mlsanityData to 7 Oct 2026

STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making

arXiv 2405.16376 ↗First posted 25 May 2024cs.CL cs.GTcited by 22

On the map: LLM reasoning → Reasoning for agents, planning and code

also in 1 more
Abstract (arXiv)

Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampered by significant limitations including poor mathematical reasoning, difficulty in following instructions, and a tendency to generate incorrect information. These deficiencies hinder their performance in strategic and interactive tasks that demand adherence to nuanced game rules, long-term planning, exploration in unknown environments, and anticipation of opponents' moves. To overcome these obstacles, this paper presents a novel LLM agent framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities. We deploy the tools in a number of economically important environments, in particular bilateral bargaining and multi-agent and dynamic mechanism design. We employ quantitative metrics to assess the framework's performance in various strategic decision-making problems. Our findings establish that our enhanced framework significantly improves the strategic decision-making capability of LLMs. While we highlight the inherent limitations of current LLM models, we demonstrate the improvements through targeted enhancements, suggesting a promising direction for future developments in LLM applications for interactive environments.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 30 of its 44 references that are in the map. First posted on arXiv 25 May 2024.

Builds on

30 of 44 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  2. 2023Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency29 Sep
  3. 2023Strategic Reasoning with Language Models30 May
  4. 2023Reasoning with Language Model is Planning with World Model24 May
  5. 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planning23 May

Other references · 25

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  7. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  8. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  9. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  10. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  11. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  12. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
13 more
  1. 2017Minimax Regret Bounds for Reinforcement Learningcited by 500
  2. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 396
  3. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  4. 2023Reward Design with Language Modelscited by 257
  5. 2023Playing repeated games with Large Language Modelscited by 213
  6. 2023Emergent autonomous scientific research capabilities of large language modelscited by 127
  7. 2024How Well Can LLMs Negotiate? NegotiationArena Platform and Analysiscited by 93
  8. 2023Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysiscited by 91
  9. 2024Can large language models explore in-context?cited by 75
  10. 2024How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environmentscited by 66
  11. 2024Evaluating Language Model Agency through Negotiationscited by 43
  12. 2024Do LLM Agents Have Regret? A Case Study in Online Learning and Gamescited by 33
  13. 2022Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approachcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 13; 2026: 3.

22 papers.

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By area:

  1. 2024Tool Learning with Large Language Models: A Surveycited by 221
  2. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  3. 2024GLEE: A Unified Framework and Benchmark for Language-based Economic Environmentscited by 19
  4. 2025Magentic Marketplace: An Open-Source Environment for Studying Agentic Marketscited by 15
  5. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  6. 2025Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crewscited by 12
  7. 2025Exploring the Necessity of Reasoning in LLM-based Agent Scenarioscited by 10
  8. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 9
  9. 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 8
  10. 2025Should You Use Your Large Language Model to Explore or Exploit?cited by 8
  11. 2025Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectivescited by 4
  12. 2025Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approachcited by 4
10 more
  1. 2025LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphscited by 4
  2. 2026Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewardscited by 4
  3. 2024Physics-Informed Autonomous LLM Agents for Explainable Power Electronics Modulation Designcited by 3
  4. 2024SEAL: Suite for Evaluating API-use of LLMscited by 2
  5. 2025Estimating the Empowerment of Language Model Agentscited by 2
  6. 2025CSP: A Simulator For Multi-Agent Ranking Competitionscited by 1
  7. 2025The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical featurescited by 1
  8. 2024WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysiscited by 0
  9. 2026LLM Nepotism in Organizational Governancecited by 0
  10. 2026Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplexcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs
  2. 2024LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
  3. 2026Beyond Scaling: Assessing Strategic Reasoning and Rapid Decision-Making Capability of LLMs in Zero-sum Environments
  4. 2025Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making
  5. 2026GameTalk: Training LLMs for Strategic Conversation
  6. 2026Competition and Cooperation of LLM Agents in Games
  7. 2025DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments
  8. 2024Game-theoretic LLM: Agent Workflow for Negotiation Games