Topic
language model
Technology Z.ai GLM 5.3 open-weight model arrives with near-frontier hacking skills
Chinese AI company Z.ai announced GLM 5.3, an open-weight model it says automates coding and cybersecurity tasks almost as well as Anthropic and OpenAI's best models. It also launched OpenVuln for code scanning. Z.ai is staging access to security partners before full release in two weeks.
Technology Anthropic to Charge Usage-Based Fees for Claude Fable 5, Breaking Subscription Model
Anthropic is introducing usage-based billing for Claude Fable 5, the consumer version of its Mythos 5 AI model. Starting July 12, subscribers to the $20, $100, and $200 monthly plans will pay additional fees per token, matching API rates. The move marks a shift from flat subscriptions and reflects data center capacity constraints.
Technology 28 Tips to Take Your ChatGPT Prompts to the Next Level: A Guide for Enterprise Leaders
WIRED's guide to 28 advanced ChatGPT prompt engineering techniques, covering methods to improve output quality, reduce sycophancy, and accelerate learning. Tips include using the Pareto principle, role-playing, camera integration, and iterative refinement.
IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources
Researchers present IHUBERT, a monolingual Persian language model pretrained on a 45GB curated subset of the Sepahr-Danesh collection using a multi-stage pipeline that includes vector-database-based semantic deduplication and domain-balanced pretraining. IHUBERT achieves top scores on extractive QA benchmarks PQuAD and ParsiNLU-RC, and best results on FarsTail NLI, while remaining competitive on NER and topic classification.
Hybrid Open-Ended Tri-Evolution Framework Boosts Deep Research AI Performance
Researchers propose the Hybrid Open-Ended Tri-Evolution (HOTE) framework that uses hybrid-mode reinforcement learning to collaboratively evolve a proposer, solver, and judge for deep research tasks. An 8B model trained with HOTE surpasses static open 8-32B models and state-of-the-art deep research training methods while requiring less time overhead.
Koshur Diacritizer: A Byte-Level Model Restores Diacritics for Kashmiri Language NLP
Researchers have developed Koshur Diacritizer, a byte-level sequence-to-sequence model based on ByT5-small, to restore missing diacritic marks in Kashmiri digital text. The model, trained on 23,700 sentence pairs, achieves a DERm of 0.2012 and word error rate of 0.2159, with a native expert accuracy of 77.5%. The dataset, model, and source code are publicly released to support low-resource language research.
Data Augmentations Offer Path to Efficient Language Model Pretraining Under Data Constraints
As AI labs face a data ceiling where compute capacity outpaces new high-quality text, researchers propose data augmentations to enable productive multi-epoch training on fixed corpora. Three categories—token-level noise, sequence permutations, and target offset prediction—are shown to delay overfitting and lower validation loss compared to standard autoregressive pretraining. Random token replacement achieved the best minimum loss among individual methods, with combined augmentations further improving results.
Technology ChatGPT's Roasting Skills: A New AI Frontier
AI chatbots like ChatGPT are now being used to roast users, offering a unique blend of humor and insight. By analyzing past interactions, these AI systems can deliver personalized and often surprisingly accurate jabs.