Inference Validation and Miner Lifecycle

Running AI inference across untrusted, decentralized networks introduces major challenges around correctness, fault tolerance, and trust minimization. Nesa addresses these through:

• Optimistic validation

• Reputation-based scoring

• Tiered miner routing

• Trial gating for new miners

• Timeout and recovery protocols

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✅ Optimistic Validation Flow

Nesa uses optimistic execution: results are assumed valid unless flagged otherwise. This allows for low-latency execution without synchronous consensus.

🔄 Execution Pipeline

1. Miner runs its assigned model shard

2. Result is returned to the orchestrator agent

3. Agent validates via:

   • Tensor structure and output shape

   • Response latency

   • Miner’s reputation history

🔍 For High-Risk Queries

Optional extra layers of validation:

• Rerun on a shadow miner

• Enforce redundant execution or zkDPS proofs

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🧪 Miner Onboarding: Trial Runs

Before joining the live query pool, every new miner undergoes a trial inference:

• A dummy task with known output is dispatched

• Miner’s result is checked for correctness

Outcomes:

• ✅ If correct: miner marked as warm, with initialized baseline reputation

• ❌ If incorrect: cooldown enforced, logs reviewed

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🧠 Miner Reputation Scoring

Nesa maintains two scoring models based on architecture type:

🏆 Top-Down Scoring (Orchestrator Assignment)

Reputation is updated per inference task as:

$$R' = R \cdot \text{Pen}^M \cdot \text{Rew}^{1 - M}$$

Where:

• $R$: current reputation

• $R'$: updated reputation

• $\text{Pen} = 0.8$: penalty factor for errors

• $\text{Rew} = 1.01$: reward factor for correctness

• $M \in \{0, 1\}$: 1 if error, 0 if correct

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⚙️ Bottom-Up Scoring (Bidding Architecture)

Adds hardware/network performance to the score:

$$R' = \alpha R \cdot \text{Pen}^M \cdot \text{Rew}^{1 - M} + \beta (w_S S + w_F F + w_B B + w_I I)$$

Terms:

• $\alpha$: trust score weight

• $\beta$: performance weight

• $S$: single-token inference throughput

• $F$: forward pass performance

• $B$: backward pass performance

• $I$: internet speed or availability

• $w_S, w_F, w_B, w_I$: normalized weights

Each metric is normalized as:

$$x \leftarrow \frac{x - x_{\text{min}}}{x_{\text{max}} - x_{\text{min}}}$$

Comparison of miner reputation dynamics across 20 time steps. Left: High penalties cause poor performers to rapidly diverge downward. Right: Higher rewards accelerate separation of good performers, aiding orchestrator selection.

🏷️ Tiered Miner Pools

Nesa dynamically organizes miners into three tiers based on reputation and activity:

• Tier 1: High-reputation miners with cached (“hot”) models and fast response

• Tier 2: Reliable fallback miners with decent scores

• Tier 3 (Trial): New or recovering miners, limited to test and low-risk jobs

Query routing favors Tier 1 for latency-critical or high-stakes workloads.

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⚠️ Failure Handling and Penalization

If a miner fails (disconnects, times out, or returns invalid results):

🔻 Miner-Side Response:

• Score penalized

• Node may be throttled or temporarily blacklisted

🔁 Agent-Side Response:

• Task is republished to a fallback sequence

• Timeout window of 2–5 seconds ensures recovery without pipeline blocking

👁️ User-Facing Behavior:

• Frontend displays fallback messaging

• If unresolved:

  • Returns partial result (if safe)

  • Retries on new path

  • Or emits an error if no alternative available

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🕒 Timeout and Retry Logic

Nesa enforces strict timeout thresholds at multiple levels:

• Shard-level timeout: Each shard must respond within $N$ seconds

• End-to-end timeout: Entire inference must resolve within 10–15 seconds

• If deadlines are missed:

  • Tasks are rerouted automatically

  • UI receives diagnostics or fallback result

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✅ Summary

Nesa’s decentralized inference system balances flexibility and trust with practical engineering:

• Validation: Optimistic + shadow rerun for high-risk tasks

• Reputation: Dual scoring strategies based on architecture

• Tiered pools: Ensure tasks land on trusted, responsive nodes

• Penalties: Prevent freeloaders and unreliable participants

• Trial gating: Avoids onboarding malicious or misconfigured miners

• Timeout safeguards: Maintain API-level responsiveness

These mechanisms collectively make Nesa’s decentralized inference network trustworthy, low-latency, and scalable.