CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning

## Abstract

Abstract Vision-Language Models (VLMs) have demonstrated their broad effectiveness thanks to extensive training in aligning visual instructions to responses.
However, such training of conclusive alignment leads models to ignore essential visual reasoning, further resulting in failures in meticulous visual problems and unfaithful responses.
Drawing inspiration from human cognition in solving visual problems ( e.g. , marking , zoom in ), this paper introduces Chain of Manipulations , a mechanism that enables VLMs to solve problems step-by-step with evidence. After training, models can solve various visual problems by eliciting intrinsic manipulations ( e.g., grounding , zoom in ) with results ( e.g., boxes , image ) actively without involving external tools, while also allowing users to trace error causes.
We study the roadmap to implement this mechanism, including (1) a flexible design of manipulations upon extensive analysis, (2) an efficient automated data generation pipeline, (3) a compatible VLM architecture capable of multi-turn multi-image, and (4) a model training process for versatile capabilities.
With the design, we also manually annotate 6K high-quality samples for the challenging graphical mathematical problems.
Our trained model, CogCoM , equipped with this mechanism with 17B parameters achieves state-of-the-art performance across 9 benchmarks from 4 categories, demonstrating the effectiveness while preserving the interpretability.
Our code, model weights, and collected data will be publicly available.

## 1 Introduction

Figure: Figure 2: In comparison with existing VLMs, CogCoM performs the multiple steps of evidential reasoning with chain of manipulations (CoM) to achieve the faithful answer to visual scene.

Benefiting from the advantage of Large Language Models (LLMs) in broad world knowledge, large Vision Language Models (VLMs) (Alayrac et al., ; Wang et al., ) that are further trained to understand visual inputs have demonstrated viabilities on broad multimodal scenarios, such as visual question answering (Liu et al., ), visual grounding (Peng et al., ), optical character recognition (Zhang et al., ).
The research employing VLMs as foundation models (Bai et al., ; Sun et al., ; Wang et al., ) usually involves two main stages of training, where the first stage develops intrinsic visual understanding ability through exposure to massive image-caption pairs, and the second stage endows the models with problem-solving capabilities through the instruction tuning.

However, existing tuning methods train models to respond to instructions with conclusive language responses upon visual inputs, which leads models to ignore the essential intermediate visual reasoning and further results in failures in meticulous visual problems, unfaithful responses, and even hallucinations. For example in the left subplot of Figure 2, we test the top-performing model CogVLM (Wang et al., ) about the details in the image (*i.e.,* texts written on a pillar), and it directly responds an incorrect answer (*i.e.,* NO SMOKING), most likely from bias to visual or linguistic priors (*i.e.,* typical scenes with a pillar in office).
The absence of the essential reasoning on the visual scene may lead to a rash response (Hwang et al., ).

Humans solve problems regarding visual details by marking or processing the given images for convenience and rigor,
which we refer to as manipulations. For example, we find targets by sequentially locating references, and concentrate on subtle details by zooming into a corresponding region.
Most of VLMs have developed numerous intrinsic capabilities (*e.g.,* grounding boxes, recognizing texts) during the first stage of training. By further imitating the fundamental human behaviours (*e.g.,* cropping, zoom in), models have the potential to perform this cognitive reasoning process.
Three major obstacles in eliciting VLMs with such reasoning are (1) flexible definitions of manipulations covering most visual problems, (2) an efficient data collection pipeline capable of producing abundant training data, and (3) a multi-turn multi-image VLM structure compatible with existing models.

Inspired by the human cognition in solving visual problems, we introduce Chain of Manipulations (CoM), a mechanism that enables VLMs to solve problems step-by-step with evidence, with each step potentially involving a manipulation on the visual input and its corresponding result, both generated by the model to facilitate the success and fidelity.
This paper studies a complete roadmap with manipulations design, data collection, model architecture and training process for training general VLMs with this mechanism.
We first formally design 6 basic manipulations upon the pilot experiments, which are capable of handling diverse visual problems. Next, we propose a cascading data generation pipeline based on reliable large language models (*e.g.,* LLMs, the linguistic annotators) and visual foundational models (*e.g.,* VFMs, the visual annotators), which can automatically produce abundant error-free training data. We collect 70K CoM samples with this pipeline. We then devise a multi-turn multi-image model architecture compatible with typical VLMs structures. Based on a data recipe incorporating the curated corpus, we finally train a general VLM equipped with CoM reasoning mechanism, named CogCoM, which possesses capabilities of chat, captioning, grounding and reasoning.
Additionally, benefiting from the expressive capability of the proposed mechanism, we further manually annotated 6K high-quality samples of graphical mathematical problems, each accompanied by a CoM reasoning process, to advance the research of VLMs in solving challenging mathematical problems.

We conduct extensive experiments on 9 benchmarks from 4 categories, including TextVQA (Singh et al., ), ST-VQA (Biten et al., ), TallyVQA (Acharya et al., ), and GQA Hudson & Manning () for detailed visual question answering, RefCOCO (Yu et al., ), RefCOCO+(Yu et al., ), and RefCOCOg (Mao et al., ) for visual grounding, POPE (Li et al., ) for hallucination validation, and MM-Vet (Yu et al., ) for general multimodal ability.
Our model achieves up to 9.0 and 1.09 accuracy improvement on the detailed VQA and grounding benchmarks, respectively, and the superior performance on the general multimodal benchmark. The results demonstrate the effectiveness of the mechanism while maintaining the interpretability of outputs.

## 2 Terminology

We first conduct pilot experiments to investigate the possible manipulations capable of handling diverse visual problems.

Figure: Figure 3: Distribution of the generated 465 actions base on GPT-4, mapped into 6 manipulations.

Specifically, given a question about an image, we prompt the advanced large language model, GPT-4, to generate solving steps by optionally utilizing possible actions on the image that facilitate problem-solving.
We conduct this experiment on 170K questions from TextVQA, a dataset requiring detailed reasoning and recognition on images.
To ensure the stability, we manually write 4 demonstrations as priors, The detailed statistics are available at Appendix C.3.

We utilize the StanfordCoreNLP toolkit to extract verb phrases referring to the actions, and the distribution of frequencies is shown in Figure 3.
Through result analysis, we find that most of the actions can be mapped to 6 fundamental manipulations on images: OCR, Grounding, CropZoomIn, Counting, Calculate, and Line.

Based on the observation, we formally predefine a set of 6 manipulations, which can either be developed from pre-training or be learned from fine-tuning with the imitation to human behaviors: $\mathcal{M}\subseteq${$\textit{OCR}(tgt)\rightarrow txt$, $\textit{Grounding}(tgt)\rightarrow bbx$, $\textit{Counting}(tgt)\rightarrow num$, $\textit{Calculate}(tgt)\rightarrow num$, $\textit{CropZoomIn}(bbx,x)\rightarrow img$, $\textit{Line}(pts)\rightarrow img$}, where the parameters or results $tgt,txt,bbx,num,x,img,pts$ refer to the bounding boxes, zoom ratio, image, target description, numbers, texts, and points, respectively.
In addition to the predefined manipulations, we also allow trained models to create new manipulations during inference to facilitate problem-solving.
We empirically find that more complicated goals can be derived from these fundamental manipulations.

We then define the standard CoM data structure to streamline the subsequent data construction and validation process.
Given a question $Q$ about an initial input image $I_{0}$, a VLM equipped with chain of manipulations mechanism solves the problem to achieve final answer as $\textit{VLM}_{\boldsymbol{\varsigma}}(A,C|I_{0},Q)$, where $\boldsymbol{\varsigma}$ refers to the reasoning chain with evidence,

$$ $\displaystyle\begin{split}\boldsymbol{\varsigma}&=(step_{1},step_{2},...)\\ step_{i}&=(f_{i},c_{i}),\quad\ f_{i}\in\mathcal{M}\end{split}$ (1) $$

where $C=(c_{i},c_{2},...,c_{|C|})$ refers to the free-form textual descriptions incorporating manipulation names $f_{i}$ and corresponding results from utilizing $f_{i}$.
This definition explicitly declares the symbolic execution process, while also being compatible with linguistic reasoning steps.
Based on this definition, we can clearly construct standard CoM samples that incorporating the manipulation executions and linguistic steps with evidence. After the data construction, we can utilize a simple method to convert the standard CoM samples to the compatible VQA samples.

## 3 Data Collection

Figure: Figure 4: A cascading data generation pipeline that automatically produces standard CoM samples. Given an original VQA sample, the linguistic annotator (LLMs) taught with usage of manipulations (prompt) is first asked to provide solving steps for the question $\mathcal{Q}$, and the visual foundational models (VFMs) are then engaged to replace the manipulations results, followed by a final traversal on the tree branched by the possible manipulation results to find positive paths terminating to the answer $\mathcal{A}$.

In this section, we first introduces the automated data generation pipeline (illustrated in Figure 4), that employs reliable LLMs as linguistic annotators and VFMs as the visual annotators to produce error-free CoM samples upon prevalent VQA corpus, and then present the manual annotation of high-quality CoM samples for the challenging graphical mathematical problems.

### 3.1 Automated Data Generation

Given a general corpus $\mathcal{D}=\{(I,Q,A)\}$ consisting of triplet samples of images with corresponding visual question-answer pairs, our automated data generation pipeline consists of a linguistic annotator and several visual annotators according to the manipulations.
For a question $Q$ in each sample, we first engage the linguistic annotator to generate manipulations-assisted solving steps with the CoM format $(f_{i},c_{i})$, where the corresponding results of the instantiated manipulation executions are set with variables as placeholders. In this paper, we adopt GPT-4 (OpenAI, ), a large language model with reliable language understanding and generation abilities as the linguistic annotator. We design a comprehensive prompt including the task requirements, usage of manipulations, and output data format, and further manually annotate 5 demonstrations for a stable generation.
The detailed implementations are available at Appendix C.4.

We then employ essential visual annotators to supply the results of manipulations requested in the solving steps by exactly performing the corresponding manipulations. By empirically analyzing the manipulations from both predefined set and newly created ones (refers to Appendix C.3 for a detailed statistics), we reveal the Grounding and OCR are two fundamental manipulations, and most of the others can be consequently derived (*e.g.,* CropZoomIn along a region of box, Counting upon recognized boxes, and Calculate for the recognized formula). Therefore, we employ two visual foundational models, GroundingDINO (Liu et al., ) and PaddleOCR (Du et al., ), and develop the implementations of these manipulations(^1^11We simply implement the CropZoomIn referring to human behaviors with a local code interpreter.).
The execution of the manipulations will transform the sequential reasoning steps into a tree $\mathcal{T}$, as the input of current manipulation $f_{1}(x_{a})$ may rely on one of the multiple results of previous manipulation $f_{2}\rightarrow(x_{b},x_{c})$, *i.e.*, $x_{a}$ rely on $x_{b}$ (*e.g.,* step 2 for finding pillars in Figure 5).
We then perform a traversal on each produced tree with Depth First Search (DFS) to find all positive paths $\{\mathcal{P}_{i}|\mathcal{P}_{i}\in\mathcal{T},i=1,2,...\}$ that can terminate with the final answer $A$ from the result of the last manipulation.
Based on this method, the generated CoM samples with positive paths are guaranteed to be error-free.
We implement this pipeline on 3 existing datasets that require detailed recognition or objects counting, TextVQA (Singh et al., ), ST-VQA (Biten et al., ), and TDIUC (Shrestha et al., ), to build 70K CoM samples (^2^22The success rate of GPT-4 to achieve the positive paths is 0.3555.).
The designed prompt, a generated example with linguistic and visual results, and detailed algorithm illustration are available at AppendixC.1.

### 3.2 Human Annotation

The analysis from Fig.1 of AlphaGeometry (Trinh et al., ) shows that outputting auxiliary lines in linguistic reasoning process helps LLMs to solve complex geometry problems.
Benefiting from the expressive capability of CoM structure, we have also manually annotated high-quality CoM samples for the graphical mathematical problems to facilitate VLMs in solving this challenging scenario.
Similar to the automated pipeline, we engage 10 human experts as the linguistic annotators and visual annotators, where each expert is asked to annotate the linguistic solving steps and the use of manipulations, as well as the results of manipulations on images.
We perform this annotation on the MathVista (Lu et al., ) and ChartQA (Masry et al., ), which include geometric and chart math problems, resulting in the collection of 6K high-quality CoM math samples.

Finally, we adapt the CoM samples to be compatible with VQA-style training samples.
For each CoM sample including $n$ images from manipulations outputs $(I_{0},Q,C_{0},I_{1},C_{1},...,I_{n},A)$, we convert it into a multi-turn VQA sample segmented by the images $[(I_{0},Q,C_{0}),(I_{1},\bar{Q},C_{1}),...,(I_{n},\bar{Q},A)]$, where $C_{i}$ represents the intermediate steps between $I_{i}$ and $I_{i+1}$, and $\bar{Q}$ is a simple prompt asking model to answer question based on history.
This transformation converts CoM samples into multi-turn VQA samples that are compatible with existing VLMs training data.
The detailed statistics of the data generation are available at Appendix C.3.

## 4 Model Training

Figure: Figure 5: Left: A compatible VLM architecture capable of multi-turn multi-image understanding. Right: An effective training process to develop a general VLM with versatile capabilities.

### 4.1 Architecture

We use the same model architecture as CogVLM (Wang et al., ), a general VLM approach that involves four fundamental components: (1) a Visual Encoder, (2) an MLP Adapter, (3) an LLM Backbone, and (4) a Visual Expert Module, for a reliable multimodal understanding.
Concretely, the pre-trained EVA2-CLIP-E (Sun et al., ) with 4B parameters and Vicuna-7B-v1.5 (Chiang et al., ) are adopted as the visual encoder and LLM backbone, respectively. A two-layer MLP (SwiGLU (Shazeer, )) is further engaged to map the output of the visual encoder into the linguistic space of the LLM backbone. The visual expert module adds the vision-specific weights into the attention layer and feed-forward layer of each block in the LLM backbone, resulting in a total of 6.5B additional parameters for the deep fusion of modalities.

Based on this general architecture, we develop a memory-based multi-turn multi-image VLM approach.
Specifically, for a multi-turn VQA sample $[(I_{t},Q_{t},A_{t})|t=1,2,...]$, where $A_{t}$ refers to $C_{t}$ in CoM, we keep the accumulated KV memories of each layer in the LLM backbone throughout these turns. And at each turn $t$ in training and inference, we calculate the attention function $att$ as:

$$ $\displaystyle\begin{split}att(\boldsymbol{X})&=softmax(\frac{\boldsymbol{Q}_{t }\boldsymbol{K}_{t}^{\prime T}}{\sqrt{d}})\boldsymbol{V}^{\prime}_{t}\\ \boldsymbol{K}^{\prime}_{t}&=\text{trunc}(\text{concat}(\boldsymbol{K}_{0}, \boldsymbol{K}_{1},...,\boldsymbol{K}_{t}))\\ \boldsymbol{V}^{\prime}_{t}&=\text{trunc}(\text{concat}(\boldsymbol{V}_{0}, \boldsymbol{V}_{1},...,\boldsymbol{V}_{t}))\end{split}$ (2) $$

where $\boldsymbol{Q}_{t}\in\mathbb{R}^{s\times d}$ is query representation of current layer, and the $\boldsymbol{K}^{\prime}_{t},\boldsymbol{V}^{\prime}_{t}\in\mathbb{R}^{(s\times
t
)\times d}$ refer to the concatenation of accumulated representations and will be further truncated if the sequence length $s\times t$ is greater than a predefined threshold.
At $t>0$, the new image $I_{t}$ will be cropped from $I_{t-1}$ and amplified with the Bicubic Interpolation (Keys, ).

### 4.2 Training

The proposed CogCoM-17B relies on two main stages of training, to develop the capabilities of general multimodal task-solving as well as the visual reasoning.

##### First Stage Pre-Training

This stage consists of two ordinal sub-phases of training for foundational visual understanding and grounded generation. Following the pre-training of CogVLM (Wang et al., ), we first train model on 1.5B image-text pairs cleaned from the LAION-2B (Schuhmann et al., ) and COYO-700M (Byeon et al., ) with 120,000 iterations and batch size of 8,192. We then train model on 40M grounded image-question-answer triples cleaned from LAION-115M (Li et al., ) with 60,000 iterations and batch size of 1,024, where each noun phrase in the answer is followed by a list of coordinates $[[x_{0},y_{0},x_{1},y_{1}],...]$(^3^33$x_{i},y_{i}\in[000,999]$ refer to the normalized pixel coordinates.) referring the phrase to the grounded objects in the image.
Both phases adopt the next token prediction objective, and train the 6.5B parameters of visual experts.

##### Second Stage Alignment

This stage further trains the model to align with human preferences on solving practical visual problems.
We fuse the produced CoM data with 3 types of corpus, including MultiInstruct (Xu et al., ), LLaVAR (Zhang et al., ), and ShareGPT4V (Chen et al., ), referring the abilities of instruction-following, texts-recognizing, and detailed-captioning.
This fusion results in a total of 570K $(I,Q,A)$ samples, where the answer $A$ in CoM data consists of multiple turns.
For the training data of CoM, we randomly prepend a lunching prompt(^4^44See Appendix D.1 for examples.) $P^{\mathcal{M}}$ to questions $Q=P^{\mathcal{M}}+Q$ asking models to optionally use manipulations for the adaption of explicitly eliciting.
We empirically show that the model can effectively learn the evidential visual reasoning by ingesting this portion of CoM data.
We train model with 14,000 iterations and a batch size of 160, where the learning rate reaches $10^{-5}$ after 280 steps of warm-up and then decays linearly. The parameters of 6.5B visual experts are trained with the objective of next token prediction.
These two stages of training result in our standard version of CogCoM involving both chat and reasoning capabilities.
More training details are available at Appendix D.2.

## 5 Experiment

To quantitatively validate the suitability and efficiency of the proposed method, we conduct experiments on 9 benchmarks corresponding to 4 categories of multimodal capabilities, as well as on a newly constructed testbed that includes the evidential reasoning paths with a keypoints-aware metric.
Following previous works, we train two generalist versions of CogCoM for adapting to the different scenarios of Visual Question Answering and Visual Grounding, and evaluate the standard version with a qualitative analysis (Hwang et al., ). We also evaluate the time complexity.

- •
Detailed Visual Question Answering. This task involves models to perform detailed reasoning or recognition on images. We use 4 prominent benchmarks including, GQA (Hudson & Manning, ), TextVQA (Singh et al., ), ST-VQA (Biten et al., ), and TallyVQA (Acharya et al., ).
- •
Visual Grounding. Visual grounding evaluates the crucial abilities of VLMs on meticulous position understanding. We evaluate our model on 3 standard benchmarks, RefCOCO (Yu et al., ), RefCOCO+ (Yu et al., ), and RefCOCOg (Mao et al., ).
- •
General Multimodal Capabilities & Hallucination. We also evaluate on a general multimodal benchmark, MM-Vet (Yu et al., ), and a hallucination detection benchmark POPE (Li et al., ), to investigate the helpfulness of visual reasoning.

### 5.1 Experiments on Detailed VQA

VLMs have demonstrated the well-known superiority in visual scenes with salient content understanding. We evaluate the effectiveness of CogCoM on VQAs on detailed understanding, which typically require models to perform multiple actions (*find, read*) or multiple reasoning steps (*recognizing and then calculating*).
Following previous studies (Wang et al., ), we train our model obtained from the first-phase of stage-1 on a mixture of data, including an instruction corpus of MultiInstruct, 13 publicly available VQA datasets (only using training set), a newly created VQA dataset built through promoting GPT-4V (OpenAI, ) for image-oriented question-answer generation, and the automatically generated 70K CoM corpus. This training results in a generalist VQA model incorporating CoM reasoning.
For all existing VQA tasks, we directly prompt CogCoM with given questions and examine the correctness of outputted answers.

**Table 1: Performance on Visual Question Answering benchmarks, where the results labeled with ^† refer to the few-shot setting. CogCoM achieves SOTA across the board, and demonstrates the effectiveness on the visual reasoning and scene texts recognition benchmarks.**
| Type | Model | GQA | TallyVQA | TextVQA | ST-VQA |  |
| --- | --- | --- | --- | --- | --- | --- |
| test-balanced | simple | complex | test | test |  |  |
| Generalist | Flamingo (Alayrac et al., ) | - | - | - | 54.1 | - |
| GIT (Wang et al., ) | - | - | - | 59.8 | - |  |
| GI2 (Wang et al., ) | - | - | - | 67.3 | - |  |
| BLIP-2 (Li et al., ) | 44.7^† | - | - | - | 21.7 |  |
|  | InstructBLIP (Dai et al., ) | 49.5^† | - | - | - | 50.7^† |
|  | Qwen-VL (Bai et al., ) | 59.3 | - | - | 63.8 | - |
|  | CogVLM (Wang et al., ) | 65.2 | 79.8 | 68.0 | 69.7 | 61.0 |
|  | CogCoM | 71.7 | 84.0 | 70.1 | 71.1 | 70.0 |
| Specialist<br>SOTAs |  | 72.1<br>(CFR) | 86.0<br>( PaLI-X) | 75.6<br>(PaLI-X) | 71.4<br>(PaLI-X) | 86.0<br>(SMoLA) |

#### 5.1.1 GQA, TextVQA, ST-VQA, TallyVQA

##### Settings

GQA is a compositional VQA benchmark with diverse reasoning questions coming from semantic functional programs. TallyVQA is an objects counting benchmark with human-annotated complex counting questions involving challenging non-zero counterparts.
TextVQA and ST-VQA are two texts understanding benchmarks requiring models to answer questions through textual cues on images.
We use the official evaluation scripts for GQA and TallyVQA, which calculate the accuracy score by the Exact Matching (EM) between model predictions and answers.
For TextVQA and ST-VQA, we submit our model predictions to the official online websites for calculating the accuracy with VQA Score metric (Antol et al., ).

##### Results

As the results shown in Table 2, CogCoM achieves the state-of-the-art performance in comparison with all generalist models, and achieves significant improvements over the baseline model.
Specifically, compared to the baseline model, our model achieves up to 5.97 and 9.0 percentage points improvement on the benchmarks that requires complex reasoning and detailed recognition, respectively.
On GQA and TextVQA, CogCoM also obtains comparable results with the large-scale specialist SOTAs.
This result demonstrates the effectiveness of the proposed approach in solving details recognition problem.

#### 5.1.2 Experiments for Reasoning Accuracy and Time Complexity

Due to the lack of resource, we build CoM-test, a benchmark with evidential reasoning chains on the TextVQA test set based on the proposed data generation pipeline, and also introduce a keypoints-aware metric to validate the correctness of reasoning paths (see Appendix C.3 for detailed statistics). We also evaluate the time complexity for model generation on a held-out benchmark, MM-Vet.

Figure: Figure 6: Left: Results on a reasoning testbed CoM-test shows CogCoM achieves satisfactory performance with only 70K training data and 2K steps. Right: Results on MM-Vet shows that CogCoM produces comprehensive reasoning content without incurring excessive time overhead.

##### Reasoning Accuracy

To validate the correctness of execution and results of manipulations in reasoning paths, we introduce a keypoints-aware evaluation metric that concentrates on these contents and their order.
Concretely, given a predicted chain-answer pair $(C^{\prime},A^{\prime})$ and the ground truth pair $(C,A)$, we first extract the keypoints (*i.e.,* the name, parameters, and results of manipulations) in $A^{\prime},A$ to form two lists, and then discretize these two lists into $K^{\prime}$ and $K$ based on a bag-of-words composed of all keypoints. Then, we calculate the normalized Levenshtein Distance $s_{K}=Levenshtein(K^{\prime},K)/N$ as the manipulation score. We also compute the BLEU (Papineni et al., ) score $s_{C}=\text{BLEU}(C^{\prime},C)$ as the paragraph score. Finally, a weighted average of these two scores serves as the ultimate reasoning score s $acc=(0.6\times s_{K}+0.4\times s_{C})/2$.

We train our first-stage model only using the 70K automated CoM data without other supervision for qualitatively evaluate the effectiveness of chains, and the results are shown in the left subplot of Figure 6.
We find that by training with the CoM chains, our model can swiftly achieve the satisfactory performance of 48.41 accuracy score with 2k training steps, and obtain the optimal result of 55.59 with 8K steps.
Additionally, the explanation scores gradually improve along with the model performance, indicating that successful reasoning steps contribute to the achieving of final answer.

##### Time Complexity

We also evaluate the time complexity and average length of tokens during model reasoning on a held-out test set, MM-Vet. Specifically, we run CogCoM and the baseline model on all 218 questions, and record the time overhead as well as the average number of outputted tokens (using the Vicuna-7B-v1.5 tokenizer).
We divide the 218 samples into 8 intervals based on the time expenditure for each sample and calculate the average values of the time complexity and the number of tokens for each interval, with the results presented in the right subplot of Figure 6.

From the results we find that compared to baseline model, CogCoM produces information-intensive reasoning content (*e.g.,* detection boxes, auxiliary lines) without incurring infeasible time overhead. For example, without quantitive optimization, CogCoM outputs 262.9 informative tokens in approximately 9 seconds. With the advantages in long-context optimization techniques (Hooper et al., ), we believe that it is crucial for models to produce informative content and accurate responses.

### 5.2 Experiments on Visual Grounding

The task of visual grounding requires models to precisely provide the corresponding coordinates of regions in an image based on the given target description.
Following the existing work (Wang et al., ), we train our model obtained by the first stage on a mixture of datasets, including an instruction corpus MultiInstruct, a high-quality grounded VQA corpus introduced in CogVLM, and the 70K CoM data. This training results in a generalist grounding model that is excelling at visual grounding while capable of reasoning.
For all benchmarks, we prompt CogOM in a chat manner to ask the model to provide grounded coordinates, such as “Where is $\langle$expr$\rangle$ answer in [x0,y0,x1,y1] format.", where the $\langle$expr$\rangle$ refers to the target expression.
We use the standard metric, that considers a prediction as correct when the intersection-over-union (IoU) between boxes is greater than 0.5.

**Table 2: Results on VG benchmarks, where the specialist SOTAs are quoted from (Bai et al., ).**
| Type | Model | RefCOCO | RefCOCO+ | RefCOCOg |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| val | test-A | test-B | val | test-A | test-B | val | test |  |  |
| Generalist | OFA-L* (Wang et al., ) | 79.96 | 83.67 | 76.39 | 68.29 | 76.00 | 61.75 | 67.57 | 67.58 |
| Shikra-7B (Chen et al., ) | 87.01 | 90.61 | 80.24 | 81.60 | 87.36 | 72.12 | 82.27 | 82.19 |  |
| Shikra-13B (Chen et al., ) | 87.83 | 91.11 | 81.81 | 82.89 | 87.79 | 74.41 | 82.64 | 83.16 |  |
| Qwen-VL (Bai et al., ) | 89.36 | 92.26 | 85.34 | 83.12 | 88.25 | 77.21 | 85.58 | 85.48 |  |
| CogVLM (Wang et al., ) | 92.51 | 93.95 | 88.73 | 87.52 | 91.81 | 81.43 | 89.46 | 90.09 |  |
| CogCoM | 92.34 | 94.57 | 89.15 | 88.19 | 92.80 | 82.08 | 89.32 | 90.45 |  |
| Specialist<br>SOTAs |  | 92.64<br>(UNINEXT) | 94.33<br>(UNINEXT) | 91.46<br>(UNINEXT) | 88.77<br>(ONE-PEACE) | 92.21<br>(ONE-PEACE) | 83.23<br>(ONE-PEACE) | 89.22<br>(ONE-PEACE) | 89.37<br>(UNINEXT-H) |

##### Results

As shown in Figure 2, CogCoM achieves the best performance in 6 out of all 8 sub-sets.
Based on the training with a mixture of broad capabilities, this result indicates that our model exhibits a superior grounding abilities while offers potential to solve a variety of tasks.

### 5.3 Experiments on General Multimodal Evaluation and Hallucination Examination

We further examine the general multimodal capabilities, and the hallucination issue.
We use the generalist VQA model and obtain model predictions by directly asking the original questions in benchmarks.
We use the challenging adversarial version and official evaluation scripts for POPE.

**Table 3: Evaluation results on the general and hallucination assessment benchmarks.**
| Method | LLM | MM-Vet | POPEadv |
| --- | --- | --- | --- |
| InstructBLIP (Dai et al., ) | Vicuna-13B | 25.6 | 77.3 |
| LLaVA (Liu et al., ) | LLaMA2-7B | 28.1 | 66.3 |
| DreamLLM (Dong et al., ) | Vicuna-7B | 35.9 | 76.5 |
| LLaVA-1.5 (Liu et al., ) | Vicuna-13B | 36.3 | 84.5 |
| CogVLM (Wang et al., ) | Vicuna-7B | 45.5^† | 87.2 |
| CogCoM | Vicuna-7B | 46.1 | 87.8 |

##### Results

As shown in Table 3, we can see that CogCoM improves the performance by 0.6 points compared to the baseline model on MM-Vet, and achieves the superior performance on POPE which is in consistent with the baseline model.
This result suggests that out model maintains superior reasoning capabilities while preserving effectiveness in general multimodal tasks, and simultaneously exhibits lower hallucination.

## 6 Conclusion

This paper studies the problems presented by the conclusive alignment training of VLMs, and proposes a mechanism, Chain of Manipulations (CoM), that enables VLMs to solve problems step-by-step by actively manipulating visual inputs as evidence.
We realize this methodology by proposing (1) a flexible data structure, (2) an efficient data generation framework capable of producing abundant samples, (3) a memory-based architecture compatible with existing VLMs, and (4) a training process for versatile capabilities.
We also annotate 6K graphical math samples with reasoning chains to facilitate the advancement of VLMs in solving mathematical problems.
Experiments on 9 public benchmarks show that our trained 17B general VLM can produce informative reasoning content while achieving superior performance on diverse multimodal problems.

## Appendix A Related Works

### A.1 Large Vision-Langauge Models as Foundations

Most of LVLMs rely on the training on publicly available image-caption pairs, including ALIGN (Jia et al., ), MSCOCO (Lin et al., ), VG Krishna et al. (), CC3M Sharma et al. (), CC12M (Changpinyo et al., ), SBU (Ordonez et al., ), LAION2B (Schuhmann et al., ), LAION400M Schuhmann et al. ().
Starting from Flamingo (Alayrac et al., ), a series of LVLMs have focused on training the adaptation layers to align the visual representation to the frozen LLMs on a mixture of image-text pairs with the above corpus, including BLIP2 Li et al. (), KOSMOS Huang et al. (), and OpenFlamingo (Awadalla et al., ). Inspired by success of instruction tuning in LLMs (Wang et al., ), a line of works have devoted efforts to build vision-oriented instruction-answer pairs through GPT4 and train models for imitation, such as LLAVA (Liu et al., ), Otter (Li et al., ), VisionLLM (Wang et al., ), MultiInstruct (Xu et al., ), Lynx (Zeng et al., ), InstructBLIP (), CleverFlamingo (Chen et al., ) and StableLLaVA (Li et al., ).
Recently, researchers have proven the efficiency of developing LVLMs with two stages of training, the first stage of abundant pretraining on image-caption pairs and the second stage of alignment on image-question-answer triples, such as PALI (Chen et al., ), PaLI-X (Chen et al., ), Qwen-VL (Bai et al., ), and CogVLM Wang et al. ().

### A.2 Large Vision-Language Models with Reasoning

To further enhance the ability of LVLMs in solving high-level visual problems, research focusing on various aspects of reasoning is attracting broad attention. We simply divide existing studies into tree broad categories.
The first line of research focus on enhance train models with a mastery of cross-modal grounded reasoning, where grounded instruction-following supervision is build through public visual grounding dataset or GPT4-V for training, including KOSMOS-2 (Peng et al., ), Shikra (Chen et al., ), and GPT4ROI (Zhang et al., ).
The second aspect of efforts have been devoted into promoting models to understand artificial visual scenes, such as figures, charts, and receipts. These studies includes CogAgent (Hong et al., ) and CHARTVE (Huang et al., ).
Some other studies address the crucial problem of hallucination in LVLMs with counterfactual or interpretable reasoning (Yu et al., ; Yin et al., ).
V* (Wu & Xie, ) also contributes efforts to enhance the details recognition of VLMs based the LLM-guided searching process.

## Appendix B Limitation and Impact

Though we try to develop an accurate and robust framework that engages remarkable LLM to provide basic solving steps, adopts reliable visual tools to obtain visual contents, and then acquires feasible paths based on traversal, there are still limitations in our methodology that we hope to improve in the future.
First, We find that the diversity of linguistic solving steps is insufficient, and the inaccuracy of visual tools (*e.g.,* the rough granularity of grounding boxes, OCR failures on slant letters) will lead to a large amount of negative paths (effectively utilizing these paths would beneficial).
We suggest to promote these limitations with dedicate prompts and improved visual tools.
Second, our current model re-input the manipulated images with a set of hard prompts, which may bring speed losses. This is expected to be improved by implementing the physical manipuations into the calculations in vector space.
This work presents a general visual reasoning mechanism that alleviate the problems caused by existing conclusion-alignment training for VLMs, introduces a data production framework involving LLMs and visual tools as reliable annotators, and devises a memory-based compatible VLM architecture.
We expect this work to bring three benefits to the community.
First, the proposed visual reasoning mechanism may push the progress of VLMs in solving complex visual problems.
Second, the introduced data production framework may be applied to widespread training scenarios to promote the development of current data-driven machine learning.
Third, we hope that the memory-based architecture will be helpful for VLMs in multi-turn long contexts.

## Appendix C Details of Data Production

In this section, we further introduce the details of CoM data production, with the overall algorithm of a pseudo code, an example of the solving steps generation with LLM and corresponding guideline, an example of the reasoning chains completion with visual tools.
We also list the details of data statistics for the synthesised training data as well as the evaluation data of CoM-test, followed by a limitation analysis for the current data production method.

### C.1 Algorithm for the Automate Data Generation Pipeline

We provide the pseudocode of the CoM synthesis algorithm to clearly explain the process of data generation, thereby facilitating understanding and reproduction 1.

Figure: Algorithm 1 Synthesising Chain of Manipulations

### C.2 The CoM-test Benchmark and Evaluation Metric

To measure the correctness of CoM chains, we introduce a keypoints-aware metric. The intuition is that we care about the key elements including actions (*i.e.,* manipulation name), targets (*i.e.,* manipulation input), and visual contents (*i.e.,* manipulation returns) of each step in the path, as well as the logical execution order of manipulations.
Given a pair of chain-answer annotation $(c,a)$ and corresponding model prediction $(c^{\prime},a^{\prime})$, we first sequentially extract the key elements from $c$ and $c^{\prime}$ to construct two ordered lists, and then replace the elements in the lists with their fixed indices in a Bag-of-Elements $\mathcal{E}=c\cup c^{\prime}$ to result in lists of $k$ and $k^{\prime}$.
We thus calculate the score as the normalized Levenshtein Distance $s_{c}=Levenshtein(k,k^{\prime})/N$ between the two lists, where $N$ is the maximum length between $k$ and $k^{\prime}$.
We adopt this simple discretization strategy with low time complexity to concentrate on the key points as well as the solving order.
We further consider the linguistic matching of paragraphs by calculating the BLEU (Papineni et al., ) score between two chains $s_{p}=\text{BLEU}(c,c^{\prime})$, and the final sore is a weighted combination as $acc=(0.6\times s_{c}+0.4\times s_{p})/2$.

### C.3 Data Statistics

We develop a strategy to extract predicate phrases based constituency parsing with StandordCoreNLP, in which we extract verb, conjunction-connected verb phrase, preposition-connected verb phrase.

Besides the standard CoM data incorporating manipulations with explicit visual evidences, the proposed data synthesising framework is compatible of producing implicit visual reasoning steps $step^{\prime}_{i}=(desc_{i})$ without involving the manipulations.
We thereby also build this partial CoM data on the corpus consisting of absurd visual questions (*i.e.,* asking unanswerable questions based on the given image) to further resist the toxic hallucinations.
Specifically, given an image $I$ with a question $Q$,we prompt GPT-4V (OpenAI, ) to solve the question step-by-step to acquire the reasoning chains.

**Table 4: Detailed statistics the the training data and evaluation data synthesised with CoM production.**
| Data Source | #QAs | #Chains | #Steps/Chain | #Manipulations Types/Chain |
| --- | --- | --- | --- | --- |
| TextVQA (Biten et al., ) | 10782 | 13766 | 2.93 | 2.41 |
| ST-VQA (Singh et al., ) | 4814 | 3959 | 2.88 | 2.43 |
| TDIUC-count (Shrestha et al., ) | 53547 | 54523 | 2.35 | 0.74 |
| TDIUC-absurd (Shrestha et al., ) | 11677 | 11677 | 4.09 | - |
| CoM-test | 4609 | 8612 | 3.26 | 2.18 |

Figure: Figure 7: Distribution of the top-50 generated manipulations out of total $465$ based on 4-shot prompting, where the first three bars are scaled with $20\%$ for a smooth visualization of all data.

Figure: Figure 8: An example shows the configuration, inputs, outputs of the linguistic annotation and visual annotation.

### C.4 Details of the Linguistic/Visual Annotations

In this work, we adopt the GPT4-turbo as the linguistic annotator for generating problems solving steps, and the API call was conducted during the period of 2023.9 - 2023.12.
For the visual annotators, we leverage the the currently best-performing tools, GroundingDINO and PaddleOCR, to acquire all visual contents requested by the manipulations.
For a clear description to the production setting and results, we illustrate the guiding prompt, and an example-based linguistic annotation results as well as the visual annotation results in Figure 8.

### C.5 Limitation Analysis for the Data Production

For the implemented data framework, we engage the remarkable LLM to provide basic solving steps, adopt two reliable visual tools (*i.e.,* GroundingDINO and PaddleOCR) to acquire corresponding visual contents, and then perform the traversal to achieve feasible reasoning paths, which ensures the correctness and robustness of data synthesizing. However, we also find that there are three major limitations caused by the employed models and could be improved in future:

- •
The lack of diversity in linguistic reasoning steps. The 5-shot prompting to the GPT-4 gains a stable solving steps, but it also results in the descriptions for executing manipulations or general thinking are similar. We suggest that this can be addressed by employing diversified prompts or requirements.
- •
The inaccuracy of visual tools. We find that there are a considerable amount of negative paths caused by the failures of visual tools, such as the rough granularity of bounding boxes and the error recognition of slated letters or long sentences. This issue can be relieved by improving the semantic understanding capabilities of visual tools.

## Appendix D Details of Training

### D.1 Launching Prompts

- •
Please solve the problem gradually via a chain of manipulations, where in each step you can selectively adopt one of the following manipulations GROUNDING(a phrase)$\rightarrow$boxes, OCR(an image or a region)$\rightarrow$texts, CROP_AND_ZOOMIN(a region on given image)$\rightarrow$new_image, CALCULATE(a computable target)$\rightarrow$numbers, or invent a new manipulation, if that seems helpful. {QUESTION}
- •
Please tackle a given question in a stepbystep manner. For each step one of the following manipulations (depicted as Name(Input)$\rightarrow$Retrun) can be optionally used: GROUNDING(a phrase)$\rightarrow$boxes, OCR(an image or a region)$\rightarrow$texts, CROP_AND_ZOOMIN(a region on given image)$\rightarrow$new_image, CALCULATE(a computable target)$\rightarrow$numbers, or develop a new manipulation yourself (if it is indeed required). {QUESTION}
- •
Please go through the question incrementally with chain of manipulations (optionally use manipulation when needed) such as GROUNDING(a phrase)$\rightarrow$boxes, OCR(an image or a region)$\rightarrow$texts, CROP_AND_ZOOMIN(a region on given image)$\rightarrow$new_image, CALCULATE(a computable target)$\rightarrow$numbers, and create a new manipulation if necessary. {QUESTION}

### D.2 Training settings

**Table 5: Training details of all stages.**
| Parameters | Stage1-1 | State1-2 | Stage-2 |
| --- | --- | --- | --- |
| Hardware Environment | 3,840 A100xdays | 256 A100xdays | 160 A100xdays |
| Objective | next token prediction | next token prediction | next token prediction |
| Images | 1.5B | 40M | 576K |
| Batch size | 8192 | 1024 | 160 |
| Iterations | 120,000 | 60000 | 14000 |
| Optimizer | AdamW | AdamW | AdamW |
| Learning rate | 1e-4 | 1e-5 | 1e-5 |
| Warm up steps | 7200 | 1200 | 280 |
| Trainable weights | 6.5B visual expert | 6.5B visual expert | 6.5B visual expert |

## Appendix E Details of Qualitative Analysis

### E.1 Qualitative Analysis

We investigate the evidential reasoning capability of CogCoM on scenarios that requires different types of meticulous reasoning, including recognizing textual details, reading time, understanding charts and counting objects. The results are shown in Figure 1.
The first case demonstrates that CogCoM finds the region corresponding to the plane logo through two steps of grounding and then achieves the answer based on zooming in the cropped region.
The second case illustrates the ability of CogCoM in reading time, by locating the device that displays time and then transforming the time into words based on the read_timne manipulation.
In the forth example, CogCoM first identifies all visible truck wheels through grounding, and then leverages the counting manipulation to consolidate the total number.

### E.2 Representative Cases

Figure: Figure 9: CogCoM demonstrates the flexible capabilities for adapting to different multimodal scenarios, including evidential visual reasoning, Visual Grounding, Grounded Captioning, Image Captioning, Multi Choice, and Detailed Captioning.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: * The paper should include a broader discussion of prior work.
* The paper should include more extensive ablations.
soundness: 3
strengths: * The paper shows promising results.
* The paper is well written and easy to understand.
* The paper includes extensive qualitative examples.
* The proposed CoM dataset has the potential to be useful to the broader community.
summary: The paper focuses on visual reasoning problems using large vision language models. The paper focuses on the problem caused by the first alignment training stage in such vision language models, where the model does not focus on the low level details and instead focuses only on the high level details of the image. The paper proposes to solve this problem by teaching the model to perform intrinsic manipulations (e.g., grounding, zoom in) with results (e.g., boxes, image). The paper shows promising performance on various visual reasoning problems.
weaknesses: * Novelty: The core idea of the paper is very similar to that of "Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models, CVPR 2024" and "Visual Programming: Compositional Visual Reasoning Without Training, CVPR 2023". The Visual Program Distillation work trains the VLM to perform several grounding tasks for more accurate visual reasoning. Somewhat similarly, "Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models, arXiv 2024" uses a visual sampler to extract relevant regions in the image.

* On the other hand, prior work such as "Look, Remember and Reason: Grounded reasoning in videos with language models, ICLR 2024" used surrogate tasks to extract low level visual information. A discussion of the relationship to prior work is necessary.

* Limited size of the CoM dataset: The proposed CoM dataset consist of only 6k images. This might limit its utility due to the lack of visual diversity.

* Ablation studies: The proposed method uses several datasets during the second stage of training along with the proposed CoM data -- this includes MultiInstruct (Xu et al., 2022), LLaVAR (Zhang et al., 2023b), and ShareGPT4V (Chen et al., 2023c). It would be beneficial to include additional ablations to highlight the utility of each of these datasets.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: See weakness.
soundness: 4
strengths: 1. The proposed method, defining operations of image manipulation to solve queries that require a detailed look is intuitive, is intuitive and clear.
2. Intensive experiment results compared with up-to-date models are shown on various tasks, leading to SoTA performance.
3. writing is clear and easy to follow
summary: This paper introduces Chain of Manipulations (CoM) to address the reasoning questions that require a detailed look of the visual information. CoM is a step-by-step strategy allowing the model to take manipulation operations like Crop/ZoomIn over the image, thus reserving more capacity for the image regions/bboxes that needs to be looked at with more detail. The contributions include both two datasets and a trained model, which shows SoTA performance on 9 datasets in 4 categories.
weaknesses: 1. One thing not very clear - in inference time, once the model is trained, is step-by-step CoM still needed? In other words, is CoM a method for collecting training data, or is it for VLM inference as well? Fig-6 right breaks down the questions into eight groups based on the time overhead - is this time the inference time using one single VLM call or multiple calls using CoM is needed?
2. In principle, how does this work compare to visual programming [1,2]? Is the defined manipulations like ZoomIn/Crop a specific case of the operations as in these works? More discussions will be helpful.
3. While the VQA results show huge improvement, the improvements on grounding on RefCOCO(+/g) are not as significant. Any discussions/analysis on this?
4. The proposed data contains 70k automatically generated data and 7k human-annotated data. How does each of them contribute to the model performance? For example, are there results using each type of the data separately? Or are there more discussions on why both types are needed and only one type is not sufficient?
[1] Gupta, Tanmay, and Aniruddha Kembhavi. "Visual programming: Compositional visual reasoning without training." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
[2] Surís, Dídac, Sachit Menon, and Carl Vondrick. "Vipergpt: Visual inference via python execution for reasoning." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 1
questions: - Do the math problems also benefits from the same "modules"? (cropping, detection...), or new ones were identified by human annotators?

- Is there any analysis on the distribution of the correct paths found during the data collection process? If only 35%, do those 35% belong to the same category of questions? Maybe there are entire categories of questions that GPT-4 just never solves, and that end up not being part of the training data.
soundness: 3
strengths: 1 - The paper's contributions are very sound. The idea of collecting data automatically, but having a way to pseudo-verify its correctness (at least end-to-end correctness) is reasonable.

2 - The paper shows good results across a large variety of benchmarks.

3 - The paper additionally contributes extra human-annotated data for mathematical visual reasoning, which can be useful for the community.
summary: This paper presents CogCoM, a model that can reason in a multi-turn way when answering VQA questions. The paper first introduces an approach to collect multi-turn data given only input-output (single-turn) VQA pairs, by using GPT-4, and then uses this multi-turn pseudo-labeled data to finetune a VLM, giving this VLM the capability of reasoning in a multi-turn way. This results in better results in a variety of benchmarks.
weaknesses: 1 - From a scientific point of view, it is hard to tell how significant the contributions are. There is one ablation (which is informative) where they remove the multi-turn training data from their training mix, and it seems like it helps overall (but very little for two out of the three tasks shown). However, it is unclear how much of the contribution comes from the single-turn vs. multi-turn setting, or the data, or the combination of both, or just any other modeling decision when training the final model. If I am not mistaken, the results that are shown from previous methods are all single-turn, which makes the comparisons unfair. 

2 - Motivation. There are no comparisons to the module-based approach (ViperGPT-like) that is actually used to collect the pseudo-annotations. If this approach is good to collect the pseudo data, why would it not be used at inference time? Would it perform worse? Is it slower or more expensive?

3 - Presentation

  - There are many grammar and orthography mistakes.
  - The related work is only presented in the appendix. This makes it harder to understand the specific contributions of this paper.

Minor weaknesses:

4 - It is mentioned that the positive paths "are guaranteed to be error free". This is not necessarily true. The model could get to the correct answer through a wrong path, or even by having the last reasoning step completely ignore all previous information. 



- Many related literature not mentioned? Or compared
Related work should not go all of it in the appendix!

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: - In Fig 2., should the “Large Language Model” be the “Vision-Language Model”?
- In the title, should ‘visual’ be ‘vision’ for consistency?
- Have you also experimented with adding CogCoM to different VLMs? Do you have experiment results showing whether the base VLMs already need to be strong to generate CoM, or can weak VLMs benefit from CoM?
soundness: 3
strengths: - Introduction of CogCoM, a technique that teaches VLMs to generate visual programs that execute visual expert modules to augment the reasoning chain of VLMs.
- CogCoM improves CogVLM performance in multiple benchmarks.
- Code/Model/Data will be publicly available.
summary: The authors provide CogCoM, a technique that augments VLMs with ‘chain-of-manipulation (CoM)’ reasoning. In CoM, given image and text input, VLMs generate visual programs that evoke 6 external modules that can perform OCR, grounding, counting, calculating, zooming-in, and drawing a line. The outputs of external modules are composed into the reasoning path of VLMs. The authors automatically collect 70K training annotations for CogCoM on TextVQA/ST-VQA/TDIUC datasets, by prompting GPT-4 to use GroundingDINO and PaddleOCR, and also collect 7K training annotations for geometry problems with human annotation. The authors compares CogVLM augmented with CogCoM to other VLMs as well as CogVLM in VQA benchmarks (GQA/TallyVQA/TextVQA/ST-VQA), a proposed benchmark for CoM reasoning chain (CoM-test), visual grounding (RefCOCO, RefCOCO+, RefCOCOg), and other general benchmarks (MM-Vet, POPE). CogCoM improves CogVLM performance in many benchmarks, especially on CoM-test.
weaknesses: - **No comparisons with other visual programming methods.** There have been many works where LM or VLM generate programs to execute visual experts for different visual reasoning tasks. Earlier works include VisProg (https://arxiv.org/abs/2211.11559) / ViperGPT (https://arxiv.org/abs/2303.08128). More recent works include GPT4Tools (https://arxiv.org/abs/2305.18752), LLaVA-Plus (https://arxiv.org/abs/2311.05437), Visual Sketchpad (https://arxiv.org/abs/2406.09403) and V* (https://arxiv.org/abs/2312.14135). As generating programs with visual experts itself is not new, qualitative/quantitative comparisons would make the contribution/usefulness of CogCoM more clear.

Meta-review: This paper introduces a mechanism called “Chain of Manipulations” inspired by human cognition, which enables VLM to solve problems step by step using evidence. With this mechanism, the model can extract unique operations and solve visual problems. In addition, it is also possible to trace the cause of errors. The paper proposes a comprehensive methodology that includes flexible operation design, an automatic data generation pipeline, a compatible VLM architecture, and a versatile training process.

The strengths of this paper are as follows. As evidence of active manipulation of visual input, the paper proposes the Chain of Manipulations (CoM), a mechanism that enables VLM to solve problems in stages. Specifically, the paper proposes a flexible data structure, an efficient data generation framework that can generate a large number of samples, a memory-based architecture that is compatible with existing VLMs, and a training process for cultivating general-purpose abilities.Furthermore, in order to utilize the expressive power of the proposed mechanism and further advance VLM research towards solving difficult mathematical problems, this paper manually annotated 7,000 high-quality mathematical graph samples with the CoM inference process.

The weaknesses of this paper are as follows. The size of the CoM dataset is limited. In order to strengthen the model's functionality and generalization, it is necessary to construct a wider range of inference data for a greater variety of scenarios. The success rate for constructing inference paths is high for common, prominent objects, but low for small, less common objects. There is a lack of comparison with research on improving the inference function of VLM.

This paper is clearly written, and all reviewers have given it positive evaluations. Based on a comprehensive judgment of the paper itself, the reviewers' comments, and the author's rebuttal, the AC believes that this paper exceeds the ICLR's acceptance threshold and that acceptance is appropriate.

Decision: Accept (Poster)