https://aijourney.space/minimax/news/ Changes at MiniMax, logged from its own announcements, documentation and price pages, never from another publisher, each with what it means for you. en https://aijourney.space/updates/2026-10-21-announced-breaks-separate-conformal-reliability-from-frequency-calibrati/ 2026-10-21-announced-breaks-separate-conformal-reliability-from-frequency-calibrati Wed, 21 Oct 2026 00:00:00 GMT What happened. On October 3, 2026, researcher Karl Li of Columbia University posted a paper to arXiv, later accepted at the 40th Conference on Neural Information Processing Systems, arguing that conformal reliability and frequency calibration are separate audits once a forecasting system hits an announced boundary, a partition visible before the first affected outcome arrives. Sources. arXiv preprint 2610.23917, Announced Breaks Separate Conformal Reliability from Frequency Calibration: https://arxiv.org/abs/2610.23917 (read 21 Oct 2026) Why it matters. A vendor that bills by the token, the way MiniMax's published pay as you go price list does, depends on forecasts of demand and error rates staying honest across a pricing change, not just on average; a reset rule that keeps a usage model's coverage close to nominal right after a known shift could let an operator avoid both overbuilt capacity and surprise shortfalls the week a new price tier lands. https://aijourney.space/updates/2026-10-07-when-does-external-guidance-help-llm-reasoning-a-bias-variance-theory-of/ 2026-10-07-when-does-external-guidance-help-llm-reasoning-a-bias-variance-theory-of Wed, 07 Oct 2026 00:00:00 GMT What happened. On July 8, 2026, researchers posted a paper to arXiv proposing Guidance-Augmented GRPO, a theoretical framework that treats external guidance used in reasoning training, such as expert traces or retrieved thought patterns, as a stochastic operator rewriting the question distribution, and derives convergence rates and an optimal weighting rule for that signal. Sources. arXiv preprint 2610.06861, submitted July 8, 2026: https://arxiv.org/abs/2610.06861 (read 7 Oct 2026) Why it matters. For a team deciding whether to add expert traces or retrieved patterns to a reasoning run, this work turns a choice that used to rest on trial and error into a weighting formula tied to a measurable divergence term, which changes how much compute budget a lab like MiniMax needs to spend testing guidance strength before settling on a production recipe. https://aijourney.space/updates/2026-09-21-sparse-priors-for-efficient-distribution-learning/ 2026-09-21-sparse-priors-for-efficient-distribution-learning Mon, 21 Sep 2026 00:00:00 GMT What happened. On September 16, 2026, researchers Saumya Goyal and Barnabas Poczos posted a paper on arXiv titled Sparse Priors for Efficient Distribution Learning, introducing a class of sparse priors and a measure called the Sparse Dimension, and showing that under a k sparse prior a distribution can be learned with a Bayesian risk bound of order the square root of k over n, rather than a rate that worsens sharply with the number of dimensions. Sources. arXiv preprint, Sparse Priors for Efficient Distribution Learning, submitted 16 September 2026: https://arxiv.org/abs/2609.20883 (read 21 Sep 2026) Why it matters. For vendors such as MiniMax that sell generation by the token, a bound that removes the usual dependence on dimension matters because it suggests fewer training samples could reach a target accuracy, which affects how much data and compute a model needs before its outputs are reliable enough to price and ship at scale.