LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems

• URL: http://arxiv.org/abs/2508.13371v1
• Date: Mon, 18 Aug 2025 21:21:21 GMT
• Title: LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
• Authors: Ronit Virwani, Ruchika Suryawanshi,
• Abstract summary: Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses.<n>Current state-of-the-art neural planning approaches struggle with complex domains.<n>LOOP is a novel neuro-symbolic planning framework that treats planning as an iterative conversation between neural and symbolic components.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic approaches use one-shot translation from natural language to formal plans, missing the opportunity for neural and symbolic components to work and refine solutions together. To address this gap, we develop LOOP -- a novel neuro-symbolic planning framework that treats planning as an iterative conversation between neural and symbolic components rather than simple translation. LOOP integrates 13 coordinated neural features including graph neural networks for spatial relationships, multi-agent validation for consensus-based correctness, hierarchical decomposition for complex task management, and causal memory that learns from both successes and failures. Unlike existing approaches, LOOP generates PDDL specifications, refines them iteratively based on symbolic feedback, and builds a causal knowledge base from execution traces. LOOP was evaluated on six standard IPC benchmark domains, where it achieved 85.8% success rate compared to LLM+P (55.0%), LLM-as-Planner (19.2%), and Tree-of-Thoughts (3.3%). This work shows that the key to reliable planning is not in choosing between neural networks or symbolic reasoners but it lies in making them actually ``talk'' to each other during the entire process. LOOP provides a thorough blueprint for building autonomous systems that can finally be trusted with critical real-world applications.

Related papers

• REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligence [7.533896918107583]
  This paper presents REASON, an integrated acceleration framework for probabilistic logical reasoning in neuro-symbolic AI.<n>REASON introduces a unified directed acyclic graph representation that captures common structure across symbolic and probabilistic models.<n>REASON is tightly integrated with GPU streaming multiprocessors through a programmable interface and multi-level pipeline.
  arXiv  Detail & Related papers  (2026-01-28T17:17:21Z)
• A Neuro-Symbolic Framework for Reasoning under Perceptual Uncertainty: Bridging Continuous Perception and Discrete Symbolic Planning [1.9236465591431287]
  We present a neuro-symbolic framework that explicitly models and propagates uncertainty from perception to planning.<n>We demonstrate the framework's effectiveness on tabletop robotic manipulation as a concrete application.
  arXiv  Detail & Related papers  (2025-11-18T14:38:01Z)
• Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning [51.54559117314768]
  Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of guidance to improve the performance of temporal planners.<n>We propose an evolution of this learning and planning framework that focuses on exploiting the information provided by symbolics during both the RL and planning phases.
  arXiv  Detail & Related papers  (2025-05-19T17:19:13Z)
• On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms [9.071347361654931]
  We assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient models.<n>We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency.
  arXiv  Detail & Related papers  (2025-02-13T03:29:42Z)
• Compositional Generalization Across Distributional Shifts with Sparse Tree Operations [77.5742801509364]
  We introduce a unified neurosymbolic architecture called the Differentiable Tree Machine.<n>We significantly increase the model's efficiency through the use of sparse vector representations of symbolic structures.<n>We enable its application beyond the restricted set of tree2tree problems to the more general class of seq2seq problems.
  arXiv  Detail & Related papers  (2024-12-18T17:20:19Z)
• NSP: A Neuro-Symbolic Natural Language Navigational Planner [6.841829967944275]
  We propose a neuro-symbolic framework for path planning from natural language inputs called NSP. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems.
  arXiv  Detail & Related papers  (2024-09-10T20:49:05Z)
• The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning [54.56905063752427]
  Neuro-Symbolic AI (NeSy) holds promise to ensure the safe deployment of AI systems. Existing pipelines that train the neural and symbolic components sequentially require extensive labelling. New architecture, NeSyGPT, fine-tunes a vision-language foundation model to extract symbolic features from raw data.
  arXiv  Detail & Related papers  (2024-02-02T20:33:14Z)
• A Framework for Neurosymbolic Robot Action Planning using Large Language Models [3.0501524254444767]
  We present a framework aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL) Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1,000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.
  arXiv  Detail & Related papers  (2023-03-01T11:54:22Z)
• Neuro-Symbolic Causal Language Planning with Commonsense Prompting [67.06667162430118]
  Language planning aims to implement complex high-level goals by decomposition into simpler low-level steps. Previous methods require either manual exemplars or annotated programs to acquire such ability from large language models. This paper proposes Neuro-Symbolic Causal Language Planner (CLAP) that elicits procedural knowledge from the LLMs with commonsense-infused prompting.
  arXiv  Detail & Related papers  (2022-06-06T22:09:52Z)
• Semantic Probabilistic Layers for Neuro-Symbolic Learning [83.25785999205932]
  We design a predictive layer for structured-output prediction (SOP) It can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space.
  arXiv  Detail & Related papers  (2022-06-01T12:02:38Z)
• SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming [15.814914345000574]
  We introduce SLASH -- a novel deep probabilistic programming language (DPPL) At its core, SLASH consists of Neural-Probabilistic Predicates (NPPs) and logical programs which are united via answer set programming. We evaluate SLASH on the benchmark data of MNIST addition as well as novel tasks for DPPLs such as missing data prediction and set prediction with state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-10-07T12:35:55Z)
• Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning [61.37385221479233]
  In this work, we take a step toward bridging the gap between model-based reinforcement learning and integrated symbolic-geometric robotic planning. NSRTs have both symbolic and neural components, enabling a bilevel planning scheme where symbolic AI planning in an outer loop guides continuous planning with neural models in an inner loop. NSRTs can be learned after only tens or hundreds of training episodes, and then used for fast planning in new tasks that require up to 60 actions to reach the goal.
  arXiv  Detail & Related papers  (2021-05-28T19:37:18Z)

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.