arXiv:2511.01386v1 Announce Type: new 
Abstract: Retrieval-Augmented Generation (RAG) quality depends on many interacting choices across retrieval, ranking, augmentation, prompting, and generation, so optimizing modules in isolation is brittle. We introduce RAGSmith, a modular framework that treats RAG design as an end-to-end architecture search over nine technique families and 46{,}080 feasible pipeline configurations. A genetic search optimizes a scalar objective that jointly aggregates retrieval metrics (recall@k, mAP, nDCG, MRR) and generation metrics (LLM-Judge and semantic similarity). We evaluate on six Wikipedia-derived domains (Mathematics, Law, Finance, Medicine, Defense Industry, Computer Science), each with 100 questions spanning factual, interpretation, and long-answer types. RAGSmith finds configurations that consistently outperform naive RAG baseline by +3.8\% on average (range +1.2\% to +6.9\% across domains), with gains up to +12.5\% in retrieval and +7.5\% in generation. The search typically explores $\approx 0.2\%$ of the space ($\sim 100$ candidates) and discovers a robust backbone -- vector retrieval plus post-generation reflection/revision -- augmented by domain-dependent choices in expansion, reranking, augmentation, and prompt reordering; passage compression is never selected. Improvement magnitude correlates with question type, with larger gains on factual/long-answer mixes than interpretation-heavy sets. These results provide practical, domain-aware guidance for assembling effective RAG systems and demonstrate the utility of evolutionary search for full-pipeline optimization.

RAGSmith هو إطار جديد مثير مصمم لتحسين طرق التوليد المعززة بالاسترجاع (RAG) من خلال اعتبار عملية التصميم بحثًا عن بنية شاملة. تتيح هذه الطريقة المبتكرة استكشاف العديد من التكوينات، مما يسهل العثور على أفضل التركيبات لمجموعات البيانات المختلفة. تكمن أهمية RAGSmith في قدرته على تحسين جودة RAG من خلال معالجة تعقيدات تفاعلات الوحدات، مما يؤدي في النهاية إلى طرق توليد أكثر فعالية.

RAGSmith es un nuevo marco emocionante diseñado para optimizar los métodos de Generación Aumentada por Recuperación (RAG) al tratar el proceso de diseño como una búsqueda de arquitectura de extremo a extremo. Este enfoque innovador permite explorar numerosas configuraciones, facilitando la búsqueda de las mejores combinaciones para varios conjuntos de datos. La importancia de RAGSmith radica en su capacidad para mejorar la calidad del RAG al abordar las complejidades de las interacciones entre módulos, lo que lleva a métodos de generación más efectivos.

RAGSmith est un nouveau cadre passionnant conçu pour optimiser les méthodes de génération augmentée par récupération (RAG) en considérant le processus de conception comme une recherche d'architecture de bout en bout. Cette approche innovante permet d'explorer de nombreuses configurations, facilitant ainsi la recherche des meilleures combinaisons pour divers ensembles de données. L'importance de RAGSmith réside dans sa capacité à améliorer la qualité du RAG en abordant les complexités des interactions entre modules, conduisant finalement à des méthodes de génération plus efficaces.

RAGSmith is an exciting new framework designed to optimize Retrieval-Augmented Generation (RAG) methods by treating the design process as an end-to-end architecture search. This innovative approach allows for the exploration of numerous configurations, making it easier to find the best combinations for various datasets. The significance of RAGSmith lies in its ability to enhance the quality of RAG by addressing the complexities of module interactions, ultimately leading to more effective and efficient generation methods.

RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets

arXiv:2601.08747v1 Announce Type: new 
Abstract: Current context augmentation methods, such as retrieval-augmented generation, are essential for solving knowledge-intensive reasoning tasks.However, they typically adhere to a rigid, brute-force strategy that executes retrieval at every step. This indiscriminate approach not only incurs unnecessary computational costs but also degrades performance by saturating the context with irrelevant noise. To address these limitations, we introduce Agentic Context Evolution (ACE), a framework inspired by human metacognition that dynamically determines whether to seek new evidence or reason with existing knowledge. ACE employs a central orchestrator agent to make decisions strategically via majority voting.It aims to alternate between activating a retriever agent for external retrieval and a reasoner agent for internal analysis and refinement. By eliminating redundant retrieval steps, ACE maintains a concise and evolved context. Extensive experiments on challenging multi-hop QA benchmarks demonstrate that ACE significantly outperforms competitive baselines in accuracy while achieving efficient token consumption.Our work provides valuable insights into advancing context-evolved generation for complex, knowledge-intensive tasks.

تشير التطورات الأخيرة في أساليب تعزيز السياق، وخاصة تقديم تطور السياق الوكيني (ACE)، إلى إطار ديناميكي يوازن بين استرجاع الأدلة والتفكير، مما يعزز المهام المعقدة التي تتطلب المعرفة. يهدف ACE إلى تحسين الأداء من خلال اتخاذ قرارات استراتيجية حول متى يجب البحث عن معلومات جديدة أو الاعتماد على المعرفة الموجودة، مما يقلل من التكاليف الحاسوبية والضوضاء في السياق.

Los recientes avances en métodos de aumento de contexto, especialmente la introducción de la Evolución de Contexto Agentivo (ACE), proponen un marco dinámico que equilibra la recuperación de evidencia y el razonamiento, mejorando así las tareas de razonamiento intensivo en conocimiento. ACE busca optimizar el rendimiento al decidir estratégicamente cuándo buscar nueva información o confiar en el conocimiento existente, reduciendo así los costos computacionales y el ruido en el contexto.

Des avancées récentes dans les méthodes d'augmentation de contexte, notamment l'introduction de l'Évolution de Contexte Agentique (ACE), proposent un cadre dynamique qui équilibre la récupération d'évidence et le raisonnement, améliorant ainsi les tâches de raisonnement intensif en connaissances. ACE vise à optimiser la performance en décidant stratégiquement quand récupérer de nouvelles informations ou s'appuyer sur des connaissances existantes, réduisant ainsi les coûts computationnels et le bruit dans le contexte.

Recent advancements in context augmentation methods, particularly the introduction of Agentic Context Evolution (ACE), propose a dynamic framework that balances evidence retrieval and reasoning, enhancing knowledge-intensive reasoning tasks. ACE aims to optimize performance by strategically deciding when to retrieve new information or rely on existing knowledge, thereby reducing computational costs and noise in the context.

To Retrieve or To Think? An Agentic Approach for Context Evolution

One More Thing in AI – Your Shortcut to AI Mastery

RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets

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