These sources provide an extensive overview of Large Language Model (LLM)-based Text-to-SQL (NL2SQL) systems, focusing on techniques like prompt engineering, supervised fine-tuning (SFT), and Retrieval-Augmented Generation (RAG) to enhance performance. Researchers evaluate models using benchmark datasets like Spider and BIRD, employing metrics such as Exact Match (EM) and Execution Accuracy (EX), while also addressing persistent challenges like hallucination and cross-domain generalization. Advanced frameworks, including multi-agent systems like SQL-of-Thought and MAC-SQL, are proposed to improve accuracy on complex queries through decomposition, reasoning (e.g., Chain-of-Thought), and structured error correction, with various studies detailing the importance of schema representation, few-shot examples, and managing long context lengths for robust query generation.

Mem0, Graphiti, Cognee, and LangMem are open-source libraries that provide persistent memory for AI agents. Mem0 uses a hybrid database to optimize personalization and reduce token costs. Graphiti creates temporal knowledge graphs for dynamic data, while Cognee builds multi-modal graphs and uses ontologies to improve reasoning and reduce hallucinations. LangMem is a framework-native solution designed for seamless integration with the LangChain ecosystem.

Text-to-SQL, translating natural language to SQL, has seen significant advancements due to Large Language Models (LLMs). However, challenges remain in handling complex database schemas, diverse SQL operations beyond simple queries, and natural language ambiguity. To address this, new approaches like MultiSQL and SGU-SQL utilize schema-integrated context, prompt engineering (Chain-of-Thought, decomposition, self-refinement), and graph-based schema linking. Evaluation has also evolved, with new metrics like Enhanced Tree Matching (ETM) and Database State Match being introduced to more accurately assess performance beyond traditional Exact Set Match and Execution Accuracy.

This briefing document synthesizes information from several recent academic papers and a commercial announcement, highlighting cutting-edge developments in enhancing Large Language Models (LLMs) with robust memory and retrieval capabilities. Key themes include the use of hierarchical memory systems inspired by operating systems (MemGPT), the integration of temporal knowledge graphs for improved factual accuracy and reasoning (Zep, TempAgent), and the application of reinforcement learning for efficient memory management in multi-objective tasks (MEM1). The integration of FalkorDB as a backend for Graphiti by Zep underscores the growing industry recognition of graph databases for scalable, real-time agent memory, particularly in multi-tenant environments.

https://arxiv.org/abs/2506.15841

The research introduces MEM1, a novel reinforcement learning framework designed to enhance language agents' efficiency and performance in complex, multi-turn interactions. Unlike traditional models that accumulate information, MEM1 uses a constant-memory approach by integrating prior knowledge with new observations into a compact internal state, strategically discarding irrelevant data. This method significantly reduces computational costs and memory usage while improving reasoning, particularly in long-horizon tasks such as question answering and web navigation. The authors also propose a scalable task augmentation strategy to create challenging multi-objective environments, demonstrating MEM1's ability to generalize beyond its training horizon and exhibit emergent, sophisticated behaviors.

https://arxiv.org/abs/2501.13956

The research introduces Zep, a novel memory service for AI agents, designed to overcome the limitations of current retrieval-augmented generation (RAG) frameworks, which struggle with dynamic and continuously evolving data. Zep utilizes Graphiti, a temporally-aware knowledge graph engine, to synthesize both unstructured conversational data and structured business information while preserving historical relationships. The paper highlights Zep's superior performance over MemGPT in the Deep Memory Retrieval (DMR) benchmark and demonstrates significant improvements in accuracy and reduced latency on the more complex LongMemEval benchmark, which better reflects real-world enterprise scenarios. Zep's architecture, inspired by human memory models, involves three hierarchical subgraphs—episode, semantic entity, and community—enabling sophisticated and nuanced memory structures. The authors also discuss Zep's advanced memory retrieval system, which employs various search and reranking functions to provide relevant context for large language model (LLM) agents.

https://machinelearning.apple.com/research/illusion-of-thinking

The document investigates the capabilities and limitations of Large Reasoning Models (LRMs), a new generation of language models designed for complex problem-solving. It critiques current evaluation methods, which often rely on mathematical benchmarks prone to data contamination, and instead proposes using controllable puzzle environments to systematically analyze model behavior. The research identifies three distinct performance regimes based on problem complexity: standard models may outperform LRMs at low complexity, LRMs show an advantage at medium complexity, but both collapse at high complexity. Crucially, LRMs exhibit a counter-intuitive decline in reasoning effort as problems become overwhelmingly difficult, despite having available token budgets, and also demonstrate surprising limitations in executing exact algorithms and inconsistent reasoning across different puzzle types.

Ref: https://arxiv.org/html/2503.06474v2

The document introduces ROGRAG, a novel GraphRAG framework designed to improve large language models' (LLMs) performance on specialized and emerging topics. It addresses the limitations of traditional RAG methods by structuring domain knowledge as a graph for dynamic retrieval. ROGRAG proposes a multi-stage retrieval mechanism that combines dual-level and logic form retrieval to enhance robustness and incorporates various result verification methods alongside an incremental database construction approach. Extensive ablation experiments demonstrate ROGRAG's effectiveness, significantly improving scores on benchmarks like SeedBench and outperforming mainstream methods. The paper also provides detailed analyses of indexing, retrieval, and generation components, highlighting the importance of fuzzy matching and the preference for logic form retrieval by domain experts due to its clear, logical progression.

The provided sources offer a comprehensive overview of the rapid and transformative evolution of Artificial Intelligence. They highlight that AI user adoption, usage, and capital expenditures are experiencing unprecedented growth, driven by declining inference costs and a surge in accessible AI models. The text details how AI is fundamentally reshaping various sectors, from enterprise operations and specialized industries like healthcare and legal services to the physical world through autonomous vehicles and robotics. It also emphasizes the intense global competition in AI development, particularly between the United States and China, underscoring AI's role not just as an economic driver but also as a geopolitical factor. Finally, the sources explore the significant impact of AI on the workforce, showcasing its ability to enhance productivity and create new job opportunities.

https://arxiv.org/abs/2501.06642

This manuscript argues that achieving true artificial intelligence (AI) autonomy requires integrating **common sense**, a fundamental ability observed in all animals, which current systems often lack. The text critiques existing benchmarks like the Turing Test and ARC challenge for not effectively evaluating this capacity, suggesting that **scaling AI models** and passing such tests is insufficient for real-world adaptability and decision-making. The authors propose a **shift in AI development**, emphasizing starting with minimal knowledge, contextual learning, adaptive reasoning, and a broader concept of **embodiment** in both physical and abstract domains. They advocate for **rethinking the AI software stack** and creating new benchmarks to prioritize common sense, asserting that this is essential to avoid performance plateaus and unlock AI's full societal and commercial value.

Model Context Protocol (MCP) is presented as a crucial emerging specification for managing how AI models access enterprise data across multiple applications. It addresses the security and permission challenges arising from AI's ability to interact with diverse data sources by ensuring models operate with proper identity, access rights, and full auditability. MCP acts as an "operating system" for AI data access, enforcing rules, tracking user requests, filtering visible data, orchestrating complex actions, and logging all activity. The increasing reliance on API-based data requests in AI-forward organizations highlights the necessity of MCP to prevent data leaks and ensure secure AI workflows. Introduced in late 2024, MCP has rapidly gained adoption by major industry players and is projected to become a foundational standard for enterprise AI integrations.

Ref: https://aiwithmike.substack.com/p/text2sql-the-art-of-teaching-machines

Mike Erlihson's Substack post explores the complexities of Text2SQL, the process of enabling machines to translate natural language questions into SQL queries. The author highlights that this task involves more than just syntax, touching upon context, user intent, and ambiguity, areas where large language models (LLMs) often mimic understanding rather than possess genuine comprehension. Erhlihson emphasizes the need for multi-layered systems with components for input interpretation, schema mapping, generation, validation, and user feedback to create robust Text2SQL applications. The piece further discusses practical challenges like synonym variations, subjective user queries, and messy database schemas that impact the effectiveness of these systems. Ultimately, the article envisions Text2SQL not as full automation, but as a collaborative tool that empowers both technical and non-technical users to interact with data conversationally and iteratively.

https://securityboulevard.com/2025/04/wizs-security-graphdb-vs-deeptempos-loglm/

This Security Boulevard article from April 2025 contrasts Wiz's Security GraphDB, a system that identifies known cloud security risks by mapping resources and their relationships, with DeepTempo's LogLM, which uses deep learning to detect novel attack behaviors. Wiz excels at finding and prioritizing "toxic combinations" of known vulnerabilities and misconfigurations, helping organizations address the most critical threats. However, the article suggests Wiz's rule-based approach may struggle against AI-powered attackers employing new, unforeseen tactics. DeepTempo's LogLM, likened to a "friendly Eye of Sauron," offers a complementary approach by learning normal activity and spotting subtle anomalies indicative of sophisticated attacks that Wiz might miss. The piece argues that a robust security strategy requires both proactively addressing known risks and adaptively detecting novel threats.

https://arxiv.org/abs/2503.10385

The provided document introduces a language-agnostic algebraic foundation for constructing knowledge graphs from diverse data sources. This formal system aims to address the current lack of a solid theoretical basis for declarative mapping languages like RML, which leads to implementation inconsistencies and hinders optimization. The paper demonstrates the algebra's utility by showing how RML can be translated into it, thereby providing a formal semantic definition for RML and enabling the proof of algebraic rewriting rules for query optimization.

https://arxiv.org/abs/2402.07630

The paper "G-Retriever" introduces a new method for question answering on textual graphs. It addresses the challenge of enabling users to interact with graphs through a conversational interface. The core innovation is a retrieval-augmented generation (RAG) approach specifically designed for textual graphs, using a Prize-Collecting Steiner Tree optimization to handle large graphs and mitigate hallucinations. A new benchmark, GraphQA, was developed to facilitate research in this area. Empirical results demonstrate that G-Retriever outperforms existing methods on various textual graph tasks. The study showcases the method's scalability and its effectiveness in reducing hallucination.

Ref: https://arxiv.org/abs/2502.21321

This document provides a comprehensive survey of post-training methodologies for Large Language Models (LLMs), focusing on refining reasoning capabilities and aligning models with user preferences and ethical standards.

It categorizes these methodologies into fine-tuning, reinforcement learning (RL), and test-time scaling, while exploring the challenges and advancements in each area. The study highlights various techniques such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO), and discusses their impact on model performance and safety. It also examines benchmarks used to evaluate LLMs, and emerging research directions that include addressing catastrophic forgetting, reward hacking, and efficient RL training.

The paper emphasizes the interplay between model, data, and system optimizations to improve the deployment and scaling of LLMs for real-world applications.

Ultimately, it seeks to guide future research in optimizing LLMs by identifying both the latest advances and the open challenges.

https://graphwise.ai/resources/white-paper/knowledge-organization-llm-rag/

This Unisphere Research report, sponsored by Semantic Web Company, examines the current state of Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) adoption among 382 knowledge management executives. The study highlights the pervasive use of LLMs, particularly for content creation and improving employee insights, while also emphasizing significant concerns around security and data quality. A considerable portion of respondents are exploring RAG to enhance LLM accuracy and efficiency by connecting LLMs to corporate databases, particularly knowledge graphs. The report concludes with recommendations for successful LLM and RAG implementation, focusing on data-centric approaches and maintaining human oversight to mitigate risks. Finally, the demographics of the survey respondents are detailed.

https://arxiv.org/abs/2411.05844

This research paper introduces LEGO-GraphRAG, a modular framework for improving Retrieval-Augmented Generation (RAG) systems that use knowledge graphs. The framework systematically categorizes existing RAG techniques and facilitates the creation of new, more efficient and effective RAG instances. The authors conduct empirical studies, evaluating various configurations on large-scale real-world graphs, to analyze the trade-offs between reasoning quality, runtime efficiency, and resource costs. Their findings highlight the importance of balancing these factors when designing GraphRAG systems and suggest a promising strategy combining structure-based and semantic-augmented methods. The paper concludes by identifying key areas for future research in this field.

https://www.sciencedirect.com/science/article/pii/S1570826824000441

This pre-print research paper investigates the use of knowledge graphs to improve the accuracy and trustworthiness of Large Language Model (LLM)-powered question answering systems in enterprise settings. The authors argue that knowledge graphs provide a crucial framework for validating LLM-generated queries, explaining results, and ensuring access to reliable data. Their research includes a benchmark study demonstrating the accuracy improvements achieved by incorporating knowledge graphs. The paper also explores lessons learned regarding knowledge engineering, explainability, governance, and effective question selection strategies. Finally, it outlines key industry needs and future research directions in this area.