Anthropic announced the release of Claude Opus 4.6, an enhanced version of its most powerful Claude model. The upgrade focuses on faster, more accurate coding and better handling of complex app tasks through a step‑by‑step reasoning approach. Opus 4.6 can self‑check its work and make multiple attempts without user prompts. The new model is available to paying Claude users on Pro, Max, Team and Enterprise plans, with the Pro tier priced at $20 per month (or $17 with annual billing). Smaller models such as Sonnet 4.5 and Haiku 4.5 remain in the lineup. Read more →

Researchers at the University of Colorado at Boulder tested popular large language models, including OpenAI's ChatGPT and its reasoning variants, on Sudoku puzzles and their ability to explain solutions. The models struggled with both 6×6 and 9×9 puzzles, often resorting to trial‑and‑error and producing inaccurate explanations. In some cases, the models gave unrelated answers, such as a weather forecast. The findings raise concerns about AI transparency, especially as the technology moves into high‑stakes domains like driving, tax preparation, and business decision‑making. The study also notes a pending Ziff Davis lawsuit against OpenAI over training data. Read more →

Researchers at the University of Colorado at Boulder tested popular large language models, including OpenAI's ChatGPT and its reasoning variants, on Sudoku puzzles and their ability to explain solutions. The models struggled with both 6×6 and 9×9 puzzles, often resorting to trial‑and‑error and producing inaccurate explanations. In some cases, the models gave unrelated answers, such as a weather forecast. The findings raise concerns about AI transparency, especially as the technology moves into high‑stakes domains like driving, tax preparation, and business decision‑making. The study also notes a pending Ziff Davis lawsuit against OpenAI over training data. Read more →

Researchers at the University of Colorado at Boulder tested popular large language models, including OpenAI's ChatGPT and its reasoning variants, on Sudoku puzzles and their ability to explain solutions. The models struggled with both 6×6 and 9×9 puzzles, often resorting to trial‑and‑error and producing inaccurate explanations. In some cases, the models gave unrelated answers, such as a weather forecast. The findings raise concerns about AI transparency, especially as the technology moves into high‑stakes domains like driving, tax preparation, and business decision‑making. The study also notes a pending Ziff Davis lawsuit against OpenAI over training data. Read more →

Researchers at the University of Colorado at Boulder tested popular large language models, including OpenAI's ChatGPT and its reasoning variants, on Sudoku puzzles and their ability to explain solutions. The models struggled with both 6×6 and 9×9 puzzles, often resorting to trial‑and‑error and producing inaccurate explanations. In some cases, the models gave unrelated answers, such as a weather forecast. The findings raise concerns about AI transparency, especially as the technology moves into high‑stakes domains like driving, tax preparation, and business decision‑making. The study also notes a pending Ziff Davis lawsuit against OpenAI over training data. Read more →

Researchers at the University of Colorado at Boulder tested popular large language models, including OpenAI's ChatGPT and its reasoning variants, on Sudoku puzzles and their ability to explain solutions. The models struggled with both 6×6 and 9×9 puzzles, often resorting to trial‑and‑error and producing inaccurate explanations. In some cases, the models gave unrelated answers, such as a weather forecast. The findings raise concerns about AI transparency, especially as the technology moves into high‑stakes domains like driving, tax preparation, and business decision‑making. The study also notes a pending Ziff Davis lawsuit against OpenAI over training data. Read more →

Researchers evaluating large language models (LLMs) discovered that the models' chain‑of‑thought reasoning collapses when faced with tasks that differ from their training data. By testing the models on novel transformations, altered input lengths, and unfamiliar symbols, the study showed sharp declines in accuracy and an inability to generalize. The authors conclude that the apparent reasoning is merely pattern replication rather than true understanding, describing it as a “simulated reasoning” mirage. Read more →