meta_ai_TeamTitans / tasks /task_3_hard.json
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{
"task_id": 3,
"name": "Full Relationship Management",
"difficulty": "hard",
"description": "The agent cannot finish all 30 emails within 360 minutes β€” it must decide which to skip. Time-trap emails (up to 180 min) appear even from VIPs, meaning blind VIP-always-respond is punished. Angry follow-ups cascade from repeat VIP ignores. A sunset penalty deducts reward for every high-priority email left unfinished. The agent must balance time efficiency, relationship health, AND priority accuracy simultaneously.",
"num_emails": 30,
"vip_count": 8,
"normal_count": 14,
"spam_count": 8,
"time_budget": 360,
"episode_length": 30,
"avg_minutes_per_email": 12,
"features": {
"action_cost_asymmetry": true,
"dynamic_followups": true,
"time_pressure": "severe",
"sunset_penalty": true,
"time_traps": true,
"repeat_ignore_penalty": true
},
"what_agent_must_learn": [
"Cannot respond to all emails β€” must prioritise strategically",
"Time-trap emails (120-180 min) from VIPs may not be worth responding to if time is low",
"Ignoring a VIP twice triggers a second follow-up with even higher urgency + extra -10 relationship health",
"Sunset penalty = sum of (base_priority Γ— relationship_health/100) for all unfinished emails",
"Time efficiency compares actual reward to a perfect greedy agent benchmark",
"Optimal policy: respond to short high-priority emails, ignore long low-priority ones regardless of sender tier"
],
"scoring": {
"formula": "0.3 * time_efficiency + 0.4 * relationship_health + 0.3 * priority_accuracy",
"time_efficiency_weight": 0.3,
"relationship_health_weight": 0.4,
"priority_accuracy_weight": 0.3,
"time_efficiency_benchmark": "Perfect greedy agent β€” picks highest priority emails until time runs out",
"sunset_penalty_formula": "-sum(base_priority * relationship_health/100) for all remaining emails",
"notes": "Relationship health still has the highest weight (0.4) β€” even under time pressure, destroying VIP relationships is costly. Time efficiency rewards staying within budget while maximising reward."
}
}