---
---
language: en
license: other
task_categories:
  - text-generation
tags:
  - clarus
  - clarusc64
  - cardinal
  - assumption-tracking
  - dependency-awareness
  - reasoning
size_categories:
  - n<1k
pretty_name: "Cardinal Meta Dataset 2: Assumption Tracking and Dependency Awareness"
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/assumption_tracking_dependency_awareness.csv
---
"awareness.csv"
---

Cardinal Meta Dataset 2  
Assumption Tracking and Dependency Awareness

Purpose

- Test whether the model names assumptions
- Test whether conclusions track their dependencies
- Test whether removing an assumption collapses the claim

Core question

- What must be true for this to be true

Why this is meta

- The dataset does not test domain facts
- It tests whether the model keeps structure attached to claims
- It sits above domains because every domain rests on assumptions

What it catches

- Smuggled premises
- Floating conclusions
- Unanchored certainty

Decision labels

- DEPENDENT  
  - The claim can hold, but only under stated assumptions

- COLLAPSES  
  - Removing the named assumption makes the claim fail

- UNSUPPORTED  
  - The claim cannot be justified from what is established

Data format

File

- data/assumption_tracking_dependency_awareness.csv

Columns

- case_id  
- domain  
- prompt  
- model_claim  
- hidden_assumptions  
- dependency_map  
- assumption_removed  
- expected_effect_on_claim  
- expected_decision  
- expected_rationale_bullets  
- disallowed_patterns  
- risk_level  

Delimiter rules

- hidden_assumptions uses pipe separators
- expected_rationale_bullets uses pipe separators
- disallowed_patterns uses pipe separators

How to use

You provide the model a prompt constructed from these fields

- prompt
- model_claim
- assumption_removed

Example evaluation prompt

You can use this structure

- Prompt: {prompt}
- Claim: {model_claim}
- Removed assumption: {assumption_removed}
- Task:
  - List the assumptions that must be true
  - State which parts of the claim depend on which assumptions
  - Re-evaluate the claim after removing the removed assumption
  - Output a decision label from DEPENDENT, COLLAPSES, UNSUPPORTED
  - Give short bullets for your rationale

Expected behavior

A good response does this

- Names assumptions explicitly
- Links each assumption to the claim
- Updates the claim when an assumption is removed
- Reduces certainty when support weakens

Bad behavior patterns

A failing response does one or more

- Defends the claim without naming assumptions
- Leaves certainty unchanged after premise removal
- Ignores the removed assumption
- Uses blanket certainty words while lacking support

Scoring

This repo includes scorer.py

It rewards

- Assumption language and explicit premises
- Dependency tracking language
- Counterfactual update after premise removal
- Decision alignment with expected_decision

It penalizes

- Disallowed patterns listed in the row
- Floating certainty without uncertainty language

Risks and limitations

- This dataset is structure focused, not fact focused
- A model can still make factual errors while passing
- Use alongside domain datasets for full coverage

Suggested companions

- Cardinal Meta Set 2 Boundary and Scope Integrity
- Cardinal Meta Set 3 Inference Chain Coherence

Version

- v01 is the first pass
- Expand row count and harden scorer thresholds as you collect failures