Large Multimodal Agents

• Large Multimodal Agents are intelligent systems that fuse large language models with various sensory inputs to enable perception, reasoning, planning, and action.
• They integrate unified cross-modal encoders, chain-of-thought planning, and memory modules with external tool interfaces to perform dynamic and collaborative tasks.
• LMAs are applied in domains such as digital assistance, robotics, and scientific document processing, while research continues to address challenges in security, evaluation, and explainability.

Large Multimodal Agents (LMAs) are intelligent systems built upon large-scale neural models—most notably LLMs extended with vision, audio, and other multimodal capabilities—that can perceive, reason, plan, and act in complex, open-ended environments by dynamically integrating input from multiple modalities. LMAs are increasingly deployed for tasks such as digital assistants, robotics, web and GUI automation, social simulation, code generation, and scientific document processing. Their architectures typically combine advanced foundation models with memory and planning modules, external tool use, and collaborative protocols, positioning LMAs as foundational building blocks for real-world generalist AI.

1. Principal Architectural Components and Modes of Operation

A standard LMA comprises four principal functional components: perception, planning, action, and memory (Xie et al., 23 Feb 2024). Perception modules ingest multimodal signals (text, images, audio, video, etc.), transforming them into a unified embedding space using joint encoders and cross-attention mechanisms. Planning modules perform stepwise decision-making—either dynamically (with chain-of-thought decomposition) or statically (using immediate feedback)—producing natural language/instructional plans or executable commands. Action modules translate these plans into concrete operations, invoking tools, performing robotic or simulated movements, or manipulating user interfaces. Memory subsystems, which may combine short-term working memory with long-term repositories, facilitate both rapid context access and retrieval of episodic or key–value structured experiences:

Component	Function	Example Mechanism
Perception	Multimodal encoding	Unified embedding via CLIP, cross-attention
Planning	Reasoning & plan creation	Chain-of-thought, tree-of-thought, in-context memory
Action	Task execution	Tool APIs, simulated input, robotic control
Memory	Persistent contextual store	Key–value, vector DB, CLIP-based multimodal recall

Advanced agents increasingly incorporate external module interfaces for knowledge retrieval (RAG), symbolic reasoning, or execution of specialized code (Jiang et al., 1 Jun 2024).

2. Categorization and Collaboration Paradigms

Recent surveys delineate LMA system designs into four progressive categories (Xie et al., 23 Feb 2024):

• Type I: Prompt-based planning via closed-source LLMs without long-term memory.
• Type II: Finetuned open-source LLMs as planners, also lacking explicit long-term memory.
• Type III: Planners that interact with long-term memory through retrieval modules or tools.
• Type IV: Native integration of long-term memory directly into the planner module.

Collaboration is increasingly central, with multi-agent frameworks distributing cognitive load. Tasks may be divided among specialist agents (e.g., perception, planning, or monitoring), coordinated via explicit protocols or emergent behaviors. Hybrid collaboration—combining horizontal (parallel) and vertical (hierarchical/sequential) task decomposition—is prevalent in scalable agent societies, as exemplified by MegaAgent’s OS-like hierarchical agent orchestration with O(log n) communication complexity for n agents (Wang et al., 19 Aug 2024). Collaborative paradigms also support data, computation, and knowledge sharing (see Section 3).

3. Multimodal Integration, Knowledge, and Computation Cooperation

LMAs are defined by their ability to seamlessly fuse multiple input modalities at both the perception and decision layers. Mechanisms such as cross-attention, unified multimodal embeddings, and joint memory spaces underpin their semantic alignment capabilities (Jeong, 1 Jan 2025). In knowledge cooperation, agents synchronize parametric and external knowledge (e.g., knowledge graphs, RAG) and engage in distributed search and explicit extraction (Wang et al., 22 Sep 2024). Computation is distributed via horizontal (parallel) and vertical (pipeline) paradigms, enabling efficient distributed problem solving on heterogeneous tasks. Digital twin technologies and AR/VR interfaces further extend the agent-environment interaction loop, enabling real-time feedback and synchronization for embodied or cyber-physical deployments.

4. Evaluation Frameworks, Performance, and Limits

LMAs are evaluated using a mix of subjective (human judgment: versatility, safety, user experience) and objective (task accuracy, completion rate, reward, SSIM, AES) metrics (Xie et al., 23 Feb 2024, Liu et al., 12 Aug 2024, Zhang et al., 5 Dec 2024). Representative benchmarks include:

• VisualAgentBench: Embodied, GUI, and visual design tasks with trajectory-based success rates and SSIM thresholds for visual similarity (Liu et al., 12 Aug 2024).
• MageBench: Vision-in-the-chain (ViC) POMDP-based reasoning, testing dynamic planning with continuous visual feedback (Zhang et al., 5 Dec 2024).
• RiOSWorld: Safety risk evaluation for agents manipulating real-world desktop environments, measured along intention and completion (Yang et al., 31 May 2025).
• SafeMobile: Chain-level jailbreak defense, combining trajectory-level risk scoring with LLM-based automated evaluation (Liang et al., 1 Jul 2025).

Current LMAs have not yet achieved human-level performance in complex, interactive settings—struggling with dynamic planning based on visual feedback, long-horizon reasoning, and robust error correction (see MageBench results (Zhang et al., 5 Dec 2024)). Scalability in collaborative frameworks (e.g., MegaAgent supporting 590 agents in 3000 seconds (Wang et al., 19 Aug 2024)) and agentic efficiency in cost-sensitive applications (e.g., two-tier phishing detection with a 4.2x–2.6x improvement in sites processed per \$100 (Trad et al., 3 Dec 2024)) have been demonstrated.

5. Security, Privacy, and Trustworthiness

Security and privacy are primary concerns. LMAs are vulnerable to cross-modal prompt injection, wherein adversaries align adversarial signals across both the visual and textual modalities to subvert agent behavior—CrossInject increases success rates by +26.4% over baselines, effectively hijacking agent policies even in autonomous driving (Wang et al., 19 Apr 2025). Agents further face risks of hallucination, adversarial attacks, poisoning/backdoor attacks, LM memorization leakage, and model/prompt stealing (Wang et al., 22 Sep 2024). Countermeasures include data sanitization, adversarial training, reinforced instruction tuning, post-processing self-checks, differential privacy, red-teaming, and advanced chain-level discrimination and preference optimization (Liang et al., 1 Jul 2025).

The agent ontology is also under question: LMAs are inherently stateless, stochastic, semantically sensitive, and linguistically intermediated, challenging the usual properties of identifiability, continuity, persistence, and consistency (Perrier et al., 4 Feb 2025). Scaffolding with external memory and planning modules offers partial mitigation but cannot fully resolve core limitations rooted in the LLM backbone.

6. Applications and Societal-Scale Impact

LMAs are deployed across diverse application domains:

• Automation: GUI/web control, RAG-based knowledge automation, enterprise process orchestration (Jeong, 1 Jan 2025).
• Robotics/Embodied AI: Real-world navigation, manipulation, and multi-agent simulation in e-commerce and social environments (Liu et al., 12 Dec 2024).
• Software Engineering: Autonomous code generation, review, integration, and project orchestration (see Software Engineering 2.0 (He et al., 7 Apr 2024)).
• Defense and Security: Phishing and cyberrisk detection using multimodal fusion and cost-optimized agentic scheduling (Trad et al., 3 Dec 2024, Yang et al., 31 May 2025).
• Scientific Document Processing: Automated layout parsing, semantic editing, and summarization with multi-agent orchestration—DocRefine achieves SCS 86.7%, LFI 93.9%, IAR 85.0% on DocEditBench (Qian et al., 9 Aug 2025).
• Game AI: Role-playing, reasoning, control of avatars or environments, interpreter-driven cognitive modeling (Xu et al., 15 Mar 2024).

Collective intelligence at scale is achieved in agent societies via memory-augmented, self-consistency prompted, and small-world topologically structured networks supporting phenomena like herd behavior and emergent trends (Liu et al., 12 Dec 2024). MLAS architectures underlie a shift toward enterprise monetization, privacy-respecting agent specialization, and agent-as-a-service business models (Yang et al., 21 Nov 2024).

7. Challenges, Limitations, and Future Research

Unresolved challenges include:

• Robustness and Rationality: Ensuring consistency, grounding, and rationality across diverse situations (formalized via preference orderings and invariance criteria) remains open (Jiang et al., 1 Jun 2024).
• Evaluation and Benchmarking: The community requires standardized, comprehensive, and risk-sensitive evaluation protocols reflecting real-world agent deployments (Yang et al., 31 May 2025, Liang et al., 1 Jul 2025).
• Security by Design: Development of robust hybrid defense frameworks that monitor cross-modal consistency, employ advanced adversarial training, and automate chain-level risk discrimination is imperative (Wang et al., 19 Apr 2025, Liang et al., 1 Jul 2025).
• Intrinsic Generalization: Moving beyond large-scale finetuning via few-shot and meta-learning adaptation (as in AdaptAgent) increases adaptability with minimal extra supervision (Verma et al., 20 Nov 2024).
• Explainability, Fairness, and Green AI: Progress toward transparent, unbiased, and energy-efficient agent architectures is essential for societal deployment (Wang et al., 22 Sep 2024).

The field is converging on modular, collaborative, and privacy-respecting LMA designs, moving from isolated prompt-based systems to rich, ecosystem-level collective intelligence guided by cooperative protocols and risk-aware alignment.

The emergent Large Multimodal Agent paradigm thus represents an overview of foundational models, modular engineering, and large-scale system-oriented design. It is defined by its capacity for data fusion, autonomous action, collaboration, and adaptation, while simultaneously challenged by unresolved questions of reliability, interpretability, and scalable safety. Ongoing progress is documented via dedicated benchmark suites, open agent frameworks, and up-to-date community resources such as https://github.com/jun0wanan/awesome-large-multimodal-agents (Xie et al., 23 Feb 2024).