Extending single-minus amplitudes to gravitons
A new preprint extends single-minus amplitude techniques to gravitons in quantum gravity, with GPT-5.2 Pro used to help derive and verify nonzero graviton tree amplitudes. The work represents a collaboration between AI assistance and theoretical physics research. This is notable as a capability demonstration of GPT-5.2 Pro contributing to frontier mathematical physics.
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GPT-5.2 derives a new result in theoretical physics
A new preprint demonstrates GPT-5.2 proposing a novel formula for a gluon amplitude in theoretical physics, which was subsequently formally proved and verified by OpenAI researchers and academic collaborators. This represents a claimed instance of an AI system producing a genuinely new scientific result rather than reproducing known work. The result was published as a preprint and announced via the OpenAI blog.
Doing Vibe Physics — Alex Lupsasca, OpenAI
A Latent Space podcast/essay featuring Alex Lupsasca of OpenAI recounts how GPT-5.x was used to derive new results in theoretical physics and quantum gravity. The piece documents a concrete case of frontier LLMs contributing to original scientific research rather than merely assisting with literature review or code. It represents an early data point on AI-driven discovery in hard sciences.
Advancing science and math with GPT-5.2
OpenAI has released GPT-5.2, described as its strongest model for mathematics and science, achieving state-of-the-art results on GPQA Diamond and FrontierMath benchmarks. The announcement highlights practical research applications including solving an open theoretical problem and generating verified mathematical proofs. The post positions GPT-5.2 as a meaningful step toward AI-assisted scientific discovery.
GPT-5 and the future of mathematical discovery
UCLA Professor Ernest Ryu collaborated with GPT-5 to solve an open problem in optimization theory, representing a concrete example of AI-assisted mathematical research. The announcement highlights GPT-5's capability in formal reasoning and scientific discovery beyond standard benchmarks. This is an OpenAI blog post showcasing a real-world research outcome involving a frontier model.
Early experiments in accelerating science with GPT-5
OpenAI has published initial research cases demonstrating GPT-5's application to scientific discovery across mathematics, physics, biology, and computer science. The examples highlight human-AI collaboration in generating mathematical proofs and uncovering novel insights. This represents OpenAI's first public documentation of GPT-5's scientific research capabilities beyond general benchmarks.
OpenAI GPT-next Solves 80-Year-Old Erdős Planar Unit Distance Problem for Under $1000
A Latent Space AINews digest reports that OpenAI's GPT-next model disproved the Erdős planar unit distance conjecture, an 80-year-old open problem in combinatorial geometry, at a compute cost under $1000. The item is framed as a notable AI-assisted mathematics result. The brief characterizes it as a quiet day overall but highlights this as a meaningful capability demonstration at the intersection of AI and formal mathematics.
Making LLMs lighter with AutoGPTQ and transformers
Hugging Face announces native integration of AutoGPTQ into the transformers library, enabling 4-bit quantized inference for large language models. The integration allows users to load and run GPTQ-quantized models directly through the standard transformers API with minimal code changes. This lowers the hardware barrier for deploying LLMs by significantly reducing VRAM requirements while maintaining competitive performance.
GPT-5.4 released with tool search, computer use, and frontier benchmark performance
OpenAI released GPT-5.4 in Thinking and Pro variants, featuring an expanded context window (up to 1.05M input tokens), native computer use, tool search capabilities, and adjustable reasoning levels. In independent testing by Artificial Analysis, GPT-5.4 Pro at xhigh reasoning achieved state-of-the-art on GDP-Val-AA, BrowseComp, Terminal-Bench-Hard, SWE-Bench-Pro, and MCP Atlas, while trailing Gemini 3.1 Pro Preview on MMMU-Pro and Humanity's Last Exam. Pricing is set at the top of the market ($30/$180 per million input/output tokens for Pro), and the release also powers Codex, OpenAI's competitor to Claude Code. The item is reported via The Batch (tier 2 commentary) and includes additional context on Andrew Ng's chub CLI tool for agent documentation sharing.