01
Predicted-future evaluation
Compare possible rollouts, identify the first implausible transition, and capture structured rationale.
- Plausibility judgments
- Failure classification
- Reusable evaluation rubrics
Human data for consequential systems
Train for what
happens next.
Consequence Labs designs and operates custom human-data programs for world models, embodied AI, simulation, and agents.
A consequence map
Actions×Outcomes×Failure×Recovery×Judgment×Counterfactuals
01 / Thesis
Beyond static labels
The useful signal is often in the transition.
Models that reason about the world need evidence about sequences and consequences: what a person observed, what they did, what changed, and whether the result made sense.
We turn those questions into bounded collection and evaluation programs—with explicit protocols, qualified contributors, structured evidence, and acceptance criteria.
02 / Pilot formats
Start with a measurable question
From unusual data need
to accepted delivery.
02
Failure and recovery data
Record attempts, failure modes, corrective actions, and outcomes as coherent episodes.
- Action sequences
- Recovery behavior
- Episode-level metadata
03
Expert process data
Capture what qualified people notice, decide, expect, and change under alternative conditions.
- Decision rationales
- Counterfactuals
- Structured expert evidence
03 / Method
A closed-loop program
Designed around the delivery.
Every engagement begins with the model decision the data must support, then works backward to the people, interactions, evidence, and quality controls required.
- 01Specify
Define the unit of data, protocol, metadata, and acceptance test.
- 02Qualify
Find and screen the right contributors or reviewers for the task.
- 03Collect
Guide consistent interactions and preserve the evidence around them.
- 04Control
Apply automated checks, human review, retakes, and provenance.
- 05Deliver
Ship accepted assets, structured metadata, and a concise quality report.
The next dataset probably does not exist yet