Agentic Multimodal LLMs

• Agentic Multimodal LLMs are defined as models that recast task execution as adaptable policies via Markov decision processes rather than static pipelines.
• They integrate specialized modules for perception, reasoning, planning, memory management, and tool use to enable proactive, multimodal workflows.
• Empirical studies highlight their effectiveness across sectors like deep research, robotics, and healthcare while addressing challenges in scalability and safety.

Agentic Multimodal LLMs (MLLMs) represent a new paradigm in artificial intelligence, characterized by systems that autonomously perceive, reason, plan, interact, and adapt across multimodal information streams and dynamic environments. Unlike conventional multimodal LLM agents that solve tasks through static, developer-designed pipelines, Agentic MLLMs implement adaptive policies capable of proactive goal-directed behavior, leveraging internal reasoning, long-horizon planning, reflection, memory management, tool-use, and physical or virtual embodiment. This agentic turn is underpinned by formalisms from Markov decision processes and reinforced by advances in multimodal learning, scalable memory architectures, and real-world tool integration, as detailed in recent foundational surveys and systematizations (Huang et al., 20 Mar 2025, Yao et al., 13 Oct 2025, Huh et al., 10 Aug 2025, Hu et al., 5 Mar 2025, Fu et al., 6 Oct 2025).

1. Conceptual Distinctions and Formal Foundations

Agentic MLLMs fundamentally diverge from conventional (static) MLLM-based agents by recasting task execution as a Markov decision process (MDP) rather than a pre-defined function pipeline. In a static agent, task solution is represented as

$$\text{Agent}_{\mathrm{MLLM}}(x_1) = f_{T}\bigl(f_{T-1}(\dots f_{1}(x_1)))\,,$$

where each stage $f_i(x_i) = \mathrm{MLLM}(p_i, x_i)$ is invoked with a fixed prompt $p_i$ determined by the developer (Yao et al., 13 Oct 2025).

In contrast, Agentic MLLMs operate via a learned (or adaptable) policy $\pi$ over an MDP:

$$s_{t+1} = \delta(s_t, a_t),\quad a_t \sim \pi(a|s_t)\,,$$

$$\pi^* = \arg\max_\pi \mathbb{E}_{x \sim \mathcal{D}}\left[\sum_{t=0}^T\gamma^t r(s_t, a_t; x)\right]\,,$$

where the model observes state $s_t$, selects an action $a_t$ from action space $\mathcal{A}$, and interacts with environment $\mathcal{E}$ (Yao et al., 13 Oct 2025). This formalism enables dynamic workflows, proactive action selection, and cross-domain adaptability, with reasoning and interaction guided by internally optimized rewards or objectives.

2. Core Architectural Components

A canonical Agentic MLLM comprises multiple specialized modules integrated into a unified agentic architecture. The following components are consistently identified in both surveys and system blueprints (Huang et al., 20 Mar 2025, Huh et al., 10 Aug 2025, Hu et al., 5 Mar 2025, Yao et al., 13 Oct 2025):

• Perception Module: Family of modality-specific encoders $$\{\mathrm{Enc}_{\text{text}}, \mathrm{Enc}_{\text{image}}, \mathrm{Enc}_{\text{audio}}, \ldots\}$$ map each input $x^{(m)}$ to an embedding $$e^{(m)} = \mathrm{Enc}_m(x^{(m)}; \theta_m)\in\mathbb{R}^d$$.
• Multimodal Reasoning Unit: Implements cross-attention or fusion mechanisms, supporting chain-of-thought (CoT) or tree-of-thought reasoning. Symbolic tool outputs (e.g., search results) are integrated into reasoning loops via protocols such as ReAct (Huang et al., 20 Mar 2025).
• Planning Engine: Models deliberation as hierarchical MDP planning. High-level planners decompose global objectives into subgoal sequences, while low-level planners solve each sub-task. The formal objective is:

$$\max_{\pi_{\text{high}}, \pi_{\text{low}}} \mathbb{E}\left[\sum_{k=1}^K\sum_{t\in T_k}\gamma^t r_t\right]$$

• Memory System: Maintains and retrieves both short- and long-term memory via attention mechanisms:

$$w_i = \frac{\exp(h_t^\top m_i/\tau)}{\sum_j \exp(h_t^\top m_j/\tau)},\quad m_{\text{read}} = \sum_i w_i m_i$$

Memory updates: $$M(u,t+1) = M(u,t) + \eta(\Delta M(u,t)), \Delta M(u,t)=\phi(h_t)$$.

• Tool-Use Interface: Maintains a registry of external tools (e.g., Search, Retrieve, Summarize, Rank) and implements adaptive, context-dependent invocation based on utility scores $U_{\text{tool}}(s_t, j)$.
• Driver and Monitoring Systems: Implement global drives, persona injection, and behavioral guardrails to enforce high-level agentic constraints (Hu et al., 5 Mar 2025).

Multimodal Fusion Mechanisms

Fusion of multimodal information is achieved via:

• Concatenation + Projection: $$e_{\text{fusion}} = W_{\text{cat}}[e^{(\text{text})}\,\|\,e^{(\text{img})}] + b$$
• Cross-Attention: See above, with $$A = \text{softmax}\left(\frac{E_q E_k^\top}{\sqrt{d}}\right)E_v$$
• Graph-Based Fusion: Graph attention treats input units as nodes, passing information via attention-weighted neighbors.

3. Internal Intelligence: Reasoning, Memory, and Reflection

Reasoning: Agentic MLLMs augment standard CoT reasoning with supervised fine-tuning (SFT) on CoT datasets (e.g., MAVIS, LLaVA-CoT-100K) and reinforcement learning (RL) objectives:

$$\mathcal{L}_{\text{MLE}}(\theta) = -\sum_{t=1}^T \log p_\theta(x_t|x_{<t})$$

$$\mathcal{J}_{\text{PPO}}(\theta) = \mathbb{E}\left[\min(\rho_t(\theta)A_t, \text{clip}(\rho_t(\theta),1-\epsilon,1+\epsilon)A_t)\right]$$

Reflection: Both implicit (emerging through RL) and explicit (via “reflection” prompts at response- or step-level) mechanisms improve error recognition and policy adjustment (Yao et al., 13 Oct 2025).

Memory: Context length is extended via token compression (Q-Former, pooling) and window extension modules; external memory is implemented as vector stores with heuristic or reasoning-driven retrieval (A-Mem, MemTool, RMM) (Yao et al., 13 Oct 2025, Huh et al., 10 Aug 2025). Decentralized RAG (retrieval-augmented generation) architectures allow per-agent memory in multi-agent contexts (Huh et al., 10 Aug 2025).

Alignment: Internal intelligence is further guided by preference-based RL (e.g., Nash Mirror Descent) and centralized LLM-based feedback (Huh et al., 10 Aug 2025).

4. Agentic Tool Use and Environment Interaction

External Tool Invocation: Tools are invoked via explicit action tokens or JSON-formatted commands:

• Search, Code Execution, Visual Processing: A diverse registry is maintained and accessed adaptively via internal policy (Yao et al., 13 Oct 2025).
• Adaptive and Contextual Tool Use: Utility-driven selection ($U_{\mathrm{tool}}(s_t, j)$) ensures invocation is contingent on environmental uncertainty or agentic intent (Huang et al., 20 Mar 2025).
• Workflow Orchestration: Closed-loop workflows are planned via graph-based controllers (e.g., ContextNav’s Operational Grammar Graph), supporting both pre-defined and adaptive sequencing of modalities, retrieval, denoising, and reasoning (Fu et al., 6 Oct 2025).

Environment Interaction:

• Virtual Embodiment: GUI and Web agents use offline demonstration-based SFT, RL-based fine-tuning, and interactive RL with error correction (Yao et al., 13 Oct 2025).
• Physical Embodiment: Robotic agents ground visual and textual commands into action sequences, leveraging both perception-driven planning and navigation modules.

5. Multi-Agentic and Adaptive Architectures

Multi-Agentic MLLMs: Extensions to decentralized, multi-agent environments employ:

• Structured Prompt Pipelines: Chains encode roles, task rules, and communication protocols.
• Decentralized Retrieval-Augmented Memory: Each agent maintains its own FAISS- or vector-database-backed memory, supporting repeated interaction and policy alignment (Huh et al., 10 Aug 2025).
• Communication Protocols and Mechanism Design: Explicit communication rounds are scored and regulated to partition “cheap talk” from incentivized strategic discourse; policy gradient RL aligns agent outputs with solution concepts such as Nash equilibria and Pareto efficiency.
• Adaptive Workflow Planning: Agents plan toolchains and reflection via feedback-linked memory, as in ContextNav’s closed-loop, graph-driven orchestration (Fu et al., 6 Oct 2025).

6. Applications and Empirical Evaluations

Application Domain	Representative Systems	Notes on Evaluation and Impact
Deep Research, Retrieval	OpenAI DR, Gemini DR, RecoWorld, LLM-ARS	Multi-step literature synthesis, proactive recommendation
Embodied/Physical Agents	OpenVLA, Wall-X, OctoNav, Nav-R1	Robotic navigation, manipulation with RL adaptation
Healthcare	MedTVT-R1, MMed-RAG, GEM-ECG, Patho-AgenticRAG	Diagnostic QA, procedural guidance, robotic surgery
GUI and Virtual Agents	UI-R1, GUI-R1, ZeroGUI, WebAgent-R1	Task automation, workflow repair, self-improvement
In-Context Learning	ContextNav	Closed-loop context curation, robust multimodal ICL gains

• Recommendation: Agentic recommenders (LLM-ARS) leverage planning, memory, and multimodal context for proactive, personalized, and interactive suggestions; impact is assessed via metrics like cumulative NDCG, recall@K, session-level retention, and click-through rates (Huang et al., 20 Mar 2025).
• Game-Theoretic Decision Making: Multi-agentic MLLMs achieve robust equilibrium strategies—e.g., higher pure NE convergence, improved exploitative play, and efficient communication—when equipped with memory, fine-tuning, and reflection modules (Huh et al., 10 Aug 2025).
• Contextual Multimodal In-Context Learning: Agentic curation mechanisms (ContextNav) yield a mean ICL gain of $16.8\%$, outperforming prior SOTA, with semantic/structural noise ablations quantitatively validating each component (Fu et al., 6 Oct 2025).

7. Key Challenges and Future Directions

Major open problems and future research avenues are articulated as follows (Yao et al., 13 Oct 2025, Huang et al., 20 Mar 2025, Fu et al., 6 Oct 2025):

• Richer Action Spaces: Expanding tool interface coverage beyond current search/code/vision APIs.
• Efficiency and Scalability: Acceleration of long-chain reasoning, tool calls, and robust training for larger, more diverse environments (e.g., multi-agent, open-ended).
• Long-Term Multimodal Memory: Architectures for scalable, cross-modal memory spanning extensive temporal horizons.
• Data and Benchmarks: Public multimodal agentic trajectory datasets for (CPT/SFT/RL) training; robust evaluation protocols for memory, tool coordination, and end-to-end agency.
• Safety, Trust, and Controllability: Guardrails via constitution modules, prompt-level constraints, human-in-the-loop overrides, adversarial robustness, provable guarantees, and reward hacking resistance.
• Stability in Multimodal Fusion: Addressing instabilities in fusion layers (e.g., cross-attention) for large-scale deployments.
• Theoretical Analysis: Need for stronger theoretical guarantees on equilibrium convergence, regret, and generalization in agentic, language-mediated settings.

Key survey and primary systematization resources include:

• "A Survey on Agentic Multimodal LLMs" (Yao et al., 13 Oct 2025)
• "Towards Agentic Recommender Systems in the Era of Multimodal LLMs" (Huang et al., 20 Mar 2025)
• "Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs" (Huh et al., 10 Aug 2025)
• "Unified Mind Model: Reimagining Autonomous Agents in the LLM Era" (Hu et al., 5 Mar 2025)
• "ContextNav: Towards Agentic Multimodal In-Context Learning" (Fu et al., 6 Oct 2025)

This comprehensive architecture and methodological taxonomy provides the current foundation for research and development in Agentic Multimodal LLMs, with forward progress depending on advances in dynamic workflow orchestration, scalable memory, safety, and robust real-world evaluation.