Overview
OpenAI released GPT-5.4 with native computer use and classified the model High capability in two dual-use domains. A separate OpenAI publication documented how GPT-5.2 Pro contributed to a graviton derivation that human researchers then checked. Google shifted coding evaluation from generated snippets to test-verified changes in public Android repositories.
Developments
1. GPT-5.4 pairs computer use with High cyber safeguards
OpenAI released GPT-5.4 in ChatGPT, the API, and Codex, with a higher-compute Pro variant. The API model supports native computer use and up to one million tokens of context, and standard pricing is $2.50 per million input tokens and $15 per million output tokens.
Its system card treats GPT-5.4 Thinking as High capability in cybersecurity and biological or chemical work, while reporting a slight regression on function-call prompt injection and lower HealthBench and HealthBench Hard scores than GPT-5.2. Computer access therefore widens the action surface at the same launch where OpenAI's own evidence shows a mixed, domain-specific safety profile.
Sources: OpenAI announcement · GPT-5.4 system card
2. GPT-5.2 Pro helps derive a nonzero graviton amplitude
OpenAI published a preprint arguing that single-minus graviton tree amplitudes can be nonzero in a restricted half-collinear regime. The authors gave an earlier gluon result to GPT-5.2 Pro, which proposed a derivation using the directed matrix-tree theorem and produced a preliminary paper draft.
Human researchers subsequently verified the formulas analytically and checked consistency with known limits and symmetry constraints. The episode assigns the model a specific role in conjecture and derivation, while the slower verification and exposition phase identifies where scientific accountability remained with the named authors.
Sources: OpenAI research note · Preprint
3. Android Bench grades AI fixes by executing repository tests
Google released Android Bench with issues drawn from public Android repositories and graders based on unit or instrumentation tests. In its initial non-agentic evaluation, completion rates ranged from 16% to 72%, with Gemini 3.1 Pro first and Claude Opus 4.6 close behind.
Executable tests improve on judging whether a patch merely looks plausible, but public tasks create a future contamination route and passing tests can preserve hidden regressions. The open harness is most useful as a baseline that teams can extend with private issues, frozen dependencies, hidden tests, and trajectory review.
Sources: Android Developers · Android Bench repository