mlsanityData to 14 Oct 2026

Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs

arXiv 2608.26641 ↗First posted 27 Aug 2026cs.CLcited by 0

On the map: Multimodal LLMs / vision-language models → Visual token pruning and efficiency

Abstract (arXiv)

Decoding quality in diffusion multimodal language models (dMLLMs) depends heavily on the order in which masked tokens are committed. Existing confidence-based strategies prioritize locally easy tokens, but confidence does not necessarily reflect contextual usefulness. As a result, structurally easy tokens such as punctuation may be committed before informative semantic anchors, weakening context propagation and increasing error accumulation. We propose Information-Guided Frontier Decoding (IGFD), a training-free decoding strategy that ranks candidates using token confidence, neighborhood uncertainty, and structural commitment risk. IGFD encourages early commitment of reliable semantic anchors while delaying fragile structural tokens, improving contextual support during decoding. A dynamic candidate frontier further constrains token selection to locally expandable regions under the same decoding budget. The method requires no additional training, auxiliary models, or extra forward passes. Experiments across multimodal understanding, reasoning, grounding, and hallucination benchmarks show that IGFD consistently outperforms existing decoding strategies across the majority of benchmarks and diffusion MLLM backbones under identical decoding budgets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Visual token pruning and efficiency, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 29 of its 48 references that are in the map. First posted on arXiv 27 Aug 2026.

Builds on

29 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026Context Tokens are Anchors: Understanding the Repetition Curse in dMLLMs from an Information Flow Perspective28 Jan

Other references · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2016Pointer Sentinel Mixture Modelscited by 3,096
  4. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  5. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  6. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,614
  7. 2025Dream 7B: Diffusion Large Language Modelscited by 544
  8. 2025Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decodingcited by 420
  9. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 327
  10. 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusionscited by 214
  11. 2025LLaDA2.0: Scaling Up Diffusion Language Models to 100Bcited by 166
  12. 2025Accelerating Diffusion LLMs via Adaptive Parallel Decodingcited by 112
16 more
  1. 2025LaViDa: A Large Diffusion Language Model for Multimodal Understandingcited by 84
  2. 2025Diffusion Language Models Know the Answer Before Decodingcited by 70
  3. 2025Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principlescited by 45
  4. 2025Flexible and Efficient Grammar-Constrained Decodingcited by 43
  5. 2025AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Sizecited by 31
  6. 2024DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinationscited by 25
  7. 2025Empirical Analysis of Decoding Biases in Masked Diffusion Modelscited by 19
  8. 2023ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrievalcited by 12
  9. 2025Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Modelscited by 9
  10. 2026R2-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reductioncited by 6
  11. 2023AMOM: Adaptive Masking over Masking for Conditional Masked Language Modelcited by 5
  12. 2023C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluationcited by 5
  13. 2025D3ToM: Decider-Guided Dynamic Token Merging for Accelerating Diffusion MLLMscited by 5
  14. 2026Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Modelscited by 5
  15. 2025WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoningcited by 4
  16. 2026Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Modelscited by 4

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  1. 2026Context-Aware Cluster Decoding: Semantic Anchor-Driven Coherence in dMLLMs
  2. 2026Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models
  3. 2026Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast
  4. 2026Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
  5. 2026Deferred Commitment Decoding for Diffusion Language Models
  6. 2026Early-Bird Decoding: Accelerating Diffusion LLMs with Learnable Block Sizes and Parallel Sampling
  7. 2026When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models
  8. 2025A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models