docs/adaptive_difficulty_spec.md

Adaptive Difficulty Specification

Purpose

After the agent masters a scenario (>70% success), the environment hardens parameters — tighter budgets, rarer subgroups, noisier data — and targets the agent's tracked weak spots.

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Mastery Detection

Tracked per scenario × per tier. A strong solid_tumor_chemo doesn't mask weakness on rare_disease_orphan.

Tier	Window	Threshold	Min Episodes
Warmup	10	70%	15
Beginner	12	65%	20
Intermediate	15	55%	25
Advanced	20	45%	30
Expert	N/A	N/A	N/A

Fast-track: ≥90% in first 5 episodes → immediate hardening (skip mastery window).

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Parameter Hardening

Applied incrementally when mastery is detected, within the same tier:

Axis	Easy	Step 1	Step 2	Step 3
Effect size multiplier	1.0×	0.85×	0.70×	0.60×
Budget multiplier	1.0×	0.90×	0.80×	0.70×
Noise multiplier	1.0×	1.2×	1.4×	1.6×
Dropout add	+0%	—	+5%	+8%
Placebo boost	+0%	—	—	+5%
Subgroup prevalence	1.0×	—	—	0.7×
Misleading Phase I	No	—	—	Yes

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Weak-Spot Targeting

The adversarial designer (adversarial_designer.py) analyzes failure patterns:

Failure Type	Counter	Hardening Response
Small effect missed	small_effect_failures	Reduce effect size further
High dropout	high_dropout_failures	Increase dropout rate
Subgroup missed	missed_subgroup_failures	Reduce subgroup prevalence
Budget exhausted	budget_failures	Tighten budget

At Expert tier, the adversarial designer creates compound challenges combining 2–3 difficulty axes simultaneously.

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Solvability Guarantee

Every generated scenario must remain solvable — at least one action sequence achieves success:

• Effect size > 0 (drug works)
• Budget sufficient for n patients needed for 80% power
• Time sufficient for enrollment + follow-up
• At least one valid inclusion criteria identifies responders

If generated params fail solvability check → regenerate with relaxed constraints.