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Monthly Mixing reports that Adobe has launched three Firefly audio-generation tools: Generate Music, Generate Speech and Generate Sound Effects, alongside new Firefly AI Assistant features and support for Google’s Gemini Omni Flash.
An arXiv preprint reports that five large language models changed harmful-content classifications across Urdu scripts and English translations, revealing a measurable “Missed-in-Urdu” safety gap.
A new arXiv preprint reports that automated metrics and LLM judges often fail to match human assessments of creativity in short stories, with judges systematically favoring AI-generated writing styles.
A preprint reports that four multilingual eighth-grade students challenged LLM classifications of their math discussions, arguing that adult annotations and standard scores can miss students’ own interpretations.
A new arXiv paper introduces AgentSpec, a speculative-decoding method designed to reduce response-time degradation when LLM agents run in large batches. The authors evaluate it across five workloads and four models from four LLM families in vLLM.
A new benchmark reports that vision-language models often changed a correct chest X-ray answer after receiving conflicting text or prior-image context, with misleading text exerting a stronger pull than misleading visual context.
A new arXiv paper proposes training language models to evaluate their own judgments without external gold labels, while using validation checks to identify when recursive improvement should stop.
Qwen describes Qwen3.8-Flash-Next as a multimodal mixture-of-experts model with 125 billion total tokens and 6 billion active parameters, and says it previews architecture planned for Qwen4.
A new arXiv paper proposes VisCache, a no-training framework that reduces visual KV-cache storage for vision-language models while retaining 19% to 28% of the cache. The authors report speedups of up to 2.35 times, but the results have not been independently validated here.
A new arXiv study reports that tuning the blend between text and image prototypes is not the main limit on few-shot vision-language model accuracy. In experiments spanning 4,800 evaluation cells, validation-free linear probes outperformed even a blend selected with test-set information.
A paper reports a two-stage AI collaboration framework that uses a multimodal language model and a vision expert to analyze breast ultrasound images, with the authors reporting improved diagnostic accuracy and interpretability across multiple datasets.
A new arXiv paper presents an LLM-based multi-agent framework that combines 3D CAD models, 2D engineering drawings and manufacturing rules to generate traceable process plans. The authors report strong benchmark results, but real-world deployment and independent validation remain unknown.
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