Last September, after it was sued for allegedly enabling the tragic death of 16-year-old Adam Raine, OpenAI announced it was working on a system that would automatically identify teens and restrict their usage of ChatGPT. Nearly a year later, the company is putting that system to work as part of a new user experience it calls ChatGPT for Teens. "There's no need for a teen to create a new account or change anything; if we predict you're under 18, or you've told us so, this becomes your default experience," says Lauren Jonas, OpenAI's head of youth and families. ChatGPT for Teens is broadly built around two pillars: learning and safety. Starting with the former, it brings together a selection of features OpenAI has released over the last year to make ChatGPT a better teacher.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Claude's Record-a-Skill cut my research from hours to 30 minutes - but the magic has limits I used Claude Cowork to automate a workflow, and the result was remarkably effective, but it exposed four drawbacks. Claude turns narrated screen recordings into reusable skills. Last month, Anthropic announced a new Claude Cowork feature: the ability to narrate a screen recording and turn it into a Claude skill . I tried it and, well, as they say, All magic comes with a price. Let's set the stage by defining what we're after here. A skill is a very detailed canned prompt that can be run like a program from within Claude (and many other AIs).

OpenAI is making a major change to how it operates ChatGPT free and Go accounts. Starting next week, the company will no longer enforce rate limits on users of those accounts for prompts that involve only text. In effect, that change will allow you to talk with ChatGPT as much as you want. The company will continue to enforce separate limits for other forms of usage. For instance, adding files and images to your prompts will see you eventually hit a limit as a free or Go tier user, as will making use of image generation and ChatGPT's recently updated voice mode.

This position paper challenges the "scaling fundamentalism" dominating AI research, where unbounded growth in model size and computation has led to unsustainable environmental impacts and widening resource inequality. We argue that LLM development should be fundamentally reoriented toward capability-perresource rather than capability alone. We present a theoretical framework demonstrating that resource-allocation decisions guided by gradient influence patterns can dramatically improve efficiency throughout the AI lifecycle. Our analysis shows that in transformer-based models, where a small fraction of parameters exert outsized influence (following heavy-tailed distributions), three critical insights emerge: (1) updating only high-influence parameters strictly outperforms full-parameter tuning on a performance-per-resource basis; (2) simple gradient norms provide computationally efficient proxies for identifying these high-influence components; and (3) coordinated parameter and data selection yields multiplicative efficiency gains, potentially reducing resource requirements by orders of magnitude. Building on these theoretical foundations, we propose a two-stage paradigm--marginalreturn pretraining for foundation developers and influence-guided adaptation for downstream users--bridged by gradient blueprints, metadata describing which parameters matter most for various tasks. This capability-per-resource perspective transforms what were once considered pragmatic hardware workarounds into theoretically optimal strategies, democratizing access to cutting-edge AI capabilities while significantly reducing environmental impact. By embedding resource consciousness into how we develop, adapt, and evaluate models, we can reshape AI progress toward a more sustainable and equitable future.

The following section is answers to questions listed in datasheets for datasets.858 A.1 Motivation859 Question: For what purpose was the dataset created? Was there a specific task in mind?860 Was there a specific gap that needed to be filled? Answer: To evaluate the linguistic robustness of language models across diverse English862 varieties by transforming Standard American English (SAE) datasets.863 Question: Who created the dataset (e.g., which team, research group) and on behalf of864 which entity (e.g., company, institution, organization)?865 Answer: The authors of this paper.866 Question: Who funded the creation of the dataset? If there is an associated grant, please867 provide the name of the grantor and the grant name and number.868

Large Language Models (LLMs) are predominantly evaluated on Standard American English (SAE), often overlooking the diversity of global English varieties. This narrow focus may raise fairness concerns as degraded performance on nonstandard varieties can lead to unequal benefits for users worldwide. Therefore, it is critical to extensively evaluate the linguistic robustness of LLMs on multiple non-standard English varieties. We introduce Trans-EnV, a framework that automatically transforms SAE datasets into multiple English varieties to evaluate the linguistic robustness. Our framework combines (1) linguistics expert knowledge to curate variety-specific features and transformation guidelines from linguistic literature and corpora, and (2) LLM-based transformations to ensure both linguistic validity and scalability. Using Trans-EnV, we transform six benchmark datasets into 38 English varieties and evaluate seven state-of-the-art LLMs. Our results reveal significant performance disparities, with accuracy decreasing by up to 46.3% on non-standard varieties.

We propose SCONE (Scalable, Contextualized, Offloaded, N-gram Embedding), a new method for extending input embedding layers to enhance language model performance. To avoid increased decoding costs, SCONE retains the original vocabulary while introducing embeddings for a set of frequent n-grams. These embeddings provide contextualized representation for each input token and are learned with a separate model during training. After training, embeddings are precomputed and stored in off-accelerator memory; during inference, querying them has minimal impact on latency due to the low complexity of embedding lookups. SCONE enables two new scaling strategies: increasing the number of n-gram embeddings and scaling the model used to learn them, both while maintaining fixed accelerator usage during inference (in terms of FLOPS and memory). We show that scaling both aspects enables a model with 1B accelerator-resident parameters to outperform a 1.9B-parameter baseline across diverse corpora, while using only about half the FLOPS and accelerator memory during inference.

Protein language models (PLMs) are often assumed to capture evolutionary information by training on large protein sequence datasets. Yet it remains unclear whether PLMs can reason about evolution--that is, infer evolutionary relationships between sequences. We test this capability by evaluating whether standard PLM usage, frozen or fine-tuned embeddings with distance-based comparison, supports evolutionary reasoning. Existing PLMs consistently fail to recover phylogenetic structure, despite strong performance on sequence-level tasks such as masked-token and contact prediction. We present Phyla, a hybrid state-space and transformer model that jointly processes multiple sequences and is trained using a tree-based objective across 3,000 phylogenies spanning diverse protein families.

The growing importance of mRNA therapeutics and synthetic biology highlights the need for models that capture the latent structure of synonymous codon (different triplets encoding the same amino acid) usage, which subtly modulates translation efficiency and gene expression. While recent efforts incorporate codon-level inductive biases through auxiliary objectives, they often fall short of explicitly modeling the structured relationships that arise from the genetic code's inherent symmetries. We introduce Equi mRNA, the first codon level equivariant mRNA language model that explicitly encodes synonymous codon symmetries as cyclic subgroups of 2D Special Orthogonal matrix ($\mathrm{SO}(2)$). By combining group theoretic priors with an auxiliary equivariance loss and symmetry aware pooling, Equi mRNA learns biologically grounded representations that outperform vanilla baselines across multiple axes. On downstream property prediction tasks including expression, stability, and riboswitch switching Equi mRNA delivers up to $\approx$ 10\% improvements in accuracy. In sequence generation, it produces mRNA constructs that are up to $\approx$ 4$\times$ more realistic under Fréchet BioDistance metrics and $\approx$ 28\% better preserve functional properties compared to vanilla baseline. Interpretability analyses further reveal that learned codon rotation distributions recapitulate known GC content biases and tRNA abundance patterns, offering novel insights into codon usage. Equi mRNA establishes a new biologically principled paradigm for mRNA modeling, with significant implications for the design of next generation therapeutics.