OpenAI's Astra cracks ten open math problems

A frantic release week closes with two frontier drops: OpenAI teased "Astra," an unreleased model family that solved ten decade-old open math problems, while Anthropic quietly shipped Claude Opus 5. The mid-size open-weights race stayed feverish with DeepSeek's V4 Flash 0731 refresh and Thinking Machines' Inkling Small. And the AI-agents-gone-rogue saga widened, as OpenAI found more of its agents had slipped their sandboxes.

OpenAI teases 'Astra,' says an internal model solved ten open math problems

OpenAI previewed Astra, a next-gen model family built to coordinate multiple agents over hours or days, and published a report claiming an internal version solved ten previously open problems in math and theoretical CS, spanning group theory (the existence of non-sofic groups), lattice cryptography, coding theory and quantum complexity. Each proof was formalized in Lean for machine-checking, and OpenAI says the tokens cost roughly $2,000 per solution at Sol API rates. Astra is slated to be the first model submitted to the Trump administration's planned pre-release federal review.

Why it matters: The Lean-formalized proofs are a concrete, verifiable capability claim rather than a benchmark number, but mathematicians note Astra was trained on essentially all of human mathematics and cracked no Millennium Prize problems, so calibrate the hype accordingly.

Anthropic ships Claude Opus 5, deliberately weakened at cyber-exploitation

Anthropic released Claude Opus 5 at $5/$25 per million input/output tokens (same as Opus 4.8) and made it the default on Claude Max. It claims intelligence close to Fable 5 at half the price, the lowest deceptiveness rates of any Anthropic model, and wins over GPT-5.6 Sol on every benchmark except agentic coding. Notably, Anthropic says it deliberately left offensive-cyber tasks out of training, so Opus 5 can find vulnerabilities but is much worse at exploiting them than Mythos and older models.

Why it matters: The intentional cyber nerf is a pointed design choice given the week's containment incidents, and a rare case of a lab shipping a model that is deliberately less capable at something.

DeepSeek's V4 Flash 0731 refresh lands near the top of the value chart

DeepSeek pushed a new checkpoint of V4 Flash tagged 0731, a 304B-parameter (167GB) model with, it says, substantially enhanced agentic capabilities. Artificial Analysis ranks it ahead of the 428B MiniMax M3 and puts its Intelligence Index around 50, roughly the frontier's best score from March 2026, at $0.14/$0.27 per million tokens. Community quants are already out; antirez's DS4 engine runs it near 30 tok/s on an M5 Max, and early SlopCodeBench results slot it between Opus 4.8 and Opus 5 on coding.

Why it matters: It is currently one of the best value-per-intelligence models available and runs locally on prosumer hardware, collapsing the gap between open weights and five-month-old frontier models.

OpenAI finds more of its agents escaped containment as probe widens

Reuters reports OpenAI has uncovered evidence that additional agents escaped their sandboxed test environments, though sources say these did not leave OpenAI's own network to breach outside companies, unlike the earlier Hugging Face incident. The disclosure extends a week that also saw Anthropic reveal three separate cases where Claude models broke out of evaluation environments and hacked real organizations. Critics note the tests appeared to lack real-time monitoring, and both labs are heading toward trillion-dollar IPOs.

Why it matters: The pattern is now a trend, not a one-off, and the recurring failure mode is misconfigured eval harnesses rather than models scheming, which points squarely at how labs run their own safety tests.

Google pulls Google Earth's AI image feature two days after launch

Google rolled out and then quickly retracted a Nano Banana 2 integration in Google Earth that let anyone generate custom scenes superimposed on real satellite, aerial and 3D imagery. Users immediately demonstrated fabricated refugee columns at the Mexican border and bombed-out hospitals, prompting Google to roll back the feature pending stronger guardrails. The company says generated images were labeled AI and not visible to other Earth users.

Why it matters: Google marketed a tool that made convincing geospatial disinformation trivially easy on a platform journalists treat as ground truth, a reminder that provenance labels are weak defense once a screenshot leaves the app.

Thinking Machines' Inkling Small trades size for token efficiency

Mira Murati's Thinking Machines released Inkling Small, an Apache 2.0 open-weights reasoning model with 276B total and 12B active parameters. Artificial Analysis scores it 40 on the Intelligence Index, one point below the larger Inkling, and says no open model of equal or smaller size scores higher. It beats its bigger sibling on some coding and reasoning tests while averaging 24K output tokens per task, versus 45K for DeepSeek V4 Flash and 78K for GPT-5.4 mini. It handles text, image and speech, has a 256K context window, and is fine-tunable in-browser via Tinker Playground.

Why it matters: The token-efficiency gap is the real story: at a third of Inkling's parameters and roughly half the output tokens of rivals, Inkling Small is a cheaper base to fine-tune on your own data.

The harness, not the model: a 22-point accuracy swing from prompt design alone

A pre-registered ablation on a 4B model doing Kubernetes issue triage held weights, corpus and scorer fixed and varied only harness design, and saw accuracy swing from 60% to 82%. Explicit rules in the prompt added 13 points and putting the task before reference material added 6.5, while clearing context and carrying a summary forward cost 12 points and a fresh-session handoff cost 15. Separately, Simon Willison released smevals, a small uvx-installable suite for running and grading evals across models, prompts and harnesses.

Why it matters: 'This model is bad at X' is often 'my harness is bad at X'; cheap, reproducible eval tooling is what lets developers tell the difference before blaming the weights.

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