MMPlanner: Multimodal Planning Approaches
- MMPlanner is a multifaceted term representing planning systems that integrate text and image modalities, transportation routing, and video keyframe generation.
- Zero-shot MMPlanner employs Object State Reasoning Chain-of-Thought prompting to maintain object-state consistency between textual instructions and visual outputs.
- Evaluation protocols in MMPlanner demonstrate measurable improvements in text planning, cross-modal alignment, and visual ordering through reference-free, automated metrics.
Searching arXiv for the specific MMPlanner usages and closely related multimodal planning papers. arxiv_search.query({"search_query":"all:MMPlanner OR ti:MMPlanner", "start": 0, "max_results": 10}) Searching arXiv for MMPlanner and adjacent multimodal planning literature. MMPlanner is an overloaded designation in the arXiv literature. Most prominently, it denotes a zero-shot multimodal procedural planning framework for generating step-by-step instructions that combine text and images, with explicit attention to object-state consistency across modalities and systematic evaluation of plan quality (Tabassum et al., 25 Sep 2025). The same or closely related name is also used for an extensible and personalizable multi-modal trip planner in transportation (Liu et al., 2019) and, in the context of Macro-from-Micro Planning, for a long-video generation system organized around Micro Planning and Macro Planning (Xiang et al., 5 Aug 2025). This suggests that “MMPlanner” is best understood as a recurrent label for multimodal planning systems rather than a single unified architecture.
1. Terminological scope and disambiguation
In current usage, MMPlanner spans several technically distinct problem settings. The common thread is planning under multiple modalities, but the meaning of “multimodal” changes substantially across domains.
| Usage | Core mechanisms | Citation |
|---|---|---|
| Multi-modal trip planning | Auxiliary geographic data, Linear Temporal Logic, preferential cost functions | (Liu et al., 2019) |
| Multimodal procedural planning | OSR-CoT prompting, LLM-as-a-judge, visual step reordering | (Tabassum et al., 25 Sep 2025) |
| Long-video generation planning | Micro Planning, Macro Planning, Content Populating, Adaptive Workload Scheduling | (Xiang et al., 5 Aug 2025) |
In transportation, multimodality refers to combining walking, biking, driving, public transit, and taxi in a single plan. In procedural planning, multimodality refers to paired textual and visual steps. In long-video generation, the term is tied to hierarchical planning over keyframes and segments. A common misconception is that these systems form a single methodological lineage; the published descriptions do not support that reading.
2. MMPlanner as zero-shot multimodal procedural planning
The most direct use of the name appears in “MMPlanner: Zero-Shot Multimodal Procedural Planning with Chain-of-Thought Object State Reasoning” (Tabassum et al., 25 Sep 2025). In that formulation, Multimodal Procedural Planning (MPP) aims to generate step-by-step instructions that combine text and images, with the central challenge of preserving object-state consistency across modalities while producing informative plans. The framework is explicitly zero-shot: no task-specific training is required, and the system relies on general-purpose models and prompting.
MMPlanner generates a sequence of step pairs,
where is the textual instruction and is a corresponding step image. Its pipeline includes a textual plan generator, an image description generator, and a visual plan generator. The textual component takes a multimodal goal , where the visual goal is synthesized from the text using Stable Diffusion. A Vision-LLM is then prompted to output an ordered sequence of concise textual steps using both goal text and visual goal.
The image description stage addresses a specific failure mode of text-to-image generation: the difficulty of depicting the correct object state when that state must be inferred from previous steps or is not explicit in the current step. To compensate, MMPlanner conditions each step description on the overall goal, the current textual step, and all previous steps. The visual plan generator then samples candidate images for each description and selects the image with the highest cosine similarity to the description embedding, using a pretrained feature extractor; BLIP-2 is reported as best in ablation. The selection rule is
3. Object State Reasoning Chain-of-Thought
The distinctive mechanism of MMPlanner is Object State Reasoning Chain-of-Thought (OSR-CoT) prompting (Tabassum et al., 25 Sep 2025). Rather than treating a step image as a direct rendering of a short textual instruction, OSR-CoT explicitly asks the model to reason about state transitions. The prompt structure proceeds in three stages: describe the current step action and focus, list the object states before and after the step, and then generate a concise image description centered on relevant objects and their physical states.
This design is meant to capture both explicit and implicit state changes. The paper’s motivating example is that a description such as “a bowl of bread flour mixture with small pieces of unsalted butter” may be less useful than one emphasizing the outcome, such as a “crumbly mixture,” because the latter better represents the physical state after mixing. The framework therefore treats object-state tracking as a first-class planning variable rather than a byproduct of generic text generation.
The methodological significance lies in how MMPlanner constrains cross-modal generation. Existing approaches often leverage LLMs to refine textual steps; however, visual object-state alignment and systematic evaluation are largely underexplored. OSR-CoT operationalizes that gap by forcing the model to account for before-and-after states at every step. A plausible implication is that MMPlanner shifts multimodal procedural planning away from caption-like generation and toward state-transition modeling.
4. Evaluation protocols and reported performance
MMPlanner couples generation with a modular evaluation framework based on LLM-as-a-judge protocols (Tabassum et al., 25 Sep 2025). The paper defines three evaluation axes: textual planning, cross-modal alignment, and visual sequence ordering. Textual planning evaluates how well the generated steps align with the overall goal, focusing on completeness, correctness, and coherent ordering. Cross-modal alignment evaluates whether each image captures both the explicit action and inferred object states described in its paired textual step. Visual sequence ordering tests whether shuffled step images can be reordered into the correct temporal sequence.
This evaluation design is notable because it is reference-free in key components and is intended to handle the fact that procedural tasks often admit multiple valid solutions. For visual ordering, the paper reports standard sequence metrics including Accuracy, Longest Common Subsequence, Kendall’s Tau, Distance, Minimum Swap, and Weighted Minimum Swap. Kendall’s Tau is defined as
On RECIPEPLAN and WIKIPLAN, MMPlanner is reported to achieve state-of-the-art performance, improving textual planning by +6.8%, cross-modal alignment by +11.9%, and visual step ordering by +26.7% (Tabassum et al., 25 Sep 2025). In the detailed tables, MMPlanner attains a textual planning score of 82.05 against 82.00 for TIP, 80.43 for GPT-3.5+SD, and 73.85 for Text-Ref+SD. For cross-modal alignment on RecipePlan, MMPlanner reaches 77.07 against 67.68 for TIP. For visual step ordering, MMPlanner reports 27.50 accuracy, 3.09 LCS, and 0.22 Kendall’s Tau, compared with 21.70, 1.81, and 0.05 for TIP. The paper also reports an inference-time reduction of approximately 46% compared to TIP and a higher correlation with human judgments for image-to-text relevance, versus 0.37 for CLIPScore.
5. Relation to adjacent text-image planning frameworks
MMPlanner belongs to a broader line of 2025 work on text-image plan generation, but its treatment of state is distinct. “Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation” proposes a step-by-step autoregressive framework in which the system drafts the next textual step, edits the last visual step to obtain the next one, extracts PDDL-like visual information, and refines the draft with the extracted visual information (Lu et al., 13 Jun 2025). That framework is explicitly organized around multimodal consistency and visual coherence, and it treats pPDDL extraction as the structured interface between image understanding and text refinement.
The contrast is methodological. MMPlanner centers Object State Reasoning Chain-of-Thought prompting and uses candidate-image selection against generated descriptions. The pPDDL framework centers iterative refinement through structured visual parsing. Both respond to the same general problem—misalignment between textual and visual steps—but they instantiate different abstractions of the latent plan state. MMPlanner reasons over before-and-after object states in natural-language prompting; the pPDDL approach externalizes visual information as objects, tools, actions, and goals.
The label also appears in adjacent planning literatures as a comparison point rather than a fixed method. Int2Planner states that “MMPlanner” and related models such as MTR and TNT typically build multi-modal plans around generic or clustered target anchors, whereas Int2Planner samples intention points directly along the route (Chen et al., 22 Jan 2025). In dynamic-environment TAMP, MMRF characterizes prior MMPlanner/TAMP extensions as having limited handling of multi-level replanning or modularity (Lin et al., 2023). These references reinforce that the term functions partly as a moving comparison class.
6. Other domain-specific uses of the name
A separate and earlier usage appears in “An Extensible and Personalizable Multi-Modal Trip Planner” (Liu et al., 2019). That planner addresses the claim that current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice because the planners fail to capture the true preferences of users. It allows users to upload auxiliary geographic data such as crime rates, specify temporal constraints and preferences over these data in combination with typical metrics such as time and cost, and supports walking, biking, driving, public transit, and taxi. Hard constraints are expressed in Linear Temporal Logic, while soft preferences are represented by preferential cost functions. The search layer is built on A* and minimizes the preferential cost function over all valid paths subject to the LTL constraints.
The long-video generation literature introduces yet another usage through Macro-from-Micro Planning, where MMPL is instantiated by MMPlanner (Xiang et al., 5 Aug 2025). In that setting, MMPlanner is a planning-then-populating framework for long video generation. Micro Planning predicts a sparse set of future keyframes within each short video segment, Macro Planning extends that planning across the entire video through an autoregressive chain of micro plans, and Content Populating generates intermediate frames in parallel across segments. Adaptive Workload Scheduling is added for balanced GPU execution and accelerated autoregressive video generation. Here, “planning” refers neither to route optimization nor to procedural instructions, but to hierarchical control of long-horizon generation.
These usages make clear that “MMPlanner” is not a standardized term tied to one benchmark suite, one mathematical formalism, or even one modality pairing. In one strand it denotes routing over transportation modes and auxiliary spatial data; in another it denotes text-image procedural planning with object-state reasoning; in another it denotes hierarchical keyframe planning for long video synthesis. A plausible implication is that disambiguation by paper title or application domain is necessary whenever the term appears without context.
7. Limitations, misconceptions, and significance
The procedural MMPlanner reports several limitations (Tabassum et al., 25 Sep 2025). Visual consistency of peripheral or background elements is not fully enforced, some residual hallucination can occur, the method fails on concepts out of the image model’s domain, and LLM-based evaluations can have imperfect calibration. These caveats matter because the framework’s strongest contributions—object-state reasoning and automated multimodal evaluation—depend on the behavior of the underlying LLMs, VLMs, and image generators.
A second misconception is that multimodal planning quality can be reduced to text quality alone. Both MMPlanner and adjacent work reject that assumption. The MMPlanner evaluation suite adds cross-modal alignment and visual temporal coherence, while the pPDDL framework explicitly targets multimodal consistency and coherence for text-image plans (Lu et al., 13 Jun 2025). Across these papers, the planning problem is not merely to generate fluent instructions, but to preserve a consistent latent state across modalities and across time.
The broader significance of MMPlanner, in its procedural sense, is therefore methodological rather than purely benchmark-centric. It treats multimodal procedural planning as a state-consistency problem, introduces Object State Reasoning Chain-of-Thought as the principal control mechanism, and pairs generation with reference-free evaluation protocols that are designed for tasks with multiple valid realizations. At the same time, the wider literature shows that the name “MMPlanner” has become polysemous. For researchers, that polysemy is itself informative: it marks a convergence on planning problems in which multiple representational channels—transport modes, text and images, or keyframes and segments—must be coordinated under explicit structural constraints.