The article explains how NVIDIA’s AI‑Q Blueprint packages a full deep‑research pipeline into a portable Agent Skill, letting developers add secure, on‑premise research capabilities to existing agent frameworks with minimal code while keeping sensitive enterprise data local.
The review dissects Andrew Ng’s 2026 “AI Prompting for Everyone” course, outlining its three modules—information retrieval tiers, AI‑assisted writing with progressive outlining, and building apps or analyzing data via code execution—while highlighting the Deep Research agentic loop, AI‑slop detection, and a decision‑tree for choosing the right AI tool.
Interviewers point out that simply enlarging the LLM’s context window cannot prevent forgetting early conclusions in long‑step Deep Research tasks; the article explains the ReAct context issues, introduces the IterResearch framework with evolving reports, and compares its accuracy, cost, and scalability against ReAct and ReSum.
Google's Gemini 3.1 Pro introduces Dual‑track Deep Research agents—speed‑optimized Deep Research and thorough Deep Research Max—capable of merging public web data with private enterprise sources, generating native charts, and delivering transparent, controllable reports that serve as a solid foundation for finance, life‑science, and market‑research workflows.
The paper introduces the Deep Research paradigm and the RecPilot multi‑agent framework, which transform traditional list‑based recommender systems into proactive decision‑support assistants that simulate user exploration, generate structured reports, and demonstrably outperform existing baselines on TMALL data.
The article explains how ChatGPT Deep Research transforms ordinary web searches into full‑fledged research reports, compares three leading Deep Research tools, outlines nine practical use cases, warns of common pitfalls, and offers prompt‑engineering tips for both individual and enterprise adoption.
OpenResearcher presents a fully open, reproducible offline pipeline that synthesizes 97,000 long‑horizon research trajectories, enabling a 30B LLM to achieve 54.8% accuracy on BrowseComp‑Plus and surpass leading closed‑source models while eliminating online API costs.
This article explains how Deep Research extends traditional Retrieval‑Augmented Generation by adding autonomous planning, multi‑step search, self‑correction, and long‑context synthesis to enable AI agents that can generate comprehensive industry analysis reports.
The paper introduces Deep Research for Recommender Systems, a multi‑agent framework called RecPilot that replaces traditional list‑based recommendations with AI‑driven exploration, trajectory simulation, and structured decision‑support reports, and demonstrates its superiority on TMALL data through extensive trajectory and report‑generation evaluations.
AgentCPM-Report is a locally deployable, privacy‑preserving AI agent that matches or exceeds the performance of top closed‑source large‑model systems on deep‑research benchmarks, offering end‑to‑end report generation without uploading any confidential data to the cloud.
This article surveys the emerging Deep Research (DR) paradigm that upgrades large language models into research agents capable of autonomous planning, multi‑source evidence gathering, memory management, and verifiable long‑form report generation, outlining its stages, core components, training pipeline, and evaluation benchmarks.
GoSkills Agent is a lightweight, framework‑less Go implementation of a Deep Research AI assistant that combines autonomous planning, real‑time web search, intelligent analysis, and markdown reporting, offering a fully native CLI experience with clear architecture and extensible components.
The article explains Google’s Test‑Time Diffusion Deep Research (TTD‑DR) paradigm, which adds iterative draft‑refinement and self‑evolution to AI agents, enabling multi‑step web retrieval, continuous “denoising” of drafts, and superior research reports compared with first‑generation Deep Research systems.
This article systematically compares major open‑source deep‑research agent frameworks—including DeerFlow, SmolAgents, LangChainAI, SkyworkAI, and Researcher—detailing their architectures, best practices, and commercial alternatives, to help developers and users choose the most suitable tool for automated research workflows.
This guide explains how to construct a deep research workflow in Dify that leverages AI agents, loop variables, and structured outputs to automatically explore complex topics, gather sources, and synthesize comprehensive reports with proper citations.
This article presents a large‑scale advertising risk‑control solution that combines deep‑research paradigms, domain‑graph constraints, and large language models to enable explainable, responsible, and high‑precision fraud detection, detailing system architecture, challenges, demo workflow, and future directions.
The article explains what Deep Research agents are, walks through a concrete example of investigating the $6 million training cost controversy of DeepSeek V3, details the multi‑step plan‑edit‑execute workflow, and discusses broader implications for AI efficiency, market dynamics, and product design.
OpenAI has launched the ChatGPT Deep Research agent, a new AI tool designed to help professionals conduct intensive, multi‑source research by providing comprehensive, cited answers, initially available to Pro users with plans to expand to Plus, Team, and Enterprise tiers.
