Retrieval Gauntlet Design: Sealed Task Inventory and Declared Assistance Conditions
Agent-Assisted Engineering Check
Prerequisites
Days 85-87 capstone integration and the completed Learning Project sequence (autograd, pretraining, real LoRA, evaluable agent, CI delivery).
Learning Objective
Assemble the fixed, sealed task inventory for unaided retrieval across five domains and declare assistance conditions before any attempt, so independent evidence is never inferred from assisted output.
Instructions
1. Understand why this day exists: retrieval practice (the testing effect) produces durable memory better than rereading; today converts the whole curriculum into fixed, unaided checks.
2. The sealed gauntlet covers exactly five domains, each traceable to real evidence days:
- Domain PY (Core Python): values, control flow, functions, containers, exceptions, files (Days 4-12).
- Domain GIT (Git & Debugging): branching, diff reading, failure diagnosis, safe recovery on disposable repositories (Days 13-15, 79-81).
- Domain MATH (Mathematics): derivatives, chain rule, probability, cross-entropy interpretation (Days 16-30).
- Domain GRAD (Gradients & Autograd): computation graphs, shared-node accumulation, finite-difference verification (Days 31-33 and projects/autograd).
- Domain MODEL (Model Reasoning): tokenization, causal masking, parameter ledgers, loss/perplexity reading, LoRA accounting, agent-loop behavior (Days 34-87).
3. For each domain, fix three task IDs now — no improvisation mid-check. Map every task to a recovery day in case it is missed.
4. Declare the assistance condition in writing: Independent Mastery Check — no AI, no notes, no browser, ≤105 minutes of the 120-minute block, start time recorded. Declared condition is honest self-report, not claimed surveillance.
5. The agent may help you *organize this inventory only*; it must not author, solve, or grade any sealed task.
Practice & Prediction Log
Produce sealed_inventory.json listing the 5 domains with 3 fixed task_ids each (15 total), each row carrying {domain, task_id, recovery_day, declared_condition: 'independent'}. Assert the inventory has exactly 15 rows and no duplicate task_id.
Use one row per task, e.g. {"domain":"GIT","task_id":"GIT-1","recovery_day":"day-14","declared_condition":"independent"}.
{
"declared_condition": "independent",
"time_cap_minutes": 105,
"tasks": [
{"domain":"PY","task_id":"PY-1","recovery_day":"day-09","declared_condition":"independent"},
{"domain":"PY","task_id":"PY-2","recovery_day":"day-11","declared_condition":"independent"},
{"domain":"PY","task_id":"PY-3","recovery_day":"day-12","declared_condition":"independent"},
{"domain":"GIT","task_id":"GIT-1","recovery_day":"day-14","declared_condition":"independent"},
{"domain":"GIT","task_id":"GIT-2","recovery_day":"day-80","declared_condition":"independent"},
{"domain":"GIT","task_id":"GIT-3","recovery_day":"day-81","declared_condition":"independent"},
{"domain":"MATH","task_id":"M-1","recovery_day":"day-19","declared_condition":"independent"},
{"domain":"MATH","task_id":"M-2","recovery_day":"day-25","declared_condition":"independent"},
{"domain":"MATH","task_id":"M-3","recovery_day":"day-27","declared_condition":"independent"},
{"domain":"GRAD","task_id":"G-1","recovery_day":"day-31","declared_condition":"independent"},
{"domain":"GRAD","task_id":"G-2","recovery_day":"day-32","declared_condition":"independent"},
{"domain":"GRAD","task_id":"G-3","recovery_day":"day-33","declared_condition":"independent"},
{"domain":"MODEL","task_id":"MR-1","recovery_day":"day-50","declared_condition":"independent"},
{"domain":"MODEL","task_id":"MR-2","recovery_day":"day-65","declared_condition":"independent"},
{"domain":"MODEL","task_id":"MR-3","recovery_day":"day-83","declared_condition":"independent"}
]
}
assert len([t for t in tasks]) == 15
Produce a complete sealed inventory mapping every task to a named recovery route, with the assistance condition declared before any attempt begins.
Authoritative Source Resources & Provenance
Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention
Relevant Section / Range: Psychological Science 17(3): 249-255, Experiments 1-2
Verified Version & Date: Psychological Science, 2006 (Verified: 2026-10-30)
Foundational experimental evidence that retrieval practice (testing) outperforms repeated study for long-term retention; grounds the Day 88 AI-free retrieval checks in peer-reviewed learning science rather than folk advice.
Basic understanding of study sessions, retention intervals, and experimental design vocabulary.
The curriculum restates the experimental design and effect sizes in original prose; every sealed task runs without the paper.
Compared Candidate Sources & Selection Analysis (2)
Strong meta-analytic coverage of spacing effects but addresses scheduling rather than the retrieval effect itself; retained as support for the Day 90 spaced-review schedule.
Equally foundational follow-up demonstrating repeated retrieval vs repeated study; the 2006 paper is cited as the standard first controlled demonstration.
Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology
Relevant Section / Range: Psychological Science in the Public Interest 14(1): 4-58; practice testing and distributed practice sections
Verified Version & Date: Psychological Science in the Public Interest, 2013 (Verified: 2026-10-30)
Detailed comparative review rating practice testing and distributed practice as high-utility techniques while rating highlighting and rereading low-utility; anchors the retrieval, correction, and spaced-review design of Days 88-90.
None beyond general academic reading.
Curriculum restates the utility ratings and application conditions in original prose; every task remains runnable offline.
Compared Candidate Sources & Selection Analysis (2)
Important negative result on learning-styles matching but narrower scope; Dunlosky et al. rates a broader set of techniques directly usable for this curriculum.
Strong trade-book synthesis of the same evidence base; the peer-reviewed review is preferred for citation precision and technique-by-technique utility ratings.