mlsanityData to 14 Oct 2026

OSCAR: Orchestrated Self-verification and Cross-path Refinement

arXiv 2604.01624 ↗First posted 2 Apr 2026cs.AI cs.CLcited by 2

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Diffusion language models (DLMs) expose their denoising trajectories, offering a natural handle for inference-time control; accordingly, an ideal hallucination mitigation framework should intervene during generation using this model-native signal rather than relying on an externally trained hallucination classifier. Toward this, we formulate commitment uncertainty localization: given a denoising trajectory, identify token positions whose cross-chain entropy exceeds an unsupervised threshold before factually unreliable commitments propagate into self-consistent but incorrect outputs. We introduce a suite of trajectory-level assessments, including a cross-chain divergence-at-hallucination (CDH) metric, for principled comparison of localization methods. We also introduce OSCAR, a training-free inference-time framework operationalizing this formulation. OSCAR runs N parallel denoising chains with randomized reveal orders, computes cross-chain Shannon entropy to detect high-uncertainty positions, and then performs targeted remasking conditioned on retrieved evidence. Ablations confirm that localization and correction contribute complementary gains, robust across N in {4, 8, 16}. On TriviaQA, HotpotQA, RAGTruth, and CommonsenseQA using LLaDA-8B and Dream-7B, OSCAR enhances generation quality by significantly reducing hallucinated content and improving factual accuracy through uncertainty-guided remasking, which also facilitates more effective integration of retrieved evidence. Its native entropy-based uncertainty signal surpasses that of specialized trained detectors, highlighting an inherent capacity of diffusion language models to identify factual uncertainty that is not present in the sequential token commitment structure of autoregressive models.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 24 of its 29 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

24 of 29 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Dream 7B: Diffusion Large Language Models21 Aug
  2. 2025A Survey on Diffusion Language Models14 Aug
  3. 2025Large Language Diffusion Models14 Feb

Other references · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  5. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  7. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  8. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
  9. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  10. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 767
  11. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 323
  12. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 293
9 more
  1. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  2. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  3. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 197
  4. 2025Steer LLM Latents for Hallucination Detectioncited by 50
  5. 2025The Illusion of Progress: Re-evaluating Hallucination Detection in LLMscited by 22
  6. 2026DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learningcited by 15
  7. 2025Towards Understanding Text Hallucination of Diffusion Models via Local Generation Biascited by 11
  8. 2025TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Modelscited by 8
  9. 2025EigenScore: OOD Detection using Covariance in Diffusion Modelscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variancecited by 0
  2. 2026TRACESTEER: Trace Driven Activation Steering for Hallucination Mitigation in Diffusion Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026TRE: Training-Free Hallucination Detection for Diffusion Language Models
  2. 2026TDGNet: Hallucination Detection in Diffusion Language Models via Temporal Dynamic Graphs
  3. 2026Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry
  4. 2026DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
  5. 2026Path-Dependent Denoising: A Non-Conservative Field Perspective on Order Collapse in Diffusion Language Models
  6. 2026Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models
  7. 2026MEND: Label-Free Detection, Localisation, and Correction of Latent Hallucination in World Models
  8. 2025Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models