Observed Signal · Feb 11, 2026 · Preprint Publication · Source: OpenAI Blog · Impact: 3/5 · Sentiment: Positive
GPT-5.2 Uncovers New Insights in Gluon Scattering Amplitudes
OpenAI published a preprint on arXiv reporting that a class of gluon tree-level scattering amplitudes long assumed to vanish (single-minus helicity configurations) are nonzero on a specific, precisely defined slice of momentum space called the half-collinear regime. The preprint, titled “Single-minus gluon tree amplitudes are nonzero,” is authored by Alfredo Guevara (Institute for Advanced Study), Alex Lupsasca (Vanderbilt University and OpenAI), David Skinner (University of Cambridge), Andrew Strominger (Harvard University), and Kevin Weil (OpenAI) on behalf of OpenAI. GPT-5.2 Pro conjectured a closed-form formula (Eq. 39) after simplifying base-case expressions, and an internal scaffolded GPT-5.2 produced a formal proof after roughly 12 hours; the result was checked against standard recursion relations and the soft theorem. The authors report extensions to graviton amplitudes and anticipate further AI-assisted developments.
The result combines a substantive theoretical-physics advance with demonstration that a large language model (GPT-5.2) can conjecture and help prove new closed-form formulas. This signals meaningful progress in AI-assisted research methodology and could influence scientific tooling and validation practices, though it is not a platform policy change or major industry-wide technical release.
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Key Takeaways & Evidence Grounding
- Preprint titled "Single-minus gluon tree amplitudes are nonzero" is available on arXiv and is being submitted for publication.
- Authors: Alfredo Guevara (Institute for Advanced Study), Alex Lupsasca (Vanderbilt University and OpenAI), David Skinner (University of Cambridge), Andrew Strominger (Harvard University), and Kevin Weil (OpenAI) on behalf of OpenAI.
- The paper demonstrates that single-minus helicity gluon tree amplitudes are nonzero on a specific momentum-space slice called the half-collinear regime.
- GPT-5.2 Pro conjectured the final closed-form formula (Eq. 39) after simplifying complex base-case expressions, and an internal scaffolded GPT-5.2 produced a formal proof after ~12 hours.
- The results have already been extended from gluons to gravitons and further generalizations are reported as forthcoming.
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ChatGPT Enables Novel Theoretical Physics Discoveries
Alex Lupsasca describes how recent OpenAI models (GPT-5 / GPT-5.2 / ChatGPT) reproduced and extended cutting‑edge theoretical physics results rapidly. Using a priming technique, GPT‑5 reportedly reproduced one of Lupsasca’s papers in minutes and later produced novel derivations (including a gluon→graviton extension) that the team turned into arXiv preprints. Lupsasca joined OpenAI’s science efforts to push model‑assisted research; OpenAI published a blog post and a full transcript of prompts and outputs. The team found the model identified simplifying limits (the “half‑collinear regime”), produced proofs using unfamiliar techniques, and generated a 110‑page graviton result later released as a preprint (arXiv:2603.04330). The story illustrates LLMs accelerating research workflows and suggests frontier scientific reasoning is being reshaped by foundation models.
GLM-5.2 Emerges as Frontier Open-Weight Model
Latent Space's AINews reports that Zhipu’s GLM-5.2 has gained broad community validation as a frontier-adjacent open-weight large language model, driven by architecture changes and strong out-of-sample performance. GLM-5.2 introduces an IndexShare mechanism to reuse sparse-attention top-k indices across layers to lower the cost of very long-context (1M-token) inference, and was rapidly made available via Hugging Face inference providers and local GGUF support (llama.cpp/Unsloth). The issue also highlights other open releases (PoolsideAI’s Laguna M.1), system and tooling advances (agent harnesses, Codex Record & Replay), and a new long-horizon agentic benchmark (Artificial Analysis’ AA-Briefcase) that ranks Claude Fable 5, Opus 4.8 and GLM-5.2 and reports per-task cost comparisons. The piece frames GLM-5.2 as a meaningful step for open-model practicality and local AI deployment.
OpenAI Disproves Erdős Conjecture
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