MATEO: A Multimodal Benchmark for Temporal Reasoning and Planning in LVLMs

• URL: http://arxiv.org/abs/2602.14589v1
• Date: Mon, 16 Feb 2026 09:41:50 GMT
• Title: MATEO: A Multimodal Benchmark for Temporal Reasoning and Planning in LVLMs
• Abstract summary: AI agents need to plan to achieve complex goals that involve orchestrating perception, sub-goal decomposition, and execution.<n>Existing research on foundational models' understanding of temporal execution is limited to automatically derived annotations, approximations of the TEO as a linear chain, or text-only inputs.<n>We introduce MATEO, a benchmark designed to assess and improve the temporal reasoning abilities of Large Vision Language Models (LVLMs) required for real-world planning.
• Score: 2.1793134762413433
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: AI agents need to plan to achieve complex goals that involve orchestrating perception, sub-goal decomposition, and execution. These plans consist of ordered steps structured according to a Temporal Execution Order (TEO, a directed acyclic graph that ensures each step executes only after its preconditions are satisfied. Existing research on foundational models' understanding of temporal execution is limited to automatically derived annotations, approximations of the TEO as a linear chain, or text-only inputs. To address this gap, we introduce MATEO (MultimodAl Temporal Execution Order), a benchmark designed to assess and improve the temporal reasoning abilities of Large Vision Language Models (LVLMs) required for real-world planning. We acquire a high-quality professional multimodal recipe corpus, authored through a standardized editorial process that decomposes instructions into discrete steps, each paired with corresponding images. We collect TEO annotations as graphs by designing and using a scalable crowdsourcing pipeline. Using MATEO, we evaluate six state-of-the-art LVLMs across model scales, varying language context, multimodal input structure, and fine-tuning strategies.

Related papers

• Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning [13.499744113926505]
  DRAPE is a prompt-learning framework that synthesizes continuous instance-specific soft prompts for MCIT.<n>DRAPE achieves state-of-the-art performance among representative prompt-based and LoRA-based continual-learning baselines.
  arXiv  Detail & Related papers  (2026-05-11T15:59:06Z)
• Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions [61.479946958462754]
  We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy.<n>We present a novel task embedding technique leveraging a new generation of semantic translations-to-automata.
  arXiv  Detail & Related papers  (2026-02-06T14:46:27Z)
• AR-MOT: Autoregressive Multi-object Tracking [56.09738000988466]
  We propose a novel autoregressive paradigm that formulates MOT as a sequence generation task within a large language model (LLM) framework.<n>This design enables the model to output structured results through flexible sequence construction, without requiring any task-specific heads.<n>To enhance region-level visual perception, we introduce an Object Tokenizer based on a pretrained detector.
  arXiv  Detail & Related papers  (2026-01-05T09:17:28Z)
• Vision Language Models Cannot Plan, but Can They Formalize? [28.52711774279781]
  We present a suite of five VLM-as-formalizer pipelines that tackle one-shot, open-vocabulary, and multimodal PDDL formalization.<n>We reveal the bottleneck to be vision rather than language, as VLMs often fail to capture an exhaustive set of necessary object relations.
  arXiv  Detail & Related papers  (2025-09-25T20:55:11Z)
• Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model [63.14883657299359]
  Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering.<n> tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert, where distribution shifts between pre-training and target datasets constrain target performance, and OpenWorld Stabilization, where catastrophic forgetting erases the model general knowledge.
  arXiv  Detail & Related papers  (2025-03-06T15:29:13Z)
• Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing [43.75154489681047]
  We propose a novel framework leveraging test-time scaling for Multi-Document Summarization (MDS)<n>Our approach employs prompt ensemble techniques to generate multiple candidate summaries using various prompts, then combines them with an aggregator to produce a refined summary.<n>To evaluate our method effectively, we also introduce two new LLM-based metrics: the Consistency-Aware Preference (CAP) score and LLM Atom-Content-Unit (LLM-ACU) score.
  arXiv  Detail & Related papers  (2025-02-27T23:34:47Z)
• Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models [93.5327725085853]
  Continual LLaVA is a rehearsal-free method tailored for continual instruction tuning in LVLMs. Experiments indicate that the proposed Continual LLaVA outperforms previous methods by significantly reducing the forgetting during the continual instruction tuning process.
  arXiv  Detail & Related papers  (2024-11-04T19:55:32Z)
• Show and Guide: Instructional-Plan Grounded Vision and Language Model [9.84151565227816]
  We present MM-PlanLLM, the first multimodal plan-following language model. We bring cross-modality through two key tasks: Conversational Video Moment Retrieval and Visually-Informed Step Generation. MM-PlanLLM is trained using a novel multitask-multistage approach.
  arXiv  Detail & Related papers  (2024-09-27T18:20:24Z)
• Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation [8.180994118420053]
  Nl2Hltl2Plan is a framework that translates natural language commands into hierarchical Linear Temporal Logic (LTL)<n>First, an LLM transforms instructions into a Hierarchical Task Tree, capturing logical and temporal relations.<n>Next, a fine-tuned LLM converts sub-tasks into flat formulas, which are aggregated into hierarchical specifications.
  arXiv  Detail & Related papers  (2024-08-15T14:46:13Z)
• Meta-Task Prompting Elicits Embeddings from Large Language Models [54.757445048329735]
  We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
  arXiv  Detail & Related papers  (2024-02-28T16:35:52Z)
• Improving Planning with Large Language Models: A Modular Agentic Architecture [6.394504490512812]
  Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning.<n>We propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of specialized modules.<n>We find that MAP yields significant improvements over both standard LLM methods.
  arXiv  Detail & Related papers  (2023-09-30T00:10:14Z)
• Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension [23.281727955934304]
  Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. In this study, we approach Procedural MultiModal Machine (M3C) at a fine-grained level (compared with existing explorations at a document or sentence level) that is, entity.
  arXiv  Detail & Related papers  (2022-04-06T03:41:13Z)
• Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language [81.73820295186727]
  We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
  arXiv  Detail & Related papers  (2021-09-16T20:36:21Z)
• CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems [56.302581679816775]
  This paper proposes Comprehensive Instruction (CINS) that exploits PLMs with task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD. Experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data.
  arXiv  Detail & Related papers  (2021-09-10T03:23:06Z)

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.