Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery
Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery explores ASRO optimizes heuristic discovery with a game-theoretic co-evolution framework for LLMs.. Commercial viability score: 3/10 in LLM Optimization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkPathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMsViability: 3.0
- Higher ViabilityAdaEvolve: Adaptive LLM Driven Zeroth-Order OptimizationViability: 7.0
- Higher ViabilityDiscovering Multiagent Learning Algorithms with Large Language ModelsViability: 5.0
- Higher ViabilityCOvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum GameViability: 7.0
- Higher ViabilityFrom AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents
Related Resources
- How can LLM optimization be used to improve the efficiency of LLM fine-tuning?(question)
- How do frameworks like OptiKIT democratize LLM optimization for non-expert teams?(question)
- How can LLM optimization techniques contribute to more sustainable AI practices by reducing energy consumption?(question)