https://aiexpert.news/en/article/heros-journey-benchmark-reveals-llm-rule-induction-limits ai|expert en 2026-06-02T14:38:15.643Z LLMs Can Induce Hidden Rules, but Procedural Execution Remains Uncracked https://aiexpert.news/pt/article/heros-journey-benchmark-reveals-llm-rule-induction-limits ai|expert pt-BR 2026-06-02T14:38:15.643Z LLMs Podem Induzir Regras Ocultas, mas Execução Procedimental Continua Sem Solução https://aiexpert.news/es/article/heros-journey-benchmark-reveals-llm-rule-induction-limits ai|expert es 2026-06-02T14:38:15.643Z Los LLM pueden inducir reglas ocultas, pero la ejecución procedural sigue sin resolver https://aiexpert.news/en/article/intel-xeon-6-drops-hyper-threading-for-ai-inference-workloads ai|expert en 2026-06-02T14:06:15.683Z Intel Clearwater Forest Sacrifices Vector Width for Inference Throughput https://aiexpert.news/pt/article/intel-xeon-6-drops-hyper-threading-for-ai-inference-workloads ai|expert pt-BR 2026-06-02T14:06:15.683Z Intel Clearwater Forest 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https://aiexpert.news/es/article/safesteer-efficient-safety-alignment-for-llms ai|expert es 2026-06-02T11:36:15.408Z SafeSteer reduce el impuesto de alineación al enfocarse en tokens de seguridad dispersos https://aiexpert.news/en/article/protoada-efficient-multimodal-continual-learning-with-prototype-guided-adapters ai|expert en 2026-06-02T11:00:53.834Z Output Format Drives Faster Accuracy Loss Than Domain Shift in Multimodal LLMs https://aiexpert.news/pt/article/protoada-efficient-multimodal-continual-learning-with-prototype-guided-adapters ai|expert pt-BR 2026-06-02T11:00:53.834Z Formato de Saída Leva à Perda de Precisão Mais Rápida do que a Mudança de Domínio em LLM Multimodais https://aiexpert.news/es/article/protoada-efficient-multimodal-continual-learning-with-prototype-guided-adapters ai|expert es 2026-06-02T11:00:53.834Z El Formato de Salida Provoca una Pérdida de Precisión Más Rápida que el Cambio de Dominio en Modelos Multimodales de Gran Tamaño 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