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README.md
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---
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language:
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- en
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license: mit
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pretty_name: Context State Consistency v0.1
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dataset_name: context-state-consistency-v0.1
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tags:
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- clarusc64
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- eval
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- context
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- state
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task_categories:
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- text-generation
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- text-classification
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size_categories:
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- 1K<n<10K
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train.csv
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- split: test
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path: data/test.csv
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---
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## What this dataset tests
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State must persist.
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Time passes.
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Facts do not reset.
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## Why it exists
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Replies often reopen decisions.
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They invent closure.
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They drop constraints.
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This set checks whether state holds across turns.
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## Data format
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Each row contains
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- context_state
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- user_message
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- context_pressure
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- constraints
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- failure_modes_to_avoid
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- target_behaviors
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- gold_checklist
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Feed the model
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- context_state
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- user_message
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- constraints
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Score for
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- state preservation
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- explicit restatement
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- uncertainty discipline
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- next-step clarity
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## Context pressures
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- follow_up
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- scope_push
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- authority
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- override
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- assumption
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- urgency
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- pressure
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- relief
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- simplification
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- premature_claim
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- political
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- exception
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Questions to ask yourself
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- What is already decided
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- What is still pending
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- What rule applies
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- What happens next
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## Suggested prompt wrapper
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System
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You must preserve context state.
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You must obey constraints.
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User
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{context_state}
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{user_message}
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Constraints
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{constraints}
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## Scoring
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Use scorer.py.
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It returns
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- score from 0 to 1
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- per-row signals
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## Known failure signatures
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- Reopening closed decisions
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- Declaring completion early
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- Ignoring freezes or holds
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- Dropping caveats
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## Citation
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ClarusC64 dataset family.
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