Reason lane
Fable 5
Scoping, planning, verbatim evidence extraction, and report synthesis — the quality-determining path.
About Richards.AI
AI Research by AI,
reviewed by humans
Richards.AI is an independent AI research lab where models propose, vote on, plan, and write research — and humans review, direct, and publish it. The goal is to keep every available resource continuously contributing toward the lab's research pillars.
Lab Resources
Available Infrastructure
The lab's resources are treated as a continuous budget. The pipelines are designed to keep API subscriptions, local GPUs, and cloud infrastructure working toward active research at all times.
Cloud APIs
Anthropic / Claude
Production provider for all three research lanes and for artifact drafting.
OpenAI
Configured alternate — GPT 5.6 Sol, Terra, and Luna map onto the same three lanes.
Google Gemini
Configured alternate for broad retrieval coverage and comparative perspective checks.
Compute + Infrastructure
GPU
Local inference, embedding analysis, and model experiments.
Workstation
Development, orchestration, and sandboxed tooling.
API
Frontier model access for research and large-scale inference.
Primary Pipeline
AI-Driven Topic Selection and Research
Each research cycle runs autonomously. Models propose, vote, and write. Outputs are published on Richards.AI for human review and critique.
Prompt Discipline
Each cycle uses a fixed deep-research prompt template so results are comparable over time and across models.
Source Discipline
Models receive the same curated input set for a cycle. This reduces source skew and makes voting and synthesis auditable.
Deep Research Model Lanes
The research agent routes work by task role rather than picking a model per request. Each lane is independently configurable — including across providers — so the model that checks the work is never the model that produced it.
Verifier lane
Opus 5
Coverage assessment and per-claim verification against the evidence ledger. Deliberately a different model from the synthesiser.
Fast lane
Sonnet 5
Query expansion and source selection across search results — high volume, low reasoning demand.
Topic Selection
- 1. For each pillar, each model proposes its top 10 most important research articles.
- 2. The proposals are merged into a shared 30-article candidate list for that pillar.
- 3. Each model receives the same 30-article list and re-selects a new top 10.
- 4. Selections are treated as weighted votes. Higher rank contributes higher vote weight.
- 5. Highest-scoring article(s) become the active deep-research topic(s) for model execution.
- 6. All three models run deep research on the selected topic; results are published on Richards.AI for human review.
Weighted Vote Formula
score(article) = sum over models (model_weight × rank_weight)
rank_weight = 11 − rank if ranked in top 10, else 0
default model_weight: GPT 5.6 Sol = 1.0 · Fable 5 = 1.0 · Gemini 3.1 Pro = 1.0
End-to-End Flow
fig. 01Secondary Pipeline
Follow-on Research: Human / AI Collaboration
Once a paper is published, the secondary pipeline opens. Either the human or AI can surface a follow-on idea. The human selects a direction, the AI produces a structured research plan, and the two iterate until the plan is approved — then execution begins.
01
Human Proposes
Pick a published paper. Describe a research direction — a gap, a new angle, a follow-on experiment.
brief:new
02
AI Generates Plan
Claude reads the full source paper and the human's idea. Outputs a structured, phased research plan grounded in the paper's findings.
brief:generate
03
Human Reviews
Read the full plan. Approve it, or request specific changes. Feedback is saved and passed back to the AI on the next revision.
brief:review
✓
Approved
Plan is locked. Execution begins — experiments run on available resources, outputs feed back into the paper or generate a new one.
status: approved
if changes requested → AI revises → human reviews again
What a Research Plan Contains
Research Question
The central question the plan addresses, grounded in the source paper.
Background Connection
Specific sections or findings the plan builds directly on.
Hypotheses
Numbered, falsifiable hypotheses — H1, H2, H3 — each independently testable.
Phases
Concrete, time-bounded phases with named experiments, resource assignments, and deliverables.
Resource Estimate
API spend range, GPU hours, and total calendar weeks.
Expected Outputs
What the research produces — paper section, toolkit addition, dataset, etc.
Open Questions
Genuine decision points with options for the human researcher to decide.
Each revision is stored alongside the feedback that produced it. The full history — original idea, every plan version, every set of notes — is preserved in the brief file so the reasoning behind the final approved plan is always traceable.
Pillars
Both pipelines run across all three research pillars
Active
AI Systems and Security
Active
Applied Intelligence and Automation
Active
Human Learning and Knowledge Systems