Agent-ScanKit: Unraveling Memory and Reasoning of Multimodal Agents via Sensitivity Perturbations

• URL: http://arxiv.org/abs/2510.00496v3
• Date: Fri, 03 Oct 2025 04:20:33 GMT
• Title: Agent-ScanKit: Unraveling Memory and Reasoning of Multimodal Agents via Sensitivity Perturbations
• Authors: Pengzhou Cheng, Lingzhong Dong, Zeng Wu, Zongru Wu, Xiangru Tang, Chengwei Qin, Zhuosheng Zhang, Gongshen Liu,
• Abstract summary: We propose textbfAgent-ScanKit to unravel the memory and reasoning capabilities of multimodal agents under controlled perturbations.<n>Results show that mechanical memorization often outweighs systematic reasoning.<n>Our findings underscore the necessity of robust reasoning modeling for multimodal agents in real-world scenarios.
• Score: 34.15274225730601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although numerous strategies have recently been proposed to enhance the autonomous interaction capabilities of multimodal agents in graphical user interface (GUI), their reliability remains limited when faced with complex or out-of-domain tasks. This raises a fundamental question: Are existing multimodal agents reasoning spuriously? In this paper, we propose \textbf{Agent-ScanKit}, a systematic probing framework to unravel the memory and reasoning capabilities of multimodal agents under controlled perturbations. Specifically, we introduce three orthogonal probing paradigms: visual-guided, text-guided, and structure-guided, each designed to quantify the contributions of memorization and reasoning without requiring access to model internals. In five publicly available GUI benchmarks involving 18 multimodal agents, the results demonstrate that mechanical memorization often outweighs systematic reasoning. Most of the models function predominantly as retrievers of training-aligned knowledge, exhibiting limited generalization. Our findings underscore the necessity of robust reasoning modeling for multimodal agents in real-world scenarios, offering valuable insights toward the development of reliable multimodal agents.

Related papers

• BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents [30.849897676091327]
  Multimodal large language models (MLLMs) are evolving into autonomous agents capable of performing multimodal web browsing and deep search in open-world environments.<n>We introduce BrowseComp-$V3$, a novel benchmark consisting of 300 carefully curated and challenging questions spanning diverse domains.<n>Our results highlight a fundamental gap between current model capabilities and robust multimodal deep search in real-world settings.
  arXiv  Detail & Related papers  (2026-02-13T12:25:13Z)
• Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems [75.78934957242403]
  Self-driving vehicles and drones require true Spatial Intelligence from multi-modal onboard sensor data.<n>This paper presents a framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal.
  arXiv  Detail & Related papers  (2025-12-30T17:58:01Z)
• Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything [12.274140974616747]
  Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs.<n>We propose an Agent- Omni framework that coordinates existing foundation models through a master-agent system.
  arXiv  Detail & Related papers  (2025-11-04T18:59:09Z)
• NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching [64.10695425442164]
  We introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms.<n>Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal generation and understanding benchmarks.<n>To advance further research, we release training details, data protocols, and open-source both the code and model checkpoints.
  arXiv  Detail & Related papers  (2025-10-15T16:25:18Z)
• MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation [64.85885900375483]
  MEXA is a training-free framework that performs modality- and task-aware aggregation of expert models.<n>We evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA.
  arXiv  Detail & Related papers  (2025-06-20T16:14:13Z)
• Rethinking Explainability in the Era of Multimodal AI [9.57008593971486]
  multimodal AI systems have become ubiquitous and achieved remarkable performance across high-stakes applications.<n>Most existing explainability techniques remain unimodal, generating modality-specific feature attributions, concepts, or circuit traces in isolation.<n>This paper argues that such unimodal explanations systematically misrepresent and fail to capture the cross-modal influence that drives multimodal model decisions.
  arXiv  Detail & Related papers  (2025-06-16T03:08:29Z)
• Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning [0.41783829807634765]
  Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex problems in robotics or games.<n>This paper advocates for direct interpretability, generating post hoc explanations directly from trained models.<n>We explore modern methods, including relevance backpropagation, knowledge edition, model steering, activation patching, sparse autoencoders and circuit discovery.
  arXiv  Detail & Related papers  (2025-02-02T09:15:27Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering [0.0]
  In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA) We propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework.
  arXiv  Detail & Related papers  (2024-08-24T19:34:04Z)
• Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
  This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
  arXiv  Detail & Related papers  (2024-07-04T14:36:49Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Multi-Agent Verification and Control with Probabilistic Model Checking [4.56877715768796]
  Probabilistic model checking is a technique for formal automated reasoning about software or hardware systems. It builds upon ideas and techniques from a diverse range of fields, from logic, automata and graph theory, to optimisation, numerical methods and control. In recent years, probabilistic model checking has also been extended to integrate ideas from game theory.
  arXiv  Detail & Related papers  (2023-08-05T09:31:32Z)
• How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents [9.840104333194663]
  We argue for hierarchy in the design of representation models and contribute with a novel multimodal representation model, MUSE. MUSE is the sensory representation model of deep reinforcement learning agents provided with multimodal observations in Atari games. We perform a comparative study over different designs of reinforcement learning agents, showing that MUSE allows agents to perform tasks under incomplete perceptual experience with minimal performance loss.
  arXiv  Detail & Related papers  (2021-10-07T16:35:23Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)

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.