent control plane must prove effective authority, cost and recovery
TCS says it is building a governance layer for its own, client and third-party agents. Buyers should test the effective path across vendors instead of treating one dashboard as control.
Industrial AI pilots need an exit rule before they multiply
China’s new guidance pairs industrial AI experimentation with warnings against bubbles and duplication. Pilot owners should define evidence thresholds for scale, redesign or stop.