<?xml version="1.0" encoding="UTF-8"?>
<urlset
  xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
  xmlns:news="http://www.google.com/schemas/sitemap-news/0.9"
>
  <url>
    <loc>https://aiexpert.news/en/article/piper-programmable-distributed-training-removes-manual-strategy-design</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T05:16:10.040Z</news:publication_date>
      <news:title>Piper compiler enables DeepSeek-style training at thousand-GPU scale</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/piper-programmable-distributed-training-removes-manual-strategy-design</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T05:16:10.040Z</news:publication_date>
      <news:title>Compilador Piper permite treinamento estilo DeepSeek em escala de milhares de GPUs</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/piper-programmable-distributed-training-removes-manual-strategy-design</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T05:16:10.040Z</news:publication_date>
      <news:title>El compilador Piper permite el entrenamiento estilo DeepSeek a escala de miles de GPU</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/entergy-ceo-disputes-ai-data-center-electricity-bill-hikes</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T04:32:10.417Z</news:publication_date>
      <news:title>$7 Billion Savings Mask Entergy Data Center Operators&apos; Long-Term Infrastructure Debt</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/entergy-ceo-disputes-ai-data-center-electricity-bill-hikes</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T04:32:10.417Z</news:publication_date>
      <news:title>Poupança de US$ 7 Bilhões Oculta Dívida de Infraestrutura a Longo Prazo dos Operadores do Centro de Dados Entergy</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/entergy-ceo-disputes-ai-data-center-electricity-bill-hikes</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T04:32:10.417Z</news:publication_date>
      <news:title>Ahorro de $7 mil millones enmascaran la deuda a largo plazo de infraestructura de los operadores de centros de datos de Entergy</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/piper-a-programmable-system-for-distributed-ml-training</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T03:42:10.199Z</news:publication_date>
      <news:title>Piper Compiler Eliminates Hand-Coding for Distributed Training</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/piper-a-programmable-system-for-distributed-ml-training</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T03:42:10.199Z</news:publication_date>
      <news:title>Compilador Piper Elimina a Codificação Manual para Treinamento Distribuído</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/piper-a-programmable-system-for-distributed-ml-training</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T03:42:10.199Z</news:publication_date>
      <news:title>Compilador Piper Elimina la Codificación Manual para Entrenamiento Distribuido</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/multi-token-inference-has-a-hidden-architectural-flawclp-proposes-a-fix</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T02:10:11.306Z</news:publication_date>
      <news:title>Single Linear Layer Outperforms 1M-Parameter Gate in MTP Speedup Test</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/multi-token-inference-has-a-hidden-architectural-flawclp-proposes-a-fix</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T02:10:11.306Z</news:publication_date>
      <news:title>Camada Linear Única Supera Porta de 1M-Parâmetros no Teste de Aceleração de MTP</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/multi-token-inference-has-a-hidden-architectural-flawclp-proposes-a-fix</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T02:10:11.306Z</news:publication_date>
      <news:title>Capa Lineal Simple Supera Puerta de 1M-Parámetros en Prueba de Aceleración MTP</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/clinenv-real-ehr-data-benchmark-for-agent-decision-making-under-uncertainty</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T01:00:19.054Z</news:publication_date>
      <news:title>Real EHR Benchmark Exposes Limits of LLMs in Clinical Action</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/clinenv-real-ehr-data-benchmark-for-agent-decision-making-under-uncertainty</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T01:00:19.054Z</news:publication_date>
      <news:title>Real EHR Benchmark Exposta Limites dos LLMs em Ações Clínicas</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/clinenv-real-ehr-data-benchmark-for-agent-decision-making-under-uncertainty</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-10T01:00:19.054Z</news:publication_date>
      <news:title>EHC Real Benchmark Revela Límites de LLM en Acción Clínica</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/microsoft-backed-d-matrix-chips-challenge-nvidia-in-ai-inference</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T13:50:10.288Z</news:publication_date>
      <news:title>Corsair inference accelerator cuts response time 12× in GPU hybrid setup</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/microsoft-backed-d-matrix-chips-challenge-nvidia-in-ai-inference</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T13:50:10.288Z</news:publication_date>
      <news:title>Aceleração de inferência Corsair reduz tempo de resposta 12× em configuração híbrida de GPU</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/microsoft-backed-d-matrix-chips-challenge-nvidia-in-ai-inference</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T13:50:10.288Z</news:publication_date>
      <news:title>Acelerador de inferencia Corsair reduce el tiempo de respuesta 12× en configuración híbrida de GPU</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/aha-wam-decoupling-temporal-resolutions-in-world-action-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T12:14:10.330Z</news:publication_date>
      <news:title>AHA-WAM achieves 4.59× faster robot control by decoupling Diffusion Transformers</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/aha-wam-decoupling-temporal-resolutions-in-world-action-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T12:14:10.330Z</news:publication_date>
      <news:title>AHA-WAM alcança controle de robô 4.59 vezes mais rápido desacoplando Transformadores de Difusão</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/aha-wam-decoupling-temporal-resolutions-in-world-action-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T12:14:10.330Z</news:publication_date>
      <news:title>AHA-WAM logra un control de robot 4.59 veces más rápido al desacoplar Transformadores de Difusión</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/fase-detecting-hallucinations-in-multi-agent-code-generation-at-scale</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T10:50:10.398Z</news:publication_date>
      <news:title>FASE Cuts Hallucination Detection to 333x Speed</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/fase-detecting-hallucinations-in-multi-agent-code-generation-at-scale</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T10:50:10.398Z</news:publication_date>
      <news:title>FASE Reduz Detecção de Alucinações para 333x Velocidade</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/fase-detecting-hallucinations-in-multi-agent-code-generation-at-scale</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T10:50:10.398Z</news:publication_date>
      <news:title>FASE Reduce la Detección de Alucinaciones a una Velocidad de 333x</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/llm-rl-trust-region-fix-tackles-ppos-long-tail-failure-mode</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T09:28:10.100Z</news:publication_date>
      <news:title>New DRPO Method Fixes Long-Tail Vocabulary Collapse in LLM RL</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/llm-rl-trust-region-fix-tackles-ppos-long-tail-failure-mode</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T09:28:10.100Z</news:publication_date>
      <news:title>Novo Método DRPO Corrige Colapso de Vocabulário de Longo-Cabeça em RL de LLM</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/llm-rl-trust-region-fix-tackles-ppos-long-tail-failure-mode</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T09:28:10.100Z</news:publication_date>
      <news:title>Nuevo Método DRPO Corrige Colapso de Vocabulario de Larga-Talla en RL de LLM</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/arxiv-fase-reduces-llm-hallucination-uncertainty-in-multi-agent-code-gen-without</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:56:09.989Z</news:publication_date>
      <news:title>FASE Cuts Hallucination Detection Cost to 0.3% of Rivals</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/arxiv-fase-reduces-llm-hallucination-uncertainty-in-multi-agent-code-gen-without</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:56:09.989Z</news:publication_date>
      <news:title>FASE Reduz Custo de Detecção de Alucinações para 0,3% dos Concorrentes</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/arxiv-fase-reduces-llm-hallucination-uncertainty-in-multi-agent-code-gen-without</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:56:09.989Z</news:publication_date>
      <news:title>FASE Reduce el Costo de Detección de Alucinaciones al 0.3% de los Rivales</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/siga-adapting-coding-agents-to-scientific-simulators-without-domain-fine-tuning</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:24:10.237Z</news:publication_date>
      <news:title>SIGA Speeds Coding Agents on Scientific Simulators by 36×</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/siga-adapting-coding-agents-to-scientific-simulators-without-domain-fine-tuning</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:24:10.237Z</news:publication_date>
      <news:title>SIGA Acelera Agentes de Codificação em Simuladores Científicos por 36×</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/siga-adapting-coding-agents-to-scientific-simulators-without-domain-fine-tuning</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T08:24:10.237Z</news:publication_date>
      <news:title>SIGA Acelera Agentes de Codificación en Simuladores Científicos por 36×</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/echo-memory-isolating-object-persistence-failures-in-world-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:38:10.210Z</news:publication_date>
      <news:title>Echo-Memory Shows World Models Fail the Revisit Test</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/echo-memory-isolating-object-persistence-failures-in-world-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:38:10.210Z</news:publication_date>
      <news:title>Echo-Memory Mostra que Modelos de Mundo Falham no Teste de Revisão</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/echo-memory-isolating-object-persistence-failures-in-world-models</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:38:10.210Z</news:publication_date>
      <news:title>Echo-Memory Demuestra que los Modelos del Mundo Fallan en la Prueba de Revisita</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/fase-fast-adaptive-semantic-entropy-for-multi-agent-code-reliability</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:00:10.319Z</news:publication_date>
      <news:title>Waterloo researchers cut uncertainty quantification cost 99.7% with FASE</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/fase-fast-adaptive-semantic-entropy-for-multi-agent-code-reliability</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:00:10.319Z</news:publication_date>
      <news:title>Pesquisadores de Waterloo reduzem custo de quantificação de incerteza em 99,7% com FASE</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/fase-fast-adaptive-semantic-entropy-for-multi-agent-code-reliability</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T07:00:10.319Z</news:publication_date>
      <news:title>Investigadores de Waterloo reducen el costo de cuantificación de incertidumbre un 99.7% con FASE</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/uk-sovereign-ai-push-with-nvidia-policy-meets-infrastructure</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T06:24:10.003Z</news:publication_date>
      <news:title>UK Deploys 120,000 Blackwell GPUs for Sovereign AI Compute</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/uk-sovereign-ai-push-with-nvidia-policy-meets-infrastructure</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T06:24:10.003Z</news:publication_date>
      <news:title>Reino Unido Utiliza 120.000 GPUs Blackwell para Computação de IA Soberana</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/uk-sovereign-ai-push-with-nvidia-policy-meets-infrastructure</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T06:24:10.003Z</news:publication_date>
      <news:title>Reino Unido despliega 120,000 GPUs Blackwell para cómputo de IA soberano</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/evaluation-cards-45-author-consortium-standardizes-ai-model-performance-reportin</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:36:10.216Z</news:publication_date>
      <news:title>EvalCards Schema Exposes Systematic AI Benchmark Metadata Gaps</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/evaluation-cards-45-author-consortium-standardizes-ai-model-performance-reportin</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:36:10.216Z</news:publication_date>
      <news:title>Esquema EvalCards Expõe Falhas Sistematizadas em Metadados de Benchmarks de IA</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/evaluation-cards-45-author-consortium-standardizes-ai-model-performance-reportin</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:36:10.216Z</news:publication_date>
      <news:title>El Esquema EvalCards Revela Brechas Metadatos de Comparación de IA Sistematizadas</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/perplexity-study-how-agentic-ai-reshapes-knowledge-work</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:04:10.136Z</news:publication_date>
      <news:title>Perplexity Agentic AI Cuts Task Time 87 Percent in Production Study</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/perplexity-study-how-agentic-ai-reshapes-knowledge-work</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:04:10.136Z</news:publication_date>
      <news:title>Perplexidade IA Agênica Reduz Tempo de Tarefa 87% em Estudo de Produção</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/perplexity-study-how-agentic-ai-reshapes-knowledge-work</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-06-09T04:04:10.136Z</news:publication_date>
      <news:title>Agente de IA de Perplexity Reduce el Tiempo de Tareas un 87 Por Ciento en Estudio de Producción</news:title>
    </news:news>
  </url>
</urlset>