<?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/specvalidator-catches-defective-llm-prompts-before-they-corrupt-code-generation</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:30:04.332Z</news:publication_date>
      <news:title>SpecValidator Hits 0.804 F1 on Prompt Defect Detection, Doubling Frontier Model MCC</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/specvalidator-catches-defective-llm-prompts-before-they-corrupt-code-generation</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:30:04.332Z</news:publication_date>
      <news:title>SpecValidator Atinge F1 de 0,804 na Detecção de Defeitos em Prompts, Dobrando o MCC dos Modelos Frontier</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/specvalidator-catches-defective-llm-prompts-before-they-corrupt-code-generation</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:30:04.332Z</news:publication_date>
      <news:title>SpecValidator Alcanza F1 de 0,804 en Detección de Defectos en Prompts, Duplicando el MCC de los Modelos Frontier</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/training-on-disagreement-how-multi-thinker-cot-supervision-outperforms-single-te</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:18:04.348Z</news:publication_date>
      <news:title>Multi-teacher CoT pooling can be computationally hard, active queries fix it</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/training-on-disagreement-how-multi-thinker-cot-supervision-outperforms-single-te</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:18:04.348Z</news:publication_date>
      <news:title>O agrupamento multi-teacher de CoT pode ser computacionalmente difícil — consultas ativas resolvem o problema</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/training-on-disagreement-how-multi-thinker-cot-supervision-outperforms-single-te</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:18:04.348Z</news:publication_date>
      <news:title>El agrupamiento multi-teacher de CoT puede ser computacionalmente difícil — las consultas activas lo resuelven</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/hylo-upcycling-pretrained-transformer-checkpoints-into-long-context-hybrid-archi</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:05:42.310Z</news:publication_date>
      <news:title>AMD HyLo Converts Transformer Checkpoints to 32x Longer Context Without Retraining</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/hylo-upcycling-pretrained-transformer-checkpoints-into-long-context-hybrid-archi</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:05:42.310Z</news:publication_date>
      <news:title>AMD HyLo Converte Checkpoints Transformer para Contexto 32x Mais Longo Sem Retreinamento</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/hylo-upcycling-pretrained-transformer-checkpoints-into-long-context-hybrid-archi</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T02:05:42.310Z</news:publication_date>
      <news:title>AMD HyLo Convierte Checkpoints Transformer a Contexto 32x Más Largo Sin Reentrenamiento</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/green-shielding-researchers-expose-how-routine-phrasing-variation-silently-shift</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:50:04.238Z</news:publication_date>
      <news:title>Safer-Looking LLM Outputs Miss More Critical Diagnoses, Green Shielding Study Finds</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/green-shielding-researchers-expose-how-routine-phrasing-variation-silently-shift</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:50:04.238Z</news:publication_date>
      <news:title>Outputs de LLMs com Aparência Mais Segura Erram Mais Diagnósticos Críticos, Aponta Estudo de Green Shielding</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/green-shielding-researchers-expose-how-routine-phrasing-variation-silently-shift</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:50:04.238Z</news:publication_date>
      <news:title>Los Outputs de LLMs con Apariencia más Segura Fallan más Diagnósticos Críticos, Revela Estudio de Green Shielding</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/hdet-repurposes-idle-gpu-replicas-to-run-learning-rate-exploration-during-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:38:04.216Z</news:publication_date>
      <news:title>HDET Converts Allocated GPU Replicas Into a Live Learning-Rate Search Engine</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/hdet-repurposes-idle-gpu-replicas-to-run-learning-rate-exploration-during-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:38:04.216Z</news:publication_date>
      <news:title>HDET Converte Réplicas de GPU Alocadas em um Motor de Busca de Learning Rate em Tempo Real</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/hdet-repurposes-idle-gpu-replicas-to-run-learning-rate-exploration-during-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:38:04.216Z</news:publication_date>
      <news:title>HDET Convierte Réplicas de GPU Asignadas en un Motor de Búsqueda de Learning Rate en Tiempo Real</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/persona-collapse-why-multi-agent-llm-simulations-converge-to-a-single-behavioral</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:24:04.290Z</news:publication_date>
      <news:title>Persona Collapse Undermines Multi-Agent LLM Simulations Across Ten Models</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/persona-collapse-why-multi-agent-llm-simulations-converge-to-a-single-behavioral</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:24:04.290Z</news:publication_date>
      <news:title>Persona Collapse Compromete Simulações LLM Multiagente em Dez Modelos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/persona-collapse-why-multi-agent-llm-simulations-converge-to-a-single-behavioral</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:24:04.290Z</news:publication_date>
      <news:title>Persona Collapse Socava las Simulaciones LLM Multiagente en Diez Modelos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/llm-rubrics-match-clinician-scoring-across-823-encounters-unblocking-clinical-ai</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:04:04.166Z</news:publication_date>
      <news:title>LLM Rubric Scoring Matches Clinician Agreement on 823 Cases at 1,000x Lower Cost</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/llm-rubrics-match-clinician-scoring-across-823-encounters-unblocking-clinical-ai</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:04:04.166Z</news:publication_date>
      <news:title>Pontuação por Rubrica de LLM Corresponde ao Acordo entre Clínicos em 823 Casos com Custo 1.000x Menor</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/llm-rubrics-match-clinician-scoring-across-823-encounters-unblocking-clinical-ai</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T01:04:04.166Z</news:publication_date>
      <news:title>La Puntuación por Rúbrica de LLM Iguala el Acuerdo entre Clínicos en 823 Casos con un Costo 1.000x Menor</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/depthkv-layer-aware-kv-cache-pruning-cuts-long-context-llm-memory-overhead</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:50:04.170Z</news:publication_date>
      <news:title>DepthKV Beats Uniform KV Cache Pruning by Allocating Memory per Layer Sensitivity</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/depthkv-layer-aware-kv-cache-pruning-cuts-long-context-llm-memory-overhead</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:50:04.170Z</news:publication_date>
      <news:title>DepthKV Supera o Pruning Uniforme de Cache KV ao Alocar Memória por Sensibilidade de Camada</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/depthkv-layer-aware-kv-cache-pruning-cuts-long-context-llm-memory-overhead</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:50:04.170Z</news:publication_date>
      <news:title>DepthKV Supera el Pruning Uniforme de Caché KV al Asignar Memoria por Sensibilidad de Capa</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/agentward-new-security-architecture-addresses-full-lifecycle-vulnerabilities-in-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:34:04.170Z</news:publication_date>
      <news:title>FIND-Lab releases AgentWard, a five-layer AI agent security framework</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/agentward-new-security-architecture-addresses-full-lifecycle-vulnerabilities-in-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:34:04.170Z</news:publication_date>
      <news:title>FIND-Lab lança AgentWard, framework de segurança em cinco camadas para agentes de IA</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/agentward-new-security-architecture-addresses-full-lifecycle-vulnerabilities-in-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:34:04.170Z</news:publication_date>
      <news:title>FIND-Lab lanza AgentWard, un framework de seguridad de cinco capas para agentes de IA</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/meta-races-to-unwind-manus-ai-deal-before-beijings-export-control-deadline</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:15:42.618Z</news:publication_date>
      <news:title>Meta Forced to Dismantle Manus AI Acquisition Under Beijing Deadline</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/meta-races-to-unwind-manus-ai-deal-before-beijings-export-control-deadline</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:15:42.618Z</news:publication_date>
      <news:title>Meta Obrigada a Desmantelar Aquisição da Manus AI sob Prazo de Pequim</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/meta-races-to-unwind-manus-ai-deal-before-beijings-export-control-deadline</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:15:42.618Z</news:publication_date>
      <news:title>Meta Obligada a Desmantelar la Adquisición de Manus AI bajo Plazo de Pekín</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/anthropic-tests-claude-models-for-safety-research-sabotageand-finds-none</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:02:04.344Z</news:publication_date>
      <news:title>Anthropic finds Claude does not start safety sabotage but will continue it when primed</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/anthropic-tests-claude-models-for-safety-research-sabotageand-finds-none</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:02:04.344Z</news:publication_date>
      <news:title>Anthropic descobre que Claude não inicia sabotagem de segurança, mas a continua quando induzido</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/anthropic-tests-claude-models-for-safety-research-sabotageand-finds-none</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-29T00:02:04.344Z</news:publication_date>
      <news:title>Anthropic concluye que Claude no inicia sabotaje de seguridad pero lo continúa cuando se le induce</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/alec-radfords-talkie-is-a-13b-llm-trained-on-260b-tokens-of-pre-1931-text-releas</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T03:14:49.577Z</news:publication_date>
      <news:title>Alec Radford Releases 13B Model Trained on Pre-1931 Text Under Apache 2.0</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/alec-radfords-talkie-is-a-13b-llm-trained-on-260b-tokens-of-pre-1931-text-releas</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T03:14:49.577Z</news:publication_date>
      <news:title>Alec Radford Lança Modelo de 13B Treinado em Textos Anteriores a 1931 sob Apache 2.0</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/alec-radfords-talkie-is-a-13b-llm-trained-on-260b-tokens-of-pre-1931-text-releas</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T03:14:49.577Z</news:publication_date>
      <news:title>Alec Radford Lanza Modelo de 13B Entrenado con Textos Anteriores a 1931 bajo Apache 2.0</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/doc-to-lora-adaptation-collapses-to-46-accuracy-when-documents-contradict-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:44:04.398Z</news:publication_date>
      <news:title>Doc-to-LoRA Accuracy Falls to 16% Against Strongly Entrenched Model Facts</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/doc-to-lora-adaptation-collapses-to-46-accuracy-when-documents-contradict-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:44:04.398Z</news:publication_date>
      <news:title>Acurácia do Doc-to-LoRA Cai para 16% Contra Fatos Fortemente Consolidados no Modelo</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/doc-to-lora-adaptation-collapses-to-46-accuracy-when-documents-contradict-traini</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:44:04.398Z</news:publication_date>
      <news:title>La Precisión del Doc-to-LoRA Cae al 16% Frente a Hechos Fuertemente Arraigados en el Modelo</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/elementsclaw-agentic-framework-closes-the-loop-on-ai-driven-materials-discovery</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:30:04.353Z</news:publication_date>
      <news:title>ElementsClaw Screens 2.4 Million Crystals in 28 GPU Hours, Finds Four New Superconductors</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/elementsclaw-agentic-framework-closes-the-loop-on-ai-driven-materials-discovery</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:30:04.353Z</news:publication_date>
      <news:title>ElementsClaw Analisa 2,4 Milhões de Cristais em 28 Horas de GPU e Descobre Quatro Novos Supercondutores</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/elementsclaw-agentic-framework-closes-the-loop-on-ai-driven-materials-discovery</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:30:04.353Z</news:publication_date>
      <news:title>ElementsClaw Analiza 2,4 Millones de Cristales en 28 Horas de GPU y Descubre Cuatro Nuevos Superconductores</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/silent-deepspeed-bug-has-corrupted-rl-fine-tuning-benchmarks-across-trl-openrlhf</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:15:35.617Z</news:publication_date>
      <news:title>DeepSpeed CPU-Offload Bug Corrupted RLHF Benchmarks in Three Major Frameworks</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/silent-deepspeed-bug-has-corrupted-rl-fine-tuning-benchmarks-across-trl-openrlhf</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:15:35.617Z</news:publication_date>
      <news:title>Bug de CPU-Offload do DeepSpeed Corrompeu Benchmarks de RLHF em Três Grandes Frameworks</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/silent-deepspeed-bug-has-corrupted-rl-fine-tuning-benchmarks-across-trl-openrlhf</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T02:15:35.617Z</news:publication_date>
      <news:title>El Bug de CPU-Offload de DeepSpeed Corrompió Benchmarks de RLHF en Tres Frameworks Principales</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/google-deepmind-signs-national-ai-partnership-with-south-korea-eyes-government-a</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:50:04.392Z</news:publication_date>
      <news:title>Google DeepMind Strikes Lab-Direct AI Partnership with South Korea</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/google-deepmind-signs-national-ai-partnership-with-south-korea-eyes-government-a</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:50:04.392Z</news:publication_date>
      <news:title>Google DeepMind firma parceria de IA Lab-Direct com a Coreia do Sul</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/google-deepmind-signs-national-ai-partnership-with-south-korea-eyes-government-a</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:50:04.392Z</news:publication_date>
      <news:title>Google DeepMind establece alianza de IA Lab-Direct con Corea del Sur</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/new-method-cuts-the-cost-of-fitting-ai-scaling-laws-by-actively-selecting-which-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:30:04.379Z</news:publication_date>
      <news:title>MSPE Fits AI Scaling Laws at 10% of Standard Compute Cost</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/new-method-cuts-the-cost-of-fitting-ai-scaling-laws-by-actively-selecting-which-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:30:04.379Z</news:publication_date>
      <news:title>MSPE Ajusta Leis de Escala de IA com 10% do Custo Computacional Padrão</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/new-method-cuts-the-cost-of-fitting-ai-scaling-laws-by-actively-selecting-which-</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:30:04.379Z</news:publication_date>
      <news:title>MSPE Ajusta Leyes de Escala de IA con el 10% del Costo Computacional Estándar</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/llms-systematically-generate-harmful-narratives-about-global-majority-nationalit</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:18:04.626Z</news:publication_date>
      <news:title>Frontier LLMs show 50x subordination bias against Global Majority nationalities</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/llms-systematically-generate-harmful-narratives-about-global-majority-nationalit</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:18:04.626Z</news:publication_date>
      <news:title>LLMs de fronteira apresentam viés de subordinação 50x maior contra nacionalidades da Maioria Global</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/llms-systematically-generate-harmful-narratives-about-global-majority-nationalit</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:18:04.626Z</news:publication_date>
      <news:title>Los LLMs de frontera muestran un sesgo de subordinación 50x contra nacionalidades de la Mayoría Global</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/white-box-probe-replaces-opaque-gnn-message-passing-with-interpretable-signal-co</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:04:04.643Z</news:publication_date>
      <news:title>WG-SRC Replaces GNN Message-Passing with Named, Auditable Signal Components</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/white-box-probe-replaces-opaque-gnn-message-passing-with-interpretable-signal-co</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:04:04.643Z</news:publication_date>
      <news:title>WG-SRC Substitui Message-Passing de GNN por Componentes de Sinal Auditáveis e Nomeados</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/white-box-probe-replaces-opaque-gnn-message-passing-with-interpretable-signal-co</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T01:04:04.643Z</news:publication_date>
      <news:title>WG-SRC Reemplaza el Message-Passing de GNN con Componentes de Señal Auditables y Nombrados</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/google-employees-demand-sundar-pichai-reject-classified-military-ai-contracts</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:50:04.204Z</news:publication_date>
      <news:title>600 Google Employees Demand Pichai Bar Classified Pentagon AI Use</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/google-employees-demand-sundar-pichai-reject-classified-military-ai-contracts</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:50:04.204Z</news:publication_date>
      <news:title>600 Funcionários do Google Exigem que Pichai Proíba o Uso Classificado de IA pelo Pentágono</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/google-employees-demand-sundar-pichai-reject-classified-military-ai-contracts</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:50:04.204Z</news:publication_date>
      <news:title>600 Empleados de Google Exigen que Pichai Prohíba el Uso Clasificado de IA por el Pentágono</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/how-ai-agents-burn-your-token-budget-first-systematic-study-of-agentic-cost-patt</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:38:04.458Z</news:publication_date>
      <news:title>First Systematic Study Finds AI Agents Burn 1,000x More Tokens Than Code Chat</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/how-ai-agents-burn-your-token-budget-first-systematic-study-of-agentic-cost-patt</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:38:04.458Z</news:publication_date>
      <news:title>Primeiro Estudo Sistemático Revela que Agentes de IA Consomem 1.000x Mais Tokens do que Chats de Código</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/how-ai-agents-burn-your-token-budget-first-systematic-study-of-agentic-cost-patt</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:38:04.458Z</news:publication_date>
      <news:title>El Primer Estudio Sistemático Revela que los Agentes de IA Consumen 1.000x Más Tokens que los Chats de Código</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/agentic-world-models-get-a-unified-taxonomy-levels-laws-and-whats-still-missing</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:18:05.014Z</news:publication_date>
      <news:title>42-Author arXiv Survey Defines Three Levels for Agentic World Models</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/agentic-world-models-get-a-unified-taxonomy-levels-laws-and-whats-still-missing</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:18:05.014Z</news:publication_date>
      <news:title>Pesquisa com 42 Autores no arXiv Define Três Níveis para Modelos de Mundo Agênticos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/agentic-world-models-get-a-unified-taxonomy-levels-laws-and-whats-still-missing</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:18:05.014Z</news:publication_date>
      <news:title>Investigación de 42 Autores en arXiv Define Tres Niveles para Modelos de Mundo Agénticos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/eu-ai-act-accountability-gap-ai-hiring-bias-fragments-across-vendor-supply-chain</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:04:04.286Z</news:publication_date>
      <news:title>EU AI Act leaves deployers liable for vendor bias they cannot audit</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/eu-ai-act-accountability-gap-ai-hiring-bias-fragments-across-vendor-supply-chain</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:04:04.286Z</news:publication_date>
      <news:title>EU AI Act deixa implantadores responsáveis por viés de fornecedores que não podem auditar</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/eu-ai-act-accountability-gap-ai-hiring-bias-fragments-across-vendor-supply-chain</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-28T00:04:04.286Z</news:publication_date>
      <news:title>El EU AI Act deja a los implementadores responsables por sesgos de proveedores que no pueden auditar</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/tencent-open-sources-hunyuanworld-10-first-simulation-ready-3d-world-generator-f</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:50:04.224Z</news:publication_date>
      <news:title>Tencent Open-Sources HunyuanWorld 1.0, a Mesh-Ready 3D World Generator</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/tencent-open-sources-hunyuanworld-10-first-simulation-ready-3d-world-generator-f</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:50:04.224Z</news:publication_date>
      <news:title>Tencent Abre o Código de HunyuanWorld 1.0, um Gerador de Mundos 3D Pronto para Mesh</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/tencent-open-sources-hunyuanworld-10-first-simulation-ready-3d-world-generator-f</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:50:04.224Z</news:publication_date>
      <news:title>Tencent Libera el Código de HunyuanWorld 1.0, un Generador de Mundos 3D Listo para Mesh</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/abstract-chain-of-thought-latent-reasoning-tokens-cut-inference-cost-without-sac</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:30:04.416Z</news:publication_date>
      <news:title>IBM&apos;s ACoT Cuts Reasoning Tokens 11.6x Without Accuracy Loss</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/abstract-chain-of-thought-latent-reasoning-tokens-cut-inference-cost-without-sac</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:30:04.416Z</news:publication_date>
      <news:title>O ACoT da IBM Reduz Tokens de Raciocínio em 11,6x Sem Perda de Precisão</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/abstract-chain-of-thought-latent-reasoning-tokens-cut-inference-cost-without-sac</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:30:04.416Z</news:publication_date>
      <news:title>El ACoT de IBM Reduce los Tokens de Razonamiento 11,6x Sin Pérdida de Precisión</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/canonical-charts-ai-integration-roadmap-for-ubuntu-linux</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:15:36.605Z</news:publication_date>
      <news:title>Canonical Sets 2026 Ubuntu AI Roadmap With On-Device Inference and Agentic Admin</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/canonical-charts-ai-integration-roadmap-for-ubuntu-linux</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:15:36.605Z</news:publication_date>
      <news:title>Canonical Define Roteiro de IA do Ubuntu para 2026 com Inferência On-Device e Administração Agêntica</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/canonical-charts-ai-integration-roadmap-for-ubuntu-linux</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:15:36.605Z</news:publication_date>
      <news:title>Canonical Establece el Roadmap de IA de Ubuntu para 2026 con Inferencia On-Device y Administración Agéntica</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/david-silvers-new-lab-raises-11b-to-build-ai-that-learns-without-human-data</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:02:15.389Z</news:publication_date>
      <news:title>David Silver&apos;s Ineffable Intelligence Raises $1.1B to Replace Human Training Data</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/david-silvers-new-lab-raises-11b-to-build-ai-that-learns-without-human-data</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:02:15.389Z</news:publication_date>
      <news:title>Ineffable Intelligence, de David Silver, capta $1.1B para substituir dados de treinamento humanos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/david-silvers-new-lab-raises-11b-to-build-ai-that-learns-without-human-data</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T23:02:15.389Z</news:publication_date>
      <news:title>Ineffable Intelligence, de David Silver, recauda $1.1B para reemplazar datos de entrenamiento humanos</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/openaimicrosoft-tear-up-exclusivity-no-more-lock-in-no-agi-clause</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:34:15.281Z</news:publication_date>
      <news:title>OpenAI Ends Azure Exclusivity and Drops the AGI License Clause</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/openaimicrosoft-tear-up-exclusivity-no-more-lock-in-no-agi-clause</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:34:15.281Z</news:publication_date>
      <news:title>OpenAI Encerra Exclusividade com Azure e Elimina Cláusula de Licença de AGI</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/openaimicrosoft-tear-up-exclusivity-no-more-lock-in-no-agi-clause</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:34:15.281Z</news:publication_date>
      <news:title>OpenAI Termina la Exclusividad con Azure y Elimina la Cláusula de Licencia de AGI</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/en/article/openai-achieves-fedramp-moderate-opening-federal-ai-deployments</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>en</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:20:15.717Z</news:publication_date>
      <news:title>OpenAI Earns FedRAMP Moderate for ChatGPT Enterprise and API Platform</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/pt/article/openai-achieves-fedramp-moderate-opening-federal-ai-deployments</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>pt-BR</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:20:15.717Z</news:publication_date>
      <news:title>OpenAI Obtém FedRAMP Moderate para ChatGPT Enterprise e Plataforma de API</news:title>
    </news:news>
  </url>
  <url>
    <loc>https://aiexpert.news/es/article/openai-achieves-fedramp-moderate-opening-federal-ai-deployments</loc>
    <news:news>
      <news:publication>
        <news:name>ai|expert</news:name>
        <news:language>es</news:language>
      </news:publication>
      <news:publication_date>2026-04-27T22:20:15.717Z</news:publication_date>
      <news:title>OpenAI Obtiene la Certificación FedRAMP Moderate para ChatGPT Enterprise y la Plataforma de API</news:title>
    </news:news>
  </url>
</urlset>