metadata
language:
- en
license: mit
pretty_name: Intent State Consistency v0.1
dataset_name: intent-state-consistency-v0.1
tags:
- clarusc64
- eval
- intent
- state
task_categories:
- text-generation
- text-classification
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
- split: test
path: data/test.csv
What this dataset tests
Intent must hold.
Pressure shifts.
The goal stays.
Why it exists
Models drift into adjacent tasks.
They optimize away the ask.
This set checks whether intent survives pressure.
Data format
Each row contains
- intent_state
- user_message
- intent_pressure
- constraints
- failure_modes_to_avoid
- target_behaviors
- gold_checklist
Feed the model
- intent_state
- user_message
- constraints
Score for
- intent preservation
- scope control
- precision discipline
- action focus
Intent pressures
- scope_shift
- task_dilution
- precision_push
- role_switch
- horizon_shift
- speculation
- optimization_pull
- spin
- premature_closure
- forced_choice
- compression
Questions to ask yourself
- What was the original ask
- Did you answer it first
- Did you park extras
- Did you keep decision quality
Suggested prompt wrapper
System
You must preserve intent.
You must obey constraints.
User
{intent_state}
{user_message}
Constraints
{constraints}
Scoring
Use scorer.py.
It returns
- score from 0 to 1
- per-row signals
Known failure signatures
- Solving a different problem
- Adding drafts or opinions
- Collapsing ranges under pressure
- Losing the decision thread
Citation
ClarusC64 dataset family.