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

• URL: http://arxiv.org/abs/2602.19633v1
• Date: Mon, 23 Feb 2026 09:19:56 GMT
• Title: TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents
• Authors: Jongwon Jeong, Jungtaek Kim, Kangwook Lee,
• Abstract summary: We propose Tool-guided Adaptive Planning with constrained Execution (TAPE)<n>TAPE planning capability by aggregating multiple plans into a graph and employing an external solver to identify a feasible path.<n>During execution, TAPE employs constrained decoding to reduce sampling noise, while adaptively re-planning whenever environmental feedback deviates from the intended state.
• Score: 16.59223734824801
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• MagicAgent: Towards Generalized Agent Planning [73.21129030631421]
  We present textbfMagicAgent, a series of foundation models specifically designed for generalized agent planning.<n>We introduce a lightweight and scalable synthetic data framework that generates high-quality trajectories across diverse planning tasks.<n> Empirical results demonstrate that MagicAgent-32B and MagicAgent-30B-A3B deliver superior performance.
  arXiv  Detail & Related papers  (2026-02-22T01:39:16Z)
• TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration [64.32072516882947]
  Diffusion Policy excels in embodied control but suffers from high inference latency and computational cost.<n>We propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP)<n>TS-DP achieves up to 4.17 times faster inference with over 94% accepted drafts, reaching an inference frequency of 25 Hz.
  arXiv  Detail & Related papers  (2025-12-13T07:53:14Z)
• Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning [52.03884701766989]
  offline reinforcement learning (RL) algorithms typically impose constraints on action selection.<n>We propose a new neighborhood constraint that restricts action selection in the Bellman target to the union of neighborhoods of dataset actions.<n>We develop a simple yet effective algorithm, Adaptive Neighborhood-constrained Q learning (ANQ), to perform Q learning with target actions satisfying this constraint.
  arXiv  Detail & Related papers  (2025-11-04T13:42:05Z)
• SDA-PLANNER: State-Dependency Aware Adaptive Planner for Embodied Task Planning [22.01842981739722]
  Embodied task planning requires agents to produce executable actions in a close-loop manner within the environment.<n>We propose SDA-PLANNER, enabling an adaptive planning paradigm, state-dependency aware and error-aware mechanisms for comprehensive embodied task planning.
  arXiv  Detail & Related papers  (2025-09-30T15:07:59Z)
• HiPlan: Hierarchical Planning for LLM-Based Agents with Adaptive Global-Local Guidance [11.621973074884002]
  HiPlan is a hierarchical planning framework for large language model (LLM)-based agents.<n>It decomposes complex tasks into milestone action guides for general direction and step-wise hints for detailed actions.<n>In the offline phase, we construct a milestone library from expert demonstrations, enabling structured experience reuse.<n>In the execution phase, trajectory segments from past milestones are dynamically adapted to generate step-wise hints.
  arXiv  Detail & Related papers  (2025-08-26T14:37:48Z)
• Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation [0.913755431537592]
  This work introduces NL2Flow, a fully automated pipeline for generating and evaluating workflow planning problems.<n> NL2Flow generates problems parametrically in a structured intermediate representation, translating them into both natural language and formal PDDL.<n>I evaluate several open-source, instruct-tuned LLMs on a dataset of 2296 low-difficulty problems generated by NL2Flow.
  arXiv  Detail & Related papers  (2025-07-03T03:02:49Z)
• Flex-TravelPlanner: A Benchmark for Flexible Planning with Language Agents [16.295418365993033]
  We introduce Flex-TravelPlanner, a benchmark that evaluates language models' ability to reason flexibly in dynamic planning scenarios.<n>Our analysis of GPT-4o and Llama 3.1 70B reveals several key findings.
  arXiv  Detail & Related papers  (2025-06-05T05:31:50Z)
• Reinforced Reasoning for Embodied Planning [18.40186665383579]
  Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals.<n>We introduce a reinforcement fine-tuning framework that brings R1-style reasoning enhancement into embodied planning.
  arXiv  Detail & Related papers  (2025-05-28T07:21:37Z)
• Improving Large Language Model Planning with Action Sequence Similarity [50.52049888490524]
  In this work, we explore how to improve the model planning capability through in-context learning (ICL)<n>We propose GRASE-DC: a two-stage pipeline that first re-samples high AS exemplars and then curates the selected exemplars.<n>Our experimental result confirms that GRASE-DC achieves significant performance improvement on various planning tasks.
  arXiv  Detail & Related papers  (2025-05-02T05:16:17Z)
• PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving [89.60370366013142]
  We propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents.<n>Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms.
  arXiv  Detail & Related papers  (2025-02-22T06:21:56Z)
• ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models [38.89166693142495]
  ET-Plan-Bench is a benchmark for embodied task planning using Large Language Models (LLMs)<n>It features a controllable and diverse set of embodied tasks varying in different levels of difficulties and complexities.<n>Our benchmark distinguishes itself as a large-scale, quantifiable, highly automated, and fine-grained diagnostic framework.
  arXiv  Detail & Related papers  (2024-10-02T19:56:38Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)
• Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning [36.300564378022315]
  We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles. The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
  arXiv  Detail & Related papers  (2022-11-03T04:12:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.