# ADR-0018: Fraud detection, verification, and reputation

## Status: Accepted

## Context

ADR-0003 established optimistic sampling with stake slashing; ADR-0015 replaced stake with **pending-balance forfeiture** as collateral. Pre-alpha audits identified two distinct fraud types:

1. **Correctness fraud** — wrong model, layer-skipping, garbage outputs.
2. **Accounting fraud** — inflated token counts or shard-span work units reported by nodes.

The validator today compares final text only and always blames the last hop (`_final_text_node` in `packages/validator/meshnet_validator/__init__.py` ~137–140) — wrong for multi-hop pipelines.

Research (`.scratch/alpha-hardening/research-verifiable-inference.md`, 2026-07-04) grounds the alpha design in deployed patterns (Prime Intellect TOPLOC, Hyperbolic PoSP, Gensyn Verde blame **patterns**). This ADR is the flagship decision record for alpha hardening.

## Decisions

### 1. Anchor technique: optimistic accept + teacher-forced audit

- Default audit probability **p ≈ 5%** — a **budget target**, not a hard cap. Anomalies, low reputation, and disputes escalate rate; veterans floor at ≥ 2% (research §6, §8).
- **Deterrence condition:** at p = 0.05, penalty L must exceed **L > g·(1−p)/p ≈ 19×** per-job gain g (research §1.1). **Full pending forfeiture** is the primary penalty; three strikes → ban. The ×0.8-per-strike multiplier applies to **routing/payout weight** (reputation decay), not to the forfeiture amount.
- Single-tracker alpha: the tracker (or a designated reference node) is the auditor — no verifier market, no verifier's dilemma (research §1.1).

### 2. Detection primitive: ADOPT TOPLOC

- **`pip install toploc`** (MIT, [PrimeIntellect-ai/toploc](https://github.com/PrimeIntellect-ai/toploc)) for activation fingerprint commit + verify (research build-vs-adopt table).
- Teacher-forced prefill re-verification — compare in **logit/activation space with tolerances**, never free-running token equality (research §2).
- Pin **one canonical precision/quantization** per served model; TOPLOC detects precision drift by design.
- Per-hop boundary fingerprints extend TOPLOC's final-hidden-state encoding for **multi-hop blame** (research §1.2, §8 layer 1).

### 3. Commit layer: on-demand activation commitments

- Nodes commit compact TOPLOC-style fingerprints of **output boundary activations** per hop when selected for audit (on-demand, not every request — lower serving latency; brief retention window for recent activations).
- Commitments are **audit pins, not proofs** — correctness requires independent recomputation (research §4).

### 4. Blame layer: hop-boundary bisection (adapt Verde pattern)

On audit failure:

1. Referee (tracker) teacher-forces claimed token sequence through reference model.
2. Compare committed hop-boundary fingerprints to reference at each cut-point.
3. **First divergent hop** is the culprit — fixes `_final_text_node` last-hop-only bug.
4. Full interactive Truebit/Verde on-chain game and bitwise RepOps kernels: **roadmap-only** (research §1.2, §9).

### 5. Accounting fraud: tracker-authoritative metering

- **Token counts** come from the tracker's proxied stream/non-stream response parsing (`server.py` ~1890–1943), not node self-reports.
- **Work units** derive from **tracker-assigned shard span** at route construction (`server.py` ~1776–1782), not node-declared ranges at billing time.
- See issue H2.

### 6. Reputation model (graduated, persisted)

Reputation derives **only from tracker-verified audit outcomes** + uptime/latency — never peer ratings (research §6, collusion surface).

| Signal | Effect |
|---|---|
| Clean audits | Slow reputation build; higher routing weight |
| Strike | ×0.8 routing multiplier per strike (graduated decay) |
| Failed audit | Full pending forfeiture + strike; audit rate → maximum |
| Ban (3 strikes) | Registration rejected; excluded from routes; pending never paid |
| New/low reputation | Elevated audit rate (20–30% target for newcomers) |
| Inactivity | Reputation decay |

Persist strike/ban/reputation in SQLite alongside billing (issue A1/A5). Probation (first N jobs unpaid) retained as re-entry cost.

### 7. Passive tripwires

Perplexity/repetition/truncation heuristics on all traffic raise audit rate without direct punishment (research §8 layer 5).

### 8. Roadmap-only (explicitly NOT alpha)

- zkML proofs of LLM inference (research §1.3)
- GPU TEE attestation on consumer cards (research §1.4)
- Fully trustless Verde interactive games + RepOps bitwise kernels (research §9)
- Decentralized verifier markets
- Peer-rating reputation (EigenTrust)
- PoW as correctness proof — registration-time hardware attestation only, optional (research §4)

## Consequences

- Validator must be rewired: TOPLOC verify, hop blame, tracker-authoritative events — not string compare on final text alone.
- Threshold calibration requires an **honest-noise corpus** across the volunteer fleet before production thresholds (research §8).
- ADR-0003 remains historical; penalty mechanics follow ADR-0015 + this ADR.
- Implementation order: auth + persistence → accounting → TOPLOC → bisection → reputation routing (`.scratch/alpha-hardening/README.md`).

## Related

- Research: `.scratch/alpha-hardening/research-verifiable-inference.md` (§8 recommended scheme, §9 roadmap, build-vs-adopt table)
- ADR-0015 (forfeiture collateral)
- ADR-0016 (alpha scope)
- ADR-0017 (validator/forfeit auth)
- Issues: `06-fraud-toploc-integration.md` through `10-fraud-penalty-calibration-wiring.md`