A survey of 80+ Deep Research systems: the three-stage capability roadmap, four core components (planning, acquisition, memory, generation), three optimization paradigms (prompting, SFT, RL), evaluation, and the open-source tool landscape.
The entire Deep Research field has 80+ implementations, but the core structure is just three-stage roadmap × four components × three optimization methods. This article maps the full landscape: from Agentic Search to Full-stack AI Scientist, from query planning to answer generation, from workflow prompting to end-to-end RL.
Two NeurIPS 2025 papers answer the same question: how to train a web research agent from scratch? WebThinker chooses 'bolt on web capability to existing reasoning models,' WebDancer chooses 'rebuild everything from data construction to RL training.' Two philosophies, four stages, one core insight: training beats prompting.
When a research agent runs 25, 100, or 2000 turns, what happens? Context suffocation: information piles up, noise increases, attention gets diluted. IterResearch solves this with Markovian state reconstruction; AREX achieves recursive self-improvement with an inner/outer loop. Both answer: how does an agent stay coherent across hundreds of search rounds?
Previous articles covered training from scratch and long-horizon memory. This one goes deeper: how to make the agent's 'planning' itself better? WebWeaver tackles it architecturally (dual-agent iterative outline optimization). DeepPlanner tackles it through training (advantage shaping for planning tokens). Both point to the same conclusion: planning is the ceiling of deep research.
Previous articles covered the landscape, training from scratch, long-horizon memory, and planning optimization. This one zooms out to see a complete system that threads all these insights together: Tongyi DeepResearch. Its core innovation is Agentic CPT — inserting an agentic mid-training stage between pre-training and fine-tuning, giving the model an inherent agent bias. MoE 30B parameters activating 3B, HLE 32.9 surpassing OpenAI o3.
Previous articles covered how to train agents. But training requires high-quality data—and deep research training data has been scarce. WebShaper solves this with mathematical formalization: define IS tasks in set theory, then use an agentic Expander to iteratively expand them. S1-DeepResearch goes further: moves training from 'search-centric' to 'real research.'
A deep research agent produces a report—maybe thousands of words with dozens of citations. How do you score it? Using LLMs as judges is biased, asking humans is too expensive, and benchmarks can't keep up. STC and other recent approaches try to solve this from the 'confidence' angle—but there's no perfect answer yet.
DeepResearch Bench II uses 9,430 expert rubrics covering 132 tasks, and finds that even the strongest agents satisfy less than 50% of criteria. This article breaks down the benchmark architecture, scoring methodology, leaders, and the overall evaluation landscape.
The previous articles covered evaluation. This one covers another dimension: how to dynamically allocate compute during reasoning. BrowseConf's core insight is that an agent's self-declared 'confidence' can predict answer accuracy. High confidence uses fewer resources; low confidence searches more rounds.
All deep research agents are 'text-first'—but the real world isn't just text. WebWatcher (NeurIPS 2025) is the first system to integrate visual reasoning into deep research, using OCR, image search, code execution, and other tools to handle charts, screenshots, videos, and other diverse information.
The deep research open-source ecosystem has evolved from 'single frameworks' to 'tool clusters.' This article compares 12+ projects: GPT-Researcher emphasizes multi-agent collaboration, STORM simulates expert conversations, smolagents focuses on state management. Each tool solves different problems.
By 2026, the deep research commercial market has differentiated: OpenAI is comprehensive, Perplexity is fast, Gemini integrates ecosystems, Claude reasons deeply, Grok is real-time. This article compares each product's differences—not who is best, but who fits your scenario.
This is the project's own deep research skill design, fully disclosed. Core choices: only Groundlane MCP for web tools, strict source-quality grading (A/B/C/D), research hands off to post skill for publishing. Not the most powerful, but the best fit for us.
10+ community deep-research skills represent 10+ philosophies of 'how to do research.' From hyperresearch's persistent vault to jamoeight v2's Co-Scientist 6-agent, from adversarial verification to benchmark alignment. This article puts them all on one table.
Deep research has already evolved from 'help you search' to 'help you research.' But the next step is bigger: self-evolving agents, swarm collaboration, scientific automation. This article covers three directions and an uncomfortable reality: Gartner predicts 40% of agent projects will be cancelled by 2027.