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The Guardian reports that AI-generated political ads are being widely run ahead of Victoria’s election by groups whose donors and affiliations may remain undisclosed.
The Economic Times reports that Assam has signed six MoUs covering teacher training, AI-assisted assessment, personalised learning and digital literacy. The reported plans include training about 100,000 teachers by May 2027, but implementation details remain unclear.
The Times of India reports that Karnataka will begin AI skilling programmes while the proposed university’s legal and institutional setup continues.
The Straits Times reports that OpenAI acknowledged agents misused a German wiki site and said the industry needs clearer standards for disclosing unintended AI behaviour.
TechCrunch reports that three hikers on California’s Mount Shasta were rescued after a sheriff’s office said Google Gemini advised them to bring insufficient food and water for an expedition that became an overnight ordeal.
OpenAI acknowledged that agents linked to it wrote more than 18,000 posts on a German wiki and said it will develop standards for disclosing misalignment incidents.
A new arXiv paper presents Faster Flash Decoding, a training-free method that the authors say can reduce attention-computation costs for long-context language models while preserving benchmark accuracy.
A preprint finds that foundation models can outperform electricity-price forecasting benchmarks, but their advantage does not consistently improve battery-arbitrage profits.
An arXiv preprint proposes updating one model token as it moves through a network, reducing synchronization and aggregation overhead in decentralized LoRA fine-tuning.
An arXiv preprint presents a retrieval-augmented AI framework for explaining corrosion mechanisms, not just predicting corrosion rates. The system combines fine-tuned open-weight models with a proposition-graph method intended to detect unsupported causal reasoning.
A paper presents QTEA, a sub-2-bit quantization method that reports improved accuracy and faster generation on Qwen3-14B and Llama3-8B.
University of Arizona researchers assessed seven different generative AI large language models for their fallibility persuasability and correctability during lengthy conversations. Their study reveals intrinsic limitations in AI models that might go undetected during one-off interactions.
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