mlsanityData to 13 Oct 2026

TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reduction

arXiv 2503.04457 ↗First posted 6 Mar 2025cs.CV cs.AIcited by 2

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

Vision-language models (VLMs) have achieved remarkable advancements, capitalizing on the impressive capabilities of large language models (LLMs) across diverse tasks. Despite this, a critical challenge known as hallucination occurs when models overconfidently describe objects or attributes absent from the image, a problem exacerbated by the tendency of VLMs to rely on linguistic priors. This limitation reduces model reliability in high-stakes applications. In this work, we have observed the characteristic of logits' continuity consistency enhancement and introduced a straightforward and efficient method, Cross-Temporal Prediction Connection (TPC), designed to enhance the semantic consistency of logits by connecting them temporally across timesteps. TPC amplifies information flow and improves coherence, effectively reducing hallucination. Extensive experiments show that TPC surpasses existing representatives, delivering superior performance in both accuracy and efficiency while maintaining robustness in open-ended text generation tasks.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 40 of its 46 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

40 of 46 references.

Closest ancestors · 18

Same direction, newest first

  1. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs31 Jul
  2. 2024Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models30 Jun
  3. 2024Hallucination of Multimodal Large Language Models: A Survey29 Apr
  4. 2024Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding27 Mar
  5. 2024Multi-Modal Hallucination Control by Visual Information Grounding20 Mar
  6. 2024Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models18 Feb
  7. 2023Hallucination Augmented Contrastive Learning for Multimodal Large Language Model12 Dec
  8. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  9. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  10. 2023HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data22 Nov
  11. 2023AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation13 Nov
  12. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Models24 Oct
6 more
  1. 2023HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models23 Oct
  2. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  3. 2023CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning5 Sep
  4. 2023Detecting and Preventing Hallucinations in Large Vision Language Models11 Aug
  5. 2023Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning26 Jun
  6. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 22

  1. 2023GPT-4 Technical Reportcited by 18,278
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,549
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,979
  4. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,703
  5. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,546
  6. 2021Learning to Prompt for Vision-Language Modelscited by 2,276
  7. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,637
  8. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,214
  9. 2023Vision-Language Models for Vision Tasks: A Surveycited by 837
  10. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 708
  11. 2018Object Hallucination in Image Captioningcited by 663
  12. 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 600
10 more
  1. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 374
  2. 2023A Survey of Hallucination in Large Foundation Modelscited by 367
  3. 2023Language Modeling Is Compressioncited by 225
  4. 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
  5. 2024Unveiling Encoder-Free Vision-Language Modelscited by 92
  6. 2020On hallucinations in tomographic image reconstructioncited by 67
  7. 2024In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigationcited by 44
  8. 2022SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answeringcited by 38
  9. 2022Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Modelscited by 28
  10. 2024Identifying Implicit Social Biases in Vision-Language Modelscited by 27

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Tracing and Mitigating Hallucinations in Multimodal LLMs via Dynamic Attention Localizationcited by 4
  2. 2026Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Modelscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models
  2. 2025Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models
  3. 2026First Logit Boosting: Visual Grounding Method to Mitigate Object Hallucination in Large Vision-Language Models
  4. 2026Dynamic Alignment Compensation for Hallucination Mitigation in Large Vision-Language Models
  5. 2025HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models
  6. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steering
  7. 2026Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
  8. 2024Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding