Papers with DLM
dLLM: Simple Diffusion Language Modeling (2026.acl-demo)
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| Challenge: | diffusion language models (DLMs) are evolving rapidly but many lack transparent implementations or are scattered across codebases. |
| Approach: | They propose an open-source framework that unifies diffusion language modeling components while remaining flexible enough to support new methods and architectures. |
| Outcome: | dLLM unifies the core components of diffusion language modeling and makes them easy to customize for new designs. |
Probing Functional Correctness in Diffusion Language Models (2026.acl-srw)
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| Challenge: | Diffusion language models generate text by iteratively denoising all tokens in parallel, but when and where hidden states encode whether output will be functionally correct remains unknown. |
| Approach: | They present the first probing study of Diffusion language models to train classifiers on hidden states to predict functional correctness. |
| Outcome: | The proposed model generates all tokens simultaneously, but when and where does it encode correctness? |
C2DLM: Causal Concept-Guided Diffusion Large Language Models (2026.findings-acl)
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Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, Kun Kuang
| Challenge: | Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities. |
| Approach: | They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts. |
| Outcome: | The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. |
Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation (2026.acl-long)
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| Challenge: | Diffusion language models (DLMs) offer advantages in parallel generation and bidirectional context modeling, but they face a critical trade-off between inference speed and output quality for tasks with strict structural constraints such as code generation. |
| Approach: | They propose an efficient sampling algorithm that reduces the number of tokens unmasked per step based on the model’s evolving confidence. |
| Outcome: | The proposed method improves Pass@1 accuracy by 1.9% while achieving 251.4% inference speedup. |
DLM: A Decoupled Learning Model for Long-tailed Polyphone Disambiguation in Mandarin (2024.naacl-long)
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| Challenge: | Grapheme-to-phoneme conversion datasets suffer from the long-tail problem . context learning for polyphonic characters often stems from a single dimension . |
| Approach: | They propose a model for long-tailed polyphone disambiguation in Mandarin that decouples representation and classification learnings. |
| Outcome: | The proposed model can decouple representation and classification learnings . it achieves transition learning of context from local to global . |
Endowing Neural Language Learners with Human-like Biases: A Case Study on Dependency Length Minimization (2024.lrec-main)
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| Challenge: | Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this preference. |
| Approach: | They propose to add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic. |
| Outcome: | The proposed conditions can contribute to a small but significant learning advantage for listeners of verb-initial languages. |
Towards Efficient and Effective Diffusion Language Model Inference via Semantic-Aware Adaptive Denoising (2026.acl-long)
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| Challenge: | Existing acceleration works cannot accurately detect semantically stabilized tokens and then skip computation, leading to sub-optimal speedup in practice. |
| Approach: | They propose a semantic-aware adaptive denoising framework that encodes scalar confidence scores into an evolution-awful feature vector and clusters vectors proactively and adaptively identify semantically converged tokens. |
| Outcome: | The proposed framework outperforms the SOTA competitor in speed and quality . it can detect semantically stabilized tokens and skip computation, resulting in sub-optimal speedup . |
SelFusion: Self-distillation for Diffusion Language Models (2026.acl-long)
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| Challenge: | Existing knowledge distillation methods for autoregressive large language models (LLMs) are not effective for reducing generation quality, but they can be useful for real-time applications. |
| Approach: | They propose a self-distillation framework that allows for effective KD without external teacher . they propose to use two modes of knowledge distillation to determine distillation direction . |
| Outcome: | The proposed framework outperforms existing methods with external teachers on instruction-following tasks. |
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation. |
| Approach: | They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model. |
| Outcome: | The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers. |
You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)
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| Challenge: | Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary. |
| Approach: | They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution. |
| Outcome: | The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity. |