Challenge: Existing Chain-of-Thought (CoT) approaches lack intrinsic correction mechanisms, rendering them vulnerable to error propagation.
Approach: They propose a multi-agent framework that enforces diagnostic rigor through adversarial dialectics.
Outcome: Empirical evaluations show that the proposed framework improves explanation faithfulness and mitigates hallucinations.

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Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models (2025.findings-acl)

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Challenge: Hallucination is a critical challenge for large language models and large vision-language models (LVLMs) however, dedicated research on medical hallucinations remains unexplored.
Approach: They provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes.
Outcome: The proposed models have demonstrated impressive performance on a variety of medical benchmarks.
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding (2026.findings-acl)

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Challenge: Multimodal large language models generate medical hallucinations due to over-sensitivity to clinical sections.
Approach: They propose a framework that integrates structured clinical signals from task-specific radiology expert models.
Outcome: The proposed framework improves overall performance on radiology report generation (RRG) on the MIMIC-CXR dataset, it yields up to 17% improvement in RadGraph-F1.
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)

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Challenge: Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework .
Approach: They propose a training-free inference framework that simulates a metacognitive self-correction process.
Outcome: The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE.
Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models (2026.findings-acl)

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Challenge: Existing hallucination benchmarks rarely test this failure mode outside Western contexts and English.
Approach: They propose a multimodal benchmark built from images spanning 17 MENA countries . they use a CFHR-based test to measure hallucination beyond raw accuracy .
Outcome: The proposed model is based on images from 17 MENA countries . it measures counterfactual acceptance conditioned on correctly answering the true statement.
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models (2026.findings-acl)

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Challenge: Existing classification-based methods capture noise and spurious correlations while overlooking the underlying causal mechanisms.
Approach: They propose a hallucination detection framework based on structural causal models that captures static and passive signals from internal states and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise.
Outcome: Experiments on four datasets and three widely used LLMs show that the proposed framework improves AUROC and interpretability.
MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs (2025.findings-emnlp)

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Challenge: Large Vision-Language Models often produce hallucinations due to the limited ability to verify information in different regions of the image.
Approach: a new decoding method improves factual grounding by modeling inter-region consistency . the method identifies salient regions using cross-attention and generates initial responses for each .
Outcome: a training-free decoding method reduces hallucinations and improves response consistency . the proposed method generates initial responses for each region and weights reliability weights among responses .
MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration (2025.findings-acl)

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Challenge: Recent advances in medical Large Language Models have demonstrated powerful reasoning and diagnostic capabilities.
Approach: They propose a modular multi-agent framework for multi-modal medical diagnosis . they decompose the medical diagnostic process into specialized roles .
Outcome: The framework decomposes the medical diagnostic process into specialized roles . it achieves significant performance improvements ranging from 18% to 365% compared to baseline models.
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)

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Challenge: Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know".
Approach: They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content.
Outcome: The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content.
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)

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Challenge: Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence .
Approach: They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges.
Outcome: The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks.
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (2023.emnlp-main)

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Challenge: Recent advances in Large Language Models have generated widespread acclaim, but hallucination has also emerged as a by-product.
Approach: They propose a fine-grained discourse on profiling hallucination based on its degree, orientation, and category . they categorize hallucines into six types: acronym ambiguity, generated golem, virtual voice, geographic erratum, time wrap .
Outcome: The proposed method categorizes hallucination into six types based on their degree, orientation, and category .

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