Hackathon #3: Cortensor Validation Ops Agent

projects/validation-ops-agent/README.md

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+# Cortensor Validation Ops Agent
+
+An agentic validation system that demonstrates Cortensor-style decentralized
+inference using redundant workers (PoI), validator scoring (PoUW), and
+auditable evidence bundles for AI outputs.
+
+## Overview
+
+The Cortensor Validation Ops Agent is a proof-of-concept agentic assistant
+designed to evaluate the trustworthiness of AI-generated outputs.
+
+Instead of relying on a single model or provider, the agent dispatches the
+same input across multiple inference workers, detects disagreement or
+outliers, and uses independent validators to compute an interpretable trust
+score. All results are packaged into machine-verifiable evidence bundles.
+
+## Why Cortensor
+
+Centralized inference pipelines provide no reliable way to verify whether an
+AI output is correct, consistent, or free from hallucination.
+
+This project demonstrates how Cortensor’s decentralized inference paradigm
+enables:
+- Redundant inference across independent workers (PoI)
+- Validator-based scoring of output quality and agreement (PoUW)
+- Transparent, auditable evidence instead of opaque AI responses
+
+## How It Works
+
+1. A user submits an input prompt or claim.
+2. The agent dispatches the input to multiple inference workers.
+3. Worker outputs are collected and hashed (PoI).
+4. Validators score the outputs based on redundancy, semantic similarity,
+   and outlier detection (PoUW).
+5. A final trust score and evidence bundle are produced.
+
+## How to Run
+
+The system can be tested by submitting an input via the demo UI or API.
+
+Example flow:
+1. Submit an input prompt.
+2. The system performs redundant inference across multiple workers.
+3. Validation is triggered automatically.
+4. A trust score and evidence bundle are returned.
+
+This demo uses simulated inference workers to demonstrate PoI/PoUW logic.
+In production, inference adapters are replaced with Cortensor Router v1.
+
+## Evidence Bundle (Example)
+
+```json
+{
+  "task_id": "task_pyx5qvjx",
+  "input_hash": "0x2dc121c6...a6ef60b2",
+  "worker_outputs": {
+    "Worker_A": "0xbf700bd9...7b7f6938",
+    "Worker_B": "0xebad39ec...180fc180",
+    "Worker_C": "0x28134955...ee574c06"
+  },
+  "validation_methods": ["redundancy", "embedding_distance"],
+  "validator_scores": [75, 89, 83],
+  "final_trust_score": 82
+}
+
+## What’s Next
+
+- Integrate Cortensor Router v1 for real decentralized inference
+- Expand validation logic with stronger embedding distance checks
+- Persist evidence bundles for long-term auditability
+- Explore optional on-chain anchoring of trust artifacts
+