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

https://arxiv.org/abs/2411.05844

This

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

This pre-print research paper