ClarusC64/mid-horizon-drift-v0.1b

What this dataset tests

Mid-range trends can lie.

Mix shifts.

Definitions shift.

Leading indicators speak first.

Why it exists

Models over-trust blended mid-horizon metrics.

They miss

• —mix drift
• —variance rise
• —definition breaks
• —confounds
• —early warning signals

This set forces those traps.

Data format

Each row contains

• —midhorizoncontext
• —user_message
• —drift_pressure
• —constraints
• —failuremodesto_avoid
• —target_behaviors
• —gold_checklist

Feed the model

• —midhorizoncontext
• —user_message
• —constraints

Score for

• —drift detection
• —use of leading indicators
• —proposal of slices or controlled checks
• —decision gates

Drift pressures

• —mix_shift
• —leading_indicator
• —distribution_shift
• —channel_mix
• —error_budget
• —variance_rise
• —pattern_emergence
• —capacity_risk
• —policy_lead
• —definition_shift
• —environment_shift
• —premature_baseline

Questions to ask yourself

• —What changed inside the average
• —Which definition moved
• —What confound entered
• —What lead signal predicts the next month
• —What gate protects action

Suggested prompt wrapper

System

You must hold comparability across time.

You must obey constraints.

User

{midhorizoncontext}

{user_message}

Constraints

{constraints}

Scoring

Use scorer.py.

It returns

• —score from 0 to 1
• —per-row signals

The heuristics reward

• —naming mix or definition shifts
• —using stratified slices
• —tying leads to near-future risk
• —setting thresholds and stop rules

Known failure signatures

• —Trusting blended averages
• —Publishing broken trends
• —Stopping reliability work early
• —Treating anomalies as baselines

Citation

ClarusC64 dataset family.