Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models
Authors: Xin Zou
Yizhou Wang
Yibo Yan
Yuanhuiyi Lyu
Kening Zheng
Sirui Huang
Junkai Chen
Peijie Jiang
Jia Liu
Chang Tang
Xuming Hu
Venue: ICML 2025 poster (2025)
Keywords: Multimodal LLMs
hallucination mitigation
deep learning
Primary area: deep_learning->large_language_models
TL;DR: We propose a "look-twice" mechanism for hallucination mitigation and general enhancement in MLLMs.

Abstract: Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) are prone to hallucinations, i.e., the generated content that is nonsensical or unfaithful to input sources.
Unlike in LLMs, hallucinations in MLLMs often stem from the sensitivity of text decoder to visual tokens, leading to a phenomenon akin to "amnesia" about visual information.
To address this issue, we propose MemVR, a novel decoding paradigm inspired by common cognition: when the memory of an image seen the moment before is forgotten, people will look at it again for factual answers. Following this principle, we treat visual tokens as supplementary evidence, re-injecting them into the MLLM through Feed Forward Network (FFN) as “key-value memory” at the middle trigger layer. This look-twice mechanism occurs when the model exhibits high uncertainty during inference, effectively enhancing factual alignment. 
Comprehensive experimental evaluations demonstrate that MemVR significantly mitigates hallucination across various MLLMs and excels in general benchmarks without incurring additional time overhead.

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models
Xin Zou 12 Yizhou Wang"
Yibo Yan 13 Yuanhuiyi lyu 13 Kening Zheng' Sirui Huang 14
Junkai Chen
Peijie Jiang?
Jia Liu Chang Tang' Xuming Hu 13
Abstract
with visual tasks and complex visual question answering. However, MILMS still face challenges, notably the "halluci- nation" issue (Huang et al., 2024b; Zheng et al., 2024; Lyu et al., 2025), where they generate contents inconsistent with original inputs, such as generating nonexistent objects or conflicting judgments. This flaw undermines their reliability, especially in areas critical to safety such as healthcare (Lin et al., 2024) and autonomous driving (Ding et al., 2024), Although the causes of hallucinations are unclear, one po- tential factor is the imbalance between their understanding of visual and textual modalities. This imbalance may induce biases when the model integrates multimodal information, leading outputs that do not match objective facts.
Despite their impressive capabilities, multimodal large language models (MLLMs) are prone to hallucinations, i.e., the generated content that nonsensical or unfaithful to input sources. Unlike in LLMs, hallucinations in MLLMs often stem from the sensitivity of text decoder to visual to- kens, leading to phenomenon akin to "amnesia" about visual information. To address this issue, we propose MemVR, a novel decoding paradigm inspired by common cognition: when the mem- ory of image seen the moment before is for- gotten, people will look it again for factual answers. Following this principle, we treat visual tokens as supplementary evidence, re-injecting them into the MLLM through Feed Forward Net- work (FFN) as "key-value memory" at the middle trigger layer. This look-twice mechanism occurs when the model exhibits high uncertainty during inference, effectively enhancing factual alignment. Comprehensive experimental evaluations demon- strate that MemVR significantly mitigates hal- lucination across various MILMM and excels in general benchmarks without incurring additional time overhead. The implementation is available from https://ittuu..com// zhou whw.ee////eooooaaaa/aaa//eee Wang/MeeWR...
Currently, numerous methods are being tried out to solve this problem. General studies can be broadly categorized into four streams: (i) Retreeval-Augmented Generation (RAG) (Qu et al., 2024) which incorporates knowledge from external databases to mitigate hallucinations, as well as (ii) through extra fine-tuning (Yu et al., 2024a) to enhance the self-consistency of generation; (iii) attention intervention (Huang et al., 2024a) and (iv) Contrastive Decoding (CD) (Leng et al., 2024) strategies, which not involve extra train- ing. Specifically, RAG and fine-tuning patterns typically employ external knowledge retrieval or robust instruction- tuning datasets to post-hoc debias (Yang et al., 2024; Liu et al., 2023a), which inevitably introduces substantial com- putational overhead or storage requirements. Attention in- tervention, though not requiring additional data, usually involves retrospection-allocatii operations, which bring about high inference latency and large memory footprint.
1 Introduction
Multimodal Large Language Models (MLLMs), known for their ability to process visual, auditory and textual data, are crucial in fields such as computer vision (Koh et al., 2024) and natural language processing (Tu et al., 2023), helping
CD-baaed methods (Li et al., 2023a; Shi et al., 2024) rep- resent a simpler and more efficient way to mitigate halluci- nations than other paradigms. Specifically, CD-based hallu- cination mitigation paradigms, represented by VCD (Leng et al., 2024), modulate logits of the next token prediction in contrastive manner. As shown in Figure left, VCD am- plifies the language priors by adding Gaussian noise to the visual inputs, reducing over-reliance on statistical biases and single-modal priors through contrasting output distributions from original and distorted visual inputs. This perturbation of original inputs requires task-specific design, inevitably
'qqual contribution 'The Hong Kong University of Science and Technology (Guangzhou) Ant Group "The Hong Kong University of Science and Technology "University of Technology Sydney 'Huazhong University Science and Technology. Correspon- dence to: Xuming Hu <xuminghu @ wwwst-ggz.aao saass.zzz.d......
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
MMN-Vet 32. 20
Tranzlate Code
Loyer
Decading Outper
115.0
77.5
Existence
95.93
70.0
150.0
VizWizz 51.30
Comm
Count
24 Lays
1234
1234
121-4
155.0
10 Laya
Contraad
UP Taser
D
1864.5
1896.72
133.3
MNE
OCR
Posh
# Lays
Dnna
64.00
121
Dasdlin Duspet
"Iook-wwiee" mocharisn
LLavk- Bench
50.0
Artwork
Color
63.75 MMS-Bench
146.0 Poster
swap peogle thom?
Hare sasr people thee?
K
46.
Landmark
157
135.5
CHAIR
Scone
Coleb
Space Spect
e ulow!
Ieforenca Spand:
LLaVA-1.5
OPERA
VCD
ICD
MemVR
Figure 2: Radar charts comparing models across benchmarks.
Visual Contrustive Decoding (VCD)
The Mamory-spacce Visual Retracing (MamVR) R)
Figure I: Comparison of the conventional CD-based hallucination miligation paradigm VCD, and our proposed efficient Mem
Method
Latency 1 (ms/token)
Throughput 1 (token/ims)
Time Cost J for 80 Tokens (ms)
Memory
Cost (MB)
Greedy
65.71 (x100)
0.015 1:000
5256.6 (x0.00
14257 (xL.0)
Sample
69.84 ox1.00 1000
0.015 1:000
c61770.xx1.0
14262 (x1.00
x doubling inference costs. More critically, contrastive dis- tributions are agnostic to visual and instructional nuances, which may not always amplify the intended hallucinations, occasionally 38 introducing potential noise into CD.
OPERA
240.59 (43660
0.004 (x027)
19247.2 (x3.00
21300 (x1.4)
ICD
70.84 or 100
0.014 (x0e3)
5666.9 oxt ney
14263 (x]
VCD
144.62 (x22200
0.007 pvoutt
11569.3
(x2222
14967
MemVR
68.32 (o
0.015 (10000)
5545.5 (x
14345 (x1.00
Table I: Performance comparison of SOTA methods and Mem in latency, throughput, time cost, and memory usage. The best and suboptimal results are highlighted in green and blue, respectively.
In this work, we delve into the challenges of hallucination mitigation in MLLMs and propose a novel paradigm that overcomes the two limitations of CD-based approaches. Our research is grounded in a common cognitive process: when the memory of an image seen the moment before is forgot- ten, it is intuitive to look twice for accurate and factual answers (Ballard et al., 1995; Horowitz & Wolfe, 1998). Following this principle, we propose Memory-span Visual Retracing (MemVR) that mitigates hallucinations through supplementing visual evidence, which also be called look-twice mechanism. As shown in Figure 1 right, MemVR reinjects visual tokens through Visual Retracing (VR), i.e., look-twice mechanism, into the middle trigger layer suf- fering from high uncertainty, without incurring additional inference cost. Compared with VCD and other approaches, our proposed look-twice mechanism optimal in terms of performance, efficiency, and memory cost as shown in Figure 2 and Table 1. Through extensive experiments multimodal hallucination benchmarks, as well as GPT-4o evaluations, including eight public benchmarks, we show the comprehensive performance improvements of Mem VR in hallucination mitigation and general capabilities. The main contributions can be summarized as follows:
Comprehensive experiments and evaluations on multi- ple models demonstrate that MemVR outperforms SOTA methods in performance and inference speed, such as +7.0% on the POPE benchmark, 15.6% improvement on CHAIR,, and total score of MME increased by +32.2 marks. The results on popular hallucination and general benchmarks validate the generalizability of our method.
2 Related Work
MLLMs and Challenges. recent years, MLLMS have made remarkable progress, particularly as they have evolved from the foundations laid by Vision Language Models (VLMs). Early based on BERT-style language decoders (Devlin, 2018), which achieved initial cross-modal integra- tion by combining visual and textual data (Li et al., 2022). Leveraging open-source Large Language Models (LLMs) such as LLaMA families (Touvron et al., 2023), MLLMs (Alayrac et al., 2022: Wu et al., 2024) have demonstrated en- hanced adaptability across a range of visual language tasks, leading to a more profound ability to interpret the world. Models like LLaVA (Liu et al., 2024b), Qwen-VL VL (Bai et al., 2023), GLM4V (Wang et al., 2023), and LLaVA-Next (Liu et al., 2024a) have further advanced this field, enabling users to interact with these agents using both image and text prompts. These models adhere to two critical train- ing phases: pre-training feature alignment and instruction fine-tuning, ensuring they better comprehend the format of instruction inputs (Yin et al., 2024). However, despite their impressive performance in many areas, multimodal large language models still suffer from hallucination issues. In this work, we conducted experiments and analysis on these representative models to validate Mem VR's applicability.
We propose MemVR, a novel, efficient, minimalist, and hou--and-paay approach that achieves both model fidelity and efficiency, which reinforces attention to visual infor- mation for enhancing modality balance during the forward pass, without eliminating beneficial language priors.
We present static and dynamic VR strategies that shift hidden states of the intermediate layer in MLLM for self- enhancement, rather than modulating logits directly in CD manner, thus avoiding multi-roun decoding.
Our analysis reveals that hallucinations triggered by the sensitivity of text decoder (i.e., LLM) to non-text modality. This finding is experimentally validated.
2

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Table 2: Comprehensive comparisons between the proposed MR and existing approaches are presented. MemVR introduces a low-latency look-twice decoding mechanism, optimizing hidden states to support multimodal integration and enhance overall performance. MemVR R uniquely achieves visual hallucination mitigation and general improvement. SOTA methods we compare are emphasized in gray.
VH
General
Inference Latency
Expand to More Modalities
Modified
Decoding
Methods
Mitigation
Improvement
Component(s)
Paradigm
D (Chuang et al., 2023)
Negative
X
Low
/
Logits
Contrastive
OPERA (Huang et al., 2024a)
Medium
X
High
Attention matrix
it-intervention
EAH (Zhang al., 2024a)
Medium
High
Attention matrix
in-interventon
CCA (Xing et al., 2024)
Medium
X
High
V
Attention matrix
At-intervention
ICD (Wang et al., 2024)
Medium
X
Medium
X
Textual input, logits
Contrastive
ID (Kim al., 2024)
Medium
X
Medium
K
Textual input, logits
Contrastive
SID (Huo et al., 2024)
Medium
Medium
Visual tokens, logits
Contrastive
VCD (Leng et al., 2024)
Low
X
Medium
Visual input, logits
Contrastive
HALC (Chen et al., 2024b)
Medium
X
Ultrahigh
X
Visual input, logits
Contrastive
VORD (Neo & Chen, 2024)
Medium
X
Medium
X
Visual input, logits
Contrastive
MemVR (ours)
High
Low
Hidden states
Look-twice
Mitigating Hallucinations in MLLMs. Researchers have made extensive efforts to uncover the causes of halluci- nations (Yin et al., 2023; Zhou et al., 2023; Bai et al., 2024). Early works to mitigate hallucinations focused on fine-grained rreegrrined modality alignment (Rohrbach et al., 2018) and reducing co-occurrence biases (Kim et al., 2023) in small- scale models. More recent strategies involve hallucination- related datasets for fine-tuning (Gunjal et al., 2024), post- hoc revisors (Zhou al., 2024), and adopting RLHF (Yu et al., 2024a). LURE (Zhou et al., 2023) trains reviser to edit the possible hallucinated words in the responses. While effective, these methods are resource-intensive. At- tentional intervention strategies (Zhang et al., 2024a; Xing et al., 2024), represented by OPERA (Huang et al., 2024a), are simpler and do not require additional data or training, but have a higher inference latency. CD-based approaches (Chuang et al., 2023; Chen al., 2024b; Neo & Chen, 2024), represented by ICD (Wang et al., 2024) and VCD (Leng et al., 2024), adjust the decoding distribution to mitigate hallucinations in MLLM, but this does not consistently im- prove performance as it introduces potential noise into the CD.
incorrect tokens through the comparison of logits. However, this brings two challenges, one is that the CD strategy may introduce noise to the output distribution, thus losing the original capability, and the other is that the CD-based mech- anisms often require multiple rounds of inference to obtain several pairs of logits for contrasting, resulting in a high latency. Significantly, these methods work negatively or fail in general-purpose testing. Compared with them, Mem VR stands "a paradigm of effectiveness and efficiency" in visual hallucination mitigation and general improvement.
3 Background and Motivation
3.1 Problem Formulation
Given an MLLM MMLLM parameterized by 6 with gen- eral architecture consisting of a text embedding layer, a vi- sion encoder, a vision-text interface module, a text decoder consisting of L number of transformer layers, and an affine layer <(-) which predicts the distribution of the next token. For an image-grounded text generation task, given textual query æ and an input image v. MLLM first extracts vision features of by the vision encoder, and then converts them into visual tokens zy by MIP or Q-Former (Wadekar et al., 2024) modules. Aligned vision tokens 2, are concatenated with the query x as input to the text decoder, and finally decoded into a textual response autoregressive, which is formulated as follows:
Comparisons. Distinct from these methods, our proposed Me offers a novel decoding strategy that effectively re- duces visual ballucinations (VH) without necessitating extra models, data, and training. Table 2 illustrates the differences and advantages of our method compared to recent represen- tative SOTA approaches. DoLa (Chuang et al., 2023) targets hallucinations in LLM and is negative for alleviating VH, al- though it exhibits low latency. Attentional intervention meth- ods (Zhang et al., 2024a; Xing et al., 2024), represented by OPERA (Huang et al., 2024a), have achieved considerable progress in mitigating VH. but are limited to the problem of high latency. Casea approaches, whether modifying the textual input (Wang et al., 2024; Kim et al., 2024) or visual input (Leng al., 2024: Chen al., 2024b: Neo & Chen, 2024), both aim to reduce the output probability of
ye ~ poplo x, Bee) x (oftmax(fo((o,,x.eee))
(I)
where yt indicates the 8t token, 8<ct is the token sequence generated up to time step and fo is the logit distribution, ie., unnormalized log-probabilities produced by MMMLLM
When the text generation y is inconsistent or in conflict with the input image v, MLLM is believed to present hallucina- tion issues. The objective of visual hallucination mitigation is to minimize the appearance of incorrect or conflicting
3

---

Look Twiee Before You Answer: Memory-Spacc Visual Retracing for Hallucination Mitigation in MLLMS
20
0.8
24
96
20
35
36
3.4
10
10
5.2
Query Please describe the image debil
Query: Wyat of the?
Uncertainty of different layers to predict the next token. Rows denote indices of the early layers, and column names are decoded tokens in each step. Uncertainty distribution is dynamic.
Figure 3:
Figure 5: Uncertainty distribution across layers during token rea- soning hallucinations. Red-outlined regions show higher uncer- tainty in middle and late layers for hallucinatory tokens: a. pom.
tokens, ensure faithfulness to e when answering the query x, and simultaneously maintain high-quality text generations.
scaled down proportionally, the contraction text features led to drastic performance drop, while the scaling down of visual features had a relatively minor impact. We can obtain three findings in this experiment: ① modality imbalance issues cause hallucinations in MLLMs, @ text decoders (i.e,, LLMs) are more text-informed, @ LLMS are harder to comprehend the visual modality than textual inputs.
3.2 Why Does MLLM Generate Hallucination?
Hallucinations in MLLM are caused by multiple factors, including inherent biases in the training data (Zhou et al., 2023), visual uncertainty resulting from the model's 's statisti- cal bias and priors (Leng et al., 2023), and the limitations of current models in accurately discerning context and fact throughout the output generation process (Daunhawer et al., 2021). Upon a more in-depth analysis, we consider that the imbalance of modalities in MILM and the autoregressive characteristic of language models are likely crucial factors causing their hallucinatory phenomena. Taking image and text as an example, since an image possesses a much higher information density than a piece of text, it is reasonable to suppose that LLMs struggle to understand or memorize vi- sion information compared to text. Moreover, autoregressive decoding causes MILMs to increasingly depend on textual information, including query z and growing tokens y.c+. in- evitably decreasing reliance on visual input. As attention sinks (Zhang et al., 2024b) and PAI (Liu et al., 2024c) pro- posed to pay more attention to images, both reflect the fact that hallucinations may be caused by modality inbalances.
This modality imbalance leads to substantial deviation from the accurate representation of visual input, eventu- ally giving rise to hallucinations, as manifested by the phe- nomenons in the aforementioned studies (Zhou et al., 2023). This is especially evident in generating longer responses, explaining the correlation between higher VH and larger maximum token lengths, as presented in Huang et al. (2023).
3.3 The Pattern of Hallucinations in MLLM
In order to further explore the potential pattern of halluci- nations in MLLM, this study employs uncertainty as the metric. As findings of Chen et al. (2024a) in LLMs: incor- rect tokens generally exhibit higher entropy than correct ones, we also observe this phenomenon in MLLMs, the vi- sualization case is shown in Figure 5. From Figure it can be noticed that when the model generates illusion tokens, i.e., a, pom -eg-ran-ate (pomegranatee) the uncertainty in the middle and last layers of the model is high in MLLM.
Total Score and Parcaption
2220
Totaa test text
... Toti
Total Score and Percestion
2000
Perception
2000
Uncertainty quantification. Following the DoLa (Chuang et al., 2023), we compute the probability of the next token via the vocabulary head s(-) on each layer during reasoning. Then, we introduce an entropy- based metric (Farquhar et al., 2024) to quantify the output uncertainty of each layer in the text decoder as u = Σ p, log log N, where (p, J
1800
k620
1600
1200
"ccaling Ratio of Feature Value"
Regular
@Moxt
@iimae
Figure 4: (Left) Performance under different scaling ratios to text / image feature value on MME; (Right) Performance changes when look-twice to text / image / text+image (i.e, @t+i), respectively.
In addition, in the context of tokens involving objects, at- tributes or relations, uncertainty is also high. We conduct a preliminary analysis with 32-layer LLaVA-1.5-7BB Specifi- cally, we compute the uncertainty in the output distributions of early layers. Figure 3 shows the uncertainty scores of different early layers when decoding the answer, we can observe that the computed uncertainty remains relatively high in later layers when predicting key entity objects, at- tributes, or relations, such as wooden, filled, foods. This phenomenon suggests that LLM is still uncertain about its
To further verify this conjecture, we scale the feature values of different modalities up or down to simulate the modality imbalance phenomenon. As shown in the left of Figure 4, it can be observed that: when the image and text features are scaled up proportionally, modality balance is disrupted in both cases, yet the performance decline caused by mag- nifying the image features is more significant than that of text features; however, when the image and text features is

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
predictions in the last few layers and may inject more factual knowledge into the predictions. On the other hand, when predicting function words and those tokens copied from the question, e.g., image, with, we observe that the uncertainty becomes very low from the middle layers. This finding implics that the model is deterministic for easy-to-predict tokens at the intermediate layer and keeps the distribution of outputs almost constant at higher layers, however, it is more uncertain for difficult-to-pret to-predict key tokens and may constantly change its predictions until the final layer.
usual D 4d. Peculiarly, W1 and W2 2 can be rewritten as
Wi k2.......... kD), W2 = (V1, 02, UD)) (3)
where k;, th E IRd denote entries of key and value, respec- tively. As result, the FFN can be reformulated
FFN( = Σ Φ ((ar, ki)) v2
(4)
Thus, the FFN function can be construed as using input as query to measure similarity with keys, find matching values, and gather values by similarity, which works like a key-value memory storing the factual knowledge as found in previous studies (Geva al., 2021; Jie et al., 2024).
3.4 Refresh Visual Memory Alleviates Hallucination
Based on the findings, we propose that both overreliance on textual information and schema imbalance contribute to a phenomenon akin to "amnesia" about visual information. It easy to understand that from the shallow the deep layers of the Transformer, attention is progressively biased towards textual tokens, resulting in visual tokens at the deeper layer that hardly affect the results, which is consistent with how the attention intervention strategies work. To address this, we try to refresh visual information for trustworthy answers when the model encounters high uncertainty.
4.2 FFN with Visual Retracing
Motivated by the findings in section $3.2 and $3.4, we pro- pose Visual Retracing (VR), i.., reinjecting visual evidence into the middle layer of the text decoder during elevated uncertainty during reasoning. This strategy treats visual tokens as anchors to recalibrate off-target predictions and re- duces uncertainties in object, attribute, relationship tokens. Ex Expermeental results also demonstrate that our method re- duces uncertainty and alleviates hallucinations shown in Figure 8. The reason we call this pattern of reinjecting visual evidence "visual retracing" is that the model finds and refreshes key visual memories based on the hidden states in this process. In particular, inspired by the fact that FFN executes analogous retrieval from its key-value memory, we consider VR serve simplified and efficient informa- tion re-retrieval process. Given a hidden token e Rd and dimension-aligned vision tokens 2u. FFN with visual retracing at l-th layer can be written as follows
We verify this hypothesis through empirical study. As shown in the right of Figure 4, the proposed look-twice strategy is used for combinations of different modalities, including text, image, text+image, where it can be found that the best performance improvement is achieved while only replenishing image information to the model, i.e., refreshing visual memory can effectively alleviate hallucinations in MLLMs. This study lends further credence to our conjecture regarding the causes of hallucinations in Sec. $3.2.
FFN (x x z.)) = aA + (1-a) (z). (5)
4 Methodology
where 2, = (Ze.1....,ZoNN.) E RdxN., x x 2. denotes execute VR A from x to visual features z,, and a E [0, 1] denotes injection ratio of visual memory through the FFN layer which proportional image complexity. Specifically, instead of performing retrieval via coss-attention layers as in previous approaches (Li et al., 2022; Alayrac et al., 2022), we consider a simple retrieval process for VR as,
Inspired by the common cognition: when the memory of an image seen the moment before is forgotten, people will look at it again for factual answers, we design look-twice mechanism, which treats visual tokens as supplementary evidences, re-injecting them into MLLMs through FFN as "key-value memory" at the middle trigger layer. This look-twice mechanism occurs when the model exhibits high uncertainty during inference, effectively enhancing factual alignment and modality balance. All details are listed below.
No
A(z x))
i== (((x,, Z0,i)) Zu,i-
(6)
From the perspective of FFN, visual retracing works by treat- ing æ as a query, and (zr.i : Zv,i) as new key-value entries (visual evidence) to supplement vision-related information in the hidden states. In this information re-retrieval process, Mem does not introduce any parameters that need be trained. Notably, since the size of key-value memory D in FFN typically far exceeds the number of visual tokens N, (for instance, D = 11008 in LLaMA-7B and N,- = 256 for ViT-L/14, N, < D), the computation of MemVR is negligible. Thus, VR operation is more efficient than the cross-attention mechanism with quadratic complexity.
4.1 Preliminary: Reformulation of FFN
Vanilla FFN comprises two fully connected layers with non- linear activation in between. We suppose x: E Rd as input token of the FFN, and FFN function can be formulated as
(#N = o (x W
(2)
where ó is activation function like ReLU or SiLU (Liu et al., 2020), and W1, 1, W₂ E RdxD are the weight matrices, in
5

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MILMS
4.3 Dynamic Triggered Visual Retracing
the proposed MemVR implemented with the dynamic and static strategies are presented in Section $5.4 and Table 10.
To magnify the effectiveness of VR, the optimal strategy should be to trigger VR dynamically based on the token, and the selection of the trigger layer should also be dynamically determined. In practice, we consider that the uncertainty of a candidate layer exceeding the threshold 7 warrants vi- sual retracing. Inspired by the fact that early exit patterss (Elbayad al., 2020; Schuster et al., 2022) have proven effective in directly employing the language heads Ç to the hidden states of the middle layers, even without special training process (Kao et al., 2020), we compute the uncer- tainty of the next token probability on the early layers for reasoning. We utilize layer-specific uncertainty to allow for dynamic premature layer injection at each time step.
4.4 Theoretical Analysis
To further understand why MemVR effectively mitigates hal- lucinations and performs robustly on general benchmarks, we explain these phenomena using three theorems below.
Theorem 4.1. Let æ be the hidden states of FFN and a be after reinjecting visual evidence zu. Mem VR enhances Mutual Information (MI) between &t and Bu as:
[( xu) 2 ((x;z))
Theorem 4.2. Let y be the target output dependent on hidden states. If MI between æ and zu increases, then conditional entropy H(y x) decreases with
Dynamic Triggered MemVR. For MLLMs with different numbers of layers, as Algorithm 1 shown, the dynamic trig- gered MemVR strategy called Mem V RRddnamiic identifies desirable premature layers among the candidate layers for visual retracing based on output uncertainty of different layers, thus better amplifying the effect of visual retracing.
H () S H x)..
(8)
Theorem 4.3. Within the Information Bottleneck (IB) frame- work, the loss of objective function, represented by the no- tation LT)), is optimized by MemVR, which is defined as [(t) < L(x)), where L(x)) = [(:; e) ( (x; y) is IB loss, c denotes input embedding and 3 is a trade-off parameter.
Algorithm 1 Dynamic Triggered Mem VR Strategy
Require: MILM MMLLM, text query x, image input output Model response ut-
The proofs are provided in Appendix A. The theo- retical basis of Mem VR draws support from the DPI (Cover et al., 1991) and the contraction properties of stochastic map- pings in deep neural networks, as evidenced in numerous IB-related studies (Achille & Soatto, 2018). By enhancing MI and reducing uncertainty in hidden states, Mem R effec- tively alleviates hallucination while maintaining efficiency.
Intuition:
1: At every decoding step t:
2: Initial set to trigger= = TRUF.
3: for prl== 1= to L - do
4: u - Σ -Po (1)
log /log N. % uncertainty
5: if trigger == TRUE and (1)) > y then
6: Execute A A(z, | 11 1) f84, Eq. (6)}
7: Select FFN(++1(( (1+1) (n((41) 2.) (84, Eq. (5)
5 Experiments
8: trigger = FALSE % set to only look-twice
9: end if
5.1 Experiment Setup
10:
end for
11:
MM MMLIM decoding, obtain current token y!-
Datasets and Evaluation. To rigorously assess the effec- tiveness of our proposed MemVR, we conduct comprehen- sive set of experiments across POPE benchmark (Li et al., 2023b), CHAIR (Rohrbach et al., 2018), VizWiz-VQA (Gu- rari al., 2018), MME (Fu al., 2023), MMBench (Liu et al., 2023b), MM-Vet (Yu et al., 2024b), LLaVA-Bench (in-the-wild) (Liu et al., 2024b), HallusionBench (Guan et al., 2024). More details can be found in Appendix B.1. Implementation Details. Usually, we set >=0.75. All set- tings of baseline methods follow the default configurations from the original papers. More details are in Appendix B.2,
Statie Triggered Mem Besides NemmR--ynami an- other more straightforward strategy worth considering is to perform brute-force experiment on all possible early layers using validation set and selecting the layer with the best average performance. We refer to this simple strategy as MemVR-statee Nonetheless, Mem Ve-VRtstttic exhibits two notable limitations. Firstly, it demands more comprehensive hyperparameter tuning across different layers. Secondly, the optimal layer is extremely sensitive to the data distribution, which mandates the use of an in-distribution validation set.
Table 3: CHAIR evaluation results of different methods.
Mem VR-dynamic vs. -stttic. In contrast to Mem MemVR-st VR-static, Mem VR-dynamic strategy effectively alleviates these chal- lenges. achieves this by narrowing down the layer search space and enhancing robustness, all without relying on an in- distribution validation set. Empirical comparisons between
Methods
CHAIRS
CHAIR, J
Average
Len
Recull 1
L.aVA-1.5
50.0 10.0
15.4 10.0
32.7 10.0
100.6
77.1 10.0
OPERA
47.8 12.2
14.6 30.8
31.2
10.5
98.6
76.8 10.3
VCD
48.6 11.
14.9 J0.5
31.8
10.5
100.4
77.3 10.2
ICD
56.2 16.2
16.3 10.9
36.3
13.6
103.4
16.3 160.
MemVR
46.6 13.4
13.0 J2.4
29.8
J0.5
99.6
80.8 13.7
6

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Table Performance evaluation on POPE benchmark. The best results are green. We report accuracy and fl-seore under three settings, e.g., Random, Popular, Adversarial, and also record the Average, to show the robustness of Mem VR compared with baseline methods.
Random
Popular
Adversarial
Average
Evaluation
Methods
Accuracy 1
Fi-score
Accuracy 1
Fi-score 1
Accuracy 1
Fi-score 1
Accuracy
Fi-score
LL5VA-1.5-7B OPERA (Huang et al., 2024a)
83.49 10.0
82.28
10.0
79.98
10.0
79.34
10.0
76.03
10.0
76.26
10.0
79.83
10.0
79.29
10.0
87.53 04.0
86.45
14.2
84.21
14.2
83.50
14.2
80.88
14.9
80.69
14.4
84.21
14.4
83.55
14.3
MSCOCO
ICD (Wang al., 2024)
84.87 11.4
83.27
11.0
82.93
13.0
81.45
12.1
81.07
15.0
79.96
13.7
82.96
13.1
81.56
12.3
VCD (Leng al., 2024)
86.84 13.4
86.83
14.6
82.65
12.7
83.37
14.0
77.31
11.3
79.28
13.0
82.27
12.4
83.16
13.9
McmVR (ours)
88.50 15.0
87.34
15.0
87.10
17.1
86.01
16.7
85.20
19.2
84.28
18.0
86,93
17.1
85,88
16.6
LLaVA-1.5-7B
83.45 70.0
82.56
10.0
79.90
10.0
79.59
70.0
74,04
10.0
75.15
10.0
79.13
10.0
79.10
10.0
OPERA (Huang et al., 2024a)
88.27 14.8
87.54
15.0
85.17
15.3
84,74
15.2
79.37
15..3
79.97
14.8
84,27
15.1
84.08
15.0
A-OKVQA
ICD (Wang et al., 2024)
85.57 12.1
85.06
12.5
81.93
12.0
81.95
12.4
77.43
13.4
78.99
13.8
81.64
12.5
82.00
12.9
VCD (Leng et al., 2024)
86.15 12.7
86.34
13.8
81.85
12.0
82.82
13.2
74.97
10.9
77.73
12.6
80.99
11.9
82.30
13.2
McmVR (ours)
91.10 17.7
90.83
18.3
87.33
17.4
87.43
17.8
80.20
16.2
81.66
16.5
86.21
17.1
86.64
17.5
LLaVA-1.5-7B
83.73 50.0
82,95
10.0
78.17
10:0
78.37
10.0
75.08
110.0
76.06
10.0
78.99
10.0
79.13
10.0
OPERA (Huang et al., 2024a)
88.27 14.5
87.52
14.6
83.07
14.9
82.93
14.6
80.77
15.7
81.05
15.0
84.04
15.1
83,83
14.7
GQA
ICD (Wang al., 2024)
84,90 11.2
84.22
11.3
78.37
10:2
78.81
10.4
75.97
10.9
76.93
10.9
79.75
10.8
79.99
10.9
VCD (Leng al., 2024)
86.65
12.9
86,99
14.0
80.73
12.6
82:24
13.9
76.09
11.0
78.78
12.7
81.16
12.2
82.67
13.5
MemVR (ours)
89.60
15.9
89.32
16.4
84.63
16.5
84,98
16.6
81.53
16.4
82.48
16.4
85,25
16.3
85.59
16.5
5.2 Results on Hallucination Benchmarks
100
Regular
MemvR
80
We conduct hallucination evaluations on CHAIR, POPE, and HallusionBench with results presented in Table 3, Table 4 and Table 5. In the POPE evaluation, MR demon- strates robust effects. Its performance significantly exceeds baseline results, with an average accuracy increase of up to +7.0% and an Fi-score increase of up to +7.5% on the A-OKVQA dataset under the Random, Popular, and Adver- sarial settings. MemVR clearly outperforms all compared SOTA methods. As shown in Table 3, compared with vanilla LLaVA-1.5, MemVR achieves 6.8% and 15.6% improve- ment on CHAIRs and CHAIR, metrics. Table 5 shows the HallusionBench evaluation results, where MR out- performs vanilla VL. and other compared methods, achieving the best performance and consistent improvement across different metrics such as hardaACC and aACC.
Accuracy (%)
Accuracy (%g
70
60
60
40-
50
LLaVA1.5
Owen-VL
GLM-4V
Reguiar
VCD
MemVR
Figure 6: (Left) Comparisons of Me with regular LL.VA-1.5. Qwen-VL and GLM-4V on LLaVA Bench (in-the-wild), (Right) The comparison between Mem VR and VCD on LLaVA-Bench shows that Me improves significantly while VCD decreases.
reasoning. The Existence, Count, and Color scores all show significant improvements, where the total score of MME in LLaVA and Qwen- VL increases by 32 and 36, respectively. Importantly, although those comparison methods alleviate some hallucinations, they deteriorate generalizability, i.e. they lead performance degradation on the general bench- marks. These results indicate that, compared to CD>based methods, Mem VR excels at hallucination mitigation and de- livers strong performance general-puree -prrpose benchmarks. More experimental results are provided in Appendix B.
Table 5: HallusionBench evaluation results of different methods.
Methods
FACC
easyA f
bardA ^
aACC t
LLaVA-1.5
17.9 10.0
8.13 -0.0
36.0 10.0
36.7 10.0
41.5 10.0
OPERA
16.2 11.8
5.49
.26
37.6
1099
35.4
11.
41.2 103
+ICD
13.9 J4.0
8.35
-02
36.9
10.2
33.5
13.4
38.2
13.3
VCD
13.9 14.0
11.4
13.
33.0
13.7
34.7
13.0
41.1
104
MeMmVR
17.9 10.0
9.01
+0.9
36.9
10.9
37.7
100
42.5 10.
Table 6: LLaVA- (In-the-Wiin evaluation results.
5.3 Results on General-purpose Benchmarks
Methods
Convs
Detail
Complex
All
Averago
LLaVA-1.5
58.8 00.0
$2.1 10.0
74.6 10.0
63.4 10.0
64.8 10.0
evaluate the performance of Mem VR on general-purpose benchmarks, i.e., LLaVA-Bench. MM-Vet. MME, MM- Bench, and VizWiz. As shown in Table 6, MR consis- tently outperforms competing models on VLBBehBench. Be- sides, MemVR achieves a significant improvement in over- all performance listed in Table 7, with an average increase of 6.1% in OCR and spatial awareness tasks, demonstrat- ing superior generalization capabilities. For MME subset evaluations (covering both object-level and attribute-level hallucinations), the results in Figure and Table 8 indicate that Mem uniformly improves in handling object-level and attribute-level hallucinations, as well as commonsense
+ OPERA
59.5 10.7
49.6
12.5
78.6
14.0
59.8
13.6
64.3 10.5
+ ICD
40.3 J18.
42.2
19.9
60,3
114.
49.8
113.
56.9 17.9
+ VCD
57.8 L1.0
50.8
11.3
77.9
133
59.1
[4.3
63.2 [1.6
MemVR
63.8
+5.0
52.6
10.5
77.9
13.3
64.0
10.6
65.2 10.4
Table 7: MM-Vet evaluation results, where G denotes language Generation, K: Knowledge S: Spatial awareness, R: Recognition.
Methods
R
OCR_S
OCR_K_R
OCR_G.S
Total t
LLaVA-1.5
67.6 10.0
17.7 10.0
21.2 10.0
10.0 10.0
31.1 10.0
+ OPERA
61.9 [5.7
21.5 13.8
11.2 110.
30.0
120.
32,0 10.9
ICD
59.5 [8.1
17.7 10.0
8.8
J12.4
40.0
130.
25.9 15.2
VCD
62.2 15.4
15.8
11.9
17.5
60.0
150.
30.2 [1.1
+ MemVR
70.3 12.7
23,8
16.1
21.2 10.0
30.0 120.
32.4 11.3
7

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Table 8: Performance evaluation on MME Hallucination subset, MM- Vet, Vizwiz, and MMBench. The best results are in green.
MME-Hall
Object-Lovel
Attribute-evel
MM-Vet
Vizwiz
MMBench
Evaluation
Methods
Total
Existence 1
Count
Position t
Color t
Accuracy t
Accuracy
Accuracy
Regular
643.3 10.0
190.0
10.0
155.0
10.0
128.3
10,0
170.0
10.0
31.10
10.0
50.00
10.0
62.80
10.0
OPERA
610.0 133.
195.0
15.0
128.3
126.
121.7
J6.6
165.0
15.0
32.00
10.9
50.76
10.8
62.80
10.0
LLVVA-1.5
ICD
583.3 160.
185.0
15.0
130.0
125.
121.7
16.6
146.7
423.
25.90
15.2
37.62
L12.
39.78
J23.
VCD
648.3 15.0
190.0
10.0
155.0
10.0
133.3
15.0
170.0
10.0
30.20
10.9
44,90
15.1
54.21
J8.6
MR (ours)
648.3 15.0
190.0
10.0
155.0
10.0
133.3
15.0
170.0
10.0
32.40
11.3
51.50
11.5
63.75
10.9
Regular
618.3 10.0
175.0
10.0
140.0
10.0
123.3
10.0
180.0
10.0
49.00
10.0
66.05
10.0
56.53
10,0
OPERA
10.0
10.0
70.0
10.0
10.0
10.0
10.0
10.0
Qwen-VI.
ICD
616.7 !1.7
170.0
15.0
138.3
41.7
148.3
125.
160.0
420..
31.70
117.
29.37
L36.
13.32
J43.
VCD
648.3 130.
175.0
10.0
130.0
110.
153.3
130.
190.0
110.
34,60
114.
34.54
L31.
39.18
J17.
Mem VR (ours)
638.3 120.
185.0 110.
145.0
15.0
123.3
10.0
185.0
15.0
49.60
10.6
66.36
10.3
56.44
J0.1
5.4 Ablation Studies
performs layer-fixed strategy, achieving the highest total score on MME benchmark, where Static-# denotes execut- ing VR on the # layer and Static-o means running VR on the specific layer with best performance. This indicates that dynamic VR leveraging layer entropy offers more effec- tive mechanism, adapting better to different scenarios and achieving optimal performance compared to static VR.
To validate the effectiveness across the factors we intro- duced in our MemVR, we conducted in-depth ablation ex- periments as detailed in Figure 7 and Table 10. All ablation experiments are conducted on LLaVA-1.5-7B, and the per- formance is assessed across benchmarks.
Impact of Threshold and Injection Ratio. Fig. 7 shows the performance under different injection ratios a and thresh- olds+ y, we find that setting 7 between 0.6 and 0.95 can im- prove performance, with the optimal threshold around 0.75. Understandably, a high threshold makes VR hard to trigger, while a low threshold would trigger VR an earlier layer. For injection rate a, find positive effect of between 5% and 35%, and negative effect above 35%, which suggests that there is an upper limit to the supplementation of vi- sual memory. To drop the hyperparameter a and derive the dynamic injection ratio, we calculate a by a = 2(u 7), where denotes uncertain score and this variant named Vem and the results are present in Table 9.
Table 10: MemVR performance with different VR strategics.
MME
Static-7
Stutic-15
Stutic-23
Static-0
Dynamic
Cognition
347.1
352.14
357.9
362.9
383,9
Perception
1500.5
1529.1
1500.3
1526.4
1512.8
Total score
1847.6
1881.2
1858.1
1889.2
1896.7
5.5 Inference Latency
Mc operates dynamically based on uncertainty. It uses VR when layer uncertainty exceeds threshold y; if uncer- tainty stays low across all layers, indicating high model confidence, it's not triggered. This mechanism enables efficient inference without extra computation. Different from CD-based and Att-intervention paradigms, which need multi-round inferences or have rollback-induce exponen- tial overheads, our proposed look-twice mechanism only requires one regular inference. The comparisons on latency, throughput, time cost, and memory are shown in Table 1.
Pital Precepton
2330
10-48 lotal Score Preception Cogrition
Total Score Irecustion Eogn
Precoepiin Fro
738
2030
2002
Total Score
2203
Coggrition
1500
1003
10:0
5.6 Cases Study
1230
200
0 Injoction Ratio (%)
Mem reduces uncertainty during inference. As shown in Figure 8, MemVR effectively reduces uncertainty after the 8th layer triggered VR, further confirming our analysis about the pattern of hallucinations in Section $3.3.
Entropy Threshold
Figure 7: (Left) Results under different thresholds y. (Right) Re- sults under different injection ratios a, both on MME benchmark.
Table 9: VR
performance on different benchmarks.
Long-text capability, Beyond single-word QA benchmark evaluation, we explore models' capacity for comprehensive long-text generation in various tasks. As shown in Figure 9, Me can accurately identify image details relevant to questions. In contrast, as detailed in Appendix B, Qwen- VL-Chat struggles to generate detailed image descriptions in VCD, especially when nuanced image interpretation is needed. This indicates Mem VR has better cross-are archtterch adaptability and more reliable long-text generation ability.
MME
POPE (COCO)
Hallusion -Bench
LLaVA -Bench
POPE
Methods
(Overall)
(GQA)
LLaVA-1.5
1864.6
83.49
41.45
64.80
83.73
MemVV
1896.7
88.50
42.34
65.17
89.60
-.MmVR
1894.2
88.40
42.27
65.87
89.57
Impact of VR Strategy. Mem triggers VR dynamically using layer entropy. Tab. 10 shows that dynamic VR out-
8

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Is there a pavement in the image? nnwer the question using single word or phrase.
W Wrong.
6.74
6.42
2:12
0.51
0:51
0.00
0.78
0:42
0.57
0.70
0.50
0:09
0.30
(0.00
422
0.17
0.10
0:30
0.30
0.30
5:30
5:20
0.29
0.20
0:29
0.30
6.32
No
GP
7-
100
21th
12
15
L4
15
10
17
18th
19
20
21
22
23R
20
23
28
25
20
22
32th
S1
32
(L722
Before triggored
Uncertainties of 9th 32th layer (MemV triggered)
Correct.
Y
2:
Is there chair in the image? A nswer the question using single word or phrase.
W Wrnng
0:33
0:46
0.73
oua
6.77
0.54
6.30
1:29
0.30
6:30
1.50
0:30
0.23
0.30
No
0.78
0.53
2.64
0.44
0.50
0.31
0.75
0.70
8.75
0.42
0.53
0.:3
0.30
0.34
0:30
6.27
0:33
5:30
0.20
0.30
0.30
0:00
0:30
0.33
0.30
0:30
0.33
0.35
0.20
0.30
Before triggered
Uncertainties of 9th to 32th layer (MemV R is triggered)
Correct
Y es
Figure& 8: Visualisation of uncertainty across layers without and with MemVR. MRmVR effectively reduces uncertainty after 8th layer.
Ground Truth: 8.00
GLM-4v-9B Default: The price for tomatnes is 9.00, as indicated by the white sign with black lettering among the red tomatoes.
GLM-4v-9B VCD: The price for tomatoes is not clearly visible in the image provided. There are price tags for some of other fruits and vegetables, but tomatoes are eithe r not labeled or the label is not in focus. If you need the exact price for tomatoes, I would recommend visiting the market person or checking with the vendor.
GLM-4v-9B MemVI (Ours): The price for tomatoes 8.00, as indicated by the white sign with black lettering among the red tomatnes.
Question:
What is the price for tomatoes?
Figure 9: A case study in long text generation. Compared with VCD, MemVR could effectively mitigate hallucinations.
Analysis of failure cases. We have collected failure cases from the MME benchmark, in the 'Celebrity," 'Scene,' and 'Landmark' sub-tasks, where MemVR underperforms pared to the default model. We categorize Mem VR's failure cases into two types. Type occurs when the default model is right but Mem VR is wrong, due to over-disturbing the default model's reasoning. In these instances, the original visual features are sufficient for reasoning. and the rein- jected tokens inadvertently disrupt this process, leading to errors. We're re exploring ways to reduce such disturbances. For failure type 2, it happens when both are wrong, caused by image complexity or MLLM's original knowledge gaps.
extend Mem VR's framework to these diverse input formats and assess its efficacy across broader range of tasks.
6 Conclusion
This paper proposes a novel decoding paradigm to miti- gate hallucination, named MemVR, and present static and dynamic VR strategies that shift hidden states of the inter- mediate layer in MLLM for self-enhancement, rather than modulating logits directly with CD manner, thus avoiding multi-round decoding. Our experiments, conducted on eight benchmarks, demonstrate the effectiveness of Mem VR in mitigating hallucination and improving general performance. Importantly, MemVE is blug-and-pu and task aggossic method compatible with any MILM, without extra fine- tuning, emphasizing its widespread applicability.
5.7 Limitations and Further Discussions
While Mem VR shows substantial potential, it not devoid of limitations. A primary challenge resides in the com- plex task of identifying the optimal hyperparameters. These hyperparameters include the injection ratio of visual infor- mation, and the strategy for selecting the triggered layers. This represents the focal point of our subsequent research efforts, as we work to perfect MemVR.
A Acknowledgement
This work is sponsored by CAAI-Ant Group Research Fund; Guangdong Provincial Department of Education Project (Grant No.2024KQNCX028), Scientific Research Projects for the Higher-educational Institutions (Grant No. 10.222431209),, Education Bureau of Guangzhou Mu- nicipality; GuangzZho--IKUSST((Z)) Joint Funding Pro- gram (Grant No.2025A0313357), Education Bureau of Guangzhou Municipality; Fundamental Research Funds for National Universities, CUG (Grant 2024XBB)...
Additionally, although our research focused on MLLMS with visual inputs, theoretically, Me can be expanded to more modalities, e.g., listen-twice for audio, scan-twice for spatial perception, check-twice for [MRI, etc. This opens up an exciting avenue for future work, where we plan to

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Impact Statement
Devlin, J. Bert: Pre-training of deep bidirectional trans-
formers for language understanding. arXiv preprint 10x1:1811.04485,, 2018.
This paper proposes MemVR to alleviate hallucinations in MLLMs. The work has potential wider implications. Ethically, it may inherit biases from pre-trained models such as CLIP, risking unfair representation. There's 's also concern that it could be misused to generate disinformation. In terms of societal impact, MemVE VR can improve system reliability in safety-critical areas such as healthcare.
Ding, X., Han, J. Xu, H., Liang, X., Zhang, W., and Li, Holistic autonomous driving understanding by bird s-rye- view injected multi-modal large models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp- 1..11666 13668-13677, 2024.
References
Elbayad, M., Gu, J., Grave, E., and Auli, M. Depth-adapting transformer. In International Conference on Learning Representations, pp. 1-14, 2020.
Achille, A. and Soatto. S. Information dropout: Learn- ing optimal representations through noisy computation. IEEE Transactions on Pattern Analysis and Machine In- telligence, 40(12):287--2903 2018.
Farquhar, S., Kossen, J., Kuhn, L., and Gal, Y. Detecting hallucinations large language models using semantic entropy. Nature, 630(8017))625-630) 2024.
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. Flamingo: visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35:23716 237361 2022.
Fu, C.,, Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al. Mme: A comprehen- sive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13333 2023.
Bai, J., Bai, S., Yang. S., Wang. S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J. Qwen-vl: A frontier large vision- language model with versatile abilities. arXiv preprint arXiv:2308. 12966, 2023.
Geva, M., Schuster, R., Berant, J., and Levy, O. Transformer feed-forward layers are key-value memories. In Proceed- ings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5484-5495, 2021.
Bai, Z., Wang, P., Xiao, T., He, T., Han, Z., Zhang, Z., and Shou, M. Z. Hallucination of multimodal large language models: A survey. arXiv preprint arXiv:2404. 18930, 2024.
Guan, T., Liu, F., Wu, X., Xian, R., Li, Z., Liu, X., Wang, X., Chen, L., Huang, F., Yacoob, Y., et al. Hallusionbench: an advanced diagnostic suite for entangled language hal- lucination and visual illusion in large vision-language models. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 14375- 14385, 2024.
Ballard, H., Hayhoe, M. M., and Pelz, J. B. Memory representations in natural tasks. Journal of cognitive neuroscience, 7(1):66-8)) 80, 1995.
Chen, S., Xiong, M., Liu, J., Wu, Z., Xiao, T., Gao, S., and He, J. In-context sharpness as alerts: An inner representa- tion perspective for hallucination mitigation. In Forty-first International Conference on Machine Learning, 2024a.
Gunjal, A., Yin. J., and Bas, E. Detecting and prevent- ing hallucinations in large vision language models. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 38, pp. 18135-18143, 2024.
Chen, Z., Zhao, Z., Luo, H., Yao, H., Li, B., and Zhou, J. Halc: Object hallucination reduction via adaptive focal- contrast decoding. In Forty-first International Conference on Machine Learning, 2024b.
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., and Bigham, J. P. Vizwiz grand challenge: Answering visual questions from blind people. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pp. 3608 0008-361,,100 2018.
Chuang, Y.S.. Xie, Y., Luo, H., Kim, Y., Glass, R., and He, P. Dola: Decoding by contrasting layers improves factuality in large language models. In The Twelfth Inter- national Conference on Learning Representations, 2023.
Horowitz, S. and Wolfe, J. M. Visual search has no memory. Nature, 394(6693):55--77,, 1998.
Cover, T. M., Thomas, J. A., et al. Entropy, relative entropy and mutual information. Elements of Information Theory, 2(1): 1991.
Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., Lin, D., Zhang, W., and Yu, N. Opera: Alleviating hallucination in multi-modal large language models via over-trus trusttrust penalty and retrospection-allose alloccaioon....on...mmmm. arXiv preprint arXiv:2311.17911 2023.
Daunhawer, I., Sutter, T. M., Chin-Cheong, K., Palumbo, E., and Vogt, J. E. On the limitations of multimodal vaes. arXiv preprint arXiv:2 10.0412), 2021.
10

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang. J., Lin, D., Zhang, W., and Yu, N. Opera: Alleviat- ing hallucination in multi-modal large language models via over-trust penalty and retrospection-alloene Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 13418-13427, 2024a.
understanding and generation. In International Confer- ence on Machine Learning, pp- 12888-12900 PMLR, 2022.
In
Li, X. L.,, Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., Zettlemoyer, L.,, and Lewis, M. Con- trastive decoding: Open-ended text generation as opti- mization. In The 6lst Annual Meeting The Association For Computational Linguistics, 2023a.
Huang, W. Liu, H., Guo, M., and Gong, N. Z. Visual hallu- cinations of multi-modal large language models, arXiv preprint arXiv:2402.144 14683, 2024b.
Li, Y., Du, Y., Zhou, K., Wang, J., Zhao, X., and Wen, J.-R. Evaluating object hallucination in large vision-language models. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023b.
Huo, F, Xu, W., Zhang. 7.., Wang, H., Chen, Z.,, and Zhao, P. Self-introspective decoding; Alleviating hallucinations for large languale models. In ICLR, 2024.
Jie, S., Tang, Y., Ding, N., Deng, Z.-H., Han, K., and Wang, Y. Memory-space visual prompting for efficient vision- language fine-tuning. In Forty-first International Confer- ence on Machine Learning, 2024.
Lin, Q., Zhu, Y., Mei, X., Huang. L.,, Ma, J., He, K., Peng, Z., Cambria, E.,, and Feng, M. Has multimodal learning delivered universal intelligence in healthcare? a compre- hensive survey. arXiv preprint arXiv:2408. 12880, 2024.
Kao, W.-T. Wu, T.-H., Chi, P.-.., Hsieh, C.-C., and Lee, H.-Y. Bert's output layer recognizes all hidden layers? some intriguing phenomena and a simple way to boost bert. arXiv preprint (xXv:2001100939, 2020.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision (ECCV), 2014.
Kim, J. M., Koepke, A., Schmid, C., and Akata, Z. Expos- ing and mitigating spurious correlations for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2585- 2595, 2023.
Liu, F., Lin, K., Li, L.,, Wang. J., Yacoob, Y., and Wang, L. Mitigating hallucination in large multi-modal models via robust instruction tuning. In The Twelfth International Conference on Learning Representations, 2023a.
Liu, H., Brock, A., Simonyan, K., and Le, Q. Evolving normalization-activatie layers. Advances in Neural In- formation Processing Systems, 33:13539-13550, 2020.
Kim, T., Kim, J., Lee, G., and Yun, S.- Instructive de- coding: Instruction-tuned large language models are self- refiner from noisy instructions. In The Twelfth Interna- tional Conference on Learning Representations, 2024.
Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., and Lee, Y. J. Llavanext: Improved reasoning, ocr, and world knowledge, 2024a.
Koh, J. Y., Fried, D., and Salakhutdinov, R. R. Generating images with multimodal language models. Advances in Neural Information Processing Systems, 36, 2024.
Liu, H., C., Wu, Q., and Lee, Y. Visual instruction tun- ing. Advances in Neural Information Processing Systems, 36, 2024b.
Leng. S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, I.. Mitigating object hallucinations in large vision- language models through visual contrastive decoding. arXiv preprint arXiv:231 169222 2023.
Liu, S.. Zheng, K., and Chen, W. Paying more attention image: A training-free method for alleviating halluci- nation in Ivims. In European Conference on Computer Vision, pp. 125-140. Springer, 2024c.
Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L. Mitigating object hallucinations in large vision- language models through visual contrastive decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13872-13882, 2024.
Liu, Y.. Duan, H., Zhang, Y., Li. B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.0628/ 2023b.
Li, B., Zhang, K., Zhang, H., Guo, D., Zhang, R., Li, F,, Zhang, Y., Liu, Z., and Li, C. Llava-next: Stronger llms supercharge multimodal capabilities in the wild. 2024.
Lyu, Y., Zheng, X., Jiang, L., Yan, Y., Zou, X., Zhou, H., Zhang, L., and Hu, X. Realrag: Retrieval- waweevvallaugmentte augmented realistic image generation via relf-reflective contrastive learning. arXiv preprint arXiv:2502.008: 2025.
Li, J.. Li, D., Xiong, C., and Hoi, S. Blip: Bootstrapping iingaage-image pre-training for unified vasion-language

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Neo, D. and Chen, T. Vord: Visual ordinal calibration for mitigating object hallucinations in large vision- laiguge models. arXiv preprint
Yang, D., Rao, J., Chen, K., Guo, X., Zhang, Y., Yang, J., and Zhang, Y. Im-rag: Multi-round retrieval-augmented generation through learning inner monologues. In Pro- ceedings of the 47th International ACM SIGIR Confer- ence on Research and Development in Information Re- trieval, pp. 730-740, 2024.
2024.
Qu, X., Chen, Q. Wei, W., Sun, J., and Dong. J. Alleviating hallucination in large vision- lasoon-a models with active retrieval augmentation. arXiv preprint arXiv:2408.000 00555, 2024.
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E. A survey on multimodal large language models. arXiv preprint arXiv:2306. 13549, 2023.
Rohrbach, A., Hendricks, A., Burns, K., Darrell, T., and Saenko, K. Object hallucination in image captioning. In Proceedings of the 2018 Conference on Empirical Meth- ods in Natural Language Processing, pp. 4035-4045, 2018.
Yin, Z., Wang. J., Cao, J., Shi, Z., Liu, D., Li, M., Huang, X., Wang, Z., Sheng, L.,, Bai, L.,, et al. Lamm: Language- assisted multi-modal instruction-tuning dataset, frame- work, and benchmark. Advances in Neural Information Processing Systems, 36, 2024.
Schuster, T., Fisch, A., Gupta, J., Dehghani, M., Bahri, D., Tran, V., Tay, Y., and Metzler, D. Confident adaptive language modeling. Advances in Neural Information Processing Systems, 35:17456-17437 2022.
Yu, T., Yao, Y., Zhang, H., He, T., Han, Y., Cui, G., Hu, J., Liu, Z.,, Zheng, H.-T., Sun, M., et al. Rlhf-v: To- wards trustworthy milms via behavior alignment from fine- the-grained correctional human feedback. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13807-13816, 2024a.
Shi, W., Han, X., Lewis, M., Tsverkov, Y., Zettlemoyer, L., and Yih, W.-t Trusting your evidence: Hallucinate less with context-aware decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pp. 183-791,220 2024.
Yu, W., Yang, Z., Li, L., Wang. J., Lin, K., Liu, Z., Wang, X., and Wang, L. Mm-vet: Evaluating large multimodal mod- els for integrated capabilities. In Forty-first International Conference on Machine Learning, 2024b.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F. et al. Llama: Open and efficient foundation lan- guage models. arXiv preprint arXiv: 2023.
Zhang, X., Quan, Y., Gu, C., Shen, C., Yuan, X., Yan, S., Cheng, H., Wu, K., and Ye, J. Seeing clearly by layer two:
Enhancing attention heads to alleviate hallucination in Ivims. arXiv preprint arXiv:24/ 1,09968, 2024a.
Tu, H., Zhao, B., Wei, C., and Xie, C. Sight beyond text: Multi-modal training enhances llss in truthfulness and ethics. arXiv preprint 2309000000 2023.
Zhang, X., Shen, C., Yuan, X., Yan, S., Xie, L., Wang, W., Gu, C., Tang, H., and Ye, J. From redundancy to relevance: Enhancing explainability in multimodal large language models. arXiv e-prints, pp. arXiv-2406, 2024b.
Wadekar, S. N., Chaurasia, A., Chadha, A., and Culurciello, E. The evolution of multimodal model architectures. arXiv preprint arXiv:2405. 240.::::00..9922.. 17927, 2024.
Zheng, K., Chen, J., Yan, Y., Zou, X., and Hu, X. Reef- knot: A comprehensive benchmark for relation halluci- nation evaluation, analysis and mitigation in multimodal large language models. arXiv preprint arXiv:2408.0944 2024.
Wang, W., Lv, Q., Yu, W., Hong, W., Qi, J., Wang, Y., Ji, J.. Yang, Z., Zhao, L., Song, X., et al. Cogylm: Visual expert for pretrained language models. arXiv preprint arXiv:2311.0307 2023.
Zhou, Y., Cui, C., Yoon, J., Zhang, L., Deng, Z., Finn, C., Bansal, M., and Yao, H. Analyzing and mitigating object hallucination in large vision-language models. arXiv preprint arXiv:2310.00754 2023.
Wang, X. Pan, J., Ding, L.., and Biemann, C. Miti- gating hallucinations in large vision-language models with instruction contrastive decoding. arXiv preprint 17X1v 2024.
Zhou, Y., Cui, C., Yoon. J., Zhang, L., Deng, Z., Finn, C., Bansal, M., and Yao, H. Analyzing and mitigating object hallucination in large vision-language models. In International Conference on Learning Representations, 2024.
Wu, S., Fei, H., Qu, I.., Ji, W., and Chua, T.-S. Next-gpt: Any-to-any multimodal llm. In International Conference on Machine Learning, 2024.
Xing, Y., Li, Y., Laptev, I., and Lu, S. Mitigating object hal- lucination via concentrie causal attention. arXiv preprint arXiv:2410. 15926, 2024.
12

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
A
Theoretical Analysis of MemVE
In Multimodal Large Language Models, ballucinations often arise due to insufficient alignment between visual inputs and the model's 's internal representations. This paper provides rigorous theoretical analysis demonstrating that re-injecting visual features into the intermediate layers of MLLMs mitigates hallucinations and enhances representation capability.
We demonstrate that MemVR increases Mutual Information (MI) between the hidden states and visual tokens, decreasing the conditional entropy of outputs given the hidden state for fidelity to the visual input. We begin by defining the relevant variables and information -ommatton-thooeticc concepts that will be used throughout the proof as, Xxeg denote concatenated tokens of text and vision, with probability distribution p( og): Zu means visual (image) features, with probability distribution pl (Z)
The output hidden states of the Transformer model at layer k, defined recursively as: Hog = (HH
(H 1 Ze).
where == is the indicator function that equals when h = m (the layer where Zu is rejected) and O otherwise, and Y denotes the target output of MLLMs.
The probability of hallucination can be expressed as:
Piollucination = P(Y + Y (xug);
where y* is the ground truth output. According to information theory, higher conditional entropy H(Y (xng) indicates greater uncertainty of Y given Xug. which increases the probability of hallucination.
Information Flow of Visual Features. In a standard Transformer model, the initial input Xug undergoes multiple layers of processing. As the number of layers increases, the initial visual information may gradually diminish (information forggettnnnn In the absence of Mem VR, the MI between the hidden states and the visual features Z, tends decrease with depth:
I( Zo) S I(H mq D; Z.):
for l >> 1. This inequality indicates that in deeper layers, H&g contains less vision-related information.
Theorem A.1. Assume that each Transformer layer acts deterministic or stochastic mapping with the Markov property. Then, the mutual information between the hidden states and the visual features decreases with depth:
I Zu) S ((; Z.).
Proof. Each Transformer layer can be modeled stochastic mapping (Markov kernel) that processes the input hidden states. Specifically, ! is function of H1 11 possibly incorporating additional inputs such as Z, at specific layers.
According to the Data Processing Inequality (DPI) (Cover et al., 1991). if A + C forms a Markov chain, then:
I(A:C) & B).
In this context, consider A Zv. B = H (1-1) and C = H Since H is generated from - without direct access to Ze we have the Markov chain Z, + H 1) + H! Applying DPI yields:
(Z, (1 S 1 [Zy; ((-))).
Thus, mutual information between the hidden states and the visual features does not increase with depth.
Visual Retracing MLLMs. We reinject vision tokens Z, on 1-th layer (ahead layer S sle << L):
O bq
OFN (H Z.).
MemVR ensures that after the (-th layer, f1 again contains cncestion-alinnee visual information.
Theorem A.2. Let Hag be the hidden states of FFN and Hog be after reinjection of visual evidence Z... Mem VR enhances Mutual Information (MI) between Hrg and Zo: T(Hgg ; Zw) Z T(H Z.)-
13

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MILMS
Proof. We aim show that reinjecting Ze layer increases the mutual information between the hidden states and Z. conditioned on vgg.
By the definition of conditional mutual information:
Zu X x]]] (( ()) Zu Xxg) = xx.., [T( by: bi
Similarly,
T(H(1) bi Zu (xrg) = Ex.., (TH bq Zu Xxg x))]
Given H. = FFN"(HH)))) (H Z.) denotes the hidden states after utilizing MemVR on 1-th, reinjection of Z, introduces
direct dependency between Ar! and Zu beyond what is present in H Since FFN() a deterministic function that incorporates Zu, the mutual information T(Ar24 Ze X is at least as large 7 [ Zv Xeg). H retains all
information in Hr and additionally incorporates information from Zy. Thus, Mem VR ensures that:
I [ ( Ze xxg) I(H Ze Xeg).
By directly incorporating Zu into the computation of (r MemVR ensures that the hidden states retain more information about the visual features relative the original hidden states H thereby increasing I (( Zu Xxg). enhancing the representation capability and utilizing visual information.
Theorem A.3. Let Y be the larget output dependent hidden states. MI between Heg and Zy increases, then conditional entropy HY Hog) decreases, leading to a lower probability of hallucinations:
H(Y H S HY (
Proof. We aim show that an increase in mutual information between f1 and Z, conditioned on Xuy leads to decrease in the conditional entropy H(Y) ( According to the definition of conditional entropy, we have,
H(Y A (1)) = H(Y) - I(Y; fr
H(Y ( HY) I(Y: H
From Theorem A.2: ! contains more information about Zu, i.e., I( Z,) 2 T(H:)) [( Ze). thus we have I ( Y) I(H!) Y). Then, assume dependency between Z, and i.i.. I(Z,:Y) > 0, and subtract the inequalities, have:
Zz). There is T(Y; (T!)) x
H(Y (1)) H(Y I(Y; H)
S HYY HQ
H(Y H
Thus, McmVR reduces the conditional uncertainty of the target output given the intermediate embedding, thereby mitigating the probability of hallucinations and improving the model's 's predictive capability.
Theorem A.4. Within the Information Bottleneck (IB) framework, reinjecting Zo at layer m optimizes the objective function:
C [(F((m)) S L(H(m)),
where the objective is defined as:
C(H) = T(H; (IHH.
and B is trade-off parameter.
14

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Proof. The Information Bottleneck (IB) objective aims to find representation H that maximizes the mutual information with the target Y while minimizing the mutual information with the input X The optimization objectives before & after RR are follows:
C T(H Xng) - 31 Y);
Co 1 ( Xuy Z.) - 31 (
where 1Hinq; Xug, Z,) I[ff Xxy) : + I 1(f10) Z Xeg) The gap in the objective function is:
AC = _6 - -L
= [1(E1,0 Xoy) + 1 ( Ze - - -I [Y] 11 Xzg) - (I(H = = xzg) - 1 (1 X + 1 [ : Z. Xxg) B - 1(11)) Y]].
(0) X To ensure that CLTT) S L(m)), require: AC < O. We define the changes in mutual information. Let Alx I(Htgq T(HT Xug), ATy = [(H22) YY) 1( ((H YY) Note that 7(H Zu Xxa) 2 0. For AIx, the change in mutual information between H and Xeg depends on how much additional information from Z, affects the dependence on X We denote the maximum possible increase as X For Ary. From Theorem A.2, ATy 2 0, and suppose we can establish minimum increase Alpin > O. T( og Zo Xng) represents supplement information about Z. in Heg that is not already explained by Xyg. and we denote this maximum as 1
To satisfy this inequality, choose / such that:
35- 2 AIx + I(H() Zu X
(9)
()) Upper Bound on A/x and [ Zv Xnn) as A S A I I(H Zo (wn) < 121 Lower Bound on Aly as: Aly 2 Alpin > 0. Then, we derive the condition with error bounds, for AL & 0, it suffices that:
A mmax
+ 2 A Immin
BARTI min 2 A ATXI
x mAX
B
(10)
This condition provides a lower bound for .3 to ensure that reinjecting Z, at layer m decreases the IB objective function. By adhering to this condition, MemVR optimizes the IB objective, balancing the trade-off between the compression of input information and the preservation of relevant information for prediction.
By reducing the IB objective function, the model focuses more information relevant to predicting Y while compressing irrelevant information. The enhanced mutual information with Y reduces the likelihood of generating hallucinated outputs not supported by the visual input.
Error Bounds Provide Guarantees. The upper and lower bounds on mutual information changes ensure that, under specific conditions (e.g., the selection of 3), theoretical improvement holds.
Estimating the Bounds.
A rpin requires knowledge of how much additional information about Y is gained by reinjecting Z,. It can be estimated based on the mutual information T T(2;; Y) and the effectiveness of Hr(m) (m) in capturing information relevant Y.
Ampax can be bounded based on the capacity of ( to represent X Specifically, it relates to how much additional information ((()) encode about X.y beyond what already captured in H
(Zw)) is bounded by the entropy of the visual features, as mutual information cannot exceed the entropy of 7
Through detailed mathematical derivations and the inclusion of upper and lower error bounds, we have established that:
15

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Increased Mutual Information: Reinjecting visual features at intermediate layer increases the mutual information between the model's embeddings and the visual input.
(b) Reduced Conditional Entropy: Mem VR reduces the conditional uncertainty of the target output given the intermediate embedding, enhaneing the model's predictive accuracy and mitigating hallucination phenomena caused by the forgetting of visual information.
(c) Optimization within IB Framework: Within the Information Bottleneck framework, Mem VR optimizes the objective function, provided certain conditions on the mutual information changes are met and appropriate choices of the trade-off' parameter are made.
B Additional Experiments, Results, and Discussions
B.1 Benchmarks and Metries
Datasets. To rigorously assess the effectiveness of our proposed method, we conduct a comprehensive set of experiments across two benchmarks specifically designed to evaluate hallucination mitigation and five general-purpose benchmarks to gauge the general performance: @ Hallucination benchmarks: Polling-based Object Probing Evaluation (POPE) (Li et al., 2023b), and Caption Hallucination Assessment with Image Relevance (CHAIR) (Rohrbach et al., 2018), Hallusion- Bench (Guan et al., 2024); @ General-purpose benchmarks: VizWiz-VQA (Gurari et al., 2018), MLLM Comprehensive Evaluation (MME) (Fu et al., 2023), Multimodal Benchmark (MMBench) (Liu et al., 2023b), Multimodal Veterinarian (MM-Vet) Vet) ( YYu et al., 2024b), LLaVA-BBnnh (in- (n-the-wild) (Liu et al., 2024b).
In this appendix, we provide additional details on the benchmarks referenced in the main paper. To evaluate hallucinations, we employ the following five benchmarks:
CHAIR (Rohrbach et al., 2018) evaluates how well the generated captions align with the content of the given image. CHAIR consists of two versions: CHAIRs, which measures the inaccuracies at the sentence level, and CHAIRi, which evaluates at the object level within the sentence by comparing the number of false objects to the total number of objects. For evaluation,
we use the va12014 split of the MSCOCO (Lin et al., 2014) dataset, which includes annotations for 80 object categories. We randomly select 500 images from the entire dataset and used the prompt "Please describe this image in detail." for the MILM. The CHAIR metric includes per-instance evaluation (CHAIRi) and per-sentence evaluation (CHAIRs), defined as
follows:
(hallucinated objects}|
CHAIR;
[all objects mentioned]]
CHAIR, { (sentences with hallucinated object]|
|f all sentences}|
Polling based Object Probing Evaluation (POPE) (Li et al., 2023b) is a VQA-based metric proposed to assess hallucina- tions in MLLMs. This metric evaluates the MI.LM's response to the prompt "Is [object] is in this image?" To emphasize that this is a binary VQA task, we appended the prompt with "Please answer yes or no." To select objects referenced the question prompt, we followed three different sampling options: random, popular, and adversarial. We evaluated performance across all sampling options.
MLLM Evaluation (MME) (Fu et al., 2023) evaluates the capabilities of MLLMs, dividing the evaluation into two major categories: perception and cognition. The perception category includes fine-graine fine-grained tasks such as existence, count, location, rough color, poster, celebrity. scene, landmark, artwork identification, and OCR. The cognition category includes tasks like commonsense reasoning, numerical calculations, text translation, and code reasoning. All questions in this benchmark are structured to be answered with simple yes or no.
Using the LLaVA-Bench (Liu et al., 2024b), we further demonstrated how well our proposed method maintains the language model performance. This benchmark involves posing various situational questions, such as dialogue, detailed descriptions, and complex reasoning, to randomly selected images from the MSCOCO val2014 dataset. A total of 60 questions are used to assess whether the model faithfully follows the instructions. The generated answers are evaluated by comparing them to the responses of a text-only GPT-4 model.
Candidate Layers. In dynamic premature layer selection, we partition transformer layers into buckets and select one bucket as the candidate layer set. For 32-le I.L 1,5-BB, we use two (nc)ets((0115,,(15,3) This design limits the
16

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
hyperparameter search space to only 2-4 validation runs. For efficiency, we use validation set (MME) to select the best bucket.
B.2 Backbones and Baselines
To evaluate our method, we utilize well-known MLLMs: LLaVA-1.5 (Liu et al., 2024b), Qwen-VL (Bai et al., 2023), and GLM4V (Wang et al., 2023), LLaVA-Next (Li et al., 2024). Further, We compare our methods with classic training- -tree SOTA methods designed to mitigate object hallucination, including visual contrastive decoding SOTA VCD (Leng et al., 2024), OPERA (Huang et al., 2024a) based on overconfidence penalty and hindsight allocation. As Dola (Chuang et al., 2023) is layer-wise contrastive decoding for LLMs and performs poorly in MLLMs, it will not be shown in the experiment. Experimental results are obtained and benchmarked using unified implementation.
Greedy search is used as the default decoding strategy in Mem for all benchmarks. For benchmarks, annotation questions are adapted to MILM templates. For POPE, COCO, A-OKVQA, and GQA are used, while MMBench DEV EN is used for MMBench. MM-Vet is assessed using MM-Vet Online Evaluator, and 1914-1106-previne is used for LLaVA- Bench. CHAIR uses images from COCO Val2014 with the query "Please describe this image in detail". In Mem VR, do sample=False, temperature=0, threshold=0.75, beam=1. All settings of the compared method follow the default configurations from the original papers.
B.3 Reproducibility
Implementation details. We employed greedy search as the default decoding strategy across all benchmark evaluations. For the hallucination benchmarks (POPE, CHAIR and HallusionBench) and general general-purpose purpose benchmarks (MME, VizWiz-VQA, MMBench, MM-Vet Vet, and ALLVA--Bencl (n--he--ildd), questions from the annotation files were used as prompts, formatted to fit the chat templates of each respective MLLM. Specifically, we utilized the COCO, A-OKVQA, and GQA datasets for POPE evaluation, and MMBench DEV EN for MMBench. In the MM- Vet evaluation, by OpenAl GPT-4 to assess generated results, while for LLaVA- --BBecch (n-the-wild) and HallusionBench, we employed OpenAl's Is model gpt4- 1061-00v-ieee and GPT-4o-min mini respectively via API. For CHAIR, randomly sampled image set from the COCO Val2014 4 dataset was used across all three models, with the prompt "Please describe this image in detail." We sampled three different sets of images using different random seeds and evaluated performance by calculating the mean and standard deviation of the results.
used online evaluator powered
All MemVE tests were conducted using greedy decoding approach, with do sassallle=Faase, temperature=0, threshold=0.75. and beam=1. For VCD tests, we set do sample=True, temperature=1, noise stse-stte-500, and the plausibility constraint hyperparameter À to 0.1, while a, which controls the degree of contrastive emphasis, was set to 1, following the default parameter settings from the original code and literature. OPERA tests were configured with beam=5, sample= True, scale factor=50, threshold=15, num attn candidates=5, and penalty weights=1. Due to OPERA's reliance on older versions of Torch and Transformers, it was incompatible with Qwen and GLM models, and thus experiments involving these models were not conducted. Additionally, our method introduces two hyperparameters: the informative layer i for activation calculations and the factor to control the influence of entropy on the next token probability distribution. To map the hidden states from selected layers I to vocabulary tokens, chose intermediate layers based on the model's depth (e.g.. layers 5 to 16 for vicuna-7b, which has 32 layers), and we set À as a fixed value (e.g.. 0.75). All parameter settings adhered to the default configurations specified in the respective papers and code repositories.
Experimental Code. To promote transparency and ensure the reproducibility of our work, we will release all experimental code, datasets, and detailed tutorials necessary for replicating our experiments. Our goal is to make it straightforward for researchers and practitioners to reproduce our results, regardless of their technical background. Additionally, by providing comprehensive documentation and clear guidelines, we aim to facilitate the extension of our method to other models and architectures, enabling the broader research community explore its potential applications and improvements. We believe that open and reproducible research is essential for advancing the field and fostering collaboration.
Computational Resources. Our experiments were conducted on eight A40 and four A800 GPUs. The computational bottleneck was not the numerical accuracy values but the collection of potential hallucinatory factors for analytical purposes, including logits and attention values for each head and layer.
B.4 Case Study
This case study aims to evaluate and present various benchmark cases across multiple domains systematically.
17

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Ground Truth Caption: A aerial view of Diamond Head in the Hawaiian Islands.
Llava-v1.5-70 Default: The famous sight in the photo the Na Pali Coast on the island of Kaua'i in Hawaii.
Llava-11.5-7b + VCD: The name of the famous sight in the photo the Na Pali Coast of Kauali Island, which is an island in Hawaii.
Llava-v1.5-7h Opera: The name of the famous sight in the photo is the Grand Canyon.
Alava-v1.5-77 + MemVR R (Ours): The famous sight in the photo is the Diamond Head, which is volcanic crater located on the island of Oahu in Hawaii.
Question: W hat is the name of this famous sight the photo?
Figure 10: A case study comparing the levels of hallucination among various basclines.
Ground Truth Caption: mousse
Alava-v1.5-77 Default: The right most bottle is of Charles Worthington's Thicker & Fuller range.
THE
THE VAL
Llava-v1.5-7b + VCD: densifying spray
Llava-v1.5-7b + MemVR (Ours): The right most bottle is the Thicker & Fuller Volumizing Mousse.
Question: What is the name of
this famous sight in the photo?
Figure 11: A case study comparing the levels of hallucination among various baselines.
there only three people in the image?
there people appear this image?
there skis the image? A nswer question using single... "5" (hallucinated answer of LLaVA) No.
Please answer yes "5"
Please answer yes no. "
(hallucinated answer of LLaVA) Yes.
(hallucinated answer of LLaVA) No.
re there only three people in the image?
O
re there six people appear in this image? Please answer yes or no. :5
Is there skis in the image? A nswer the question using single... 1 (correcl answer given by MemV R) Yes.
Please answer yes or no. 1
(correct answer given by MemVV R) No.
(correct answer given by MemV R) Yes.
Figure 12: A case study comparing the levels of hallucination among various basclines.
18

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
a a dog' b a.rellace( 'og', 'cat' print(b)
The image shows python code. Is the output of the code ' cat? Please answer yes no.
Is this artwork created by courbet, gustave? Please answer yes or no. *X
(hallucinated answer of LLaVA) A) No.
(hallucinated answer of LLaVA) No.
O
The image shows python code. Is the output of the code aa cal? Please answer yes no.
Is this artwork created by courbet, gustave? Please answer yes or no.
(correct answer given by MemV R) Y es.
(correct answer given by MemVRI R) Y es.
Figure case study comparing levels of hallucination among various baselines.
$#
Is it appropriate to translate the Chinese in the image into English 'very happy' in the picture?InA nswer the question using single word or phrase.
I want to supplement protein. Is it appropriate to eat the food in the picture?luAnswe nswer the question using single word phrase.
(hallucinated answer of LLavA) Yes.
(hallucinated answer of LLaVA) No.
it appropriate to translate the Chinese in the image into English 'vry happy in the picture?InA nswer the question using single word phrase.
I want to supplement protein. Is it appropriate to eat the food in the picture?InA nswer the question using single word phrase.
(correct answer given by MamV R) No.
(correct answer given by MemV R) Y es.
Figure 14: A study comparing the levels of hallucination among various basclines.
19

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Is this picture Smithfield, D Dublin?'nA nswer the question using single word phrase. "
Is this photo taken in a place of nursing home?InA nswer the question using single word or phrase. %
(hallucinated answer of Qwen) Unknown.
(hallucinated answer of Qwen) No.
this picture Smithfield, D Dublin?'nA nswer the question using single word phrase.
Is this photo taken in place of nursing home?inA nswer the question using single word or phrase.
(correct answer given by MemV R)
(correct answer given by MemV R) Y es.
Figure 15: A case study comparing the levels of hallucination among various basclines.
COFFEESHOP
= 'a dog'
HIGH TIME
== a.replace( dog', 'cat') print(b)
9
The image shows python code. Is the output of the code cat? Please answer yes or no. %
Is the word in the logo l'high tite cofeee shopt" ??IAA nswer the question using single word or phrase. *
(hallucinated answer of Qwen) No.
(hallucinated answer Qwen) es.
The image shows python code. Is the output the code 'a cat? Please answer yes or no. 1
Is the word the logo ("high tite cofeee shop) ?inAA nswer the question using single word phrase. 1
(correct answer given MemV R)
(correct answer given by MemV R) No.
Figure 16: A case study comparing the levels of hallucination among various haselines.
20

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
10 3 =
Cale Ballon Shi
Homm Torm
Aee there five people this image? Please answer yes or no.
Is the answer to the arithmetic question in the image 12?(nA nswer the question using single word or phrase. %
(hallucinated answer of ChatG es.
(hallucinated answer of ChatGLM) Yes.
there five people in this image? Please answer yes or no.
Is the answer to the arithmetic question in the image 12?\nA nswer the question
using single word or phrase.
(correct answer given by MemV R) No.
(correct answer given by MemV R) No.
Figure 17: A case study comparing the levels of hallucination among various baselines.
'a dog' b replace ('dog', 'cat' print(b)
The image shows python code. Is the output of the code cal?? Please answer yes or no.
O
Is this an image of Sampsonievsky Cathedral InnA nswer the question using single word phrase. %
(hallucinated answer of ChatG No.
(hallucinated answer of ChaiGLM) No.
The image shows python code. output of the code cat?? Please answer yes or no.
Is this image of Sampsonievsky Cathedral7InA nswer the question using single word or phrase.
(correct answer given by MemV es.
(correct answer given by MemV R) Yes.
Figure case study comparing levels hallucination among various haselines.
21

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
B.5
Additional Experiments and Results
MME
Method
Overall
Perception
Cognition
LLaVAI.5-7B
1864.68
10.0
1508.97
10.0
355.71
10.0
VCD (Leng et al., 2024)
1872.87
18.2
1515.01
16.0
357.86
12.2
OPERA (Huang et al.. 2024a)
1784.34
180.3
1473.62
135.3
310.71
145.0
ICD (Wang al., 2024)
1594.77
J269.9
1306.91
1202.1
287.86
J67.9
MemVR (Ours)
1896.72
132.0
1512.80
13.8
383.92
128.2
Qwen-Vi Cwen-VL-Chat
1784.93
10.0
1442.79
10.0
342.14
10.0
VCD (Leng ct al., 2024)
1721.06
J63.9
1403.17
439.6
317.89
424.3
+ OPERA (Huang ct al., 2024a)
ICD (Wang al., 2024)
1833.75
148.8
1472.06
129.3
361.69
119.6
MemVR (Ours)
1820.95
136.0
1473,45
130.7
347.50
15.4
GLM-4V-9B
2160.54
10.0
1680.89
10.0
479.64
10.0
VCD (Leng ct al., 2024)
2105.53
455.0
1624.10
J56.8
481.43 .43
11.8
OPERA (Huang ct al., 2024a)
ICD (Wang al., 2024)
2074.30
J86.2
1566.09
1114.8
508.21 21
128.6
MemVR (Ours)
2170.17
19.6
1683.03
12.1
487.14 17.5
Table A1: Results on the MME dataset. Best- Best-performing method model size is in bold. Arrows indicate improvement (1) or degradation (1) vs. the baseline model.
LLa (in-the-wil) wild)
Method
Average
AlI_1
All.2
AlI.3
LLaVAI.5-BB
64.80 10.0
63.40
10.0
80.20
10.0
50.80
10.0
+ VCD (Leng al., 2024)
63.20
11.6
59.10
J4.3
82.00
11.8
48.50
J2.3
+ OPERA (Huang al., 2024a)
64.30 [0.5
59.80
13.6
83.30
13.1
49.80
11.0
+ ICD (Wang et al., 2024)
56.90
17.9
49.80
113.6
80.70
10.5
40.20
110.6
+ MemVR (Ours)
65.17 10.4
64.00
10.6
80,20
10.0
51,30
10.5
Qwen VL-Chat
68.50
10.0
70.40
10.0
79.30
10000
55.80
10.0
+ VCD (Leng al., 2024)
53.77 L14.7
41,00
J29.4
85.30
16.0
35.00
420.8
+ OPERA (Huang et al., 2024a)
+ ICD (Wang et al., 2024)
56.60
111.9
45,30
$25.1
85.70
16.4
38.80
117.0
+ MemVR (Ours)
69.50 11.0
69.50
.0.9
82.00
12.7
57.00
11.2
GLM-4V-9
75.30
10.0
88.40
10.0
73.00
10.0
64.50
10.0
+ VCD (Leng et al., 2024)
74.23
41.1
86.70
41.7
72.80
J0.2
63.20
41.3
+ OPERA (Huang et al., 2024a)
+ ICD (Wang et al., 2024)
70.97
14.3
82.10
J6.3
71.80
11.2
59.00
15.5
+ MemVR (Ours)
76.73
11.4
88.90
10.5
74.80
1..8
66.50
12.0
Table A2: Results LLaVABench (in-the-wild) dataset. Best-performing method per model size and dataset highlighted in bold; arrows indicate improvement or degradation over the baseline, where higher values indicate better performance.
22

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Method
Total
LLaVAI.5-75
31.1 10.0
VCD (Leng al., 2024)
30.2 '0.9
OPERA (Huang al., 2024a)
32.0 10.9
ICD (Wang et al., 2024)
25.9 15.2
MemVR (Ours)
32.4 11.3
Qwen-VI.-Cho
49.0 10.0
VCD (Leng et al., 2024)
34.6 $14.4
OPERA (Huang et al., 2024a)
ICD (Wang et al., 2024)
31.7 .17.3
Mem (Ours)
49.6 10.6
GLM-4V-9B
63.4 10.0
VCD (Leng al., 2024)
59.4 44.0
OPERA (Huang et al., 2024a)
+ ICD (Wang et al., 2024)
57.7 15.77
Mem VR (Ours)
65.0 11.6
Table A3: Results on MM-Vet dataset.
Best-performing method per model size is highlighted in bold. Arrows indicate improvement ( (1)
degradation (1) the baseline.
Method
Accurwcy
LLaVAI.5-7B
50.00
10.0
VCD (Leng et 2024)
44.90
15.1
OPERA (Huang et al., 2024a)
50.76
10.8
ICD (Wang et al., 2024)
37.62
L12.4
MR (Ours)
51.50 f1.5
Owen-VL-Chat
66.05
10.0
VCD (Leng et al., 2024)
34.54
431.5
OPERA (Huang et al., 2024a)
ICD (Wang et al., 2024)
29.37
L36.7
MemVR (Ours)
66.36
10.3
GLM-4V-9B
$7.39
10.0
VCD (l.eng et al., 2024)
48.04
49.4
OPERA (Huang et al.. 2024a)
ICD (Wang et al., 2024)
50.01 J7.4
Mem (Ours)
58.00
10.6
Table A4: Results on the Vizwiz dataset. Best-performing method per model size is highlighted in bold; arrows indicate improvement ( ()) degradation relative the baseline.
CHAIRS
Method
Cs
Ci
Recall
Len
LLaVA115-7B
47.60
10.0
13.30
10.0
80.60
10.0
99.70
10.0
+ VCD (Leng et al., 2024)
55.00
17.4
15.80
12.5
77.40
[3.2
101.20
+1.
+ OPERA (Huang et al., 2024a)
47.60
10.0
13.50
10.2
79.00
[1.6
93.20
J6.5
+ ICD (Wang et al., 2024)
56.20
18.6
16.30
13.0
16.30
164.3
103.40 13.7
+ MemVR (Ours)
46.60 41.0
13.00
10.3
80.80
10.2
99.60 J0.1
Qwen-VL-10B
VL-10B
6.80
10.0
5.30
10.0
53.40
10.0
17,60
10.0
+ VCD (Leng et al., 2024)
13.00 16.2
12.30
17.0
47.90
45.5
115.70
198.1
+ OPERA (Huang et al., 2024a)
+ ICD (Wang et al., 2024)
18.40 111.6
14,30
19.0
37.60
115.8
48.10
130.5
+ MemVR (Ours)
4.80 12.0
3.30
12.0
52.30
11.1
15.00
J2.6
GLM-4V-9B
40.40 10.0
9,00
+0.0
10.00 72.80
72.70
218.20
10.0
+ VCD (Leng et al., 2024)
42.20 11.8
9.60
10.6
10.1
239.80
121.6
+ OPERA (Huang et al., 2024a)
ICD (Wang et al., 2024)
43.40
13.0
9.10
10.1
73.60
11.9
239.80
121.6
MemVR (Ours)
39.40
$1.0
9.00
10.0
70.70
42.0
214.00
J4.2
Table AS: Results on CHAIRS dataset. Best-performing method per model size and dataset is highlighted in hold; arrows indicate improvement or degradation over the baseline, where lower values indicate hetter performance.
23

---

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MILMS
Method
Existence
Count
Position
Color
Scene
Artwork
OCR
Numerical
Text
Code
LLaVA-Next (Llama3-8BB)
195.0
165.0
143.3
185.0
161.6
159.2
118.0
125.0
50.0
77.5
+MemVR
195.0
170.0
143.3
185.0
163.6
161.0
124.0
125.0
52.5
77.5
LLaVA-Next (Mistral-7B)
190.0
130.0
133.3
190.0
144.2
163.5
113.0
122.5
60.0
67.5
+MemVR
195.0
155.0
133.3
190.0
145.2
165.0
113.8
122.5
60.0
67.5
LLVV--e (Vicuna-1.6-27)
195.0
135.0
143.3
165.0
162.2
123.2
132.5
42.5
107.5
55.0
+MemVR
195.0
135.0
135.0
170.0
163.0
123.5
140.0
42.5
115.0
57.5
Table A6: Performance comparison across different LLaVA-Next models with and without Mem VR.
MMBench
Method
AR
CP
FP-C
FP-$
I.R
RR
Overall
LLaVA115-7B
72.86 10.0
75.68
10.0
58.04 10.0
63.48
70.0
28.81 10.0
51.30
10.0
62.80 10.0
+ VCD (Leng et al., 2024)
60.30
68.58
51.75
53.24
18.64
48.70
54.21
+ OPERA (Huang et al., 2024a)
69.85
75.00
56.64
66.21
28.81
53.04
62.80
+ ICD (Wang et al., 2024)
J30.7 42.21
52.36
123.3
15.7 52.36
J23.9 39.59
L12.7 16.10
32.17
119.1
39.78
[23.0
+ MemVR (Ours)
71.86 f1.2
76.69
+1.0
57.34
J0.7
64.16
70.9
31.36
12.5
56.52
f5.2
63.75
10.9
Qwen-VL--1B
60.30 10.0 34.67
71.28
10.00 52.36
11.00 20.28
45.45
62.80
70.0 55.63
28.81 10.0
38.26 10.0
56.53 10.0
+ VCD (Leng al., 2024)
11.86
22.61
39.18
+ OPERA (Huang al., 2024a)
+ ICD (Wang al., 2024)
12.56 J47.7
17.57
U53.7
2.10
J43.4
22.53
J40.3 62.80
2.54
426.3
5.22
133.0
13.32
J43.2
+ MemVR (Ours)
61.31 $1.0
71.28
10.00
44.06
41.4
70.0
27.97
40.8
38.26
10.0
56.44
J0.1
GLM-4V-9B
88.44 10.0 86.43
10.0 85.47
86.49
10.0 68.53
69.93
70.0 84.64
10.0 61.86
10.0 81.74
10.0 80.58
82.39
VCD (Leng al., 2024)
OPERA (Huang al., 2024a)
ICD (Wang al., 2024)
83.92
14.5
84.46
J2.0
62.94
17.0
81.23
J4.4
60.17
15.9
80.00
45.2
78.01
J4.4
VeMMVR (Ours)
88.94 10.5
86.49
70.63
10.7
86.01
10.4
66.10
10.0
85.22
10.0
82.65
10.3
Table A7:
Results on MMBench dataset with newly added ICD rows. Best-performing method per model size and dataset is highlighted bold; arrows indicate improvement ( (f) or degradation (L) over the basclinc.
Strategy
1-Token Len
5-Token Len
10-Token Len
20-Token Len
30-Token Len
50-Token Len
80-Token Len
Greedy
661.7
897.9
1273.1
1880.3
2501.8
3617.6
5256.6
Sample
786.8
1056.2
1314.9
1998.5
2568.5
3593.0
5587.0
VCD Sample
1747.74
2767.52
4027.07
4537.42
5031.39
7690.77
11569.3
Opera Beam
1566.
3094,9
4166.4
6242.7
8436.9
12672.3
19247.2
MR Sample
750.8
1197.6
1780.5
2339.2
2631.7
3718.0
6011.0
Mem VR Greedy
775.1
974.2
1337.5
1861.7
2742.8
4000.9
5545.5
Table A8: Time cost for generating tokens. All based on LLaVA1.5-BB
B.6 VCD Implement Details
The code of VCD (Leng al., 2024) is also released. However, the result of VCD evaluated in experiments POPE lower than the original paper. Therefore, report the results in the original paper.
24

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Examples of capability integrations
C.1
Others
Q: How can the model understand information directly from the vision encoder, especially if has a different vision system? ? To ensure that MemVR adaptable across diverse vision systems, we conducted experiments on multiple VLM architectures, including LLaVA, which utilizes a Visual- Instructional--Tmme Qwen-VL--Chat, which employs a Q-Former-like architecture for visual processing, and ChatGl MM-vV9,, which integrates a large pre-trained visual encoder. These architectures encompass a broad range of vision models, providing confidence that Mem VR applicable to most MLLMs in use today.
framework with different sizes of ViT-based CLIP models,
Artifacts and licenses We report list of licenses for all datasets and models used in our experiment in Table A10. We strictly follow all the model licenses and limit the scope of these models academic research only.
25

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMS
Gasoline
Self Serve
Requler 3.49
9 0
Plus 3.55
Supreme 3.65
Diesel
3.60 9
No.2
Question: Which room is bigger, the double garage the living room?
Question: How many gallons of supreme gasoline can I get with $50?
Ground Truth: Double garage
Ground Truth: 13.6 13.7
Required Capabilities: OCR, Spatial Awareness, Math
Required capabilities: OCR, Math
5-1331
Question:
Which car is on the parking spot 33?
Question: Is this apple organic?
Ground Truth: No Empty
Ground Truth: Yes
Required Capabilities: Recognition, OCR, Spa- tial Awareness
Required capabilities: Recognition, OCR
Dlue whale
Kiler whole
3x3=
7x2=
11-2=
Lecoard
Seogu
Elephant
Sould
Phytopbarkton
Cub
Question: What will the girl on the right write on the board?
Question: Which are producers in this food web?
Ground Truth: 14
Ground Truth: Phytoplankton & Seaweed
Required capabilities: Recognition, OCR, Spatial Awareness, Math
Required Capabilities: OCR, Knowledge, Spatial Awareness
Table A9: Six samples on MM- Vet benchmark requiring different capability integrations.
26

---

Look Twiee Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in MLLMs
Data Sources
URL
License
MSCOCO 2017
Link
CC 4.0
ADE20K
Link
BSD-3-Clause
VQA Val
Link
CC BY 4.0
LL.VA--ench-ii--the--ii
Link
Apache-2.0
ImageNet
Link Custom License
MMBench
Link
Apache-2.0
Software Code
URL License
LLaVA
Link
Llama Community Licence
Qwen-VI. VL
Link
Tongyi Qianwen Licence
GLM-4V
Link THUDM GL.M44 Licence
GPT-4V/40
Link
OpenAl Term of Use
Table A10: License information for the scientific artifacts.
27


Reviews:
[{"review_id": "YuejwLgdfh", "reviewer": "Reviewer_2zfj", "summary": "This paper introduces Memory-Space Visual Retracing (MemVR), a novel decoding approach to mitigate hallucinations in Multimodal Large Language Models (MLLMs). The authors posit that hallucinations often occur due to the model's tendency to \"forget\" visual information during text generation, and they address this by developing a \"look-twice\" mechanism that reinjects visual tokens into the model's middle layers when uncertainty is detected. Unlike existing contrastive decoding approaches, MemVR modifies intermediate hidden states rather than output logits, avoiding the need for multiple decoding passes. The authors evaluate their method across seven benchmarks and demonstrate superior performance in both hallucination mitigation and general capabilities while maintaining efficiency in inference time.\n\n\n## update after rebuttal\nThe authors' rebuttal has addressed my concerns. However, I still doubt the effectiveness of MemVR compared to retrieval-based methods, and I'm concerned that the novelty is somewhat limited, as the reviewer 3FtN mentioned. In my opinion, it's a borderline paper considering the technical contributions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments and kind suggestions.\n﻿\n> Q1: The contribution of revisiting visual tokens.\n\nMemVR is fundamentally different from the current approaches, you may refer to Table 2 in the paper, where we show comprehensive comparisons with existing methods.\n﻿\n1) Retrieval-based approach incorporates knowledge from the external database, which brings about a large memory footprint. *MemVR achieves self-improvement without external knowledge.*\n\n2) CD-based and attention intervention strategies both bring about high inference latency, due to multiple rounds of inference or the rollback operation. *MemVR mitigates hallucinations and excels beyond SOTA methods without incurring additional time overhead.*\n\n3) Retrieval-based, CD-based, and attention intervention methods generally act on textual/visual input (i.e., instruction/image), output logits, or attention matrix. *MemVR reinjects visual information at hidden states.*\n\n4) Compared with other methods, which succeed in hallucination benchmarks but fail on general benchmarks, *MemVR enables constant performance boosting in both hallucination and general benchmarks.*\n\n> Q2: Add analysis of failure cases.\n\nThank you for your valuable and helpful suggestions. We are keen to explore how reinjecting similar visual features without external data might affect model biases.  To address this, we have collected failure cases from the MME benchmark, in the 'Celebrity,' 'Scene,' and 'Landmark' sub-tasks, where MemVR underperforms compared to the default model, as follows.\n﻿\n| Right numbers | existence | count | position | color | posters | celebrity | scene | landmark | artwork | OCR | CommR | numerical_cal | translation | code |\n|--- | :----: | :-----: | :----: | :-----: | :-----: | :----: | :----: | :-------: | :----: | :----: | :---: | :---: | :-----: | :-----: |\n|Total| 60| 60 | 60| 60| 294| 340| 400 | 400 | 400| 40  | 140 | 40| 40 | 40  |\n|LLaVA1.5-7B|58| 51|45 | 54| 241| 266| 342 | 352 | 286 | 32 | 97  | 18  | 27  | 21 |\n|MemVR | 58 | 51 | 46| 54| 241| 264| 341| 351 | 288 | 32 | 102 | 18  | 28 | 23|\n\nWe categorize MemVR's failures into two types:\n\n1) Cases where the default model provides the correct answer, but MemVR outputs an incorrect one.\n\n2) Cases where both the default model and MemVR produce incorrect answers.\n\nFor failure type 1, we attribute the failure to the over-disturbance of the default model's reasoning process.  In these instances, the original visual features are sufficient for reasoning, and the reinjected tokens inadvertently disrupt this process, leading to errors.  We are actively investigating methods to mitigate such disturbances.\nFor failure type 2, the failures arise from either the excessive complexity of the image or gaps in the LLM's knowledge base, which prevent correct reasoning even after VR.\n\nWe will be including the bad cases as well as the analysis in our paper.\n\n> Q3: Why are specific layers selected for VR\n\nThe layer selection for the dynamic trigger is theoretically grounded in information entropy analysis. Specifically, we employ layer-wise entropy $H(x)=-\\sum p(x) \\log p(x)$ as an information bottleneck metric to identify critical transition points where feature uncertainty reaches local maxima, which guides information entropy of the trigger layer from high to low state under the Data Processing Inequality[1]. While our implementation adopts an efficient thresholding mechanism, the core selection criterion stems from established analysis of hierarchical feature stabilization patterns in [2], rather than arbitrary heuristics. \n\n[1] Cover, T. M. et al. Entropy, relative entropy and mutual information. Elements of Information Theory, 2(1):12–13, 1991. \n\n[2] Teerapittayanon et al., BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks, ICPR 2016.\n\n> Q4: detailed breakdown of computational overhead.\n\nLLaVA components include ViT+MLP+LLM (self-attention and FFN). Suppose $L_v$ and $L_l$ are the number of layers of ViT and LLM, $d_v$,$d$, and $D$ are dimensions of ViT, self-attention, and FFN, $N_v$ and $N_t$ denote the number of vision/text tokens.\n\n1) Computational overhead of VCD: FLOPs$_{\\text{VCD}}$=$2*(L_v(N_v^2 d_v+N_v d_v^2)+N_v d_v d+L_l[(N_v+N_t)^2 d+(N_v+N_t) dD])$\n\n2) Computational overhead of MemVR: FLOPs$_{\\text{MemVR}}$=$L_v(N_v^2 d_v+N_v d_v^2)+N_v d_v d+L_l[(N_v+N_t)^2 d+(N_v+N_t) dD] + \\underline{(N_v+N_t) d{N_v}+L_o (N_v+N_t) d}$. where $L_o \\lt L_l$.\n\nClearly, MemVR adds $\\underline{\\text{underline}}$ overhead terms, but it is\nnegligible as $(N_v+N_t) d$ is low computation, and  $N_v \\ll D$, for instance $D = 11008$ and $N_v = 256$\nfor LLaVA, thus $(N_v+N_t) d{N_v}\\ll (N_v+N_t) d D$, which makes VR operation efficient in total.\n\nWe hope the content above can address your concerns. Please let us know if you have further questions. \n\nWe sincerely thank you for your kind suggestions again.", "claims_and_evidence": "The paper claims MemVR:\n1. mitigates hallucinations more effectively than existing methods；\n2. maintains or improves general MLLM capabilities;\n3. computationally efficient compared to alternatives;\n4. plug-and-play and task-agnostic;\n\nEvidence is provided through extensive evaluations on hallucination benchmarks (POPE, CHAIR) and general benchmarks (MME, MM-Bench, LLaVA-Bench, etc.)", "methods_and_evaluation_criteria": "Yes. This paper adopts several multimodal benchmarks to evaluate the proposed method, such as POPE and CHAIR for evaluating hallucination.", "theoretical_claims": "The paper provides a theoretical framework based on information theory:\n* MemVR enhances mutual information between hidden states and visual evidence;\n* MemVR optimizes the Information Bottleneck objective.", "experimental_designs_or_analyses": "The experimental design is comprehensive, evaluating on:\n1. Hallucination-focused benchmarks: POPE (using COCO, A-OKVQA, GQA datasets) and CHAIR\n2. General capability benchmarks: MME, MM-Bench, MM-Vet, VizWiz, LLaVA-Bench\n\nExperiments compare MemVR against baseline MLLMs and state-of-the-art hallucination mitigation methods (OPERA, VCD, ICD) using various models (LLaVA-1.5, Qwen-VL).\n\nThe authors also conduct ablation studies examining the impact of different injection ratios, uncertainty thresholds, and static vs. dynamic triggering strategies.", "relation_to_broader_scientific_literature": "The authors clearly position MemVR as addressing limitations of contrastive decoding approaches, particularly their inference overhead and potential introduction of noise.", "essential_references_not_discussed": "The paper covers the most relevant literature.", "other_strengths_and_weaknesses": "## Strengths\n\n1. The paper introduces a compelling and intuitive mechanism for hallucination mitigation based on the human cognitive process of \"looking twice\" when uncertain. The approach of reinjecting visual information at the feature level rather than modifying logits is conceptually different from existing methods.\n2. The experiments are extensive, covering both hallucination-specific benchmarks and general capabilities across multiple datasets and model architectures. This demonstrates the method's robustness and general applicability.\n\n## Weaknesses:\n1. The paper proposes a \"look-twice\" mechanism for hallucination mitigation, but the core idea of revisiting visual tokens resembles existing retrieval-based and contrastive decoding approaches. The contribution is incremental rather than fundamentally novel.\n2. While the proposed method shows good improvements, a detailed analysis of failure cases could strengthen the paper.\n3. The implementation of the \"dynamic trigger\" mechanism lacks theoretical justification for why specific layers are selected for visual retracing. The selection seems heuristic rather than rigorously derived.\n4. While the paper claims that MemVR has minimal computational cost, it still introduces additional FFN operations and dynamic triggering logic. The efficiency advantage over contrastive decoding is clear, but a detailed breakdown of computational overhead is missing.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "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": "Lyg51gq5Yv", "reviewer": "Reviewer_bEEH", "summary": "This paper addresses the hallucination issue in Multimodal Large Language Models (MLLMs) by proposing MemVR, a novel decoding paradigm. MemVR uses visual tokens as supplementary evidence and re-injects them via FFN at the middle trigger layer. Theoretical analysis shows MemVR can mitigate hallucinations by enhancing mutual information, reducing conditional entropy, and optimizing the objective function. Experiments on multiple benchmarks prove its superiority in reducing hallucinations and improving performance.\n\n## update after rebuttal\nI have carefully read other reviewers' comments and the rebuttal. Most of the concerns are well addressed. I will keep my original rating as accept.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We sincerely thank Reviewer bEEH for the constructive comments on our work. \nWe are very grateful to the reviewer for recognising the novelty of our idea and the richness and rationality of our experiments.**\n\n> Q1: Missing comparisons with cross-attention based retrieval.\n\nSorry for the confusion, we wish to clarify that the ‘cross-attention’ mentioned in our paper refers specifically to a text–image alignment strategy used during the training, rather than the specific method for mitigating VLM hallucinations. We are simply stating that the VR strategy has less overhead than injecting information through cross-attention. Besides, the cross-attention mechanism needs to be trained.\n\nWe make complexity analyses between cross-attention and FFN, and VR operations. Let $d$ be the dimension of the hidden state, $D$ denote the dimension of FFN, and $N_v$ and $N_t$ denote the number of vision/text tokens. We have computational complexity (CO) analysis, CO_cross-attn=$\\mathcal{O}((N_v+N_v)N_v d+N_vd^2 )$; CO_FFN=$\\mathcal{O}((N_v+N_t) d D)$; CO_VR=$\\mathcal{O}((N_v+N_t) d N_v)$. Where CO_VR<  CO_FFN < CO_cross-attn.\n\n> Q2: The layout can be further improved and Typo.\n\nThanks for your helpful suggestion. We have revised our paper accordingly, including layout and typos, so that it will be easier to read.\n\n> Q3: Hyperparameter Tuning.\n\nWe value your concerns. We develop MemVR++, which alters the injection rate of visual information from fixed to self-adaptive, thus discarding the hyperparameter $\\alpha$. Specifically, the retracing ratio $\\alpha$ is determined by 2*(layer_entropy – entropy_threshold) when MemVR is triggered. This ensures that the higher layer_entropy is, which indicates that the model is more confused, the higher $\\alpha$ would be. This is a global strategy for MemVR. We've tested it on several benchmarks, and the evaluation shows that this dynamic retracing strategy is also significantly outperforming the default one. The results are as follows,\n\nLLaVA-Bench:\n\n|Method|Average|all|complex|conv|detail|\n|--------|:--------:|:--------:|:--------:|:--------:|:--------:|\n|LLaVA1.5|64.80|50.80|74.60|52.90|52.10|\n|VCD|63.20|48.50|77.90|52.40|50.80|\n|ICD|56.90|40.20|78.20|35.30|42.20|\n|MemVR|65.17|51.30|77.90|55.90|52.60|\n|MemVR++|65.87|51.70|81.80|51.20|49.60|\n\nPOPE (MSCOCO):\n\n|Method|Random|Popular|Adversarial|Average|\n|---|:---:|:---:|:---:|:---:|\n|LLaVA1.5|83.49|79.98|76.03|79.83|\n|VCD|86.84|82.65|77.31|82.27|\n|ICD|84.87|82.93|81.07|82.96|\n|MemVR|88.50|87.10|85.20|86.93|\n|MemVR++|88.40|87.17|85.17|86.92|\n\nPOPE (A-OKVQA):\n\n|Method|Random|Popular|Adversarial|Average|\n|---|:---:|:---:|:---:|:---:|\n|LLaVA1.5|83.45|79.90|74.04|79.13|\n|VCD|86.15|81.85|74.97|80.99|\n|ICD|85.57|81.93|77.43|81.64|\n|MemVR|91.10|87.33|80.20|86.21|\n|MemVR++|91.03|87.50|80.23|86.25|\n\nPOPE (GQA):\n\n|Method|Random|Popular|Adversarial|Average|\n|---|:---:|:---:|:---:|:---:|\n|LLaVA1.5|83.73|78.17|75.08|78.99|\n|VCD|86.65|80.73|76.09|81.16|\n|ICD|84.90|78.37|75.97|79.75|\n|MemVR|89.60|84.63|81.53|85.25|\n|MemVR++|89.57|84.60|81.57|85.25|\n\nMME Benchmark:\n\n|Method|Overall|Perception|Cognition|\n|--------|:--------:|:--------:|:--------:|\n|LLaVA1.5|1864.68|1508.97|355.71|\n|VCD|1872.87|1515.01|357.86|\n|OPERA|1784.34|1473.62|310.71|\n|ICD|1594.77|1306.91|287.86|\n|MemVR|1896.72|1512.80|383.92|\n|MemVR++|1894.14|1512.00|382.14|\n\nAnd we supplement the testing of MemVR on HallusionBench.\n\nHallusionBench:\n\n|Method|$fAcc$|$qAcc$|$easy aAcc$|$hard aAcc$|$aAcc$|\n|--------|:--------:|:--------:|:--------:|:--------:|:--------:|\n|LLaVA1.5|17.92|8.13|36.04|36.74|41.45|\n|VCD|13.87|11.43|36.92|34.65|41.10|\n|OPERA|16.19|5.49|37.58|35.35|41.19|\n|ICD|13.87|8.35|32.97|33.49|38.18|\n|MemVR|18.50|9.01|36.48|37.67|42.34|\n|MemVR++|18.50|8.35|36.48|36.98|42.07|\n\nwhere the evaluation is conducted on the GPT-4o-mini.\n\nThe results demonstrate that MemVR and MemVR++ achieve superior performance. We'll include this part in our revised version.\n\nWe sincerely thank you for your kind suggestions.", "claims_and_evidence": "The claims are well-supported by comprehensive experiments.", "methods_and_evaluation_criteria": "By re-injecting visual tokens, MemVR directly addresses the cause of hallucinations in MLLMs, which is the imbalance between visual and textual modalities. \n\nThe evaluation criteria, including the use of various benchmark datasets, are appropriate. These datasets cover different aspects of MLLM performance, such as hallucination mitigation and general-purpose capabilities, enabling a comprehensive evaluation of MemVR.", "theoretical_claims": "To be honest, I did not carefully check the correctness of the theoretical proofs. The proofs are based on established information theoretic concepts such as mutual information, conditional entropy, and the Data Processing Inequality (DPI).", "experimental_designs_or_analyses": "The paper compares MemVR with multiple state-of-the-art methods on various benchmarks. The selection of baselines is comprehensive, covering different types of methods for mitigating hallucinations. The analyses of the experimental results are also valid.", "relation_to_broader_scientific_literature": "The key contribution of MemVR is closely related to the existing literature. Prior works have explored various methods to mitigate hallucinations in MLLMs, such as Retrieval Augmented Generation (RAG), extra fine-tuning, attention intervention, and Contrastive Decoding (CD).", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "**Strengths:**\n1. Originality: MemVR is a highly original approach. It combines the concept of visual retracing inspired by human cognitive behavior with the architecture of MLLMs. This novel combination offers a new perspective on solving the hallucination problem in MLLMs.\n2. Experiments: The experimental designs are sound. The paper compares MemVR with multiple state-of-the-art methods on various benchmarks. The selection of baselines is comprehensive, covering different types of methods for mitigating hallucinations. The analyses of the experimental results are also valid. \n3. Significance: The research is significant as hallucinations are a major obstacle to the widespread application of MLLMs. MemVR's ability to mitigate hallucinations without sacrificing efficiency can greatly improve the reliability of MLLMs, which is crucial for applications in safety-critical fields such as healthcare and autonomous driving.\n4. Clarity: The paper is mostly clear. The concepts, methods, and experimental results are clearly presented. (Minor modifications are needed to further improve its structure, please see below for details).\n\n**Weaknesses:**\n1. Missing comparisons with cross-attention based retrieval. In Eq.6 a simple  retrieval process for VR is adopted instead of cross-attention layers as in previous approaches (Li et al., 2022; Alayrac et al., 2022). But no direct experimental comparisons are reported.\n2. The organization of the paper, especially the layout can be further improved. For example, in Sec4.3, It would be more clear, if \"Static Triggered MemVR\" is introduced before \"Dynamic Triggered MemVR\". Other comments can be found in below Section \"Other Comments Or Suggestions\".\n3. Hyperparameter Tuning: The process of determining the optimal hyperparameters for MemVR, such as the injection ratio of visual information and the strategy for selecting the triggered layers, is complex. This may limit the practical application of MemVR as it requires significant effort to finetune these parameters for different models and tasks.", "other_comments_or_suggestions": "Typo: Line245 \"a input\".\n\nAdjust the layout: There are significant distances between the positions of figures and their citations in the main text, which can indeed cause inconvenience to readers. For example, Figure1 is in Page1, and the citation for Figure1 is in Page5 Line232. Similarly, Figure8 is mentioned in Page5 (Line220 right), but Figure8 is in Page8. Moreover, Figure3 (Line198 right) is mentioned after Figure4 (Line199 left) and Figure5 (Line182 right). Please re-plan the page layout of the paper and shorten the distance between the figure and the corresponding citation position in the main text as much as possible.", "questions_for_authors": "No", "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": {"ethical_review_concerns": "NA"}}, {"review_id": "FY0Az3iTqd", "reviewer": "Reviewer_3FtN", "summary": "This paper presents Memory-space Visual Retracing (MemVR), a decoding strategy aimed at mitigating hallucinations in Multimodal Large Language Models (MLLMs). The primary insight is that MLLMs tend to lose visual information during the decoding process, leading to hallucinations due to an over-reliance on textual context. Inspired by human cognition, MemVR introduces a “look-twice” mechanism, where visual tokens are re-injected into intermediate transformer layers based on model uncertainty. This approach leverages the Feed Forward Network (FFN) as a key-value memory module, supplementing missing visual information dynamically.MemVR mitigates hallucinations by balancing modality contributions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We sincerely thank Reviewer 3FtN for the constructive comments on our work. We promise to revise the paper.**\n\n> Q1: About textual uncertainty\n\nYes, textual uncertainty cannot be a sufficiently necessary condition for the existence of hallucinations, but when hallucinations are occurring, textual uncertainty tends to be above the threshold, and MemVR triggers override the cases in which hallucinations occur.\n\nBesides, regarding the ablation in Fig. 7 (left), the curve may not be obvious due to the value being over 1800 and the change being within 100. Specificaly, we set $\\gamma$ from 0.5 to 1.0, when $\\gamma$ is set to less 0.6 MemVR is triggered early without performance improvement, while **between 0.6 and 0.95 can improve performance, with the optimal threshold around 0.75 (can increase 32 scores)**, which means MemVR works well under higher uncertainty.\n\n> Q2: The analysis in Section 3.2 not beyond\n\nThere are many works that have discussed why MLLM generates hallucinations. Thus, our discussion in Section 3.2 firstly introduces previous insights, including inherent biases in the training data, visual uncertainty resulting from the model’s statistical bias and priors, and the limitations of current models in accurately discerning context and fact throughout the output generation process. \n\nThen, we presented our argument that the imbalance of modalities leads to a substantial deviation from the accurate representation of visual input, eventually giving rise to hallucinations, which prior work did not raise. We will continue our efforts to delve into the causes of hallucinations.\n\n> Q3: Regarding \"text feature\"\n\nThe 'text feature' here refers to the embedded text tokens, which are concatenated with visual tokens to form the input of LLMs. \n\n> Q4: Does VR introduce image-related bias?\n\n[1] constructs the image-biased model by adjusting the attention weight matrices (i.e., an amplification to the image tokens) within the vanilla model. \n\nIn Equation (6), **VR only modifies the hidden state in the FFN layer (i.e., memory about visual information) that uses the original hidden state as query to search supplemental visual feature**, rather than a direct amplification to the image tokens in attention weight matrices, thus VR does not introduce image-related bias as [1].\n\n[1] IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding\n\n> Q5: Does MemVR amplify image redundancy errors?\n\nIn conclusion of [2], image tokens are highly redundant after the cliff layer, where truncating image tokens no longer affects the answers in the middle or deep layers. **This is because LLMs are more text-informed with \"attention sinks\", and token embedding of image tokens may not align well with the model’s text-based training.** This does not mean that image redundancy leads to errors.\n\nIn MemVR, VR supplements visual information to the middle or deep layers of LLMs by taking the original hidden state as queries to search for visual features that need to be supplemented, without introducing image-related bias, nor amplifying image redundancy errors. Experimental results on 8 benchmarks show that VR effectively improves performance.\n\n[2] From Redundancy to Relevance: Enhancing Explainability in Multimodal Large Language Models\n\n> Q6: Why not use the uncertain score as $\\alpha$?\n\nThank your nice suggestion. The uncertain score is usually between 0.5 and 0.99, which is large, and the original knowledge would be confused if we use the uncertain score as $\\alpha$. To achieve a dynamic injecting ratio, we calculate $\\alpha$ by 2*(uncertain_score – threshold), which this named MemVR++, and the results are as follows,\n\nLLaVA-Bench:\n\n|Method|average|all|complex|conv|detail|\n|--|:--:|:--:|:--:|:--:|:---:|\n|LLaVA1.5|64.80|50.80|74.60|52.90|52.10|\n|VCD|63.20|48.50|77.90|52.40|50.80|\n|ICD|56.90|40.20|78.20|35.30|42.20|\n|MemVR|65.17|51.30|77.90|55.90|52.60|\n|MemVR++|65.87|51.70|81.80|51.20|49.60|\n\nPOPE (MSCOCO):\n\n|Method|Random|Popular|Adversarial|Average|\n|--|:--:|:--:|:--:|:--:|\n|LLaVA1.5|83.49|79.98|76.03|79.83|\n|MemVR|88.50|87.10|85.20|86.93|\n|MemVR++|88.40|87.17|85.17|86.92|\n\nThe results show that, compared with MemVR, MemVR++ can also achieve the same improvement, and even better.\n\n> Q7: Tested benchmarks and contribution\n\nFollow your suggestion, we test MemVR on the challenging **HallusionBench**, the results are as follows,\n\n|Method|$fAcc$|$easy aAcc$|$hard aAcc$|$aAcc$|\n|--|:--:|:--:|:--:|:--:|\n|LLaVA1.5|17.92|36.04|36.74|41.45|\n|VCD|13.87|36.92|34.65|41.10|\n|OPERA|16.19|37.58|35.35|41.19|\n|ICD|13.87|32.97|33.49|38.18|\n|MemVR|18.50|36.48|37.67|42.34|\n|MemVR++|18.50|36.48|36.98|42.07|\n\nwhere the evaluation is conducted on the GPT-4o-mini.\n\nThe results demonstrate that MemVR and MemVR++ achieve superior performance on HallusionBench.\n\n> Q8:Minor typos\n\nWe have revised our paper accordingly, the typo \"preception\" is revised to \"perception\".\n\nWe sincerely appreciate your valuable suggestions again.", "claims_and_evidence": "Claims:\n\nMemVR mitigates hallucinations by balancing modality contributions.\nMemVR is computationally efficient, introducing minimal latency.\nThese claims are supported from the experiments results. \n\nHowever, uncertainty-based heuristics may be brittle, as textual uncertainty higher than a fixed threshold does not always indicate a hallucination problem. The ablation in fig.7 (left) doesn't show the usefulness of the threshold.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes. The authors provide an information-theoretic justification, showing that MemVR increases mutual information between hidden states and visual tokens, leading to a decrease in hallucinations.", "experimental_designs_or_analyses": "Yes.\n\nComparisons across multiple benchmarks.\n\nDifferent uncertainty thresholds (γ) and their impact.\n\nAnalysis of hallucination uncertainty per layer.", "relation_to_broader_scientific_literature": "The paper situates MemVR well within multimodal hallucination mitigation literature, contrasting:\n\nRetrieval-Augmented Generation\n\nContrastive Decoding\n\nAttention-based hallucination correction", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "#### **Strengths**\n- The paper is well-organized and easy to follow, with a logical flow of ideas.  \n- The study provides a structured analysis of why VLMs hallucinate, supported by attention visualizations and informative figures.  \n- MemVR achieves good performance on the benchmarks tested, outperforming prior hallucination mitigation methods.  \n\n---\n\n#### **Weaknesses**\n- **Limited novelty in the analysis of hallucinations**  \n  - The discussion in Section 3.2 about VLMs failing to correctly process images is already a well-established issue in the field. The analysis does not provide particularly novel insights beyond existing consensus.  \n  - Figure 4 lacks clarity regarding \"text feature\"—does this refer to the text prompt’s feature representation?  \n  - Since VLMs are inherently trained to answer text-based questions, they naturally rely on textual features. A decrease in text feature importance may lead to text with issues such as incoherence, redundancy.\n\n- **Uncertain about the effectiveness of the method and potential bias amplification**  \n  - Equation (6) injects additional image information, but does this also introduce image-related bias?  \n  - Prior works, such as [1], highlight how image-biased hallucination can degrade response quality.  \n  - [2] discusses how image redundancy can lead to errors. Does MemVR amplify such errors?  \n  - Why not using the uncertainty score as $\\alpha$?\n  - A more thorough analysis or potential solution for these problems would strengthen the paper.  \n\n- **tested benchmarks**  \n  - While the proposed method appears simple, its contribution lacks significant novelty in the VLH domain.  \n  - Given the concerns outlined above, is MemVR overfitting to specific benchmarks rather than providing a robust, generalizable solution? The evaluation could be improved by testing MemVR on more recent and challenging hallucination benchmarks, such as HallusionBench [3], or other reasonable ones.  \n\n- **Minor issues and typos**  \n  - Figure 4 and some others contain a typo in the word \"perception\".  \n\n---\n\n#### **References**  \n**[1]** *IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding*  \n**[2]** *From Redundancy to Relevance: Enhancing Explainability in Multimodal Large Language Models*  \n**[3]** *HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models*", "other_comments_or_suggestions": "See weakness", "questions_for_authors": "See weakness", "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": "pjixmwDVn1", "reviewer": "Reviewer_8UAJ", "summary": "The paper addresses object hallucination, a well-known issue for existing multimodal large language models (MLLMs), where they prone to generate plausible yet incorrect responses that are not aligned with given images. Authors attribute this to weak robustness and high uncertainty of LLM representations (section 3.2), which they refer to as “amnesia” of MLLMs like LLaVA. From this perspective, authors propose to enhance representations by retracing.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We sincerely thank Reviewer 8UAJ for the insightful comments.**\n\n> Q1: Given that most layers are not aligned with the vocabulary head, what such experiments indicate is doubtful and the conclusions do not make sense...\n\nThe idea of applying language heads directly to the hidden states of the middle layers, known as **early exit** [1][2][3], has proven to be effective even without the special training process [4][5] for alignment to the vocabulary head.\n\nWe also presented it in the paper (*Line 263, right*). \n\n[1] Teerapittayanon et al. Branchynet: Fast inference via early exiting from deep neural networks. ICPR, 2016.\n\n[2] Maha Elbayad et al. Depth-adaptive transformer. ICLR, 2020.\n\n[3] Tal Schuster et al. Confident adaptive language modeling. NeurIPS, 2022.\n\n[4] Wei-Tsung Kao et al. Bert's output layer recognizes all hidden layers? some intriguing phenomena and a simple way to boost bert. arXiv, 2020.\n\n[5] Chuang Y S et al. DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models. ICLR 2024.\n\n> Q2: About 'amnesia' and not seeing memory-involved design\n\n\"Amnesia\" is similar to the concept of the information flow of image tokens converges at shallow layers and diverges at deeper layers in [6], where truncating image tokens no longer affects the answers in the middle or deep layers.\n\nVR is implemented in FFN, and FFN stores knowledge from training data in the form of KV memories [7], thus we call VR in memory space.\n\n[6] From Redundancy to Relevance: Enhancing Explainability in Multimodal Large Language Models, 2024.\n\n[7] Geva M, et al. Transformer Feed-Forward Layers Are Key-Value Memories. EMNLP, 2021.\n\n> Q3: Figure 5 case is not a clear hallucination problem, which is not a language prior or statistical bias\n\nThe ground-truth of Figure 5 case is \"*four* **mangosteen**\", but LLaVA outputs \"*a* **pomegranate**\", the count (four->one) and object (mangosteen->pomegranate) are wrong. We don't know why it is not a clear hallucination case. \n\n1) About this case, Reviewer 8UAJ thinks it is not a language prior or statistical bias. We get the reviewer's view that only examples with a language prior or statistical bias, like the misidentification of a black banana as a yellow banana, can be called hallucinations. **However, the language prior or statistical bias is merely a possible source of the hallucinations** proposed in the Contrast Decoding series of papers, e.g., VCD, but not the only source.\n\n2) We're not imitating VCD or other papers to present a language prior or statistical bias as the cause of hallucination, so we did not purposely choose like “black bananas“ with a strong language prior for this example.\n\n3) Further, **the other hallucination cases with a language prior or statistical bias in our experiments also show the same regularity, i.e., hallucination tokens exhibit higher entropy than correct ones.**\n\n> Q4: No theoretical claims\n\nWe have provided a theoretical framework based on information theory in the paper, **Reviewers 2zfj,bEEH,3FtN mentioned**.\n\n> Q5: The experimental designs or analyses has weak relation to experimental designs\n\nFirstly, we actually don't know much about what you mean by weak ties.\n\nWe conducted comprehensive experiments and analysis on 8 benchmarks and ablation studies examining the impact of different injection ratios, uncertainty thresholds, and static vs. dynamic triggering strategies.\n\nWe've already provided 5 MLLMs with 8 kinds of benchmark evaluations. These MLLMs used different Language models, text-image aligning methods, and training strategies to ensure effectiveness and generalization across the various testing environments. And, we have demonstrated that MemVR can be easily applied to almost all open-source MLLMs. Compared with SOTA methods, we have done the most complete experiments, as follows, ﻿ ﻿\n\n|Method|Evaluation benchmarks| \n|--------|:--------|\n|OPERA|POPE, MME, CHAIR, MMBench (4)|\n|HALC|POPE, MME, LLaVA-Bench (3)|\n|VCD|POPE, MME, LLaVA-Bench (3)|\n|ICD|POPE, MME, LLaVA-Bench (3)|\n|MemVR (ours) |POPE, MME, LLaVA-Bench, CHAIR, HallusionBench, MMBench, MM-Vet,  VizWiz-VQA **(8)**|\n\n**If you still think our experimental design has problems, could you be more specific? We are willing to discuss with you.**\n\n> Q6: VR appears to be similar to V*, about the differences\n\nWe actually don't understand why Reviewer 8UAJ suddenly gave us a paper that is irrelevant to hallucinations, nor in a similar field. Nonetheless, **we follow your comment for comparison.**\n\n**In short, the difference between V\\* and ours is as follows,**\n\n1) The goal is different. V* is an LLM-guided visual search algorithm, not a method to mitigate hallucination.\n\n2)  The framework of V* is the normal structure of MLLMs, which is completely different from our \"look-twice\" where an extra bypass is created.\n\n3) V* needs training by LoRA, but MemVR is a plug-and-play method.\n\n[1] V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs.", "claims_and_evidence": "Some claims made in this submission are not supported well. A few things should be taken care of more meticulously. To reveal amnesia phenomenon of existing MLLMs, authors pass representations from all layers in LLaMA to a vocabulary head, which is aligned with the last layer solely during LLaMA pre-training. Given that most layers are not aligned with the vocabulary head, what such experiments indicate is doubtful and the conclusions do not make sense. This is problematic and misleading analysis. Another thing is, authors name this to amnesia and memory, however, I do not see memory-involved design in their approach. Meanwhile, authors seem confuse the difference between hallucination and error. Specifically, the Figure 5 case is not a clear hallucination problem, which is not a language prior or statistical bias.", "methods_and_evaluation_criteria": "The proposed method is not sensical from technical perspective, as most layers are not aligned with the vocabulary head during pre-training, the analysis in section 3 is problematic.", "theoretical_claims": "No theoretical claims are made in main paper.", "experimental_designs_or_analyses": "The experimental designs or analyses has weak relation to experimental designs.", "relation_to_broader_scientific_literature": "The proposed method is technically viable to be applied at a general task than object hallucination, such as MMBench and MMStar.", "essential_references_not_discussed": "The visual retracing appears to be similar to V*, where authors should discuss common ideas and differences more clearly, along with its follow-up studies.\n\n[a] Guided Visual Search as a Core Mechanism in Multimodal LLMs.", "other_strengths_and_weaknesses": "No other strengths and weaknesses so far.", "other_comments_or_suggestions": "No other comments so far.", "questions_for_authors": "No questions for now.", "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:

Meta-review: Following the rebuttal, all reviewers have recommended the acceptance of the paper (3 weak accept and 1 accept). 

In the initial review, multiple concerns were raised by the reviewers. The AC thoroughly reviewed all of these concerns in conjunction with the rebuttal comments, while also checking the paper itself. 
The authors have adequately addressed the majority of the reviewers' concerns.

The primary remaining issue pertains to the significance of the contribution, as highlighted by Reviewers 2zfj and 3FtN. However, the AC finds that the proposed method is sufficiently distinct over the references the reviewers referred to and demonstrates its own clear merits. The AC concludes that the contribution is well-defined, and that the strengths of the paper significantly outweigh the identified weaknesses.

In light of this, the AC concurs with the unanimous recommendation of the reviewers and, therefore, recommends the acceptance of the paper.
Decision: Accept (poster)