Web3 & Smart Contract Logic Benchmark Challenge

Build cryptographic algorithms to verify Merkle Tree proofs and optimize Web3 smart contract security

Overview

Welcome to the Web3 & Smart Contract Logic Benchmark Challenge!

Goal: Design and implement robust cryptographic benchmarks (such as Merkle Tree proof verifiers) to evaluate and push the limits of Large Language Models (LLMs) in Web3 security and smart contract reasoning.

About the Challenge

This challenge tests the reasoning capabilities of Large Language Models (LLMs) in Web3 infrastructure, cryptographic validation, and smart contract logic.

Objectives

• Evaluate LLM performance on Merkle Tree Proof Verification.
• Test resilience against edge cases in cryptographic hashing (SHA-256).
• Benchmark AI accuracy in automated code review for Web3 security.

Submission Requirements

Valid Submission Requirements

To enter a valid submission for this hackathon, participants must submit:

• Kaggle Notebook: A public Kaggle Notebook containing the Python source code for your benchmark, evaluation harness, or smart contract logic test suite.
• Project Repository: A link to a public GitHub repository containing complete setup instructions and reproducible code.
• Evaluation Report: A summary of execution results demonstrating how your benchmark evaluates LLM reasoning capabilities on Web3 / cryptographic logic (e.g., Merkle Tree verification).

Note: All final submissions must be submitted before the deadline to be evaluated by the judges.

Tracks and Awards

Web3 Cryptographic Logic Benchmark

This track focuses on evaluating Large Language Models (LLMs) on cryptographic algorithms and smart contract logic, specifically focusing on Merkle Tree proof verification and edge-case handling.

• Leaderboard Glory & Digital Certificate - Non-monetary

Evaluation

Evaluation Criteria (Total: 100 Points)

Criteria	Description	Points
Cryptographic Logic	Accuracy in verifying Merkle proofs and cryptographic operations.	35 Points
Edge-Case Resilience	Proper handling of invalid tree depths and malformed inputs.	25 Points
Benchmark Design	Quality of evaluation scripts and LLM prompt testing framework.	25 Points
Documentation & Clarity	Clean, reproducible code with clear README instructions.	15 Points