A Neurosymbolic Agent System for Compositional Visual Reasoning

• URL: http://arxiv.org/abs/2506.07778v3
• Date: Sat, 04 Oct 2025 20:40:32 GMT
• Title: A Neurosymbolic Agent System for Compositional Visual Reasoning
• Authors: Yichang Xu, Gaowen Liu, Ramana Rao Kompella, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Zachary Yahn, Ling Liu,
• Abstract summary: Existing vision-language models (VLMs) remain challenged by compositional visual reasoning.<n>This paper presents a neuro-symbolic approach to developing a Vision-Language Agent system for efficient compositional visual reasoning.
• Score: 31.649454833851863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advancement in large language models (LLMs) and large vision models has fueled the rapid progress in multi-modal vision-language reasoning capabilities. However, existing vision-language models (VLMs) remain challenged by compositional visual reasoning. This paper presents VLAgent, a neuro-symbolic approach to developing a Vision-Language Agent system for efficient compositional visual reasoning with three novel features. First, VLAgent develops an interpretable visualization-enhanced two-stage neuro-symbolic reasoning system. The first stage is managed by a front-end engine that generates a structured visual reasoning plan (symbolic program script) for each compositional visual reasoning task by utilizing a pre-trained LLM powered with few-shot chain-of-thought in-context learning. The second stage is managed by a high-performance back-end engine. It transforms the planning script into executable code based on visual input (image or video) and the combination of neural models and symbolic functions and then performs a sequence of actions for the compositional visual reason task. Second, to ensure and enhance the quality of mapping the logic plan to a sequence of executable instructions, VLAgent introduces the SS-parser, which examines the syntax and semantic correctness of the planning script, detects and repairs the logic errors found in the LLM-generated logic plan before generating the executable program. Third, VLAgent introduces the execution verifier in critical reasoning steps to validate and refine its compositional reasoning results in a stepwise manner, for example, ensemble methods for critical visual reasoning and caption analysis for low-confidence compositional reasoning. Extensive experiments on six visual benchmarks compared to a dozen SoTA visual reasoning models show that VLAgent outperforms existing representative approaches to compositional visual reasoning.

Related papers

• Synthesizing Visual Concepts as Vision-Language Programs [39.89156658712712]
  Vision-Language models (VLMs) achieve strong performance on multimodal tasks but often fail at systematic visual reasoning tasks.<n>We propose Vision-Language Programs, which combine the perceptual flexibility of VLMs with systematic reasoning of program synthesis.
  arXiv  Detail & Related papers  (2025-11-24T10:30:33Z)
• MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning [66.35003870737727]
  A promising approach uses code as an intermediate representation to precisely express and manipulate the images in the reasoning steps.<n>Existing evaluations focus mainly on text-only reasoning outputs, leaving the MLLM's ability to perform accurate visual operations via code largely unexplored.<n>This work takes a first step toward addressing that gap by evaluating MLLM's code-based capabilities in multi-modal mathematical reasoning.
  arXiv  Detail & Related papers  (2025-07-24T07:03:11Z)
• LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning [26.098281158573748]
  We introduce LLaPa, a vision-language model framework designed for multimodal procedural planning.<n>LLaPa generates executable action sequences from textual task descriptions and visual environmental images.<n>We enhance LLaPa with two auxiliary modules to improve procedural planning.
  arXiv  Detail & Related papers  (2025-07-11T11:18:49Z)
• VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots [44.99833362998488]
  We propose an architecture for automatically verifying high-level task plans before their execution in simulator or real-world environments.<n>The module uses the reasoning capabilities of the Large Language Models to evaluate logical coherence and identify potential gaps in the plan.<n>We contribute to improving the reliability and efficiency of task planning and addresses the critical need for robust pre-execution verification in autonomous systems.
  arXiv  Detail & Related papers  (2025-07-07T15:31:36Z)
• Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification [22.871255950998016]
  We introduce a novel framework for inference-time visual tokens scaling that enables MLLMs to perform verifier-guided reasoning over visual content.<n>Our method significantly outperforms existing approaches across diverse visual reasoning benchmarks.<n>These results demonstrate the promise of dynamic inference mechanisms for enabling fine-grained, context-aware visual reasoning in next-generation MLLMs.
  arXiv  Detail & Related papers  (2025-06-08T17:38:49Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• Integrating Visual Interpretation and Linguistic Reasoning for Math Problem Solving [61.992824291296444]
  Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language models (LLMs)<n>This paper proposes a paradigm shift: instead of training end-to-end vision-language reasoning models, we advocate for developing a decoupled reasoning framework.
  arXiv  Detail & Related papers  (2025-05-23T08:18:00Z)
• VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making [21.61801132083334]
  VIPER is a novel framework for multimodal instruction-based planning.<n>It integrates VLM-based perception with LLM-based reasoning.<n>We show that VIPER significantly outperforms state-of-the-art visual instruction-based planners.
  arXiv  Detail & Related papers  (2025-03-19T11:05:42Z)
• VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use [74.39058448757645]
  We present VipAct, an agent framework that enhances vision-language models (VLMs) VipAct consists of an orchestrator agent, which manages task requirement analysis, planning, and coordination, along with specialized agents that handle specific tasks. We evaluate VipAct on benchmarks featuring a diverse set of visual perception tasks, with experimental results demonstrating significant performance improvements.
  arXiv  Detail & Related papers  (2024-10-21T18:10:26Z)
• ExoViP: Step-by-step Verification and Exploration with Exoskeleton Modules for Compositional Visual Reasoning [27.725814615823687]
  We propose a "plug-and-play" method, ExoViP, to correct errors in both the planning and execution stages. We employ verification modules as "exoskeletons" to enhance current vision-language programming schemes.
  arXiv  Detail & Related papers  (2024-08-05T03:22:10Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning [79.38140606606126]
  We propose an algorithmic framework that fine-tunes vision-language models (VLMs) with reinforcement learning (RL) Our framework provides a task description and then prompts the VLM to generate chain-of-thought (CoT) reasoning. We demonstrate that our proposed framework enhances the decision-making capabilities of VLM agents across various tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:50:19Z)
• VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding [65.12464615430036]
  This paper introduces a Video Understanding and Reasoning Framework (VURF) based on the reasoning power of Large Language Models (LLMs)<n>Ours is a novel approach to extend the utility of LLMs in the context of video tasks, leveraging their capacity to generalize from minimal input and output demonstrations within a contextual framework.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models [73.40350756742231]
  Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are under-explored.
  arXiv  Detail & Related papers  (2024-02-12T18:21:14Z)
• TIC: Translate-Infer-Compile for accurate "text to plan" using LLMs and Logical Representations [0.0]
  We study the problem of generating plans for given natural language planning task requests. Our approach comprises of (a) translate: using an LLM only for generating a interpretable intermediate representation of natural language task description. We observe that using an LLM to only output the intermediate representation significantly reduces LLM errors.
  arXiv  Detail & Related papers  (2024-02-09T18:39:13Z)
• Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents [39.53593677934238]
  Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, current LLM-based agents frequently generate invalid or non-executable plans. This paper proposes a novel "Formal-LLM" framework for LLM-based agents by integrating the expressiveness of natural language and the precision of formal language.
  arXiv  Detail & Related papers  (2024-02-01T17:30:50Z)
• ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning [7.701407633867452]
  Large Language Models (LLMs) offer the potential to enhance the generalizability as task-agnostic planners. We introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process. We show that ISR-LLM is able to achieve markedly higher success rates in task accomplishments compared to state-of-the-art LLM-based planners.
  arXiv  Detail & Related papers  (2023-08-26T01:31:35Z)
• Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting [83.21164539349273]
  Pre-trained language models (PLMs) have played an increasing role in multimedia research. In this paper, we focus on exploring PLMs as a stand-alone model for vision-language reasoning tasks. We propose a novel transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP)
  arXiv  Detail & Related papers  (2023-06-01T07:19:28Z)
• See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning [60.43585179885355]
  We propose a novel framework named Interactive Prompting Visual Reasoner (IPVR) for few-shot knowledge-based visual reasoning. IPVR contains three stages, see, think and confirm. We conduct experiments on a range of knowledge-based visual reasoning datasets.
  arXiv  Detail & Related papers  (2023-01-12T18:59:50Z)

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.