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ChainMPQ: Interleaved Text-Image Reasoning Chains for Mitigating Relation Hallucinations

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Relational Hallucination; Interleaved Chain of Image and Text; Large Vision-Language Models
Abstract:

While Large Vision-Language Models (LVLMs) achieve strong performance in multimodal tasks, hallucinations continue to affect their reliability. Among the three categories of hallucinations, which include object, attribute, and relation, relation hallucinations account for the largest proportion but have received the least attention. To address this challenge, we propose \textbf{ChainMPQ} (\textbf{M}ulti-\textbf{P}erspective \textbf{Q}uestions guided Interleaved Text-image Reasoning \textbf{Chain}), a training-free method that improves relational inference in LVLMs by utilizing accumulated textual and visual memories. ChainMPQ first extracts subject and object keywords from the question to enhance the corresponding image regions. It then constructs multi-perspective questions that focus on the three core components of a relationship: the subject, the object, and the relation that links them. These questions are sequentially input to the model, with textual and visual memories from earlier steps providing supporting context for subsequent ones, thereby forming an interleaved chain of image and text that guides progressive relational reasoning. Experiments on multiple LVLMs and benchmarks show that ChainMPQ substantially reduces relation hallucinations, while ablation studies further validate the effectiveness of its three core modules.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

ChainMPQ proposes a training-free method for mitigating relation hallucinations in large vision-language models by constructing multi-perspective reasoning chains that decompose relational questions into subject, object, and relation components. The paper resides in the 'Relation-Focused Reasoning Chains' leaf under 'Specialized Mitigation Approaches', which currently contains only this single paper. This indicates a sparse research direction within the broader taxonomy of 50 papers across 36 topics, suggesting the specific focus on relation-targeted decomposition and chained reasoning represents a relatively unexplored niche in hallucination mitigation research.

The taxonomy tree reveals that ChainMPQ's parent branch 'Specialized Mitigation Approaches' includes neighboring leaves like 'Visual Evidence Prompting' (which uses region-level prompts for grounding) and 'Semantic Reconstruction Methods' (which penalize inaccurate relationships through reconstruction objectives). While these adjacent directions share the goal of improving visual grounding, they differ in mechanism: visual prompting emphasizes explicit evidence presentation, semantic reconstruction uses inverse objectives, whereas ChainMPQ focuses on sequential multi-perspective question chains. The broader 'Decoding-Based Mitigation Methods' branch contains more crowded leaves like 'Contrastive Decoding Approaches' and 'Attention-Based Interventions', indicating that general inference-time methods have received substantially more research attention than relation-specific reasoning strategies.

Among the three contributions analyzed, the 'Interleaved text-image reasoning chain with multimodal memory transfer' shows one refutable candidate among ten examined, suggesting some overlap with prior work on memory-guided reasoning approaches. The 'Multi-perspective question decomposition' and 'ChainMPQ framework' contributions each examined ten candidates with zero refutations, indicating these aspects appear more novel within the limited search scope of thirty total candidates. The statistics reflect a focused semantic search rather than exhaustive coverage, meaning the analysis captures the most semantically similar prior work but may not encompass all potentially relevant literature across the broader field of vision-language model hallucination mitigation.

Based on the limited search scope of thirty candidates from top-K semantic matching, ChainMPQ appears to occupy a relatively novel position by specifically targeting relation hallucinations through structured reasoning chains. The sparse population of its taxonomy leaf and the low refutation rate across contributions suggest distinctiveness, though the single refutable pair for the memory transfer component indicates partial conceptual overlap with existing memory-guided decoding methods. The analysis covers semantically proximate work but does not claim exhaustive field coverage.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Mitigating relation hallucinations in large vision-language models. The field has organized itself around five main branches that reflect different intervention points and research priorities. Decoding-Based Mitigation Methods adjust inference-time generation through techniques like contrastive decoding (Visual Contrastive Decoding[3], Instruction Contrastive Decoding[9]) and attention manipulation (Opera[17], Self-Introspective Decoding[20]). Training-Based Mitigation Methods modify model parameters via instruction tuning (Reflective Instruction Tuning[13], Targeted Instruction Tuning[50]) or preference optimization (V-DPO[29], RRHF-V[37]). Hallucination Detection and Evaluation develops benchmarks and metrics for identifying errors (Hallucination Evaluation Analysis[8], Unified Triplet Evaluation[27]), while Hallucination Analysis and Understanding investigates root causes through attention patterns (Attention Causality[48]) and information flow (Information Flow Constraint[32]). Specialized Mitigation Approaches target specific problem types, including relation-focused reasoning chains, entity-centric methods (Entity-Centric Optimization[42]), and visual grounding techniques (Object Grounding Reduce[47]). Recent work reveals a tension between lightweight decoding interventions and deeper training-based solutions, with many studies exploring hybrid strategies that combine both. Relation hallucinations—where models incorrectly describe spatial, functional, or semantic relationships between objects—remain particularly challenging because they require coordinated visual grounding and compositional reasoning. ChainMPQ[0] sits within the Specialized Mitigation Approaches branch, specifically targeting relation-focused reasoning chains. Unlike broader decoding methods such as Visual Contrastive Decoding[3] that apply general contrast mechanisms, or training approaches like Reflective Instruction Tuning[13] that reshape model behavior globally, ChainMPQ emphasizes structured reasoning pathways tailored to relational content. This positions it alongside works that decompose complex visual understanding into interpretable steps, addressing the gap between generic hallucination reduction and the nuanced demands of accurate relationship prediction in vision-language models.

Claimed Contributions

Multi-perspective question decomposition for relational reasoning

The authors propose decomposing relational questions into five complementary sub-questions that systematically examine the subject, object, and their relationship from different perspectives. This decomposition strategy guides the model to analyze individual components before making final relational judgments.

10 retrieved papers
Interleaved text-image reasoning chain with multimodal memory transfer

The authors develop a mechanism that sequentially processes questions while accumulating both textual answers and visual attention patterns from previous steps. This interleaved chain uses accumulated multimodal evidence to guide progressive reasoning rather than relying on single-step inference.

10 retrieved papers
Can Refute
ChainMPQ training-free framework for mitigating relation hallucinations

The authors introduce ChainMPQ, a complete training-free framework that combines text-guided attention enhancement, multi-perspective question construction, and interleaved reasoning chains to reduce relation hallucinations in large vision-language models without requiring additional training.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multi-perspective question decomposition for relational reasoning

The authors propose decomposing relational questions into five complementary sub-questions that systematically examine the subject, object, and their relationship from different perspectives. This decomposition strategy guides the model to analyze individual components before making final relational judgments.

Contribution

Interleaved text-image reasoning chain with multimodal memory transfer

The authors develop a mechanism that sequentially processes questions while accumulating both textual answers and visual attention patterns from previous steps. This interleaved chain uses accumulated multimodal evidence to guide progressive reasoning rather than relying on single-step inference.

Contribution

ChainMPQ training-free framework for mitigating relation hallucinations

The authors introduce ChainMPQ, a complete training-free framework that combines text-guided attention enhancement, multi-perspective question construction, and interleaved reasoning chains to reduce relation hallucinations in large vision-language models without requiring additional training.

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