MLLMs as First-Class Operators

• MLLMs as first-class operators are multimodal models that internalize robust reasoning and control tasks through nuanced multi-level feedback and integrative computations.
• The AMP framework leverages automated multi-level preference ranking and MDPO to reduce hallucination errors by up to 43.3% and improve nuanced task handling.
• Hybrid architectures and advanced compression strategies enable scalable deployment, balancing computational cost against localized accuracy in complex real-world tasks.

Multimodal LLMs (MLLMs) as first-class operators represent a paradigm in which MLLMs advance from tools for ad hoc prediction or analysis to integral, dependable computational primitives capable of robust reasoning, decision-making, and control across diverse modalities. This elevation is realized not only through advances in architectural design but also via rigorous supervision strategies, robustness methodologies, cross-modal optimizations, and system-level integrations. The following sections synthesize current research and techniques delineating the transition and realization of MLLMs as first-class operators.

1. Transition from Binary to Multi-level Feedback: The AMP Framework

Traditional MLLM alignment frameworks employ binary preference classification (superior/inferior), which is inadequate for modeling subtle inter-response differences and typically fails to capture micro-hallucinations or nuanced context alignment. The Automated Multi-level Preference (AMP) framework generalizes this approach using multi-level ranks (e.g., superior, medium, inferior), thereby narrowing gaps among comparison levels and encouraging MLLMs to recognize fine-grained distinctions. Cross-level preference comparisons, rather than adjacent-only, supply the model with a richer array of negative samples, targeting hallucination behaviors more effectively.

AMP’s dataset generation is fully automated, leveraging two strategies:

• Multi-size Expert Generation: Parallel generations from models of varying parameter sizes (e.g., the LLaVA family) yield natural quality stratification.
• Incremental Generation: Fine-tuning subsets partitioned into $K-2$ increments bootstrap a ranked set, further augmented with the pre-trained and ground-truth responses.

An auto-check mechanism, based on noun-chunk semantic similarity ($S[m, n]$ and $s[m] = \max_n S[m, n]$; accuracy $\text{Acc} = \sum_m p[m]/M$ with threshold $\tau$), ensures only semantically and structurally sound rankings are retained.

Learning is driven by Multi-level Direct Preference Optimization (MDPO), generalizing DPO to $K$-level rankings. For $K$ response levels, all $K(K-1)/2$ comparisons are incorporated, and a penalty term is introduced in comparisons with the best response, suppressing violations and encouraging true superiority:

$$\mathcal{L}_{\mathrm{MDPO}}(\pi_t, \pi_\text{ref}) = -\left[\sum_{j=0}^{K-1} \mathcal{L}_{\mathrm{DPO-P}}(x, y_0, y_j) + \sum_{i=1}^{K-1} \sum_{j=i}^{K-1} \mathcal{L}_{\mathrm{DPO}}(x, y_i, y_j)\right].$$

Empirical results on hallucination (MRHal-Bench) and general performance benchmarks show substantial hallucination reduction (up to 43.3% reduction) and improved nuanced task handling, positioning MLLMs to deliver the reliability and subtlety required of first-class operators (Zhang et al., 18 May 2024).

2. Robustness, Misleading Prompts, and the MMR Paradigm

A core requirement for first-class operators is the ability to maintain robust performance on both clean and adversarially misleading inputs. The MultiModal Robustness (MMR) benchmark scrutinizes whether MLLMs can avoid being misled (answering incorrectly in presence of misleading or adversarial questions) even when they correctly parse the visual input. This evaluation is systematically quantified via:

• Misleading Rate (MR):

$$\text{MR} = \frac{N_{\text{UF}}}{N_{\text{UR}} + N_{\text{UF}}}$$

• Robustness Accuracy (RA):

$$\text{RA} = \frac{N_{\text{UR}}}{N_{\text{UR}} + N_{\text{UF}} + N_{\text{NR}} + N_{\text{NF}}}$$

Key empirical findings highlight that current MLLMs, including high-performing models such as GPT-4o, are vulnerable: high accuracy on standard questions is not replicated for misleading prompts, resulting in elevated MR.

Remedies focus on paired positive/negative data pipelines (contrasting straightforward and misleading questions) and attention refinement (content-guided textual prompts and visual attention strategies), which demonstrably increase RA and suppress MR (Liu et al., 15 Jun 2024). The implication is that, with robust bias mitigation and proper attention allocation, MLLMs can achieve the dependability essential to first-class operators across adversarial contexts.

3. Operational Scalability and Performance Limitations

Operationalizing MLLMs as first-class computational primitives demands scalability across accuracy, computational efficiency, and deployment contexts. Large models (GPT-4V, GPT-4o) outperform small ones (LLaVA series, Phi-3-Vision) in complex reasoning and multimodal tasks due to their superior semantic compositionality, detailed chain-of-thought reasoning, and robust multimodal fusion.

However, the practical trade-off is significant:

• Computational Cost & Inference Latency: Large models are resource-intensive, limiting real-time and edge deployment.
• Local/Domain Generalization: Small MLLMs, while better suited to resource-constrained environments, struggle with compositional reasoning, structured data extraction, and precise localization.

Failure cases—spanning prompt misinterpretation, object miscounting, and scene misclassification—occur in both large and small models, but the severity and prevalence scale down with model size.

Approaches such as hybrid architectures, knowledge distillation, and domain-specific fine-tuning are highlighted to bridge the gap. Robust prompt engineering and advanced geometric reasoning modules are also recommended directions (Feng et al., 4 Jan 2025). The current landscape is thus one where first-class operator status is reserved for large MLLMs in complex domains, while efficiency-oriented research pushes for closing this gap in smaller models.

4. Compression and Preprocessing: Enabling MLLM-First Paradigms

Cloud-edge deployment scenarios position signal compression as a principal bottleneck for first-class operator deployment, as traditional codecs prioritize human perceptive fidelity rather than downstream computational performance. CoTAM (Codec TAilored to MLLMs) is proposed as a machine-perception-aware codec, allocating bits according to shallow-layer CLIP attention-inspired importance maps and employing a multi-level loss (combining patch-level low fidelity and semantic-level high fidelity):

$$\mathcal{L}_{\text{total}} = \lambda_{\text{low}}\mathcal{L}_{\text{low}} + \lambda_{\text{high}}\mathcal{L}_{\text{high}}$$

Bit allocation decisions are made on quantized importance maps (e.g., via the rule $\mu \pm k\sigma$), delivering up to 35.99% bitrate savings with negligible task performance loss. A lightweight latent adapter infuses high-level context at the decoding stage, ensuring cross-layer semantic continuity.

This approach signals a broader paradigm shift: system preprocessing and codecs are best designed for MLLM downstream utility, and not merely for human viewing. As a result, compression, preprocessing, and adaptation modules should be conceived as integral, MLLM-facing operator interfaces (Liu et al., 29 Sep 2025).

5. Cross-Domain Integration and MLLM Operator Extensions

The extension of MLLMs to arbitrary retrieval and prompt optimization tasks underlies their generalist first-class operator status. Frameworks like FreeRet illustrate that careful layer selection—e.g., extracting representations before the lexicalization MLP—preserves semantic depth and enables training-free, plug-and-play retrieval. Reframing reranking to a multiple-choice (MCQ) setting mitigates pretraining-related framing biases. This two-stage strategy provides performance that matches or exceeds heavily trained retrieval-specific models, and is both backbone- and modality-agnostic (Zhu et al., 29 Sep 2025).

Multimodal Prompt Optimization (MPO) advances this further by automating the discovery and alignment of optimal prompt pairs (text + non-textual input), applying alignment-preserving operators (generation, edit, mix) and prior-inherited Bayesian UCB search. By performing prompt optimization in the multimodal space, MLLMs can natively integrate contextual signals across modalities, adapting to tasks as diverse as molecular analysis, fine-grained classification, and scenario understanding (Choi et al., 10 Oct 2025).

6. Operator Structure and Direct Operator Learning

The operator interpretation of MLLMs is exemplified in operator learning for PDEs, where neural operator architectures trained via Multi-Level Monte Carlo (MLMC) gradient estimation serve as data-driven surrogates for complex nonlinear operators (e.g., in fluid dynamics). The MLMC gradient estimator

$$\mathbb{E}_{a\sim\mu}[\nabla_\theta X_\theta^m(a)] \approx \sum_{i=1}^m \frac{1}{N_i} \sum_{j=1}^{N_i} \left[ \nabla_\theta X_\theta^i(a_{i,j}) - \nabla_\theta X_\theta^{i-1}(a_{i,j}) \right]$$

enables efficient hierarchical learning, reducing computational cost while maintaining solution accuracy. Empirical results demonstrate a Pareto frontier between accuracy and computational resource, with up to 60% training time savings.

This methodology reframes MLLMs—now neural operators for function spaces—as first-class computational objects in scientific computing, with applications in multi-scale simulation, pretraining/coarse-to-fine transfer, and large-scale operator learning systems (Rowbottom et al., 19 May 2025).

7. Mathematical and Structural Interpretability of Operators

Research dissecting the latent computation of operator precedence in LLMs reveals that even arithmetic operations are encoded in an interpretable, linear fashion in the model’s latent state. Techniques such as logit lens (projection of residuals onto output vocabulary), linear classification probes, UMAP visualization, and partial embedding swap empirically demonstrate that:

• Intermediate computations are explicitly present in hidden activations.
• Operator precedence is linearly embedded post-attention.
• Structured interventions on operator embeddings can modify computational output, confirming that these operator “entities” are first-class objects within the model space.

This supports the notion that MLLMs’ reasoning mechanisms—arithmetic and otherwise—are both robustly encoded and amenable to causal manipulation, thereby reinforcing the operator analogy (Yugeswardeenoo et al., 14 Oct 2025).

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In summary, MLLMs as first-class operators denote models that (i) internalize robust, nuanced, and controllable computational entities (operators), (ii) can be precisely supervised and evaluated via multi-level feedback mechanisms, (iii) are supported by architectures and pipelines that respect the semantics of machine perception, (iv) scale across deployment demands, and (v) can be configured or merged for domain-specialist reasoning (e.g., mathematical reasoning by IP-Merging (Hu et al., 16 Oct 2025)). This operator-centric view provides a systematic path for advancing MLLMs from high-capacity predictors to core computational and reasoning modules in complex, real-world multi-modal systems.