Human-centered AI-assisted research can no longer sustain the next great leaps of our civilization. What we need is not just more tools, but an AI researcher that thinks and acts independently — a new entity to replace the human role in science. This is DARE.

🧠 DARE — De-Anthropocentric Research Engine

🚧 Personal side project. Actively under development.

DARE is not a tool that helps you do research. It is the researcher. You set the direction — DARE searches, reads, discovers gaps, generates ideas, designs experiments, and executes them on GPUs. Autonomously. Iteratively. Without asking for permission.

⚡ What It Does

• 📚 Autonomous literature survey — searches Google Scholar, downloads full papers (not just abstracts), reads them cover-to-cover with a three-pass protocol
• 🔍 Gap discovery — identifies what the field is missing, not what you tell it to find
• 💡 Idea generation — 31 ideation methods across 5 categories (SCAMPER, component surgery, cross-domain, perspective forcing, structural deconstruction), filtered by MAP-Elites quality-diversity algorithm
• ⚔️ Adversarial debate — Proposer-Critic-Judge architecture validates every gap, insight, and idea through structured 4+3 round debates
• 🔬 INSIGHT pipeline — 7-step deep analysis (root-cause → stakeholder → tension → HMW → abstraction → assumption → validation)
• 🔄 Self-review loop — an independent AI process reviews all outputs, scores them, and selectively re-runs weak stages
• 📏 Research depth enforcement — quantitative budget floors (S/M/L topic tiers), state ledgers, budget gates, and adversarial completeness probes ensure the AI cannot take shortcuts or produce shallow results
• 🌍 Forced cross-domain discovery — before ideation, mandatory search across 3+ unrelated domains (biology, physics, economics, etc.) to fuel cross-domain collision methods
• 🧪 Experiment design & execution — designs experiments and runs them on remote GPU pods, autonomously
• 🧬 Method evolution (planned) — AlphaEvolve-inspired evolutionary improvement of DARE's own methods (mutation + crossover + Elo ranking). Core tools implemented, full loop coming in v3.2+
• 🌐 Deep reference exploration — traces citation graphs via Semantic Scholar
• 💾 Full-text caching — every paper and web page converted to markdown, cached locally
• 🚀 Git-based context transfer — research context pushed to GitHub, cloned on remote GPU pod, executed by a fresh AI instance

🗺️ Roadmap

Active development continues. Near-term priorities:

• 🔬 Search pipeline overhaul — major refactor of dare-web and dare-scholar search flows to better integrate with AlphaXiv MCP and upcoming Perplexity MCP, reducing redundancy and improving retrieval quality
• 🧬 Method-evolve full loop (v3.2+) — AlphaEvolve-inspired evolutionary improvement of DARE's own research methods. Core tools (mutate, crossover, evaluate) implemented; full autonomous Elo-tournament loop next
• 📝 Paper-writing implementation (v3.1+) — automated academic paper composition from research outputs. Strategy interface defined, implementation pending
• 🔗 GitHub MCP integration — native GitHub MCP adapter for issue tracking, PR-driven experiment workflows, and automated result reporting
• 🌐 Perplexity MCP adapter — leverage Perplexity's search-augmented generation as an additional web research backend

🎯 Design Philosophy

🤔 Why "De-Anthropocentric"?

The bottleneck in modern research is not data or compute — it's the human in the loop. Every existing "AI research assistant" still requires a human to decide what to search, what to read, which gaps matter, and which ideas are worth pursuing. DARE removes this bottleneck entirely. The human provides only the initial direction; everything after that is autonomous.

This isn't about replacing researchers — it's about creating a parallel research capacity that operates on timescales and breadths impossible for any individual.

🎖️ The Military Metaphor: Four-Layer Command Structure

DARE's architecture follows a military command hierarchy — not because research is war, but because the decomposition pattern is remarkably effective:

General (Meta-Strategy)  →  "Take that hill"         →  WHAT to research
Colonel (Strategy)       →  "Flank from the east"    →  WHEN and WHY
Captain (Tactic)         →  "Squad A cover, B move"  →  HOW to combine
Sergeant (SOP)           →  "Fire, reload, advance"  →  HOW to execute

Each layer has a single concern and calls only the layer directly below it. A Strategy never touches MCP tools directly; a Tactic never decides research direction. This strict layering means every component is independently testable, replaceable, and composable.

🤖 Micro-Agent Paradigm: Every Tool Thinks

Traditional MCP tools are dumb functions — they take input, return output, no reasoning involved. DARE's dare-agents tools are fundamentally different. Each of the 49 tools is a single-responsibility LLM micro-agent with its own system prompt, personality, and reasoning chain.

When DARE runs "root-cause-drilling", it's not calling a template — it's spawning an AI agent whose entire existence is devoted to drilling from surface symptoms to root causes. When "debate-critic" runs, it genuinely tries to destroy the idea it's reviewing. This is what makes DARE's outputs qualitatively different from prompt-chaining systems.

Built on pi-ai — a lightweight framework for building LLM-powered tools as MCP servers.

⚔️ Adversarial Validation: Ideas Must Survive Attack

Every significant output in DARE goes through adversarial debate before being accepted. The Proposer-Critic-Judge architecture isn't decoration — it's the core quality mechanism:

1. A Proposer presents the gap/insight/idea
2. A Critic attacks it from every angle (4 rounds of critique)
3. A Defender responds to each attack (3 rounds of defense)
4. A Judge evaluates the exchange and scores the result

Ideas that survive this gauntlet are genuinely robust. Ideas that don't are discarded or refined. No hand-waving, no "sounds good to me."

🎲 Quality × Diversity: Not Just Good Ideas, Different Ideas

Most AI systems optimize for a single quality metric — they'll give you 10 variations of the same good idea. DARE uses MAP-Elites, a quality-diversity algorithm that maintains a population of ideas spanning multiple dimensions of variation. The result: you get the best idea in each niche, not 10 copies of the same insight.

📏 Research Depth & Breadth Enforcement

AI agents naturally take the path of least resistance — searching a handful of papers and declaring victory. DARE embeds hard enforcement mechanisms directly into every skill to prevent this:

• Research Budget: Every strategy declares quantitative floors with three topic-size tiers (Small / Medium / Large). A literature survey on a Medium topic must fetch 40+ papers and 50+ web pages.
• State Ledger: A progress table printed before every iteration — the AI cannot lose track of where it stands.
• Budget Gate: A <HARD-GATE> that blocks the strategy from exiting its loop until 80% of the budget is met. The AI cannot stop early no matter how "satisfied" it feels.
• Adversarial Completeness Probe: After the budget is met, a qualitative self-check probes for blind spots (missing sub-areas, unchecked citations, unexplored perspectives). Up to 2 extra iterations if gaps are found.
• Yield Reports: Every tactic prints execution metrics (papers fetched, ideas generated, methods used) that feed the calling strategy's ledger.
• Cross-Domain Discovery: Before any ideation method runs, a mandatory phase searches 3+ unrelated domains for analogical inspiration — because the best ideas come from unexpected collisions.

You ask a question
    ↓
┌────────────────────────────────────────────────────────────────┐
│  Phase 0: Brainstorming (structured requirement clarification) │
│  Phase 1: Intake (research brief)                              │
│  Phase 2: Research Loop (Stages 1-3, up to 7 rounds)           │
│    ├── Literature Survey (S:20 / M:40 / L:60+ papers)          │
│    ├── Gap Analysis (S:10 / M:15 / L:25+ papers)               │
│    ├── Insight (7-step pipeline)                               │
│    ├── Ideation (cross-domain discovery → 31 methods × 5)      │
│    └── Review → Selective Redo → Review (score ≥ 8/10)         │
│  Phase 3: Experiment Design                                    │
│  Phase 4: GPU Execution (remote pod, fully autonomous)         │
└────────────────────────────────────────────────────────────────┘
    ↓
Results returned via git

Each stage runs SEARCH → READ → REFLECT → EVALUATE cycles with autonomous gap discovery and dynamic stopping. No human in the loop.

🏗️ Architecture (v3.0)

Four-layer skill hierarchy where each layer calls only the layer below:

┌───────────────────────────────────────────────────────────────┐
│  META-STRATEGY (/dare)                                        │
│  Entry point — orchestrates the full research pipeline        │
├───────────────────────────────────────────────────────────────┤
│  STRATEGY (8)                                                 │
│  intake, lit-survey, gap-analysis, insight, ideation,         │
│  round, paper-writing, method-evolve                          │
├───────────────────────────────────────────────────────────────┤
│  TACTIC (15)                                                  │
│  academic-research, web-research, insight, multiagent-debate, │
│  review, idea-generation, idea-augmentation, scamper,         │
│  component-surgery, cross-domain-collision, and more          │
├───────────────────────────────────────────────────────────────┤
│  SOP (60)                                                     │
│  Single-responsibility wrappers around dare-agents tools      │
├───────────────────────────────────────────────────────────────┤
│  TOOL LAYER (MCP servers — atomic operations)                 │
│  dare-agents, dare-scholar, dare-web, apify, brave, runpod    │
└───────────────────────────────────────────────────────────────┘

• Meta-Strategy = WHAT to research (entry point, pipeline orchestration)
• Strategy = WHEN and WHY (iteration loops, state management, stopping conditions)
• Tactic = HOW to combine (orchestrates multiple SOPs into coherent workflows)
• SOP = HOW to execute (single dare-agents tool wrapper with protocol)
• Tool = WHAT to do (atomic MCP operations)

🧩 dare-agents — LLM-Powered Micro-Agent Tools

The core engine of v3. 49 tools built with pi-ai, each a single-responsibility LLM micro-agent with its own system prompt.

📁 Monorepo Structure

dare/
├── packages/
│   ├── agents/           # dare-agents MCP — 49 LLM micro-agent tools (72 tests)
│   ├── scholar/          # dare-scholar MCP — academic paper pipeline (5 tools)
│   ├── ss/              # dare-ss MCP — Semantic Scholar API (8 tools, 75 tests)
│   ├── web/              # dare-web MCP — web page fetching & caching (2 tools)
│   └── session/          # dare-session — pod provisioning scripts
├── skills/
│   ├── dare/             # /dare meta-strategy (entry point)
│   ├── strategy/         # 8 strategies (lit-survey, gap-analysis, insight, ...)
│   ├── tactic/           # 15 tactics (debate, scamper, surgery, ...)
│   └── sop/              # 60 SOPs (one per dare-agents tool)
├── package.json          # Root workspace config
└── .mcp.json             # MCP server configuration (gitignored)

🔌 MCP Servers

🚀 Quick Start

1. Clone and install:

git clone https://github.com/Pthahnix/De-Anthropocentric-Research-Engine.git
cd De-Anthropocentric-Research-Engine
npm install

1. Install external MCP servers:

npm install -g @apify/actors-mcp-server @brave/brave-search-mcp-server @runpod/mcp-server

1. Copy .mcp.example.json to .mcp.json and fill in your API keys and paths.

2. Claude Code will auto-discover all tools from the configured MCP servers.

⚙️ Configuration

dare-agents

dare-scholar

dare-ss

dare-web

dare-session

📄 License

Apache-2.0

Built by Pthahnix