Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization
Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization explores Develops a novel methodology and tailored LLM agents to significantly improve optimization solution quality through interactive conversations, bridging AI and operations research.. Commercial viability score: 7/10 in LLM Agents.
Use This Via API or MCP
Use the canonical paper page as a proof artifact
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Owned Distribution
Subscribe to the weekly brief
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
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
- Builds On ThisGameTalk: Training LLMs for Strategic ConversationViability: 3.0
- Builds On ThisChoose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party NegotiationViability: 4.0
- Builds On ThisToward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future DirectionsViability: 4.0
- Prior WorkReinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented DialogueViability: 7.0
- Prior WorkOptimizing Service Operations via LLM-Powered Multi-Agent Simulation