arxiv: 2602.22586 · v2 · submitted 2026-02-26 · 💻 cs.LG · cs.AI· cs.CL

Recognition: no theorem link

TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion

Donghong Cai , Jiarui Feng , Yanbo Wang , Da Zheng , Yixin Chen , Muhan Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-15 19:27 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL

keywords tabular data generationdiffusion modelsmasked diffusionsynthetic datamultimodal tabular datanumerical embeddingscross-modality modeling

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The pith

TabDLM generates mixed tabular data by running masked diffusion on text and categories alongside continuous diffusion on numbers inside one bidirectional model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Real-world tables mix precise numbers with free-form text such as reviews or notes, yet prior diffusion methods degrade text quality while language-model methods distort numerical precision. TabDLM solves this by extending masked diffusion language models into a joint numerical-language framework. Textual and categorical entries are generated via masked diffusion, numerical entries use continuous diffusion through learned specialized numeric token embeddings, and bidirectional attention links the modalities in a single network. Experiments on diverse benchmarks show the resulting synthetic tables outperform both pure diffusion and LLM baselines for data augmentation and privacy tasks.

Core claim

TabDLM is a unified framework built on masked diffusion language models that models textual and categorical features through masked diffusion, while modeling numerical features with a continuous diffusion process through learned specialized numeric tokens embedding; bidirectional attention then captures cross-modality interactions within a single model.

What carries the argument

Joint numerical-language diffusion model that applies masked diffusion to text and categories, continuous diffusion to numbers via specialized numeric token embeddings, and bidirectional attention to integrate modalities.

If this is right

Synthetic tables preserve both fluent open-ended text and accurate numerical statistics in one generation pass.
A single model replaces the need for separate pipelines for numerical versus textual columns.
Downstream tasks such as data augmentation and privacy release receive higher-utility synthetic data.
Training complexity drops because cross-modality interactions are learned inside the shared attention layers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same embedding trick could let diffusion models handle other precise continuous signals without discretization.
Joint modeling may scale to tables that also include time-series or image fields attached to each row.
If the numeric embeddings generalize well, similar hybrids could replace tokenization in other multimodal generative tasks.

Load-bearing premise

Learned specialized numeric token embeddings and bidirectional attention can preserve precise or wide-range numerical values and cross-modality dependencies without information loss.

What would settle it

A head-to-head test on datasets containing wide-range numerical columns where the generated numerical distributions deviate markedly from the originals while text fluency stays comparable to LLM baselines.

read the original abstract

Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy. However, real-world tabular datasets increasingly contain free-form text fields (e.g., reviews or clinical notes) alongside structured numerical and categorical attributes. Generating such heterogeneous tables with joint modeling of different modalities remains challenging. Existing approaches broadly fall into two categories: diffusion-based methods and LLM-based methods. Diffusion models can capture complex dependencies over numerical and categorical features in continuous or discrete spaces, but extending them to open-ended text is nontrivial and often leads to degraded text quality. In contrast, LLM-based generators naturally produce fluent text, yet their discrete tokenization can distort precise or wide-range numerical values, hindering accurate modeling of both numbers and language. In this work, we propose TabDLM, a unified framework for free-form tabular data generation via a joint numerical-language diffusion model built on masked diffusion language models (MDLMs). TabDLM models textual and categorical features through masked diffusion, while modeling numerical features with a continuous diffusion process through learned specialized numeric tokens embedding; bidirectional attention then captures cross-modality interactions within a single model. Extensive experiments on diverse benchmarks demonstrate the effectiveness of TabDLM compared to strong diffusion- and LLM-based baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

TabDLM combines masked diffusion on text/categorical tokens with continuous diffusion on learned numeric embeddings inside one bidirectional model, which directly targets the text-quality and number-precision problems in prior tabular generators.

read the letter

The main thing to know is that TabDLM runs masked diffusion on the text and categorical parts while using continuous diffusion on numerical features through learned specialized numeric token embeddings, then mixes everything with bidirectional attention in a single model. This setup is the actual new piece; it avoids forcing text into continuous diffusion or numbers into discrete LLM tokens. The abstract spells out the motivation cleanly by naming the exact failure modes of the two existing camps, and the proposed architecture follows from that diagnosis without obvious internal contradictions. If the joint training works as described, it could produce more usable synthetic tables that contain both free-form notes and precise measurements. The experiments are presented as extensive across diverse benchmarks and stronger than the diffusion and LLM baselines, which is the right kind of evidence to ask for. The soft spot is the specialized numeric embeddings themselves. It is not obvious from the high-level description how well they retain exact values or wide dynamic ranges without some loss, and the bidirectional attention may or may not fully compensate for the modality gap. Without the actual equations, training details, or ablation numbers it is hard to judge whether the cross-modal interactions deliver measurable gains or just add complexity. This paper is aimed at people who generate or augment tabular data that mixes structured fields with open text, such as clinical or customer records. A reader working on diffusion models for heterogeneous data would get concrete value from seeing how the two diffusion styles are kept distinct yet coupled. It deserves a serious referee because the problem is practical, the proposed fix is specific, and the claims are falsifiable once the full results and code are examined.

Referee Report

2 major / 2 minor

Summary. The paper proposes TabDLM, a unified joint numerical-language diffusion framework for generating synthetic free-form tabular data containing numerical, categorical, and open-ended textual fields. Textual and categorical features are modeled via masked diffusion, numerical features via continuous diffusion on learned specialized numeric token embeddings, and cross-modality interactions via bidirectional attention in a single model. Experiments on diverse benchmarks are reported to demonstrate superiority over diffusion-based and LLM-based baselines.

Significance. If the central claims hold, the work would be significant for synthetic tabular data generation, a growing area relevant to privacy, data augmentation, and foundation-model training. The unification of masked diffusion for language/categorical tokens with continuous diffusion on numeric embeddings directly targets documented weaknesses of prior approaches (distortion of numerical precision in LLMs and poor text quality in diffusion models) and could enable higher-fidelity heterogeneous data synthesis.

major comments (2)

The integration of continuous diffusion on specialized numeric token embeddings with masked diffusion on text/categorical tokens is load-bearing for the central claim of joint modeling without information loss. The manuscript must provide the precise formulation (including how the two diffusion processes are scheduled and how gradients flow across the shared bidirectional attention layers) to allow verification that numerical precision is preserved for wide-range or high-precision values.
The experimental section must include ablations that isolate the contribution of the specialized numeric token embeddings and the bidirectional attention mechanism; without them, it is impossible to confirm that the reported gains over baselines arise from the proposed cross-modality design rather than from other implementation choices.

minor comments (2)

Notation for the numeric token embedding and the joint loss function should be introduced with explicit equations early in the method section to improve readability.
The abstract would benefit from naming the specific benchmark datasets and reporting at least one key quantitative metric (e.g., average improvement in a fidelity or utility score) to give readers an immediate sense of effect size.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential significance of TabDLM. We address each major comment below and will revise the manuscript to incorporate the requested details and experiments.

read point-by-point responses

Referee: The integration of continuous diffusion on specialized numeric token embeddings with masked diffusion on text/categorical tokens is load-bearing for the central claim of joint modeling without information loss. The manuscript must provide the precise formulation (including how the two diffusion processes are scheduled and how gradients flow across the shared bidirectional attention layers) to allow verification that numerical precision is preserved for wide-range or high-precision values.

Authors: We agree that the precise formulation is essential for verifying the preservation of numerical precision. The current manuscript describes the high-level architecture and the use of specialized numeric token embeddings with bidirectional attention in Section 3, but we acknowledge that the detailed scheduling equations and gradient flow analysis are not fully expanded. In the revised version, we will add the exact joint objective function, the timestep scheduling strategy that interleaves continuous and masked diffusion steps, and a description (with accompanying diagram) of how gradients propagate through the shared attention layers without distorting numeric values. revision: yes
Referee: The experimental section must include ablations that isolate the contribution of the specialized numeric token embeddings and the bidirectional attention mechanism; without them, it is impossible to confirm that the reported gains over baselines arise from the proposed cross-modality design rather than from other implementation choices.

Authors: We concur that targeted ablations are needed to isolate the contributions of the specialized numeric embeddings and bidirectional attention. The existing experiments compare TabDLM against diffusion and LLM baselines but do not contain these component ablations. In the revision, we will add results from two new ablation variants: (1) replacing specialized numeric embeddings with standard tokenization, and (2) replacing bidirectional attention with separate modality-specific processing. These will be evaluated on the same benchmarks using the same metrics to demonstrate that the reported improvements arise from the joint cross-modality design. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces TabDLM as a new unified architecture: masked diffusion on text/categorical tokens combined with continuous diffusion on learned specialized numeric token embeddings, plus bidirectional attention for cross-modal interactions. The abstract and description contain no equations, no fitted parameters renamed as predictions, no self-citations invoked as uniqueness theorems, and no ansatzes smuggled via prior work. The central claim is an architectural proposal whose components are defined directly rather than reduced to inputs by construction. No load-bearing step collapses to self-definition or self-citation chains.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on extending masked diffusion language models to numerical data via invented specialized tokens and assuming effective cross-modality capture through attention, with no independent evidence supplied.

free parameters (1)

specialized numeric tokens embedding
Learned embeddings introduced to map numerical values into the diffusion language model space.

axioms (1)

domain assumption Masked diffusion language models can be extended to continuous numerical features via token embeddings while preserving precision
Core assumption enabling the joint model on MDLMs.

invented entities (1)

specialized numeric tokens no independent evidence
purpose: To embed and process numerical features within the masked diffusion language model framework
New component introduced to bridge numerical and textual modalities.

pith-pipeline@v0.9.0 · 5538 in / 1281 out tokens · 58289 ms · 2026-05-15T19:27:26.800691+00:00 · methodology