mlsanityData to 7 Oct 2026

DIP: Dynamic In-Context Planner For Diffusion Language Models

arXiv 2601.03199 ↗First posted 6 Jan 2026cs.CL cs.AIcited by 1

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context Learning (ICL) approaches largely inherit the practice of autoregressive language models (ARLMs), incorporating all examples into a fixed prompt. However, applying this rigid, static-prompt paradigm to DLMs incurs substantial computational overhead, as the model must evaluate the maximum context length at every step. We address this inefficiency with a key discovery: the block-wise KV-cache mechanism inherent to DLM inference enables the textitlow-cost dynamic adjustment of the context. Following this intuition, our core idea is to start generation with a minimal prompt and progressively insert additional examples on the fly only when the generated tokens are of low confidence. Through rigorous empirical evaluations, we observe that average verified token confidence correlates strongly with generation accuracy, making it a reliable and computationally efficient signal of token quality. Formally, we propose Dynamic In-Context Planner (DIP), a context-optimization algorithm based on average verified confidence that dynamically ranks and inserts in-context examples during generation, rather than providing all examples up front. Experimental results on math and coding benchmarks with LLaDA-1.5 and LLaDA-8B-Instruct show that DIP achieves up to 1.59× and 1.36× speedups, respectively, while largely preserving the generation quality of the fixed-prompt baseline. Code: https://github.com/wmd3i/DIP

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 28 of its 44 references that are in the map. First posted on arXiv 6 Jan 2026.

Builds on

28 of 44 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Diffusion Language Models Know the Answer Before Decoding27 Aug
  2. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct

Other references · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  6. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  7. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  8. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  9. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  10. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  11. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
  12. 2022A Survey on In-context Learningcited by 717
14 more
  1. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  2. 2025Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decodingcited by 404
  3. 2025LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Modelscited by 273
  4. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 253
  5. 2025Scalable Best-of-N Selection for Large Language Models via Self-Certaintycited by 182
  6. 2025dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Cachingcited by 168
  7. 2025Accelerating Diffusion LLMs via Adaptive Parallel Decodingcited by 108
  8. 2025Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Evictioncited by 64
  9. 2025dParallel: Learnable Parallel Decoding for dLLMscited by 59
  10. 2024In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translationcited by 21
  11. 2025Thinking Inside the Mask: In-Place Prompting in Diffusion LLMscited by 13
  12. 2024Better RAG using Relevant Information Gaincited by 7
  13. 2025OPTAGENT: Optimizing Multi-Agent LLM Interactions Through Verbal Reinforcement Learning for Enhanced Reasoningcited by 4
  14. 2025Dynamic Retriever for In-Context Knowledge Editing via Policy Optimizationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context Learning for Diffusion LLMs via Decoding Dynamicscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Diffusion Language Models: An Experimental Analysis
  2. 2025A Survey on Diffusion Language Models
  3. 2025Context-Aware Initialization for Reducing Generative Path Length in Diffusion Language Models
  4. 2026Diffusion Language Models Are Natively Length-Aware
  5. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Models
  6. 2026From Position Risks to Block Survival: Faster Generation for Diffusion Language Models
  7. 2025FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
  8. 2025Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models