#multi-hop reasoning
101 related articles
·3 min
Poth Labs: Reshaping Enterprise Customer Knowledge Management with Relationship Networks
Poth Labs models customer knowledge as a dynamic relationship network, using cross-source reasoning and adaptive surveys to help enterprises understand churn drivers and feature adoption.
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·4 min
Poth Labs: Reshaping Enterprise Customer Knowledge Management with Relationship Networks
Poth Labs models customer knowledge as a dynamic relationship network, using cross-source reasoning and adaptive surveys to help enterprises understand churn and feature adoption.
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·3 min
SeaTicket: An AI Agent That Automatically Resolves GitHub and Discord Issues
SeaTicket is an AI Agent that automatically resolves GitHub Issues and Discord questions. This article analyzes its architecture, use cases, challenges, and value for open-source maintenance.
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·3 min
SeaTicket: An AI Agent That Automatically Resolves GitHub and Discord Issues
SeaTicket is an AI Agent that automatically resolves GitHub Issues and Discord questions. This article analyzes its architecture, use cases, challenges, and value for open-source maintenance.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·7 min
DeepSeek's Mysteriously Retracted Paper: Breaking AI's Multimodal Reasoning Bottleneck with Visual Primitives
DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.
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·6 min
30-Minute Hands-On: Building a Working AI Agent from Scratch
Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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·5 min
DeepSeek V4 Enters Grayscale Testing, Grok 5.0 Ready to Launch | Latest AI Model Developments
This AI Daily covers five sectors: DeepSeek V4 reportedly enters grayscale testing with improved chain-of-thought; Musk previews Grok 4.6 and 5.0 with 2T parameters; Intern-S2 open-source model beats Claude Opus 4.8; xAI builds 2000MW Colossus cluster; NVIDIA RoboTTT breaks long-horizon robotics barriers.
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Knowledge Graph-Enhanced RAG: Building a More Powerful Q&A System
A deep dive into the DeepLearning.AI & Neo4j course 'Knowledge Graphs for RAG' — covering core concepts, vector retrieval synergy, and hands-on SEC filing demos.
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Enterprise RAG Knowledge Isolation in Practice: Physical Isolation vs. Adaptive Soft Boundaries
A deep dive into two enterprise RAG knowledge isolation strategies: physical isolation vs. adaptive soft boundaries — covering metadata tagging, dynamic user-profile filtering, hybrid retrieval architecture, and data quality best practices.
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Microsoft Open-Sources Ontology Playgr…
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Microsoft Open-Sources Ontology Playground: A Free Tool for Learning Ontology Visually
Microsoft's open-source Ontology Playground is a zero-backend static web app for visually designing ontologies, with RDF/XML export and Microsoft Fabric IQ integration.
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Cognee: The Open-Source Knowledge Grap…
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Cognee: The Open-Source Knowledge Graph Engine for Persistent AI Agent Memory
Cognee is an open-source AI memory platform using knowledge graphs to give AI agents persistent cross-session memory. Self-hosted, graph + vector hybrid retrieval. 28K+ GitHub stars.
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·3 min
The Complete Guide to AI-Native Architecture: RAG, Agents, and MCP
A comprehensive guide to AI-native application architecture: LLM inference, RAG retrieval (vector DB/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability — end-to-end.
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·3 min
Claude Opus 4 + RAG + Knowledge Graph: A Real-World Architecture for AI Novel Writing
Microsoft MVP Michael shares how AI Story Builders uses Claude Opus 4, RAG, and Knowledge Graphs to solve consistency in long-form AI fiction writing.
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From RAG to Agent: A Complete Architecture Guide for AI-Native Systems
A comprehensive guide to modern AI-native system architecture: LLM reasoning, three RAG paradigms (vector/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability for enterprise AI.
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·2 min
The Reasoning Trap in Diffusion Language Models: A One-Line Fix for the 'Flexibility' Flaw
ICML Outstanding Paper 'The Flexibility Trap' reveals how dLLMs' arbitrary-order generation bypasses logical connectives, breaking reasoning. JustGRPO fixes this with autoregressive training + parallel inference, achieving 89.1% on GSM8K in under 60 lines of code.
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New Retrieval Paradigms: Hobbit's Hard Negative Construction and Disco's Collaborative Coverage Index Design
A deep dive into two frontier dense retrieval works: Hobbit uses gradient analysis to automatically construct hard batches; Disco replaces single-document competition with submodular collaborative coverage, reshaping Top-K retrieval.
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Understanding RAG from Scratch: A Complete Guide to Retrieval-Augmented Generation
RAG (Retrieval-Augmented Generation) is a key technology for solving LLM hallucinations. This guide breaks down how RAG works, its advantages, and real-world use cases — no math required.
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·3 min
Arcaide: Understanding Codebase Structure Through Multi-Level Call Graph Visualization
Arcaide is a code comprehension tool based on multi-level call graphs, helping developers explore function calling relationships from macro to micro, speed up onboarding, assess change impact, and identify technical debt.
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