TL;DR
This paper introduces Self-adaptive Schema Scaffolding ($S^3$), a novel framework leveraging diffusion-based large language models' global context awareness to improve controllable structured output generation, such as JSON, with higher reliability and fidelity.
Contribution
The paper proposes $S^3$, a new method that enhances diffusion LLMs' ability to generate reliable structured outputs by utilizing innate reverse reasoning and global context, surpassing prompt optimization techniques.
Findings
$S^3$ improves structure adherence in generated outputs.
Enhanced content fidelity and faithfulness demonstrated.
Method outperforms existing prompt-based approaches.
Abstract
Controllable generation is a fundamental task in NLP with many applications, providing a basis for function calling to agentic communication. However, even state-of-the-art autoregressive Large Language Models (LLMs) today exhibit unreliability when required to generate structured output. Inspired by the current new diffusion-based large language models (dLLM), we realize that the architectural difference, especially the global information-sharing mechanism for language modeling, may be the key to unlock next-level controllable generation. To explore the possibility, we propose Self-adaptive Schema Scaffolding (), a novel framework that enables dLLM to stably generate reliable structured outputs (e.g., JSON) by utilizing its innate reverse reasoning capability and global context awareness. initiates a schematic template directly in the output context as a starting state for…
Peer Reviews
Decision·ICLR 2026 Poster
To ensure global awareness, the paper employs a diffusion model for generation. Furthermore, it introduces a schema scaffolding mechanism to enable controllable generation and provides theoretical proof of its feasibility.
1. The paper (Figure 1) points out that autoregressive models lack global awareness, which is an advantage of diffusion models. To validate this perspective, the authors should provide experimental results from an autoregressive model baseline. 2. The equations subsequent to Equation 3 are unnumbered, resulting in an inconsistent presentation. 3. The experimental setup is relatively simplistic, employing a very limited number of baselines, which consequently lacks persuasiveness.
1. Generating reliable structured outputs is an important research direction and has many practical downstream applications, as evidenced by the fact that it is widely discussed and investigated in autoregressive LLMs literature. This work extends this research direction to a relatively less explored model of diffusion LLMs (dLLMs) and proposes a new method to improve generation of structured outputs. 2. It is well-motivated to use dLLMs for structured outputs, as this task (which requires look
1. Generalization across tasks: The experiments only use one dataset (Wikibio by Lebret et al., 2016), so it is unclear how well the proposed method generalize to other, especially more difficult, datasets. 2. Generalization across types of structured outputs: Following the previous point, it would also be nice to include experiments on other types of structured outputs, such as XML or YAML. 3. Although the authors state they use the Wikibio dataset, they didn't mention what's the input and wh
1. Clear motivation: Exploring diffusion LLMs for controllable text generation is a fresh and under explored research area. 2. Conceptually good idea: The use of schema scaffolding as a structural prior aligns well with diffusion’s iterative refinement mechanism. No additional fine-tuning or retraining is needed. 3. Improved results: The paper demonstrates substantial improvement over baseline diffusion models.
1. Limited experimental scope: Only one dataset (WikiBio) and one diffusion model (LLaDA) are tested. Broader validation tasks (e.g., code generation, dialogue structure, form filling) would strengthen generality. 2. Lack of comparison to AR-LM baselines: Although the paper motivates dLLMs as alternatives to AR models, it doesn’t include a direct comparison with strong AR methods like structured prompting or constrained decoding (e.g., CodeLLaMA, T5, or GPT-style JSON control). 3. There is no
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