Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: other
+task_categories:
+  - evaluation
+  - text-generation
+tags:
+  - clarus
+  - clarusc64
+  - cardinal
+  - meta-eval
+  - assumptions
+  - dependency-awareness
+  - reasoning
+  - safety
+size_categories:
+  - n<1k
+pretty_name: "Cardinal Meta Dataset 2: Assumption Tracking and Dependency Awareness"
+dataset_name: "assumption-tracking-dependency-awareness-v01"
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "data/assumption_tracking_dependency_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