The decision harness for predictions
Language-model judgment at machine-learning speed. More accurate, explainable and almost free to run. By Vela Research and the University of Oxford. Accepted at IEEE, NeurIPS'26 Workshop and more.
Will this startup become a unicorn?
Nuances ML features can't capture
- Research-stage science, not a workflow tool3.3×
- Founder made real decisions, not just held a title2.7×
- Worked at a breakout company in its early years1.9×
+12%vs GPT-6 Sol
$0.00087per prediction
Millisecondsper prediction
Works where ML can'tPitch videosMore use cases
Why Think-Reason-Learn
The strengths of both, without their weaknesses.
Machine learning
TRL keeps
- Speed
- Low cost
- Same answer every time
- Learning from outcomes
TRL fixes
- Can't read text or video
- Needs thousands of rows
- Numbers, not reasons
AccuracyBaseline
Language models
TRL keeps
- Reads anything
- Knows the world
TRL fixes
- Slow and costly per call
- Can't show how it decided
- Changes from run to run
- Doesn't learn from your outcomes
Accuracy+7% over ML
Think-Reason-Learn
Reads like a language model. Runs like machine learning. Shows its reasons.
- Learns from 50 examples
- Milliseconds, $0.00087 a prediction
- Readable rules, questions and policies
- #1 on VCBench
Accuracy+20% over ML
Inside the harness
Six open-source methods. Each turns a language model's judgment into questions, rules or policies you can read and check.
Question tree
GPTree
Decision trees whose questions a language model writes.
Docs →Question ensemble
Random Rule Forest
Many yes-or-no questions combined into one transparent model.
Docs →Written policies
Policy Induction
Short policies learned from examples, plain enough to check.
Docs →Weighted rules
Reasoned Rule Mining
If-then rules with calibrated confidence, cheap checks first.
Docs →Next best question
Verifiable RL
Learns what to check next, and stops when it is sure.
Docs →Rules as code
Feature Generation
Rules written as small Python functions that run without a model.
Docs →Frequently asked questions
What is Think-Reason-Learn?
Think-Reason-Learn (TRL) is the decision harness for predictions. It joins machine learning and language models: a language model reads each case and answers plain-language questions, and TRL learns from past outcomes which questions matter and by how much. The result is language-model judgment at machine-learning speed, with the reasons behind every prediction.
How is TRL different from machine learning?
Machine learning needs clean columns and thousands of examples, and it cannot read text, transcripts or video. TRL reads them directly and learns from about 50 examples, while keeping machine learning's speed and consistency. On VCBench it is 20% more accurate than the best machine learning model.
How is TRL different from asking a language model?
A language model is slow and costly per call, can change its answer from run to run, and cannot show how it decided. TRL gives the same answer every time, shows the questions behind each prediction and runs in milliseconds. On VCBench it is 12% more accurate than GPT-6 Sol.
How fast and how cheap is a prediction?
Milliseconds, and about $0.00087 per prediction on VCBench. A language model writes the questions once, during training. At prediction time a small, fast classifier answers them in parallel, so no large model has to reason about each case.
What can TRL predict?
Any outcome where the evidence is text, transcripts, video or images and context matters: which startups break out, how a pitch comes across, court litigation, earnings calls, clinical trials, insurance claims, loan applications and policy petitions.
How do I start?
Choose Add to your agent to set it up in Claude Code, Codex or Cursor, or install it from GitHub with pip install git+https://github.com/vela-research/think-reason-learn.git. TRL is open source under the MIT licence and works with Anthropic, Google, OpenAI and xAI models.
Who builds TRL?
A research lab within Vela Partners and the University of Oxford. Its methods are accepted at IEEE, a NeurIPS'26 workshop and more. Meet the team.