Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems
To address this limitation, we propose a Tool-Augmented Strategic Agent (TASA) which summarizes and exploits trajectory to improve solution quality.
ProofPaper ↗
Key points
- Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting.
- Moreover, we leverage LLM to construct problem-specific tools and introduce a strategy-guided adaptive tool planner, which efficiently utilizes the information of trajectory to update solutions.
- We establish local convergence under persistent exploration and evaluate the complete framework under a shared candidate-evaluation budget.
- The results support its solution quality and the traceability of strategy-guided search.
Sources (1)
- [1]Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial ProblemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:57 AM
To address this limitation, we propose a Tool-Augmented Strategic Agent (TASA) which summarizes and exploits trajectory to improve solution quality.
Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 10, 2026Uniform Information Density-Based Preference Optimization
- Oct 10, 2026UltraBERT: Beyond the Limit of BERT Pre-Training
- Oct 10, 2026ProgAgent: Learning Tool Orchestration through Programmatic Training
- Oct 10, 2026Vela: User Behavior as Supervision Sparks LLM Intelligence
- Oct 9, 2026SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers
- Oct 9, 2026Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding