Monte Carlo Tree Search for Table-to-Multimodal Report Generation
06:00 · August 6, 2026 · arXiv cs.AI RSS
Automatically generating professional multimodal reports comprising both textual analysis and visual charts from structured tabular data is a critical challenge in data intelligence. Existing methods suffer from fixed linear pipelines and isolated subtask processing, which hinder joint optimization of factual accuracy, visual quality, and narrative coherence. To address these issues, this paper proposes MCTS-Report, a Monte Carlo Tree Search (MCTS)-driven framework that formulates multimodal table-to-report generation as a progressive construction process over a structured search space. The core idea is to decompose report generation into atomic actions, including chapter planning, visualization task identification, chart generation, insight organization, and narrative refinement, each executed by an LLM based on dynamic reasoning conditioned on the current report state. We use an LLM to generate step-by-step reasoning and actions during MCTS, storing the reasoning trajectory in each node for context-aware, coherent report construction. To guide the search, we design a multi-dimensional reward function that jointly evaluates numerical fact consistency (via SQL), chart quality, chart-text alignment, and structural completeness, while incorporating a diversity penalty to suppress repeated charts and a precondition check to prune invalid actions. We also construct MMRBench, a comprehensive benchmark comprising real-world tables from six domains, paired with expert-refined reference report structures and verifiable key insights. Experiments on MMRBench demonstrate that MCTS-Report significantly outperforms strong baselines across structural completeness, numerical accuracy, chart-text alignment, and insight novelty, achieving a 77.9 overall score.
Summary
MCTS-Report reframes multimodal report generation from tabular data as a structured search problem rather than a fixed sequence of subtasks. Instead of committing early to a linear pipeline that parses tables, produces charts, and then writes text, the method models partial reports as nodes in a tree. Each edge represents an atomic action such as chapter planning, identification of visualization-worthy insights, chart rendering, insight organization, or narrative refinement. A single large language model supplies both the reasoning trace and the chosen action at every step, storing the trajectory inside each node to maintain context across iterations.
Monte Carlo Tree Search guides exploration of this space by balancing the discovery of new report structures against refinement of promising partial solutions. A multi-dimensional reward function evaluates every completed node on numerical consistency checked through SQL queries, visual chart quality, alignment between charts and surrounding text, and overall structural completeness. Additional terms penalize duplicate charts and prune actions that violate preconditions, allowing the search to optimize factual accuracy, visual quality, and narrative coherence jointly without external judges during training.
To support systematic evaluation, the authors introduce MMRBench, which contains 185 real-world tables drawn from finance, manufacturing, healthcare, education, retail, and IT operations. Each table is paired with expert-refined reference report structures and a set of verifiable key insights. Experiments on this benchmark show that MCTS-Report surpasses twelve strong baselines across structural completeness, numerical accuracy, chart-text alignment, and insight novelty, reaching an overall score of 77.9. Ablation results confirm that the combination of tree search, unified LLM action generation, and self-supervised multi-dimensional rewards accounts for the observed gains.
Why it matters
High technical depth and novelty make it directly usable by Dutch AI researchers working on LLM agents, data-to-insight pipelines, and evaluation frameworks; the self-supervised reward and search formulation are actionable for enterprise data intelligence tools.
