ChainMPQ: Interleaved Text-Image Reasoning Chains for Mitigating Relation Hallucinations
Authors: Yike Wu
Yiwei Wang
Yujun Cai
Venue: ICLR 2026 Poster (2026)
Keywords: Relational Hallucination; Interleaved Chain of Image and Text; Large Vision-Language Models
Primary area: applications to computer vision, audio, language, and other modalities

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 ChainMPQ (Multi-Perspective Questions guided Interleaved Text-image Reasoning 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.

Published as a conference paper at ICLR 2026
CHAINMPQ: INTERLEAVED TEXT-IMAGE REASON- ING CHAINS FOR MITIGATING RELATION HALLUCI- NATIONS
Yike Wu-
Yiwei Wang Yujun Cai
University of Queensland
University of California, Merced
3 Ant Group
yi kekku k6 com; viwei www.iaag@euummrrdeeeee.
https://yike wu, github. 10/
ABSTRACT
While Large Vision-Language Models (LVLMs) achieve strong performance in multimodal tasks, ballucinations continue to affect their reliability. Among the three categories of hallucinations, which include object, attribute, and re- lation, relation hallucinations account for the largest proportion but have re- ceived the least attention. To address this challenge, propose ChainMPQ PerspiiPerreeetivee Questions guided Interleaved Text-image Reasoning Chain), a training-free method that improves relational inference 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-perspection questions that focus on the three core compo- nents of relationship: the subject, the object, and the relation that links them. These questions are sequentially input to the model, with textual and visual mem- ories from earlier steps providing supporting context for subsequent ones, thereby forming interleaved chain of image and text that guides progressive relational reasoning. Experiments on multiple LVLMs and benchmarks show that Chain- MPQ substantially reduces relation hallucinations, while ablation studies further validate the effectiveness of its three core modules.
INTRODUCTION
Large Language Models (LLMs) have made substantial advances in language understanding, gener- ation, and reasoning Touvron et al. 2023b al: Bai et al. (2023a). Extending these capabilities with visual inputs, Large Vision-Language Models (LVLMs) achieve strong performance
range of
multimodal tasks, including image captioning and visual question answering Ye et al. (2024); Li et al.) (2023a); Bai et al. (2023b) Li et al. (2023b); Dai et al. (202)); Liu et al. (2024b). Never- theless, LVLMs still exhibit hallucinations, producing outputs that contradict or overlook the visual evidence. Hallucinations in LVLMs can be broadly categorized into object, attribute, and relation hallucinations [Bai et al. (2024). Object hallucinations refer to failures in recognizing entities, while attribute hallucinations involve misidentifying properties such as color or shape. Relation hallucina- tion occurs when models correctly recognize objects but fail to infer the relationship between them, as shown in Figure !
Although prior work has substantially reduced object and attribute hallucinations through preference optimization Wu et al.] (2025a); Yang et al. (2025). contrastive decoding Wu et al. (2025c); Zhang et al. 2025), and intermediate- modification Jiang et al. (2024) 2025 } Wang et al, (2024). rela- tion hallucination, which accounts for nearly 40% of all hallucinations Zheng et al. (2024), remains salient challenge. Existing efforts to mitigate relation hallucinations have made some progress: dataset-driven approaches Xic et al. (2025) focus on constructing high-quality training and fine- tuning data, prompt-enginerring methods Wu et al. (2025a) employ constrained perception prompts, Zheng et al. (2024) selectively adjust the final logits with intermediate layer, and Triplet Description Wu et al. (2025b) converts the image into triplets, allowing the model to answer questions based on these structured descriptions instead of the raw image.
"Corresponding author.

---

Published as a conference paper at ICLR 2026
Input Question:
ChainMPQ
"Does man stand on a surfboard in the image?"
[S]; man [O]: surfboard [R]: stand
Intericaved Text-image Reasoning Chain
asking subject and object location
Question
Answerl
Input Image:
Multimodal contoext
Construet Question
Questionl: What is [S] [R]?
Answer2
Question2
LLaVA
Question2: What is [O]]
>>
Multimodal contcext
Question3: What the
relationship between [S] [O]?
Question3
Answer3
Output LLAVA-155:
Output LLAVA-1.5: "No, the man is riding on a surfboard in the image."
"Yes, man is standing on surfboard the image."
input-conflicting hallucination
solve relation hallucination
This figure illustrates a case in which an LVLM exhibits a relation hallucination, misrec- ognizing the true relation "riding" as "standing". With ChainMPQ, the model is guided to reason step by step and infer the correct relation.
Figure 1:
Despite their promising results, existing methods targted at relation hallucinations treat relational reasoning single-step inference, expecting models simultaneously identify entities and deter- mine relationships. This approach is likely to produce errors because it relies heavily on language priors rather than systematic visual analysis. Human relational reasoning, by contrast, follows more structured process: first locating and identifying relevant objects, then examining their in- teractions, and finally synthesizing visual evidence draw conclusions about relationships. This multi-step approach allows for more careful consideration of visual information and reduces the likelihood of inference errors.
Inspired by human reasoning process and drawing insights from Interleaved Modal Chain-of- Thought (ICoT) Gao et al. (2025), we propose ChainMPQ MMulti-Perspectivee Questions guided Interleaved Text-image Reasoning Chain), training-free framework that decomposes relational inference into manageable steps while maintaining relevant reasoning through accumulated multi- modal memory. Our approach addresses the challenge of relation hallucination by combining systematic question decomposition with progressive multimodal reasoning.
Specifically. ChainMPQ first extracts subject and object keywords from the question and uses them to drive cross-attention. Next, relations are decomposed into three components, and multi- perspective questions are constructed by masking chain is built by sequentially feeding the constructed questions into the model, using previous an- swers as textual context and earlier relevant visual tokens adjust later attention maps. This process enables the model to reason over prior textual and visual memories and progressively resolve the re- lation. We demonstrate the effectiveness of ChainMPQ on four advanced and widely used models, where it consistently reduces relation hallucinations on celation-focuseee benchmarks. Our contribu- tions are summarized as follows:
component at time. Finally, the interleaved
1. We introduce a tions, encouraging the model focus on each core element of relationship.
decomposition to generate multi perspeccive ques-
2. We design interleaved chain mechanism that transfers textual and visual memories by using answers and attention maps from earlier steps to refine subsequent reasoning, thereby enabling progressive relational inference.
3. We apply ChainMPQ to multiple LVLMs and relation-focused benchmarks, demonstrating consistent reductions in relation hallucinations.
RELATED WORK
2.1 LARGE VISION-LANGUAGE MODELS (LVLMS)
Large Vision-Language Models (LVLMss) enhance the capabilities of traditional Large Language Models (L.LMs) by incorporating visual inputs, thereby enabling them to perform complex multi-
2

---

Published as a conference paper at ICLR 2026
modal tasks such image captioning and visual question answering. Prominent LVLMs, including InstructBLIP Dai et al. (2023) and LLaVA et al. (2024b), integrate pre-trained vision encoders with language models, effectively bridging the gap between the visual and textual modalities.
While these models have achieved remarkable progress in visual-language understanding Lai et al. Li (2025): Peng al. (2024), they continue to face challenges of ha lucinattts particularly in the recognition of relationships [Zheng et al.] (2024); Nie et al.] 2024 between objects. Our work extends these models by specifically addressing the problem of relation hallucinations, employing step-by-step reasoning process guided by both textual and visual information.
(202)) |aurençon al. (2024);
2.2 RELATION HALLUCINATION IN LVLMS
Despite significant advancements in mitigating object and attribute hallucinations, relation hallu- cination remains a critical and underexplored issue Liu et al. (2024a); Wu et al. (2024). These hallucinations occur when a model accurately detects objects but fails correctly identify the rela- tionships between them, leading to misleading or incorrect responses in tasks such Visual Ques- tion Answering (VQA) Shhhhii et al. (2024). The significance of addressing relation hallucinations is highlighted by their prevalence and studies indicate that nearly 40% of hallucinations in LVLMs are related to relations between objects Zheng et al. (2024), yet this issue has not received as much attention as object or attribute hallucinations.
Recent research has proposed several evaluation benchmarks aimed assessing the handling of inter-object relations. For example, MMRel Nie et al. (2024) features over 10k question answer pairs across multiple domains, specifically designed to evaluate spatial, action, and comparative relations. Additionally, R-Bench Wu et al. (2024) includes diverse set of questions that balance perception and cognition tasks, exposing limitations in the relation reasoning capabilities of contem- porary models. Tri-HE Wu al. (2025b) further extends evaluation by using triplets represent knowledge, which allows it to assess both object and relation hallucinations at the same time. These benchmarks are valuable for systematically evaluating and enhancing the management of relation hallucinations. For considerations of dataset size and usage frequency, we evaluate our method using MMRel and R-Bench, which are larger and more widely used.
2.3 INTERLEAVED MODAL CHALN-OF-THOUGHT (ICoT)
Interlcaved Modal Chain-of-Thoughh (ICoT) Gao et al. (2025) extends the Chain-of-Thouuge (CoT) reasoning paradigm Wei et al. (2022) to multimodal tasks by integrating visual and textual reasoning steps. While traditional CoT methods, which primarily focus on text, struggle handle dynamic visual state transitions, ICoT addresses this limitation by progressively updating intermediate visual states throughout the reasoning process. This enables the model to maintain coherent and grounded reasoning across both modalities. The dynamic interaction between visual and textual information in ICoT closely mirrors human cognitive processes, where visual and textual elements evolve concur- rently to support decision decision-making, Recent advances in multimodal reasoning, including approaches such as Uni-CoT Qin et al. (2025) and visual reasoning frameworks! Lin et al. (205)). emphasizes the importance of integrating visual feedback into reasoning. ICoT offers flexible, scalable, and efficient framework for multimodal reasoning, establishing new basic method for future research in this field. Given the promising potential of ICoT, leverage its principles build our own methods.
METHODS
3.1 PROBLEM FORMULATION
We address relation hallucination in visual question answering, where models correctly identify entities but fail to infer their relationships. Given image and relational question Q. the task is to generate an accurate Yes/No answer A. Relation hallucination occurs when a model detects both subject and object entities but provides incorrect relational judgments.
3

---

Published as conference paper at ICLR 2026
Text-guided
Construction of Multi-
Input
Attention Enhancement
perspective Aware Text Prompt
onhanced visual tokens
output from Q1-
Q4
Original question Does the dog chase disc?
b
biss4
Inner VLM
attention modify
Does the dog chase disc the imauee?
Decompose into components [S]: dog (O): disc [R]: chase
atteation map
KWTEETT
textial
oncodor
eneoder
vistal
Construct questions
visual
encoder
textual
encoder
[S] mask [0]-
attention
ask for [o]-Q2
mask [S
Ouiput
mask [R] QQ5
bias5
A5
enhaanzdd visual tokens
Interleaved Text-image Reasoning Chain
What's the relarionship berween the deg and the disc??
dog
Q1: Where dog the image?
Q2: Where is disc image?
Q3: What is dog chasing??
Q4: What is disc being chased by?
chase disc in the image!
bins3-5 "By"
No context
No context
A1-A2
A A1-A3
AI-A4
"Bali
A1-A5
A5: dog jumping the chase the disc.
Al: The dog is jumping
A2; The dise at the center the image.
A3: The dog is chasing
A4: A dog is chasing
Final answer Vex/No
Figure 2: Overview of our proposed ChainMPQ. comprises three modules. Text-guided Attention Enhancement: extracts subject, object, and relation, using cross-attention to emphasize relation- relevant visual regions; Multi--errseectvve Aware Text Prompt: constructs five new questions based on these elements from different perspectives; Interleaved Text-image Reasoning Chain: sequen- tially inputs the questions, using each answer A, and its top-K active visual tokens to form mask M; bias when calculating subsequent attention maps. The original question is then answered to produce the final output and evaluation metrics.
3.2 OVERVIEW
ChainMPQ is training-free framework designed to mitigate relation hallucinations through pro- gressive multimodal reasoning. Unlike existing approaches that rely solely on textual prompting. our method leverages both textual answers and visual attention patterns across reasoning steps. For procedural details the algorithm, see Algorithm[]
The framework operates in three stages: (1) enhancing visual representations of entities mentioned the question through text-guided attention mechanisms, (2) decomposing the original relational query into multiple complementary questions that target individual components of the relationship, and (3) constructing an interleaved chain where accumulated textual and visual information guides subsequent reasoning. This progressive approach enables the model to systematically analyze rela- tionships rather than making immediate judgments based on surface patterns.
3.3 TEXT-GUIDED ATTENTION ENHANCEMENT
Accurate relational reasoning requires precise entity localization. We extract subject and object key- words from the input question using the spaCy NLP toolkit. These keywords are encoded to obtain representations X E Nx,, where N denotes the number of keywords (typically two, correspond- ing to the number subject and object), and de represents the text feature dimension.
The input image is processed by the visual encoder to obtain visual features V Adx, where M is the number of image patches and d, is the dimension the visual features. We apply cross- attention to enhance image regions corresponding the extracted keywords, where the image fea- tures V serves as the Query vector and the keyword text X as both the Key and Value vectors:
T
V = softmax
X
(1)
/de
4

---

Published as a conference paper at ICLR 2026
Algorithm 1: ChainMPQ
Input: Image relational question
Output: Final answer A
Hyperparams: X, kmax
Symbols: Textual context T, visual memory V
Module I: Enhance visual tokens
Extract keywords K from Q;
Encode V 4- VisualEncoder([1 and X <- TextEncoder
Obtain enhanced visual tokens V + CrossAttn(X, V).
Module 2: Construct sub-questions
Decompose Q into [S] [R]
Generate complementary sub-questions Q1..5-
Module 3: Interleaved text-image reasoning chain
Q1 and Q2: localize [S] and [Q] using V', update T:
Q3 to Q5: ask relation-focused questions and build top-k mask M. with S Amaxi Apply bias scaled by A. update T and V/
Answer original question: decode with V", and V to produce A.
This operation produces enhanced visual tokens V that emphasize subject and object regions, tablishing foundation for accurate relational inference in subsequent steps.
3.4 CONSTRUCTION OF MULTI-PERSPECTIVE AWARE TEXT PROMPT
We decompose the original question into five complementary questions that examine different as- pects of the relationship. Specifically, the original input question typically contains three compo- nents: the subject, the object, and the relation between them. The first two questions focus on entity localization by asking about the location of the subject and object respectively. The remaining three questions employ a masking strategy where one component is masked while the other two are used to construct new queries.
To be specific, we mask the object to generate a question about what the subject is interacting with, mask the subject ask what the object is being affected by, and mask the relation to inquire about the general relationship between the entities. For example, "Does the dog chase a disc the image?" becomes five questions: entity localization ("Where is the dog/disc?") and three relation-focused queries generated through systematic masking, as shown in Figure 2
This decomposition strategy encourages the model analyze individual components before making reducing reliance on language priors and improving systematic reasoning.
final relational judgments, juttrionnliuddmettts
3.5
INTERLEAVED TEXT-IMAGE REASONING CHAIN
Unlike previous text-only prompting methods Wu al. (025a)), we sequentially process the en- hanced visual tokens from Section 3.3 and constructed questions from Section 3.4 using an inter- leaved chain that transfers both textual and visual information across reasoning steps. For each question Q; we encode it using the text encoder to obtain question tokens, while maintaining context C₁ that accumulates answers from previous steps.
The model generates answer A; for question Q; using the enhanced visual tokens V", current context C. and any accumulated visual memory from earlier steps. The first and second questions are answered directly without adding any contextual information. Starting from the third question, we extract attention weights associated with the keyword tokens each question from the final n decoder layers to capture the model's focus visual regions:
Attn
Aun [t,:]
(2)
IET t=L-n
where contains indices of keyword tokens, L the total number of decoder layers, and Attn ( represents the attention matrix layer l.
5

---

Published as a conference paper at ICLR 2026
We select the top-k visual tokens with highest attention scores using entropy-basee adaptive strategy that adjusts based on attention concentration:
k = kimux (Attt))))
Tupk, = (Att(A k)
(3)
where (Attn:) represents the normalized entropy of the attention distribution, lopk. denotes set of visual tokens most attended to for the i-th question and kmnz is set to 20 in our experiments.
The selected tokens form bias mask M, that guides attention in subsequent steps:
Aling
j Lopk.
Ma
En A
othercases
where entries corresponding to h are assigned normalized attention scores and all others are set to zero, and Mi, denoting the j-th element in M,.
For subsequent questions, the attention mechanism incorporates this visual bias:
QK
0g = À Conf prev,
Attn; = softmax
+ a; M, V
(5)
ddz
where a; is a confidence-buee based weight that increases with answer certainty. For multi-round previous visual bias, we calculate weighted average:
QKT
Attn;+1 = softmax
Oj
Mj
V
(6)
Vdg
Σ ==3
This mechanism enables the model to maintain visual focus across reasoning steps while progres- sively building understanding the relationship. The accumulated multimodal evidence culminates in final answer that reflects systematic relational analysis rather than surface-level pattern match- ing-
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Evaluated LVLMs. We evaluate our method four open-source LVLMs: LLaVA-1.5-755 Liu al. (2024)). InstruciBLIP-7B Dai et al. (2023), Qwen2.5-VL-7B Bai et al. (0225) and Intern VL3.5- 8B Wang al. (2025). The first two models are widely used and represent classic LVLM de- signs. The last two models are more recent and show stronger performance in many multimodal tasks. LLaVA- combines a CLIP visual encoder with Vicuna-7B for multimodal reasoning, while InstructBI.IP77B follows the BLIP-2 design, linking frozen vision encoder to a language model through a Q-Former. Qwen2.5-VL is built on the Qwen2.5 language model and uses a redesigned VIT architecture to provide strong visual understanding. Intern VL3.5 integrates an Intern visual encoder with a Qwen3 series language model to support robust image reasoning. These distinct architectures allow us assess the generalization of our method across different design paradigms.
Dataset and Benchmarks. Commonly used benchmarks for visual question answering, such as POPE et al. (2023c) and CHAIR Rohrbach et al. (2018), primarily focus object hallucinations. Since our task aims to alleviate relation hallucinations, we evaluate on two widely used benchmarks specifically designed for this purpose: MMRel Nie et (2024) and R-Bench (image-level) Wu al.) (004)) The datasets details are illustrated Appendix.
Baselines. In addition standard multimodal large model, compare approach with stan- dard Chain-of Thought prompting Wei et al.] (2023) and several training-fie free methods specifically designed to mitigate relation hallucinations. These include Constraint-Aware Prompting Wu et al.

---

Published as a conference paper at ICLR 2026
Table I: Results on MMRel and R-Bench benchmark. Higher (f) accuracy, precision, and Fl indi- cate better performance. The best results are bolded, and the second- best are underlined.
MMRel
R-Bench
Model
Method
Acc
Prec
F1
Acc
Prec
F1
Vanilla
59.02
56.81
66.40
71.23
64.27
77.23
Prompting Wu et al.
(2025a)
62.33
60.58
68.07
75.86
67.28
79.60
LL5
Calibrate Zheng et al. CoT Wei et al. 2023
2024
63.50
60.79
70.54
74.28
67.86
78.31
61.88
59.14
66.25
72.91
65.88
77.87
Ours
65.20
64.75
71.21
76.04
72.03
81.54
Vanilla
57.58
55.32
66.79
69.31
62.76
76.04
Prompting Wu et al.
(2025a)
64.52
62.28
71.23
73.65
68.74
80.21
InstructBLIIP
Calibrate hhng et al. CoT Wei et al. 2023
20224)
62.05
61.02
69.85
72.96
66.23
78.54
59.90
57.12
65.41
72.15
65.79
78.45
Ours
65.14
64.12
74.12
75.86
70.59
81.12
Vanilla
66.10
63.07
72.14
79.85
75.19
80.88
Prompting Wu et al.
(2025a)
70.01
68.59
75.23
81.02
78.38
82.46
Qwen2.5-VL
Calibrate Zheng et al. CoT Wei al. 2023
2024
71.36
67.20
76.28
80.98
77.05
81.52
67.35
65,49
72.08
80.24
76.94
81.64
Ours
73.52
69.65
77.45
83.92
80.23
84.63
Vanilla
71.44
67.07
75.86
82.33
78.87
83.21
In3. Prompting Wu et al. (2025a)
73.01
67.65
77.12
83.97
80.31
84.87
Calibrate Zheng ct (2044)
74.45
71.46
78.64
82.65
79.82
83.75
CoT Wei et al. (2023
72.10
68.74
76.35
82.87
79.41
84.02
Ours
75.21
72.54
78.65
85.05
82.85
85.91
2025a), which relies text-hased prompting, and Caaectthhen-Clllirttee/eiii et al. (2024) which calibrates the final output layer using hidden states from intermediate layers. We chose them be- cause they represent two main ideas in this area: text-prompt-banee tion. Comparing with them allows us to show the benefit of our full text-image reasoning chain in clear way. The performance of these baseline methods is obtained by re-mmplementing their exper- iments our chosen datasets and experimental setup using the latest public code and their official instructions.
reasoning and output calibra-
4.2
MAIN RESULTS
Overall Performance. Table 1 shows ChainMPQ consistently outperforms baselines across both models and benchmarks. On MMRel, ChainMPQ achieves 65.20% accuracy with LLaVA-1.55, im- proving over the best baseline by 1.7%. Similar improvements are observed with InstructBLIP (65.14% vs 64.52%). On Qwen2.5-VL and IL3.5, which already have higher base perfor- mance, ChainMPQ also improves accuracy from 71.36% to 73.52% and from 74.45% to 75.21 % respectively. The method shows particularly strong precision gains (4.17% higher than the best baseline using LLaVA in R- R-Bench), indicating reduced false positive relation predictions while si- multaneous gains in F1 further confirm that the method improves overall reliability without sacrific- ing recall.
Cross-Model Generalization. ChainMPQ demonstrates consistent improvements across different architectures (LLaVA, InstructBLIP, Qwen-VL and InternVL), suggesting the approach is model- agnostic rather than exploiting specific architectural features.
4.3 PERFORMANCE EXPERIMENT
To further improve the efficiency ChainMPQ, we explored two optimization methods to achieve trade-off between accuracy and latency.

---

Published as a conference paper at ICLR 2026
Table 2: Accuracy and latency comparison of different methods on LLaVA-L.5-7B.
Benchmark
Method
Acc (%)
Time (s/sample)
Acc / ATmme
Vanilla
59.02
0.9
Full ChainMPQ
65.20
3.3
2.58
MMRel
Light1
63.78
1.5
7.93
Light2
64.25
2.1
4.36
Vanilla
71.23
0.9
Full ChainMPQ
76.04
3.3
2.00
R-Bench
Light1
74.50
1.5
5.45
Light2
75.08
2.1
3.21
LightI: We keep only QI, Q2, and Q5. Since Q1 and Q2 don't need to pass text or visual information to each other, they can run in parallel. When answering Q5, we use the answers QI and Q2 as the text context, and their bias masks MI and M2, constructed from the attention map, the visual context.
2. Light2: We keep only Q3, Q4, and Q5, and all other settings remain the same. This means the reasoning chain starts from Q3 with no text or visual context, and the remaining steps (from Q3 to Q5) follow the same procedure described in Section 3.5.
The results in Table show that the Light has the highest A ATmm having higher accuracy improvement shorter time, which means it achieves the best overall trade-off. Therefore, when accuracy is the top priority, we use the full ChainMPQ. When balance between accuracy and latency needed, Light the preferred choice.
4.4 ABLATION STUDY
We conduct ablation studies to evaluate the impact of key components and hyperparameters in our method, using LLaVA-1.5 on the MMRel benchmark.
4.4.1 ABLATION STUDY OF CORE COMPONENTS.
We examine the contributions of ChainMPQ's core components. To assess the role of "TText-gudded Attention Enhancement", we remove the enhancement of visual tokens subject and object regions, such that in the multimodal chain we use V rather than VT test the importance of the "Construc- tion of Multi-perspective Aware Text Prompts", we replace the five constructed questions with only the relationship question (Q5), meaning that the model only answers Q5 and then the original ques- tion. And The model answers the original question using the text context and visual bias produced when answering Q5. To evaluate the contribution of the "Interleaved Text-image Reasoning Chain", we remove the multimodal chain and only use the text context from the previous answer, which means that we keep the entire multi-ture text context, not only the last answer.
As shown in Table 3a the full ChainMPQ achieves an accuracy of 65.20%. Removing text-guided attention reduces accuracy by 1.14%, suggesting that enhancing subject- and object-related tokens provides limited but measurable benefit in reducing hallucinations. Omitting multi-perspective ques- tions decreases accuracy by 3.68%, confirming their importance in guiding step-by-step inference, which forms the cornerstone of the chain. Eliminating the interleaved chain lowers accuracy by 3.08%, indicating that transferring visual memory plays an equally important role in the reasoning process. Despite these degradations, all three ablated variants still outperform the baseline LLaVA (59.02% accuracy), while the complete ChainMPQ demonstrates the strongest effect.
4.4.2 SENSITIVITY ANALYSIS OF HYPERPARAMETERS
We also analyze the sensitivity of kmax. the maximum value for top-K in Eq. We set kmax to 5%. 10%, 30%, and 50% of the total number of patches, corresponding approximately 10. 20, 70, and 120 tokens. We also vary the maximum bias weight parameter X, which is the coefficient
of in Eq. 5 and test values of 3, 5, and 7, As shown in Figure 3b) performance peaks at X
8

---

Published as a conference paper at ICLR 2026
+ 15 2-5
Model Variants
Acc
Prec
F1
65
Ac
59.02
56.81
66.40
60
ChainMPQ(Full)
65.20
64.75
71.21
k=3
w/o Enhancement
64.06
63.25
69.42
w/o Multi-penn perppppprsee
61.52
60.84
67.53
k-7
w/o Interleaved
62.12
61.47
68.01
10
20
70
120
Rmax
(a) Ablation study with different model variants.
(b) Sensitivity of Accuracy to kmax and A.
Figure 3: Ablation Results on MMRel using LLaVA-1.5; (a) Ablation study; (b) Sensitivity analysis.
Input Image
Normal Process:
x
Input Image
Normal Process:
Q: Does a man stand on surfboard in the image? standing
Q: Is there chair left trash bin in the image? Yes, the chair is left to trash image.
surfboard in the image.
ChainMPQ Process:
ChainMPQ Process:
Q1: Where the man?
Q1: Where the chair?
A: The man is in the cocan.ridingg/ surlboard on wave.
A: The chair is located in a hotel room.
Q2: Where the surfboard?
Q2: Where is the trash bin?
surfboard
water, with the man riding
A: The trash bin is located under the desk.
recognition
Q3: What the man standing on? Correct the errors
Q3: the chair
to something?
A: The man isn't standing, he ridiignn surfboard.
Yes, the chair is left desk.
Q4: Who is standing on the surfboard?
Q4: Is the trash bin right something?
A: No one is standing on surfboard.
A: No, is not right to anything, it's under the desk.
Q5:
the relationship between
and
QS: What the relationship between the chair and the
surfboard?
trash
Correct recopsition
A: man is soriding on the surfboard.
The chair positioncd right
trash
Q: Does a man stand on a surfboard in the image?
Q: Is there chair left trash bin in the image?
A: No. he rlding on the surfboard.
A: No, the chair is right to the trash bin.
(a) Case study for action category
(b) Case study for spatial category
Figure 4: The full ChainMPQ answering process vs. directly output
and kiux = 20, where the accuracy reaches 65.20% When kimax too large, the model uses almost all image tokens, which weakens its focus important regions and breaks the normal attention process. In contrast, when kmax is too small, the model may miss key information, especially for questions that rely scattered visual features. Similarly, if X is too large, the model becomes overly dependent on past memory, which disturbs attention propagation and reduces accuracy. is too small, the bias is weak and the performance is close to the original LLaVA baseline.
4.5
CASE STUDY
To intuitively illustrate the ChainMPQ process, present real examples from MMRel, namely an action case and a spatial case, which together account for over 90% of the MMRel dataset Nie al. (2024). Due page limits, another case study of competitive type is provided in Appendix A.6.2.
Figure illustrates the complete processing pipeline of ChainMPQ. In the action case "Does man stand on surfboard in the image?", the baseline answers "yes", confusing the true relation "riding" with "standing". ChainMPQ first localizes the subject and the object with two guiding questions, identifies the action "riding," and propagates this cue through textual context and attention transfer to produce the correct answer "no". the spatial case "Is there a chair left to a trash bin in the image?", the baseline is again incorrect. ChainMPQ retrieves the locations of the chair and the bin under the desk, constrains subsequent reasoning to the correct regions, and then verifies the directional relation conclude that the chair is to the "right" of the bin. Across both cases, decomposition helps the

---

Published as a conference paper at ICLR 2026
Before ChainMPQ
After MPQ
Before ChainMPQ
After ChainMPQ
(a) Case study action category
(b) Case study for spatial category
Figure 5: Comparison of attention maps between directly answering the original question and an- swering it using ChainMPQ.
model locate the correct regions, reduce false language priors, and follow a clear path verify relations, leading to answers that aligned with the image.
Sigureeppresentt that across both action and spatial cases, ChainMPQ produces attention maps that are more sharply concentrated on task-relevant regions, especially the subject and the object. This pattern indicates that the focused attention triggered by earlier related subquestions is propagated through the chain via textual context and attention transfer, so that when answering the original question the model attends more precisely to the related areas. In both examples, ChainMPQ can suppress attention the subject and the target object, aligning the attention trajectory with the queried relation. The resulting foeus correlates with corrected predictions and fewer contradictions between the image and the answer, which suggests improved grounding and more faithful relational inference.
irrelevant background areas and focus attention the interaction between
5 CONCLUSION
Questions guided Interleaved Text-image Reasoning Chain), training-free free framework that enhances the ability of LVLMs to recognize relationships by utilizing accumulated textual and visual memories. ChainMPQ first strengthens the relevant vi- sual tokens using keywords extracted from the question. It then constructs questions from different perspectives, each closely centered on the subject, the object, and their rela- tionship. These questions are sequentially fed into the model, with both textual and visual memories from carlier rounds propagated to subsequent ones, thereby forming an intericaved chain of image and text. Experimental results demonstrate that ChainMPQ effectively reduces relation hallucina- tions and improves factuality across multiple LVLMs and benchmarks, providing a simple yet robust framework for step-by-step relational inference.
We propose ChainMPQ
of complementary
FUTURE WORK
Although ChainMPQ consistently improves the mitigation relation hallucinations, still relies on attention distributions as a proxy for visual evidence, which may not fully capture the underly- ing reasoning process. Future work will incorporate causality-based attribution mechanism. By detecting and correcting outputs that contradict the image, it prevents errors from cascading.
worth mentioning that major challenge in relation hallucination lies in the spatial granular- ity of visual tokens, which often misalign with real-world object boundaries. This misalignment, observed in lower performance for the spatial category in MMRel our experiments, can hinder accurate relational reasoning. Future research may alleviate this issue by exploring multi-scale vi- sual representations or by integrating explicit scene graph representations to improve robustness in fine-grained relational understanding.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
The work is partially supported by the U.S. National Science Foundation (NSF) Grant CRI 2451683, an NVIDIA Academic Grants Program, U.S. Bank Academic Research Award, the Uni- versity of California, Merced, and a UC Merced Faculty Research Award. The views and conclu- sions are those of the authors and do not necessarily reflect the official policy or position of the U.S. Government.
REFERENCES
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang. Shijie Wang. Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang. Jian Yang, Shusheng Yang, Yang Yao. Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu. Qwen technical report, 2023a. URL http://axxvvv.oooeo
2309. 16609
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang. Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, local- ization, text reading, and beyond, 2023b. URL
abs 2308 12966
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025. URL
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, and Mike Zheng Shou. Hallucination of multimodal large language models: A survey. arXiv preprint
arXiv:2404. 18930, 2024.
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. Instructblip: Towards general-purpose vision-language models with instruction tuning. Advances in neural information processing systems, 36:49250-49267, 2023.
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xi- awu Zheng, Ke Li, Xing Sun, Yunsheng Wu, Rongrong Ji, Caifeng Shan, and Ran He. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2025. URL
https:://arxtt wrggoogaae wttg/////////.oooooooo//ooo /2006 13394
Jun Gao, Yonggi Li, Ziqiang Cao, and Wenjie Li. Interleaved-modal ch In Proceed- ings of the Computer Vision and Pattern Recognition Conference, pp. 19520-19529, 2025.
Nick Jiang, Anish Kachinthaya, Suzie Petryk, and Yossi Gandelsman. Interpreting and editing vision-language representations to mitigate hallucinations. arXiv preprint arX:v.2410.02762, 2024.
Zhangqi Jiang, Junkai Chen, Beier Zhu, Tingjin Luo, Yankun Shen, and Xu Yang. Devils middle layers of large vision-language models: Interpreting, detecting and mitigating object hallucina- tions via attention lens. In Proceedings of the Computer Vision and Pattern Recognition Confer- ence, pp. 25004-25014, 2025.
Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reason- ing segmentation via large language model, 2024. URL 00692
Hugo Laurençon, Andrés Marafioti, Vietor Sanh, and Léo Tronchon. Building and better under- standing languaee models: insights and future directions, 2024. URLhttps : : //arxiv. org/ 2408 12637
11

---

Published as a conference paper at ICLR 2026
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang. Chunyuan Li, and Ziwei Liu. Mimic-it: Multi-modal in-context instruction tuning, 2023a. URL https:
abtpg//bbs//a /20/2/366.. 05425
Haoxin Li and Boyang Li. Enhancing vision-language compositional understanding with multi- modal synthetic data, 2025. URL
wwtp://www.g/a///////aaaa/ 01167
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023b. URL https:// arxiv.org abs 2201 22233111220 12597
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:230S. 10355, 2023c.
Zhiyu Lin, Yifei Gao, Xian Zhao, Yunfan Yang, and Jitao Sang. Mind with eyes: from language reasoning to multimodal reasoning. arXiv preprint 1xXiv:2503.18771 2025.
Hanchao Liu, Wenyuan Xue, Yifei Chen, Dapeng Chen, Xiutian Zhao, Wang, Liping Hou, Rongjun Li, and Wei Peng. A survey on hallucination in large vision-language models, 2024a.
URL /atpps://axxi...e
abs/ 00253
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF conference on computer vision and patlern recognition, pp. 26296-26306, 2024b.
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, and Dahua Lin. Mmbench: your multi-modal model an all-around player?, 2024c. URL httpst/t::///// arxx vvv org. abs 2307. 06281
Jiahao Nie, Gongjie Zhang, Wenbin An, Yap-Peng Tan, Alex C Kot, and Shijian Lu. Mmrel: A relation understanding dataset and benchmark in the mllm era. arXiv e-prints, pp. 2406, 2024.
Wujian Peng, Sicheng Xie, Zuyao You, Shiyi Lan, and Zuxuan Wu. Synthesize, diagnose, and optimize: Towards fine -raigrr vision-language understanding, 2024. BRLpttts://arat
org/abs/233 2312. 00081
Luozheng Qin, Jia Gong, Yuqing Sun, Tianjiao Li, Mengping Yang. Xiaomeng Yang, Chao Qu, Zhiyu Tan, and Hao Li. Uni-cot: Towards unified chain-of-thought reasoning across text and vision. arXiv preprint arXiv:2508.05606 2025.
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. Object hallucination image captioning. arXiv preprint arXiv:1809.02156 2018.
Haz Sameen Shahgir. Khondker Salman Sayeed, Abhik Bhattacharjee, Wasi Uddin Ahmad, Yue Dong, and Rifat Shahriyar. Illusionvga: A challenging optical illusion dataset for vision language models, 2024. htt.ttppeee/aaaii.ooo///////////
2403.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Maric-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023a. http::tttt:/ttttt http://wwww..oraa/////22222223
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fermandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshor, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuming Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian. Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, lliyan Zarov, Yuchen
12

---

Published as a conference paper at ICLR 2026
Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Llama 2: Open foundation and fine-tuned chat models, 2023b. URL htttps::/aaxi..oootttt/ wwt.://rrggab/ssoeoooooooo 233//30//222...//////////// 09288
Chenxi Wang, Xiang Chen, Ningyu Zhang, Bozhong Tian, Haoming Xu, Shumin Deng, and Huajun Chen. MIIm can see? dynamic correction decoding for hallucination mitigation. arXiv preprint arXiv:2410.11779, 2024.
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang. Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin. Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuchui Wang, Zhi Hou, Haoran Hao, Tiayyi Zhang. Songze Li, Xiangyu Zhao, Haodong Duan, Nianchen Deng, Bin Fu, Yinan He, Yi Wang. Conghui He, Botian Shi, Junjun He, Yingtong Xiong, Han Lv, Lijun Wu, Wenqi Shao, Kaipeng Zhang. Huipeng Deng, Biqing Qi, Jiaye Ge. Qipeng Guo, Wenwei Zhang, Songyang Zhang, Maosong Cao, Junyao Lin, Kexian Tang, Jianfei Gao, Haian Huang, Yuzhe Gu, Chengqi Lyu, Huanze Tang, Rui Wang, Haijum Lv, Wanli Ouyang, Limin Wang, Min Dou, Xizhou Zhu, Tong Lu, Dahua Lin, Jifeng Dai, Weijie Su, Bowen Zhou, Kai Chen, Yu Qiao, Wenhai Wang, and Gen Luo. Internv13.5: Advancing open-s multimodal models in versatil- ity, reasoning, and efficiency, 2025. h
https: //ts:///axivie wwtpaawwww.rrooeaaeeeeeeeeeeeeee abs 2508 18265
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models, Advances in neural information processing systems, 35:24824-24837, 2022.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. Chain thoouuhh prompting elicits reasoning in large language models, 2023. URL hhtpps: :/tpp:///aae //arxiv..vvv.///a///a org/ abs abs/2201..199
Jiarui Wu, Zhuo Liu, and Hangfeng He. Mitigating hallucinations in multimodal spatial relations through constraint-awart prompting. arXiv preprint arXiv:2502.083/7, 2025a.
Junjie Wu, Tsz Ting Chung, Kai Chen, and Dit-Yan Yeung. Unified triplet-level hallucination eval- uation for large vision-language models, 2025b. URL
23114
Mingrui Wu, Jiayi Ji, Oucheng Huang, Jiale Li, Yuhang Wu, Xiaoshuai Sun, and Rongrong Ji. Eval- uating and analyzing relationship hallucinations in large vision-language models. arXiv preprint arXiv:2406. 16449, 2024.
Tsung- Han Wu, Heekyung Lee, Jiaxin Ge, Joseph E Gonzalez, Trevor Darrell, and David M Chan. Generate, but verify: Reducing hallucination in vision-language models with retrospective resam- pling. arXiv preprint arXiv:2504. 13169, 2025c.
Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang, Dawei Leng, and Yuhui Yin. Fg-elip: Fine-grained visual and textual alignment. arXiv preprint arXiv:2505.05071, 2025.
Zhihe Yang, Xufang Luo, Dongqi Han, Yunjian Xu, and Dongsheng Li. Mitigating hallucinations in large vision-language models via dpo: On-policy data hold the key. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10610-10620, 2025.
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, Chenliang Li, Yuanhong Xu, Hehong Chen, Junfeng Tian, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou, mplug-owl: Modularization empowers large language models with multimodality, 2024. httpthttt:///aate www.rrwwwv...geeeee abs 2304.34422 14178
Ce Zhang, Zifu Wan, Zhehan Kan, Martin Q Ma, Simon Stepputtis, Deva Ramanan, Russ Salakhut- dinov, Philiip Morency, Katia Sycara, and Yaqi Xic. Self-correcting decoding with gen- erative feedback for mitigating hallucinations in large vision-language models. arXiv preprint arXiv:2502.06 10,, 2025.
Kening Zheng. Junkai Chen, Yibo Yan, Xin Zou, and Xuming Hu. Reefknot: A comprehensive benchmark for relation hallucination evaluation, analysis and mitigation in multimodal large lan- guage models. arXiv preprint arXiv:2408. 09429, 2024.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 DETAILED ALGORITHM
Algorithm describes the complete execution process of ChainMPQ in detail.
Algorithm 2: ChainMPQ ttraining-free, interleaved text-image reasoning)
Input:
Image I, relational question Q
Output: Final answer A
Retained Intermediates: (Atta., .M))
Symbols: Visual tokens V, enhanced visual tokens V", complementary questions Qı.s, textual memory
T (accumulates (Q,, A.)). visual memory y (accumulates M,).
Hyperparams: X, kinax
STEP I: Text-quided Attention Enhancement Extract subject/object keywords K from Q.
&- VisualEncoder((), X <- TextEEcoderr(A K). 3. Use cross attention on X and V to enhance visual tokens.
STEP II: Merspe-eesee Prompt Construction
4. Extract subject [S]. object [O], relation [R] from Q-
Build five complementary questions Q1:5 with mask strategy:
Q1: Where [S]?
Q2: Where is [O]?
Q3: What is [S] [R]? (mask object)
Q4: What is [R] [O]? (mask subject)
Qs: What the relationship between [S] and [O]? (mask relation)
Initialize + Ø, V + Ø.:
STEP II: Interleaved Text-Image Reasoning
7. Direct answers for Q1, Q2 (no context, no bias):
For E 21, A, + Qi using V'; T TTU ((Q,, A1)
8. Context- and mask-aware answers for Qs-Qs:
i 3..5:
(a) Compute aggregated attention (t)
(b) Form top-k set build M:(kmax) )
(c) Decode with textual visual memory:
Q: using V T; Ms, T [I); (M.) Final answer: A Q T.
A.2 DATASET DETAILS
A vision-language benchmark specifically designed to assess and improve large multi- modal models' understanding of inter-object relationships. approximately 10.14K Yes/No ques- tion-answer pairs and covers three relation types: spatial, action, and comparative. MMRel in- Genome) and synthetic images (via SDXL and DALLEE),
MMRel:
corporates both real-world dataset allowing for diverse relational reasoning scenarios, including adversarial and counter-intuitive cases. The dataset is constructed through a combination of GPT-4V-assisted annotations and human val- idation to ensure high-quality labels. Beyond evaluation, MMRel serves as valuable resource for instruction tuning and fine-tuning, showing clear improvements in relational grounding when used for training. It reveals that many current vision-languas models rely heavily on linguistic priors, often hallucinating relations supported by visual evidence.
R-Bench: A diagnostic benchmark focused on detecting relationship hallucinations in large vision- language models (LVLLMs). It comprises image-level and instance-level yes/no questions that test model's ability to identify whether specific relations between objects truly exist. The benchmark categorizes hallucinations into three co-occurrence types: relation-relation, subject-relation, and relation-object to isolate sources of error. R-Bench samples are sourced primarily from NoCaps, ensuring minimal training overlap. Experiments show that LVLMs frequently over-rely on com- monsense priors, misjudging relations in visually ambiguous R-Bench is especially effective for fine- tine-grained analysis relational reasoning errors, offering in- sights into model biases and areas for improvement. We clarify that experiments use the image-
adversarial contexts. As result,
14

---

Published as a conference paper at ICLR 2026
level setup of R-Bench. The image-level and instance-level setups share almost the same images and questions, but the instance-level setup also provides bounding box coordinates and focuses more on the relationship between specific object instances. Since most datasets do not provide bounding boxes, and locating the objects involved in a relation is itself an essential ability of LVLMs, we choose the image-level setup for main experiments to give fair and clear evaluation of how ChainMPQ improves relational understanding.
A.3 IMPLEMENTATION DETAILS OF MULTI-PERSPECTIVE QUESTION GENERATION
First, we use an NLP tool to extract the subject S, object O, and relation R from the original question. For example, the question "Does the dog chase disc in the image?" can be decomposed into the subject "dog", the object "disc", and the relation "chase". We then build two questions to locate the subject and the object. In this example, they are "Q1: Where is dog in the image?" and "Q2: Where is a disc in the image?"
Next, we apply masking strategy. In each step, one of the three elements is masked, and the other two are used to form a new question. This helps the model focus on the remaining elements and check the original relation from different views. For this example, after masking and reconstruction, we obtain three new questions: "Q3: What is the dog chasing?", "Q4: What is the disc being chased by?", and "Q5: What is the relationship between the dog and the disc?" We use GPT-3.5 Turbo to refine each question so that the wording is clear and grammatically correct.
A.4 EXPERIMENT IMPLEMENTATION DETAILS
In the Construction of Purte-Perseective Aware Text Prompts step, we employ the spaCy NLP toolkit to extract salient keywords from the input questions. These keywords are then used to refine the generated prompts, ensuring that the decomposed questions remain semantically coherent and closely aligned with the original query.
In the Construction of the Interleaved Chain of Image and Text step, we set n as to capture the last 3 layers' attention in decoder. We set kmux 10% of the total number of visual patches (i.e.. kmax = 20). This proportion provides a balance between capturing sufficient visual context and avoiding noise from irrelevant regions. The maximum bias weight parameter (the coefficient of a) is fixed at 5, which we found to be a stable value across different datasets and models.
Regarding the comparative experiments with other methods, we reproduced both baseline methods using the latest public code and their official instructions. We only replaced the base models with LLaVA- 1.5-7B and InstructBLIP-7B, and we changed the datasets to MMRel and R-Bench match our setting. All comparisons are made under a fully fair setup. For Constraint-Aware Prompting Wu al. (225a)), we used the combined-promp which Prompt 10, as it gives the best accuracy. For Seneettthen-Calibrre Zheng et al. (2024), ran their released scripts without any change to the parameters.
A.5 EXPERIMENTS ON GENERAL REASONING BENCHMARKS
To explore whether improved relation reasoning also lead better general reasoning or broader application outcomes. We also evaluated ChainMPQ on two general multimodal benchmarks, MM- Bench [Lu et al. (2024c) and MME Fu al. (005).. And the results show that ChainMPQ brings consistent gains not only in relation reasoning but also in broader multimodal abilities.
These tasks cover broad range of multimodal abilities, such as visual logic reasoning, scene un- derstanding, OCR, and object grounding, and are not limited to relation reasoning. Specifically, MMBench defines three capability levels and twenty fine-grained ability dimensions for evaluat- ing different reasoning and perception skills of MLLMs. MME includes tests for both perception and cognition. The perception part covers coarse- and fine-grained skills such as object existence, counting, location, color recognition, poster recognition, celebrity recognition, scene and landmark identification, and artwork recognition. The cognition part includes commonsense reasoning, nu- merical reasoning, translation, and code reasoning. It contains fourteen sub-tasks in total.
The MMBench results are shown in Table Here Overall the weighted average over all tub-tasks and is the most important indicator of general ability. RR (Relation Reasoning) refers to relation
15

---

Published as a conference paper at ICLR 2026
Table 3: MMBench results (Overall & Relation Reasoning). Higher is better.
Model
Overall
AOverall
RR ARR
LLaVA-vI.5 .5-7B
66.5
58.8
ChainMPQ
67.8
+1.3
61.3
+2.5
InstruetBLIP-7B
63.9
52.5
ChainMPQ
65.5
+1.6
55.2
+2.7
Qwen2.5-VL-7B
83.2
78.2
ChainMPP
84.7
+1.5
81.8
+3.6
InternV33-88
83.6
82.5
ChainMPPQ
84.2
+0.6
83.9
+1.4
Table MME results (Overall, Perception, Cognition). Higher better.
Model
Overall
AOverall
Perception f
APere
Cognition t
ACog
LLaVA-v1.5-7B
1808.4
1506.2
302.1
ChainMPQ
1898.3
+89.9
1545.6
+39.4
352.7
+50.6
InstructBLIP-7B
1391.4
1137.1
254.3
ChainMPQ
1484.6
+87.2
1176.0
+38.9
308.6
+54.3
Qwen2.5-VIL-777
2312.1
1698.1
613.9
ChainMPQ
2350.8
+38.7
1719.2
+21.1
631.5
+17.6
Intern VL3-8B
2422.0
1748.4
673.6
ChainMPP 2457.1
+35.1
1771.4
+23.0
685.7
+12.1
reasoning. The MME results are shown in Table Here Overall is the average score across all fourteen tasks. Perception measures detection, counting, color, position, and OCR skills, where we do not expect large gains. Cognitionn measures high-level semantic understanding such as reasoning, description, and complex question answering.
In sum, these results show that improving relation reasoning does lead to better general reasoning. ChainMPQ brings consistent gains on the Overall scores of both MMBench and MME, especially in the Relation Reasoning and Cognition part, confirming that stronger relation understanding supports broader multimodal reasoning abilities.
A.6 DETAILED CASE STUDY
A.6.1 ATTENTION ANALYSIS IN CASE STUDY
As shown in Figure 6 ChainMPQ progressively guides the model's reasoning from object local- ization to relational understanding. The first two sub-questions help the model correctly identify the man and the surfboard, and the attention maps show a clear shift toward the regions that match each answer. The following three sub-questions gradually move the attention toward the interaction between these objects, which helps the model understand the relationship more clearly.
Compared with the direct-answer setting, where the attention maps scattered and often point to irrelevant areas, ChainMPQ produces more focused and meaningful patterns. particular, for the question "What is the man standing on", the attention highlights the surfboard instead of the water. This prevents the model from being confused by the wording of the original question.
This case shows that ChainMPQ not only breaks down complex question into simple and clear steps but also adjusts the attention process in a helpful way. By guiding the model step by step, ChainMPQ improves both the reasoning path and the final answer, which leads more stable and accurate relational predictions.
A.6.2 CASE STUDY FOR COMPETITIVE CATEGORY MMREL
We only showed two case studies the main text because of space limits. This does not mean that method works better action spatial questions than other types. ChainMPQ is also
16

---

Published as a conference paper at ICLR 2026
Figure 6: For each generated question, the attention map during the model's answer generation process using ChainMPQ is shown.
Input Image
Normal Process:
x
Q: Is light bigger than mirror in the image? Yes, there is light bigger than mirror in the image.
ChainMPQ Process:
Q1: Where is the light?
A: The light is above the sink in the bathroom.
Avoid false
Q2: Where is the mirror?
recognition
A: The mirror is above the sink in the bathroom.
Q3: Is the light bigger than something?
A: Yes, the light is bigger than the mirror in the image.
Q4: Is the mirror smaller than something?
A: No, it's the in the bathroom. Correct the
Q5: What is the relationship between light and mirror?
A: The light is above the mirror, and the mirror is above
the sink in the bathroom.
Q: Is light bigger than mirror in the image?
A: No, the light is smaller than the mirror in the image.
Figure 7: Case study for competitive category
effective on competitive cases. Figure 7 we present an example from the competitive domain. This case shows that ChainMPO can handle competitive questions with the same level of stability as action and spatial questions. It also suggests that our method not restricted to one type relation and can work well across different scenarios.
17


Reviews:
[{"review_id": "1MowSIRd6L", "reviewer": "Reviewer_STpS", "summary": "Hallucinations have hindered LVLMs’ reliability, which are categorized into three aspects. The paper focuses on relation hallucinations in LVLMs in which models correctly recognize subjects and objects but misidentify relationship between them. The proposed ChainMPQ (Multi-Perspective Questions guided Interleaved Chain of Image and Text) is a training-free, multi-step, interleaved text–image reasoning framework. It extracts subject/object keywords from the input question to enhance corresponding relation-relevant image regions via cross-attention. Furthermore, it constructs a set of multi-perspective aware text prompt, where sub-questions are raised centered on subject, object, and relation through entity localization and masking-based queries. Then, it builds an interleaved text-image reasoning chain where both textual and visual information are taken into considerations, where text encoder introduces question tokens while context is accumulated using answers from precious steps. From the third sub-question onward, it extracts attention over keyword tokens from late decoder layers, selects top‑k visual tokens via an entropy‑based rule, and applies a confidence‑weighted visual bias to steer subsequent steps; these biases are aggregated across rounds, and the original relation question is finally answered using the enhanced visual tokens and the accumulated textual/visual memories. Ablations indicate that multi-perspective question construction and the interleaved visual memory are the main contributors to the method's effectiveness. As a training‑free, model-agnostic plug-in, the approach can contribute to strengthen relational grounding in LVLMs, and by highlighting a simple decomposition-plus-memory transfer design that others can build on. It may further motivate work on causality-based attribution mechanism and more robust visual representations to reduce relation hallucinations in real applications.", "questions": "This paper presents ChainMPQ, a novel, training-free framework designed to mitigate relation hallucinations in Large Vision-Language Models (LVLMs). The method is built on a compelling core idea: decomposing relational reasoning into a sequence of interleaved text and image steps, inspired by human-like reasoning and extending the Interleaved Modal Chain-of-Thought (ICoT) concept. The work is timely, addressing a significant and under-explored problem (relation hallucinations) with a method that demonstrates clear and consistent improvements across multiple models and benchmarks. However, some revisions are needed to enhance the readability and clarity for the reader.\n\tThe use of cross-attention with subject/object keywords to enhance visual tokens is a solid foundation. It would be interesting to know if the performance gain from this module is more pronounced for images with cluttered backgrounds where object localization is inherently harder.\n\tThe masking strategy for generating questions is excellent. However, the dependency on the spaCy NLP toolkit for keyword extraction is a potential point of failure, especially for ambiguous or complex subjects/objects. A quick error analysis of the extraction step would strengthen this section.\n\tThe adaptive top-k selection based on attention entropy is a nice touch. The description of the chain's operation is generally clear, but as noted in the weaknesses, the exact mechanics of combining multiple previous visual biases (Eq. 6) could be elaborated. The assumption that confidence in the textual answer correlates with the reliability of the visual attention map is reasonable but could be discussed briefly.\n\tWhile the text mentions 4x inference time overhead, the table contains no efficiency metrics (inference time, computational cost, memory usage). This omission hides the practical trade-off between accuracy gains and computational cost.\n\tThe \"Vanilla\" baseline represents standard model performance, but there's no comparison to other relevant baselines like standard Chain-of-Thought prompting The methods selected for comparison may not represent the current state-of-the-art in relation hallucination mitigation\n\tNo t‑tests, Wilcoxon tests, or other statistical comparisons are reported; these would confirm that observed gains are not random.\nThis is a strong paper with a novel contribution, solid experimental validation, and clear presentation. The weaknesses, primarily related to computational overhead and baseline reproducibility, are manageable and do not undermine the core contributions. The paper is likely to be of significant interest to the ICLR community.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "\tInnovative Methodology: The core concept of ChainMPQ—decomposing a relationship into subject, object, and relation components and constructing a multi-perspective, interleaved reasoning chain—is creative and well-grounded. The integration of both textual memory (previous answers) and visual memory (attenuation maps) is a key differentiator from text-only prompting methods.\n\tStrong and Extensive Evaluation: The experimental design is robust. Evaluating on two state-of-the-art LVLMs (LLaVA-1.5 and InstructBLIP) and two dedicated relation-focused benchmarks (MMRel and R-Bench) provides convincing evidence of the method's effectiveness and generalizability.\n\tComprehensive Ablation Studies: The ablation study is a major strength. It clearly demonstrates the individual contribution of each of the three core modules (Attention Enhancement, Multi-Perspective Questions, Interleaved Chain), showing that they work synergistically. The sensitivity analysis of hyperparameters k_max and λ adds rigor.\n\tClear and Illustrative Case Studies: The qualitative examples are excellent. They effectively translate the complex methodology into an intuitive narrative, showing precisely how the step-by-step process corrects the model's reasoning and attention, which greatly enhances the paper's persuasiveness.", "weaknesses": "\tComputational Overhead and Practicality: The most significant weakness is the substantial increase in inference time (∼4x). While the authors rightly note that the method is \"training-free\" and can process thousands of samples in a few hours, this overhead limits its practicality for real-time applications. A discussion on potential optimizations or a trade-off analysis between accuracy and latency would be beneficial.\n\tBaseline Comparability and Reproducibility: The paper states that baseline results (e.g., for Prompting Wu et al. and Calibrate Zheng et al.) were obtained by \"re-implementing their experiments.\" It is crucial to clarify if these re-implementations were performed with the authors' best effort or if they were validated against the original papers' results. Slight differences in implementation (e.g., prompt wording, calibration parameters) could affect the fairness of the comparison. Providing the exact prompts used for all methods would improve reproducibility.\n\tLimited Analysis of Failure Cases: The paper shows where ChainMPQ succeeds but provides little insight into where it fails. A brief analysis of typical failure modes (e.g., errors in the initial keyword extraction, cases where the attention bias propagates errors) would provide a more balanced view and offer valuable directions for future work.\n\tClarity on \"Visual Memory\" Propagation: While the concept of propagating attention maps is clear, the description of how these maps from different steps are combined and applied (Eq. 6) could be more precise. Specifically, it would be helpful to clarify if the masks M_i from all previous steps (3, 4...) are applied with equal weight α_j or if there's a decay mechanism.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OPNiPZBt4K", "reviewer": "Reviewer_ySgp", "summary": "The paper proposes ChainMPQ, a training-free method designed to reduce relation-hallucinations in LVLMs. To do so, they first extract subject and object keywords from a given question,  then generate multi-perspective follow-up questions focusing on subject, object, and the relation.  Experiments across multiple existing LVLMs reportedly show a reduction in relation-hallucination rates, with ablation studies proving the importance of different modules.", "questions": "Please see my reviews above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed training-free hallucination mitigation method is useful and could be used to mitigate the hallucination issues.\n2. The motivation of this paper is clear, along with detailed method descriptions and experiments.", "weaknesses": "1. My main concern on this paper is the tested baseline models, which are a little it outdated. It will be better if the authors could add more advanced LVLMs in the experiment, which will largely increase the reliability of the results.\n\n2. The evaluation seems to focus mainly on reduction of relation-hallucination rates, but less so on downstream multi-modal task performance (e.g., does improved relation reasoning lead to better general reasoning or application outcomes).\n\n3. Missing related work on relation hallucination of LVLMs: Wu J, Chung T T, Chen K, et al. Unified triplet-level hallucination evaluation for large vision-language models[J]. arXiv preprint arXiv:2410.23114, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6F6dqn63J6", "reviewer": "Reviewer_MyiZ", "summary": "The goal of this work is to tackle relational hallucination in LVLMs which is under explored in previous literature in comparison to object and attribute based hallucinations. The proposed method is called ChainMPQ, which is essentially a training free pipeline that converts subject and object keywords from the prompt into a series of questions about the subject, object, and their relation, and uses these to guide cross-attention via an interleaved chain-of-thought mechanism. The goal is to sharpen attention on relation-relevant regions and reduce errors without additional training.\n\nThe main findings of the paper show that across two different model families, ChainMPQ is able to reduce relational hallucination on two benchmarks that measure relation hallucinations. More ablation studies are provided to show which types of the questions contribute to the overall improvement of the final results. Their main finding here states that multi-perspective questions and the interleaved chain are the biggest contributors to performance gain.", "questions": "- Can the authors elaborate on the questions raised above on distinguishing details about the ablations: (i) replaces five constructed question with a single question, and (ii) removal of multimodal chains that was raised in the Weaknesses section?\n- The case study section says \"we present two real examples from MMRel, namely an action case and a spatial case, which together account for over 90% of the MMRel dataset\" -- are there any experiments to check the diversity of cases in the two datasets beyond action and spatial? My worry is that this question construction approach is more geared towards the templates most suitable for action spatial domains.\n- Can the authors highlight how the exact differences between ChainMPQ and the baselines compared (Constraint-Aware Prompting and Detect-then-Calibrate) and the rationale behind choosing these over other related works?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Interesting application of interleaved chain-of-thought prompting to reduce relational hallucinations. The staged prompts around subject, object, and relation give the model clear subgoals and make the intervention easy to understand.\n- Results clearly demonstrate the benefit of this approach on relational hallucination benchmarks. The ablations help isolate where gains come from, e.g. multi-stage enhancement of visual tokens seems to help the most.\n- Training free and low cost to deploy, which makes the method practical for settings where fine-tuning is not feasible.", "weaknesses": "- Organization and detailing of content can be improved a lot. For instance, implementation details of question generation from the text prompt are not included in the main text. Similarly, the baselines are not described in detail (trained/training-free, how does their approach differ from ChainMPQ, etc). In the ablations section, the description of ablations was not very clear to me (e.g. \"five constructed questions with a single relationship question\" -- how is this constructed?, \"we remove the multimodal chain and only use the text context from the previous answer.\" -- is this the entire multi-turn text context or just the last turn answer?). The paper would benefit by including these details/examples in the main/appendix sections.\n- I think the multi perspective question generation aspect of this work is interesting, but currently lacks a general framework for the benefit of future practitioners who might want to extend this work to new benchmarks or new domains. Some discussion or a framework around that can help enhance the generality of this work. \n- Contributions seem siloed around relational hallucinations so the novelty seems limited. The work can be improved by showing how this module complements more general hallucination mitigation strategies.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hGUB6xdfpP", "reviewer": "Reviewer_HnMX", "summary": "The paper introduces ChainMPQ, a novel method designed to mitigate relation hallucinations in large vision-language models (LVLMs). Relation hallucinations, a type of error in multimodal reasoning, occur when the model fails to accurately infer relationships between recognized objects. The proposed method works by first extracting subject and object keywords, enhancing the visual tokens associated with these entities, and then constructing multiple interleaved questions focusing on different components of a relationship. These questions guide the model step-by-step, leveraging both textual and visual memory to progressively refine relational inference. The paper demonstrates the effectiveness of ChainMPQ through experiments on multiple LVLMs and benchmarks, showing significant improvements in reducing relation hallucinations.", "questions": "See weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The decomposition of relational reasoning into multiple, focused questions is a unique approach. By systematically addressing each component of a relationship (subject, object, and relation), ChainMPQ offers a more structured and interpretable reasoning process compared to previous methods.\n\n2. The method does not require additional training or fine-tuning, making it a practical solution for mitigating hallucinations in pre-trained models. This is a key advantage in real-world applications where retraining large models may not be feasible.\n\n3. The experiments show consistent improvements across different LVLMs (LLaVA-1.5 and InstructBLIP) and benchmarks (MMRel and R-Bench), demonstrating that ChainMPQ is effective in various contexts.\n\n4. The method’s success across different architectures further validates its generalizability and robustness.", "weaknesses": "1. There are other training-free methods[1,2] that adjust attention maps to enhance MLLM performance. These works should be cited and discussed in comparison to ChainMPQ to provide a broader context for the proposed method.\n\n2. R-Bench contains both image-level and instance-level setups. Which setup is used for the experiment in Table 1? If the results in Table 1 are based on the image-level setup, can this method also perform well in the instance-level setup?\n\n3. The current baselines only include LLaVA-1.5 and InstructBLIP. Given the rapid advancements in LVLMs, it would be beneficial to include newer models, such as Qwen2.5-VL[3] and InternVL3.5[4], to demonstrate the method’s effectiveness on state-of-the-art models.\n\n[1] Controlmllm: Training-free visual prompt learning for multimodal large language models\n\n[2] MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs\n\n[3] Qwen2.5-VL Technical Report\n\n[4] Internvl3. 5: Advancing open-source multimodal models in versatility, reasoning, and efficiency", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)