mlsanityData to 7 Oct 2026

TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents

arXiv 2602.19633 ↗First posted 23 Feb 2026cs.AIcited by 4

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment. However, they remain vulnerable in environments where a single error often leads to irrecoverable failure, particularly under strict feasibility constraints. We systematically analyze existing agent frameworks, identifying imperfect planning and stochastic execution as the primary causes. To address these challenges, we propose Tool-guided Adaptive Planning with constrained Execution (TAPE). TAPE enhances planning capability by aggregating multiple plans into a graph and employing an external solver to identify a feasible path. During execution, TAPE employs constrained decoding to reduce sampling noise, while adaptively re-planning whenever environmental feedback deviates from the intended state. Experiments across Sokoban, ALFWorld, MuSiQue, and GSM8K-Hard demonstrate that TAPE consistently outperforms existing frameworks, with particularly large gains on hard settings, improving success rates by 21.0 percentage points on hard settings on average, and by 20.0 percentage points for weaker base models on average. Code and data available at here.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 53 of its 62 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

53 of 62 references.

Closest ancestors · 14

Same direction, newest first

  1. 2026Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic23 Jan
  2. 2025Budget-Aware Tool Use Enables Effective Agent Scaling21 Nov
  3. 2025SAND: Boosting LLM Agents with Self-Taught Action Deliberation10 Jul
  4. 2025Distilling LLM Agent into Small Models with Retrieval and Code Tools23 May
  5. 2025ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection21 May
  6. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks12 Mar
  7. 2025MPO: Boosting LLM Agents with Meta Plan Optimization4 Mar
  8. 2024WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration28 Aug
  9. 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning9 Jun
  10. 2024Agent Planning with World Knowledge Model23 May
  11. 2023Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency29 Sep
  12. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
2 more
  1. 2023AgentBench: Evaluating LLMs as Agents7 Aug
  2. 2023AdaPlanner: Adaptive Planning from Feedback with Language Models26 May

Other references · 39

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  8. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  9. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  10. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  11. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  12. 2025OpenAI GPT-5 System Cardcited by 910
27 more
  1. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  2. 2023Reasoning with Language Model is Planning with World Modelcited by 846
  3. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  4. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
  5. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  6. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 204
  7. 2022TALM: Tool Augmented Language Modelscited by 197
  8. 2023Efficient Guided Generation for Large Language Modelscited by 192
  9. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
  10. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 174
  11. 2023ADaPT: As-Needed Decomposition and Planning with Language Modelscited by 172
  12. 2023An LLM Compiler for Parallel Function Callingcited by 170
  13. 2019Planning with Goal-Conditioned Policiescited by 144
  14. 2023ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Searchcited by 103
  15. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  16. 2023AgentSims: An Open-Source Sandbox for Large Language Model Evaluationcited by 86
  17. 2023Humanoid Agents: Platform for Simulating Human-like Generative Agentscited by 73
  18. 2023Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agentscited by 67
  19. 2023Dynamic Planning with a LLMcited by 60
  20. 2024Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Toolscited by 47
  21. 2023CoPAL: Corrective Planning of Robot Actions with Large Language Modelscited by 39
  22. 2024Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programmingcited by 35
  23. 2024Thought of Search: Planning with Language Models Through The Lens of Efficiencycited by 32
  24. 2024LiP-LLM: Integrating Linear Programming and dependency graph with Large Language Models for multi-robot task planningcited by 23
  25. 2024Planning in the Dark: LLM-Symbolic Planning Pipeline without Expertscited by 15
  26. 2024Budget-Constrained Tool Learning with Planningcited by 11
  27. 2025Infected Smallville: How Disease Threat Shapes Sociality in LLM Agentscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Exploration and Exploitation Errors Are Measurable for Language Model Agentscited by 2
  2. 2026Human-on-the-Loop Orchestration for AI-Assisted Legal Discoverycited by 0
  3. 2026SGG-ReflAct: Sub-Goal Guided ReflAct with Structured Planning for Reliable Long-Horizon Reasoningcited by 0
  4. 2026Consistent Plan-Act for Long-Horizon Agentic Taskscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints
  2. 2025Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning
  3. 2026From Task Solving to Robust Real-World Adaptation in LLM Agents
  4. 2026OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
  5. 2026Harness Engineering for Predictable Agentic Systems: An Empirical Study of Deterministic Execution Constraints
  6. 2024One STEP at a time: Language Agents are Stepwise Planners
  7. 2026Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
  8. 2026Toward Efficient Agents: Memory, Tool learning, and Planning