ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning
ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning explores ToolTree enhances LLM agent tool planning through advanced Monte Carlo tree search techniques for improved decision-making.. Commercial viability score: 7/10 in Agents.
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 ThisTask-Aware LLM Council with Adaptive Decision Pathways for Decision SupportViability: 6.0
- Builds On ThisEvoTool: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware SelectionViability: 5.0
- Prior WorkSpend Less, Reason Better: Budget-Aware Value Tree Search for LLM AgentsViability: 7.0
- Prior WorkHierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task PlanningViability: 7.0
- Higher ViabilityTAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
Related Resources
- AgentSpeak(glossary)
- Mixture-of-Agents(glossary)
- Agents(glossary)
- What is the future of AI agents according to Nothing's CEO?(question)
- How do LLM efficiency advancements impact the development of AI agents?(question)
- How does AgentXRay contribute to the explainability of AI agents in complex decision-making processes?(question)
- Agents – Use Cases(use_case)