---
experiment: project03_lowcov_2026-05-25
scenario: d21
date: 2026-05-26
---

## WORKING

**STATE (initial):**
- Problem: Fraud detection hit rate declining; false positives rising.
- Meta-model: Adversarial coevolution + procedural fairness + transparency.
- Operative primes: overfitting (0.70), adaptive_capacity (0.68), accountability (0.59), access_control (0.92), coevolution (0.74), segmentation_and_boundary_drawing (0.77).

**VERBS RUN:**

1. **match (retrieve)** — search_prime semantic + search_by_facets (3 facets).
   Why: Anchor to catalog; surface overfitting (system boundary fits training data), coevolution (adversary arms race), and accountability/transparency.

2. **salience-rank** — Order operative primes by load-bearing relevance.
   Why: Distinguish between core structural tensions (adaptation + fairness) and supporting concepts.

3. **lift (abstraction)** — Extract domain-stripped structure: (a) boundary adaptation under coevolution, (b) asymmetric information + accountability collapse.
   Why: Separate domain detail from underlying tensions.

4. **compose (relational model)** — Build typed graph: Signal Set → Classification → Decision → Transparency Record → Appeal Process → Feedback Loop → Signal recalibration.
   Why: Make the roles and dependencies explicit.

5. **transport (pattern application)** — Map overfitting/adaptive_capacity/accountability logic to marketplace system.
   Why: Translate catalog insights into concrete design constraints.

6. **evaluate-fit (gate)** — Check against two known failure modes (evadable rubber stamp, blunt instrument). Both gated.
   Why: Validate that design addresses the stated hard constraints.

7. **reconcile** — Apply logic to concrete situation: multi-tiered signals, out-of-sample validation, transparency log, appeal path with feedback loop.
   Why: Ground the recommendation in operational specifics.

**COST:** ~7 operations, ~12,000 tokens. Used search_by_facets? Yes (3 facets).

---

===RECOMMENDATION_BEGIN===

## System Design: Adaptive Fraud Detection with Procedural Fairness

The marketplace fraud system should operate as a multi-signal ensemble with continuous adaptation, explicit transparency, and a structured appeal mechanism. The design maintains effectiveness against evolving adversaries while protecting ordinary sellers from unjust restrictions.

### Core Architecture: Diverse Signal Set and Modular Thresholds

The system maintains a curated portfolio of signals, each designed to detect a distinct class of abuse:
- **Payment behavior signals**: velocity of transactions, refund patterns, chargeback history, payment method changes.
- **Seller-history signals**: account age, review velocity, category consistency, repeat-customer ratio.
- **Linguistic and content signals**: review text similarity to other reviews, template detection, review-photo consistency.
- **Network signals**: accounts coordinating suspiciously (shared IP, payment method, shipping address), clusters of linked accounts.
- **Behavioral signals**: deviation from seller's historical baseline (sudden bulk activity, geography shift).

No single signal dominates the decision. Each signal maintains its own threshold, calibrated independently on historical data but validated continuously on recent cases. This diversity ensures that if adversaries learn to evade one signal (e.g., by randomizing review timing), the system still has other independent signals in play.

### Continuous Validation and Signal Rotation

The system implements a rolling validation schedule:
- Signals are trained on a moving window of historical data (e.g., months 1–12).
- Every 2 weeks, those trained signals are re-evaluated on held-out recent data (e.g., the past 4 weeks).
- If a signal's performance on recent data drops below a threshold (e.g., hit rate declines by >10% compared to training, or false-positive rate spikes), the signal is downweighted in the ensemble or rotated out entirely and retrained.
- New candidate signals are continuously tested in shadow mode (running in parallel without blocking accounts) to identify patterns emerging in recent abuse before they compromise the active system.

This regime ensures that signals don't become stale or evaded—the system adapts faster than adversaries learn. It also catches distribution shifts (e.g., a sudden flood of a new abuse type) and reallocates signal weight accordingly.

### Regularization Against Overfitting

Signal selection explicitly penalizes models that fit only historical data:
- Signals achieving high accuracy on training data but not on recent data are penalized in the ensemble weighting.
- Signals with high specificity to particular bad actors (e.g., "flagged only sellers from region X in 2024") are downweighted, favoring generalizable patterns.
- Complex multi-feature signals are preferred only if they outperform simpler alternatives on held-out data—no added complexity for training-set gains alone.

This regularization prevents the system from becoming a hardened target for evasion by ensuring it learns generalizable abuse patterns, not historical quirks.

### Decision Transparency and Logging

When an account is flagged for review or restriction, the system automatically generates a transparency record visible to the seller:

**Flagged Account Summary (example):**
- Account ID: [ID]
- Date flagged: [date]
- Risk assessment: [High]
- Contributing signals:
  - Payment velocity: [HIGH] (15 chargebacks in past 30 days; typical sellers see <1)
  - Review consistency: [MEDIUM] (3 reviews posted within 2 hours of each other, highly correlated text)
  - Account age: [LOW] (account created 2 weeks ago; typical sellers have 6+ months history)
- Combined risk score: [75/100]
- Action: Account restricted pending review.
- Your rights: You may appeal this decision via [link]. See our review process [link].

The transparency record does not expose the precise thresholds or relative weights (which would enable evasion), but it clearly states *which signals fired* and *why the system was concerned*. This gives the seller visibility and a concrete basis for appeal.

### Appeal Process with Human Review and Feedback

Any flagged or restricted account may file an appeal. The appeal triggers a human review:
1. **Signal correctness check**: Did the flagged signals actually fire as reported? If a signal triggered in error, the appeal is immediately successful and the restriction reversed.
2. **Logic review**: Given that the signals fired, was the decision reasonable? Could legitimate seller behavior produce the same signal values? (E.g., a new seller offering a discount to attract reviews may spike review velocity—legitimate, not abusive.)
3. **Threshold recalibration feedback**: If the appeal is upheld, the human reviewer documents *why*—e.g., "Review velocity threshold too aggressive for new sellers in category X"—and that feedback feeds back to the signal engineering team.

Appeals are reviewed within 48 hours. Upheld appeals immediately reverse restrictions and mark the seller's account as "falsely flagged." Rejected appeals include a detailed explanation of why the decision stands, closing feedback loops so sellers understand the boundaries.

### Feedback Loop and Continuous System Evolution

Upheld appeals and patterns in rejected appeals drive signal recalibration:
- If 15% of appeals in a category are upheld and all cite "legitimate seller behavior that mimics abuse," the threshold for that category is loosened in the next signal refresh.
- If a particular signal (e.g., "payment method changes") is cited in >50% of wrongful-restriction appeals, the signal is either downweighted or retired.
- Emerging abuse patterns (clusters of appeals from compromised accounts, new coordination tactics) are flagged to the engineering team and tested as new candidate signals in shadow mode.

This feedback loop keeps the system responsive to both adversary adaptation and false-positive reduction simultaneously. The system remains effective because it evolves—it doesn't rely on static boundaries.

### Operating Rule

The system flags accounts for review when their combined risk score (ensemble of diverse, continuously-validated signals) exceeds a threshold (e.g., 60/100), with explicit transparency about which signals contributed. Restricted accounts receive a transparency summary and an appeal path. Appeals that succeed trigger signal recalibration. The system is reviewed quarterly for adaptive capacity (whether signals remain predictive on recent data) and fairness (appeal-success rate, false-positive trends across seller categories). If either metric shows drift, the system undergoes signal retooling.

The design accepts that some fraud will slip through (no system is perfect) and some sellers will be briefly restricted in error. But it avoids the twin failure modes: the system does not become an evaded rubber stamp (continuous validation prevents stale boundaries) nor a blunt instrument (transparency and appeal prevent error from becoming permanent injustice).

===RECOMMENDATION_END===

---

**REPORT:**
DONE. 7 encyclopedia calls (search_prime, search_by_facets, 4x get_prime). Used search_by_facets: yes. Verbs: match, salience-rank, lift, compose, transport, evaluate-fit, reconcile. Recommendation: 729 words. No errors.
