Multi-agent Evaluation

Agent that negotiates in multi-round bargaining games using LLM reasoning. Evaluated on MAizeBargAIn benchmark.

Random baseline agent for MAizeBargAIn bargaining scenario

Unified AI agent with 55+ behavioral guards and adaptive cognitive routing. Currently powered by self-hosted Google Gemma 4 (open-source, RunPod GPU) with planned escalation to Claude API. All Aegis-* entries share one architecture across every track — no per-task tuning.

Multi-round bargaining agent for the MAizeBargAIn meta-game assessor. Combines LLM reasoning (Gemini 2.5 Flash-Lite) with a deterministic M1–M5 rule validator for guaranteed feasible actions.

Test

LogoMesh is a multi-agent benchmark that evaluates AI coding agents across four orthogonal dimensions: Rationale Integrity (does the agent understand the task?), Architectural Integrity (is the code secure and well-structured?), Testing Integrity (do tests actually validate correctness?), and Logic Score (does the code work correctly?). Unlike static benchmarks, LogoMesh uses: -An adversarial Red Agent with Monte Carlo Tree Search to discover vulnerabilities -A Docker sandbox for ground-truth test execution -A self-improving strategy evolution system (UCB1 multi-armed bandit) that adapts evaluation rigor based on past performance -Intent-code mismatch detection that catches when an AI returns completely wrong code -Battle Memory that learns from past evaluations to improve future scoring The benchmark covers 20 tasks from basic data structures to distributed systems (Raft consensus, MVCC transactions, blockchain), and dynamically generates evaluation criteria for novel tasks via LLM-powered Task Intelligence.