Thinking as Society: Multi-Social-Agent Self-Distillation for Multimodal Misinformation Detection
Authors: Yifei Gao
Ning Xu
Wenhui Li
Hongshuo Tian
Lanjun Wang
Anan Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Misinformation Detection
Multimodal Large Language Models
Multi-Social-Agent Self-Distillation
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Multimodal Misinformation Detection (MMD) in realistic, mixed-sourced scenarios must incorporate robust reasoning capabilities to handle the social complexity and diverse types of forgeries. While MLLM-based agents are increasingly used for MMD task due to their powerful reasoning abilities, they suffer from a critical trade-off: on one hand, single-agent methods provide only the limited, single-view analysis; on the other hand, multi-agent methods introduce high computational costs and significant optimization difficulties. To address this gap, we propose a novel Multi-Social-Agent Self-Distillation framework that internalizes collective social reasoning capabilities into a unified model. Our framework consists of two core stages: (1) we simulate multi-perspective judgments from a diverse society of MLLM agents and synthesize their collective feedback into high-quality Social Chain-of-Thought (SCoT) data; (2) Building on this, we propose the Social Correction Value-Driven Preference Optimization (SCPO), a new alignment algorithm that leverages the degree of social misjudgment as a verifiable signal to dynamically focus training on the most challenging samples. Extensive experiments on the challenging MFC-Bench and MMFakeBench benchmarks demonstrate the effectiveness of our framework. Our 7B Qwen2-VL-based model significantly outperforms various MLLM baselines, multi-agent methods, and even competes or surpasses proprietary models like GPT-4o and Claude, facilitating advanced multimodal misinformation reasoning and detection via thinking as society.

Published as a conference paper at ICLR 2026
THINKING AS SOCIETY: AGENT SELF-DISTILLATION FOR MULTIMODAL MISINFORMATION DETECTION
MULTI-SOCIAL-
Yifei Gao, Ning Xu, Wenhui Li, Hongshuo Tian, Lanjun Wang & An-An Liu"
Tianjin University
Tianjin, China
ningxuêtjuu wew.uuueeudueeo. cn, aman0422@gm. com
ABSTRACT
Multimodal Misinformation Detection (MMD) in realistic, mixed-sourced scenar- ios must incorporate robust reasoning capabilities to handle the social complexity and diverse types of forgeries. While MI.LM-based agents are increasingly used for MMD task due to their powerful reasoning abilities, they suffer from critical trade-off: on one hand, single-agent methods provide only the limited, single-view analysis; the other hand, multi-agent methods introduce high computational costs and significant optimization difficulties. To address this gap, we propose a novel
Seff-Distillation framework that internalizes collective social reasoning capabilities into a unified model. Our framework consists of two core stages: (1) simulate nulti-perspective judgments from diverse society of MLLM agents and synthesize their collective feedback into high-quality So- cial Chain-of- -fouuht (SCoT) data; (2) Building on this, we propose the Social Correction Value-Driven Preference Optimization (SCPO), new alignment algo- rithm that leverages the degree of social misjudgment as a verifiable signal dy- namically focus training the most challenging samples. Extensive experiments on the challenging MFC-Bench and MMFakeBench benchmarks demonstrate the effectiveness of our framework. Our 7B Weeen--L model significantly outperforms various MLLM baselines, multi-agent methods, and even competes surpasses proprietary models like GPT-4o and Claude, facilitating advanced multimodal misinformation reasoning and detection via thinking as society.
INTRODUCTION
Multimodal misinformation, including fake news, often contains convincing yet deceptive content that spreads easily across social networks Vosoughi al. (2018). In particular, recent generative models like GpenApenAT (2024) and Stable Diffusion Rombach et al. (2022) have made it easier to create such misleading multimodal content[Chen Shu (2024). This realistic, mixed-sourced misin- formation often combines diverse forgery types and leverages social context, posing a serious threat public trust Akhtar et al. (2023). Therefore, effectively addressing these threats requires detection models that beyond just basic classification Qi et al. (2024). They must be capable of understand- ing social dynamics, performing robust reasoning and making comprehensive judgments Wang et al.) 2025) to handle such complexity and achieve generalization, robustness and interpretabilltyy
Recently, MLLM-based agents are increasingly adopted for Multimodal Misinformation Detection (MMD) owing to their powerful reasoning and generalization capabilities Liu et al. (2025b); Cekinel al. (2025); Tahmasebi al. (2024); [ee et al.) (2024). However, as shown Fig they suffer from a critical trade-off in their application: on one hand, single-agent methods Cekinel et al. (2025); Tahmascbi et al. (2024) provide only a limited, single-view analysis, which is insufficient for socially complex tasks and casy to be misled. On the other hand, while multi-agent methods Liu et al. (2025b): Wan et al, (2024); Nan et al. (2024) can conduct analysis from diverse social roles, they consume extensive computational resources and have difficulty for end-to-end optimization. This
"Corresponding authors.

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Published as a conference paper at ICLR 2026
Single-Agent Approach
Multi-Agent Approach
High Efficiency
Multi- Perspective Analysis
Misleading Alignment
Misleoding Information
Social
Low Efficiency
Damage
Perspectives
Hard Optimization
Unified Social Agent (Ours)
Pfizer CEO Secretly Admits COVID-19 Vaccine Causes Permanent Brain Damage In Vaccine Recipients.
Social Modeling
Social Distillation Inference
High Efficlency
Multi- Perspective Analysis
Ground Truth: Fake
Figure I: Overview of existing MLLM-based detection paradigms. The single-agent approach (top left) is efficient but lacks diverse social perspectives, which can be easily misled. The multi-agent approach (top right) captures multiple viewpoints but suffers from low efficiency and optimization challenges. In contrast, our method leverages social modeling and distillation to build a unified social agent that combines the high efficiency with the robust reasoning capability.
dilemma between inference efficiency and multi multi-perspecctve analysis motivates the requirement for new framework that can internalize collective social reasoning within unified model.
This objective to internalize the collective reasoning exposes two fundamental challenges. (1) Data Limitation: the lack of rich and multi- pellpiterseettvee reasoning data to teach MLLMs how to integrate
diverse social viewpoints and apply valuable evidence to improve decision- maaiing (2) Optimiza-
tion Challenge: existing fine-tuning algorithms like SFT and DPO Ratailov al. (2023) fail to
distinguish and prioritize high-value samples reflecting social cognitive differences. Consequently, even after building high-quality reasoning data, training results remain suboptimal.
In response to these challenges, we introduce a novel work. For the data limitation, we simulate a diverse of MLLM agents, each providing its own perspective to assess the authenticity of given multimodal content. Then, we integrate all of their feedback create high-quality Social Chain-of-Thouus lenge, we propose new fine-tuning algorithm, Social Correction Value-Driven Preference Opti- mization (SCPO). Building on our SCoT data, SCPO quantifies the degree of social misjudgment into an explicit and verifiable value. This value helps the model dynamically adjust optimization process to focus on the most challenging samples with cognitive gaps. By incorporating this social correction value into preference optimization process, our approach achieves effective and sta- ble training procedure. Through our multi-social-agees self-distillation framework, we can achieve "thinking as society"", enabling a single model to reason from the perspectives of multiple social roles, thereby realizing deep understanding and decision-making for the MMD task.
Self-Distillation frame-
(SCoT) data. For the optimization chal-
We conduct comprehensive evaluation Bench Wang et al. (2025) and MMFakeBBnch Liu et al. (2025b). Our 7B ---na-VL-bbssed model significantly outperforms stronger open-source MLLM baselines Wang et al. (2024a); Bai et al. (205);; Zhu et al. (2025), multi-agent frameworks Liu et al. (2025b), and even competes or sur- passes proprietary models like GPT-4o and Claude, facilitating advanced reasoning and detection for multimodal misinformation in challenging mixed-sourced scenarios via thinking as society.
latest MIL.M-oriented MMD benchmarks: MFC-
In summary, our main contributions are: (1) We introduce novel multi-social-agee aggent self-distillation framework for advanced reasoning and detection of realistic multimodal misinformation, resolving the critical trade-off between the single-agent limited perspective and multi-agent inefficiency. (2) We develop complete self-distillation pipeline to generate high-quality Social Shain-of-Thouglee (SCoT) data by simulating and synthesizing multi-perspective social feedback, addressing the data limitation. (3) We propose the Social Correction Value-Driven Optimization (SCPO), a new align- ment algorithm that introduces social misjudgement as a verifiable signal to prioritize challenging training samples, solving the optimization challenge. (4) Extensive experiments MFC-Bench and AMFaaeeBench demonstrates our 7B Qwen2- VL-based model significantly surpasses larger open- source models, dedicated multi-agent frameworks and powerful proprietary systems.
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Multi-Source Data Curation
Social Simulation for Collective Feedback
Original Dataset
Data
High-quality Training Data
Social User Agents
Qwen2-VL
Selection
reaitame: fun, Jnser canner biot Living life the fullee, one ont moment at time!
NewsCLiPpings
Semantic Similarity
DGM4
gonder: male
Validation Set
Related Topics
AutoSpfice
profession: Manulactunng
persona: Connor wn Hlypwweold gay who war the manafecturnn
Finefake
Distributional Similarity
#
Topic-driven Sampling
Industry He social butte
Potential Interest Group
interested_ topics Fun
Figure 2: Overall pipeline of multi-source data curation and social simulation.
2 RELATED WORKS
Multimodal Misinformation Detection. Multimodal misinformation detection is to automati-
cally identify false or mislcading contents different modalities (Abdaa et al. (2025). Early re- searches focus developing specialized multimodal feature extraction Abdelnabi et al. (2022)
Khattar et al. ) Wang et al. (2018), integrating other pattern analysis such as writing style
Przybyla (220)) and sentiments Ghanem et al. (2021). In addition content-based analysis, due to
the social attributes of misinformation such as news domains Luo et al. (2021); Shao et al. (2023);
Zhou et (2024).
works explore social-context-basse detection, such as propagation patterns Zhou & Zafarani (2019): Xu et al. (2024), user feedback Min et al. (2022). and social networks Nguyen et al. (2020). Recently, some works apply MILMs knowledge bases Cekinel et al. [2225 Tahmasebi et al. (2024). simulated comments Wan et al. (2024); Nan et all (2024), and ratio- nale generation [ee et al. (2024); ; (uu al. (2024), enhancing detection accuracy and explanability. The wide application of MLLMs also leads to more comprehensive benchmarks like MFC-Bench Wang et al. (2025) and MMFakeBench Liu et al. (2025b), designed evaluate the reasoning and detection capabilities required by mixed-soures multimodal misinformation. Our work enhances the intrinsic reasoning MILMs for complex real-world multimodal misinformation detection.
Preference Optimization for MILM Alignment. Aligning MLLMs with human preferences is critical for ensuring reliable deployment (hi et al. (2024); Sun et al. (2024). Traditional Reinforce- ment Learning from Human Feedback (RLHF) includes reward model training and reinforcement learning Ouyang et al. (2022). To address the training instability and computational costs in RLHF, Rafailov et al. (003) reframes alignment supervised learn- ing without explicit reward model. This innovation has inspired numerous variants such as TDPO ZZen et al. (024b)), SimPO Meng et al. (2024), ORPO Hong et al. (0024) and TIS-DPO Liu et al. (2025a). DPO framework also has wide application in multimodal domain. For example, MM-DPO Zhang et al. (2025) uses reward margins control the preference strength. Our SCPO introduces a signal to adjust training priority dynamically.
Direct Preference Optimization
verifiable and
3 METHODOLOGY
In this section, we detail our multi-social-ggent self-distillation framework which consists of two parts, i.e., the pipeline of multi-agent social chain-of-thought generation (Sec 3.1) and the algorithm of social correction value-driven preference optimization (Sec 3.2).
3.1 MULTI-AGENT SOCIAL CHAIN-OF-THOUGH GENERATION
3.1.1 MULTI-SOURCE DATA CURATION AND SOCIAL SIMULATION
To generate high-quality SCoT data, our framework first requires two foundational components: diverse set of multimodal misinformation to employ social discussion, and user agents with related interests and various backgrounds to provide realistic social feedback.
Misinformation Curation and User Profiles. To ensure the diversity and representativeness of multimodal misinformation, aggregate multiple existing MMD benchmarks into comprehen-
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Inputs Image
Feedback Classification
Reasoning Augmentation
Data Synthesis
Correct User Responses
Ground Truth fake
Confidence
Correct Consensus
As (an boer contos my basiground Jocal buttorfy. the ded state that Chump and the comediio do not store lough, whin contradems voun exiterce Thich mod Varly foim.
Thi on Image-tert par shousing togethes with aodi claiming they tab shore lough.
aff
rethin
If a contrdiciion benween the image jihnwdog fiiendly inferaction) the givcn list (doiiiing no shored lsughter).
Partially Correct User Responses
apprars to
approsch scciety..tee
imagedaud
Ohama and comedian Joel Mctfale do not shere laugh
Text Claim
confidence 0.55
Coordinator Agent
This exitenze show the Image dext
exisence otone, Furter verfttttion shrough offecal
Potential Correct
Incorrect Consensus
Sampled Users
Incorrect Responses
the gives image depacts too men event coremory..
As Liy Naren gibnrmmnnt Jamm 2Sppar-oed ligh whoor museet J might by more restoed to batieer in the authentecty of Mmage alges with Interests
The text motes daln sbout relatonship bcoween obenz... appears be authontic.
Ground Truth: fake
Summarizer
thelf
Agent
Figure 3: Overall pipeline of social ooiin-of-thought generation.
sive dataset. These include both synthetically generated examples via controlled manipulation (e.g., NewsCLIPpings Luo et al. 2021). DGM4 SSao et al. (2023). AutoSplice Jia et al. (2023) and real- world samples collected from social media (e.g., FineFake Zhou al. (2024)). To handle scale and redundancy, we rank the training samples based on metries relative to their validation set, including both CLIP semantic similarity Radford al. (202T) and Wasserstein gradient- based distributional consistency Kolouri et al. (217).. By top selection and label balancing, we obtained high-quality diverse misinformation base-dataset.
To capture social judgment diversity, we collect a large set of user profiles for realistic simulation. As shown in Fig user agents are constructed based on the profiles with wide range of demographic (e.g., global representation across countries), psychological (c.g., enthusiastic. calm), professions (e.g., journalists, engineers, educators), and anterest-based attributes (e.g.. politics, science) from OASIS platform Yang al. (2024). This breadth allows for the feedback with distinct social roles and cognitive backgrounds, facilitating the reasoning of collective intelligence.
Topic-Driven User Sampling. To simulate contextually relevant social interactions, is crucial to model how users engage with specific content. On social platforms, users preferentially interact with content aligned with their personal interests. To mirror this dynamic, we sample users who offer meaningful perspectives through topic- matching.
The sampling process involves three steps. First, for each multimodal sample in the base-dataset, we extract its topic categories (e.g., a news article about "AI deepfakes in political campaigns" might be tagged with both "Politics" and "Information Technology"), denoted as T., Next, for each user with interested topic set Tu, we compute matching score using the normalized overlap between Th, and TG: w [T.NT.
[Tal e, where is a small positive constant ensuring non-zero selection probability for all users. This simulates the real-world scenario where users occasionally engage with content outside their primary interests. Finally, perform weighted sampling from the user pool based on matching scores. This approach ensures users with higher topic relevance are more likely to be selected, while still introducing controlled randomness of social media interactions.
For each sampled user, we instantiate an MLLM Wang et al. (2024a) agent to simulate their social role. This is achieved by injecting the user's profile into structured system prompt (See specific prompts in A.7. which guides the agent to generate feedback consistent with its background and per- spectives. Each feedback reflects a unique reasoning pattern, capturing the agent's potential biases and background-influene
judgments. This collection of multi-perspectio feedback comprises the raw social reasoning data for our subsequent synthesis and optimization pipeline.
3.1.2 SOCIAL CHAIN-OF- THOUGHH SIIHHTSSYTTE SYNTHESTS
To convert the diverse but unstructured social feedback into high-quality reasoning data, we develop a multi-stage data generation pipeline for self-distillation. Instead of generating a single correct answer, first use ILLM-as-a-Judge paradigm classify the full spectrum of social responses,
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then deepen the thoughts of promising responses for quality and relevance, and finally synthesize these refined views into well-organized preference SCoT data for our optimization algorithm.
Answer-Centre Feedback Classification. Rather than simple binary classification, our approach is designed to discover any potentially valuable insights from diverse social feedback. Specifically, we introduce the soft boundary through fine- fin-grained verification principles and prompt LLM-judger to provide self-critiqued confidence scores. First, the LLM judger categorizes each user feedback into one of three distinct sets based on the consistency between the user conclusion and ground- truth answer. 1) Correct set: The conclusion is correct with supporting reasoning. 2) Incorrect set: The conclusion misaligns with the answer and the reasoning contains critical flaws (e.g., neglecting key multimodal evidence). 3) Partially-correct set: The feedback shows partial alignment, such as incorrect conclusions while containing valid observations. Second, the LLM-judger assigns confidence score (ranging from O to 1) to each classification as judgment reliability. Finally, to form solid basis for subsequent augmentation, we define the potential correct group as the subset of feedback from the correct set and partially-corect set with confidence over 0.5, This threshold effectively balances the inclusion diverse perspectives with the filtering of noisy feedback.
Role-Specific Reasoning Augmentation. To deepen and optimize the reasoning in the potential correct group, we employ two-fold strategy that augments feedback with external knowledge and adaptively handles edge cases. First, we perform knowledge injection by providing the ground-truth answer to each user in the potential correct group and prompting them to refine their explanation from their assigned role's perspective. This process encourages more elaborate reasoning and the incorporation of previously overlooked evidence, while still preserving their unique role-specific insights. Second, we apply adaptive enhancement edge cases to improve robustness. For easy samples (where all agents respond correctly), we generate adversarial negatives by prompting all agents to justify the opposite conclusion, counterfactual process that exposes potential cognitive biases and prevents overfitting, For hard samples (where no agent respond correctly), we broad- cast the answer and prompt all agents to construct valid role-specifie reasoning toward the correct conclusion for the most difficult examples.
Preference Data Synthesis. We synthesize the augmented feedback into the preference SCoT dataset by two specialized MLLM-driven agents. The Coordinator Agent is responsible for gener- ating the positive (chosen) samples. It processes refined feedhack from the potential correct group to generate a unified positive SCo'T. By merging overlapping reasoning paths while preserving role- specific insights that uniquely contribute to final judgment, it yields fine-grained social reasoning chains as high-quality positive data. The Summarizer Agent constructs the negative (rejected) SCoTs. It processes responses from the incorrect set, and its goal is to capture the most representa- tive mistakes for negative data by maintaining core misleading reasoning links and critical flaw
Overall, the synthesized chosen and rejected SCoTs form the core preference dataset for our subse- quent optimization, enabling MLLMs to reason complex tasks from diverse social perspectives.
3.2 SCPO: SOCIAL CORRECTION VALUE-DRIVED PREFERENCE OPTIMIZATION
Existing preference alignment frameworks like Rafailov et al. (2023) typically treat all training samples with the same importance, making insufficient use of social cognitive differences. To effec- tively utilize the social attributes of our SCoT data for guiding MI.LM optimization, we propose a novel learning framework, Social Correction Value-Driven Preference Optimization (SCPO). SCPO measures the degree of social misjudgement as verifiable correction value to adjust the preference optimization dynamically, enhancing the efficiency and robustness of alignment process.
Social Correction Value Function. The core mechanism in SCPO is to quantify the correcting requirements sample's learning value based on social feedback. For a given multimodal sample x, we introduce the social correction value function sc(z), which measures the sample difficulty as its priority for preference learning.
No N
Np
1 N
sc(x) =1
N
Ne
'The detailed architecture of coordinator and summarizer agents in Appendix A.3.1
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where N denotes the total user numbers for the sample Nc is the numbers of correct set, and Np is the user numbers of partially correct set. The intuition behind sc(z) is two-fold: The coarse-grained term Ne captures the proportion users who correctly judge the sample. Its higher Ne value indicates easier samples with lower correction requirements. The fine-graines term N-Ne accounts for partial correctness among the remaining users. It represents discounted weight over N to avoid overshadowing the dominant effect of correct users.
Overall, sc(z) is natural preference scaling signal. Specifically, samples with higher sc(r) are more challenging with more incorrect responses. These samples are required to amplify preference strengths, making MLLMs focus on resolving social misjudgement. Otherwise, easier samples with widespread correct conclusion are downweighted, preventing overfitting to trivial cases.
SCPO Loss Function. We design the SCPO loss function under the ORPO integration framework Hong et al. 2024) of Supervised Fine-Tuning (SFT) and Preference Optimization (PO). The SCPO
loss is as follows:
LsCro = LSFT + X (1 (1+ w sc(x)) LoR
LSFT
B(zyya)Darr [log no (yan x)]
logo a
[ogg 1 (o
To (V x)) log
T x)
COR = -B -B (o. Doutt
1 (o 2))
SFT loss LSFT guides MLLMs learn the social reasoning patterns DSFT. which denotes the dataset of positive social CoTs synthesized by the coordinator agent. To further align MLLMs to avoid misleading reasoning traces, we introduce Odds Ratio Alignment Loss LOR, which compares the generation likelihood of high-quality (yw)) with flawed reasoning (yt). Dpeet is a dataset of pref- erence pairs (yw. Ur), where you is a high-quality social CoT (from the potential correct group) and y1 is flawed reference given by summarizer agent (from the incorrect set).
Social Correction-Driven Preference Scaling. A key innovation of SCPO its dynamic weight- ing of OR loss via the social correction value (1 + w sc(x)). Existing efforts have explored using reward margins to adjust sample prioritization under DPO framework Zhang et al. (2025). However, these methods rely reward model to learn implicit preference relationships, making the resulting margins unverifiable Lambert et al. (2025). In contrast, our social correction value function sc(x) serves more reliable indicator for dynamic preference weighting. First, sc(z) is computed from observable statistics of user feedbacks with clear semantics, making it verifiable and interpretable. Second, the bounded range of sc(x)) [0, prevents overly aggressive training, ensuring opti- mization stability. Our SCPO makes MLLMs self-distill collective cognition effectively, enabling to reason complex multimodal tasks from diverse social perspectives.
4 EXPERIMENT
4.1 EXPERIMENTAL SETUP
Benchmarks. Our multi-social-agee self-distillation framework is evaluated on two MMD bench- marks. MFC-Bench Wang et al. (2025) is a comprehensive benchmark for assessing the factual accuracy of MILMS in multimodal fact-checking. It encompasses three key stages of verdict pre- diction: Manipulation Classification, Out-of-Context (OOC) Classification, and Veracity Classifi- cation, covering 35K multimodal samples from diverse social backgrounds like world news. MM- FakeBench Liu al. (2025b) focuses on mixed-soure multimodal misinformation detection. covers three critical misinformation sources: distortion of textual veracity, visual veracity and cross- modal consistency, containing 12 sub-categories under realistic mixed-source scenarios.
Baselines. We uniformly use Beenn---V-7B--nsteree Wang et al. 2024a) in our self-distillation framework, and the baselines cover various MLLMs and multi-agent methods for comprehensive comparison. We evaluate main proprietary MLLMs like GPT-4o, Claude Slaude3.5-Sonett S-nnontntttttee and open- source
MLLMs Yao al. (2024); Wang et al. (2024b) including Qwen- Bai et al. (2023); Wang et al.
(202a)) Bai et al. (2025). Intern VL Chen et al. (2024); /hhu et al. (2025). LLa | et al. (2023]
2024 Li al. 2025 series. Notably, we include ML.MMs stronger than base Qwen2-VI, such as

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Table 1: Comparison between Qwen2-VL VL..baedd self-distillation framework and other MILMM baselines on MFC-Bench in open-prompting setting. The accuracy and macro-averaged Fl score(%) are reported as the metrics. The best and second test results are in bold and underlined, respectively.
Manipulation
OOC
Veracity
Overall
Models
Accuracy
F Accuracy
FT
Accuracy
FI
Accuracy
FT
Proprictary Models
GPT-4o
68.03
67.00
82.15
82.11 75.34
69.85 80.68
69.04
69.11
68.49
Claude3.5-Sonne
65.39
61.51
75.80
78.26
64.32
Open-Source Source Models
Inter VI.
25.5B
53.77
53.38
63.15
62.28
66.55
65.65
55.03
54.35
CogVLM
17B
58.85
54.30
53.85
47.30
57.30
60.51
58.47
54.44
LLaVA-NeXT
13B
59.96
58.03
55.60
52.81
43.40
47.45
58.77
56.84
Pixtral
12B
56.97
56.94
68 68.20
68.17
74.85
72.09
58.62
58.64
MiniCPM-V-2. V-2.6
8B
56.20
56.25
61.65
61.49
64.90
64.52
$7.00
57.01
LLaVA-One
59.38
57.04
58.25
55.01
45,25
49.40
58.51
56.27
Qwen2-VL.
57.36
57.02
63.79
48.95
52.78
57.24
56.91
Intern-VI.
59.21
57.82
62.25
61.64
59.85
61.41
59.43
58.35
Qwen2.5-VL
57.29
57.45
67.40
67.35
63.60
58.23
58.34
Intern-VL.3
55.10
54.30
64.80
63.67
66.70
65.21
56.32
55.22
SCPO
65.21
74.60
74.52
80.75
76.98
66.83
Human
Human
75.7
75.6
74.0
73.5
96.0
91.7
76.8
80.3
Qwen2.5-VV VL and Intern VL3, to prove the effectiveness not from foundation model but our frame- work. For multi-agent baseline, we adopt the MMD-Agent framework using task decomposition and knowledge integration paradigms and compare it on MMFakeBench.
Settings. For fair comparison, implement two prompt strategies. Open-Prompting (main set- ting): The specific misinformation type is unknown with only "You are a professional multimodal information analyzer..." " in system prompts. This setting explores the effectiveness and generaliza- tion in real situations. We use 1eenn..57B-Instiie We clearly inform the misinformation type of each sample in system prompts explore in-domain performances. Our model always uses open prompting to show its generalization capability.
to parse model predictions. Glosed-Promppiing
4.2
MAIN RESULTS
Tab presents the overall performance of SCPO and other leading MLLMs on MFC--ench under the open-prompting setting
Comparison of Different MLLMs. Base Model: Our SCPO framework significantly enhances the performance of base model Qwen2-VL. SCPO achieves an overall accuracy 67. 65.15% and Fl score of 66.83%, marking substantial improvement of 9.91% % in accuracy and 9.92% in Fl score over DW. This demonstrates the remarkable effectiveness our SCoT data and SCPO algorithm. Stronger Open-Source Models: Our 7B SCPO model not only surpasses its founda- tion but also outperforms stronger and larger open-source models. For instance, SCPO surpasses Qwen2.5-VI. (58.23% accuracy) and 8B Intern (56.32% accuracy), indicating internalizing cial reasoning is more effective than simply enhancing the foundation. Proprietary Models: SCPO achieves highly competitive results against advanced proprietary models. It surpasses Claude3.5- Sonnet (66.85% accuracy) and closely approaches GPT-4o (69.1 1% accuracy). This shows that our framework can elevate a open-source model competitive level with SOTA proprietary systems, making advanced multimodal misinformation detection more accessible and reliable.
Comparison across Different Subtasks. SCPO exhibits robust performance across all three dis- tinct subtasks on MFC- BFFC-Bench: Manipulation, OOC, and Veracity classification. We achieve the best among all open-source models and also competitive with proprietary models. For example,
Thhe closed-prompting comparison is in Appendix Tab and evaluation details in Appendix A.2.3

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Table 2: Comparison with different models MMFakeBench test set with standard prompting (Standard) and MMD-Agent framework. Top-1 indicates the selection of the most likely answer labels, and Recall indicates the extraction of all involved predicted labels from model responses to detect whether the ground-truth is included. The accuracy, macro FI score(%), precision and recall are reported as the metrics. The results marked by are reproduced.
Answer
Test
Models
Size
Generation
F
Precision
Recall
Accuracy
Proprietary Models (Close Prompting)
Standard
54.0
62.1
48.8
61.5
GPT-4V
MMD-Agent 67.7
63.0
48.7
59.1
Open-Source Models (Closed Prompting)
Qwen- VL
7B
Standard
25.0
30.0
7.5
31.5
LLaVA-Next
7B
Standard
19.0
16.5
26.9
32.3
Standard
31.8
36.8
41.2
41.2
Qwen2-VL VL
7B
MMD-Agent*
32.8
46.9
35.7
35.6
Standard
41.0
40.6
25.0
37.4
LLaVA-Next
13B
MMD-Agent
40.6
42.7
31.2
37.5
Standard
33.7
48.7
25.7
46.6
LLaVA-Next
34B
MMD-Agent
48.7
44.1
49.6
40.5
Open-Source Models (Open Prompting)
LLM--uusiig (Top-1)
37.8
42.9
41.0
Owen2-VL
7B
LLM--ursing (Recall)
49.2
50.7
50.8
LLM-Pausing (Top-1)
57.3
62.4
59.2
SCPO
7B
LLM--Prsing (Recall)
66.1
70.1
66.0
Table Comparison with other LLM technologies using the same SCoT data based on Qwen2- VL.
Manipulation Accuracy
OOC
Veracity Accuracy
Overall
Models
Size
F1
Accuracy
F1
F1
Accuracy
F1
Qwen2-VL
7B
57.49
57.09
63.85
63.84
50.55
54.21
57.46
57.09
Self-Comsistency
7B
62.83
60.44
52.60
52.58
52.20
55.56
61.63
58.35
SFT
7B
63.14
61.51
67.65
67.03
77.20
75.33
64.20
63.10
SFT+DPO
7B
55.97
54.44
68.75
67.64
78.45
71.39
57.98
56.09
ORPO
7B
64.81
64.27
74.70
74.67
80.90
77.45
66.30
66.01
SCPO
7B
65.80
65.21
74.60
74.52
80.75
76.98
67.15
66.83
surpass GPT-4o (69.85% accuracy) and Claude3.5 --onnet (80.68% accuracy) for veracity classifi- cation. These results indicate the self-distilled collective cognition equips MLLMs with a compre- hensive and nuanced understanding of different misinformation types.
Comparison with Multi-Agent Baselines. FakeBench, where we focus our analysis on the performance of the MMD-Agent framework. The performance of MMD-Agent reveals that its agent-based decomposition strategy only partially effective. Although MMD-AV Agent improve Fl scores mostly, when applied Qwen2-Vi VL and 34B LLaVA-Next, it lowers the accuracy 5.6% and 6.1%, respectively. This suggests that while agent- based reasoning can structurally identify potential misinformation signals, its multi-step process may introduce cumulative errors or flawed logic.
a comprehensive comparison on MM-
In contrast, SCPO model demonstrates consistently superior performance across all metries. Notably, our 7B SCPO model (59. 2% accuracy) substantially outperforms the MMD-Agent applied the much larger 34B LLaVA-NeXT (40.5% accuracy). This result illustrates that our social self- distillation paradigm provides a more robust and effective path to advanced reasoning than simply applying complex inference-time agentic framework a larger MLLM.
4.3 EXPERIMENTAL ANALYSIS
Effectiveness of SCPO. To validate SCPO framework, conduct ablation study with other widely-used prompting and fine-tuning strategies. As shown in Tab all fine-tuning methods utilize the same high-quality social reasoning data eurated by our pipeline for fair comparison.
8

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Published as a conference paper at ICLR 2026
image and degict group of children standing by a window stone building, the claiming that another dead body has been taken from their village The visual evidence suggests that the image might be still from video photograph, capturing moment in rural traditional settingg of dead body being taken the vilage highly sensetional and unusual, making difficutt to verity its authenticity without additional context or evidence.
The blurriness the image and the dramatic nature of the text raise quastions about the veracity of the claim. the image showing
looking outside window, which not directly correlate with the text's cloim of dead body being taken from the village. The image lacks clear evidence of recent event confict, making difficutt to verlfyy the teod's claim based solely the visual content. presence children the image, combined
image test inconsistent,
Text: Childrenn watch as another dead hody taken from their village
Ground Truth:
tragic claim, significant disconnect that raises questions about the veracity the text. The lack alignment between the Image and the text suggests that the image may not be directly related to the text's claim. Given lack direct evidence techniques, the image-teat pair likely to contain misinformation. The comoinatiin of plousible Image with sensational text cloim is commen tactic used to manipulate public opinion and create fear or confusion. Therefore, essential to verify information from multiple sources before accepting as
the
serious
Fake Prediction:
daim the potential manipulation
Text suspiclousness
Image ambiguity
Cross-Modal
Potential Manipulation
Inconsistency
Conclusion: Fake
Figure 4: Visualization of our SCPO model on MFC- MFC-Bench.
Table 4: Comparison of the reasoning quality judged by GPT-4 on MFC-Bench.
Models
Size
Misleadingness |
Informativeness
Soundness
Readability f
7B
3.82
2.96
3.30
4.39
LLaVA-NeXT
13B
3.61
3.07
3,48
4.49
Qwen2-VL
7B
2.82
3.34
4.03
4.86
SCPO
7B
2.51
3.69
4.19
4.93
Value of Social Chain-of-Thought Data. We first observe that standard Supervised Fine-TTuning (SFT) on the positive SCoT data establishes strong baseline, achieving an overall accuracy of 64.20%. This confirms the high quality and effective pattern of our synthesized social reasoning data. The SFT results also achieve performance gains against self-consistency prompting strategy (61.63% accuracy), indicating the effectiveness of integrating diverse social perspectives. Prefer- ence optimization methods like ORPO, which integrate SFT with a preference loss term, further improve performance to 66.30% accuracy, showing advantages of learning to distinguish between high-quality and flawed reasoning paths.
Superiority of Socially-Driven Scaling Signals. Our SCPO model consistently outperforms all other methods, demontrasting the advantage of social correction value-driven scaling mechanism. Through verifiable social signals, our model focuses its efforts on resolving the most complex cog- nitive gaps, providing more robust and stable optimization.
Model Interpretability. A reliable misinformation detection model must provide convincing ev- idence in reasoning process. We evaluate the reasoning quality following the GPT4 evaluation provided by MFC-Bench. The results in Tab[qinclude four dimensions: Misleadingness, Informa- tiveness, Soundness, and Readability, and SCPO achieves the best performance across all four met- rics: The lowest misleadingness (2.51) indicates less logical mistakes or deceptive arguments. The highest on informativeness (3.69) and soundness (4.19) confirms the reasoning is logically coherent and sull-supported by relevant multimodal evidence. The readability (4.93) shows the explanations are clear, well-structured and easily understandable This superior interpretability is direct result of our social self-distillation strategy, which integrates diverse analytical perspectives into the model's reasoning process, thereby enhancing both the quality and transparency
Visualization. We provide qualitative illustration of multi- perspeccire reasoning of our SCPO model in Fig/4] The image depicts children looking out of a window, with textual claim children watch another dead body is taken from their village. Our generated reasoning reveals detailed and traceable analytical process. It first identifies the textual suspiciousness of the unverifiable claim and image ambiguity due blurriness. Crucially, pinpoints the core cross-modal inconsistency, noting that the visual evidence does not correlate with the text narrative of a dead body. The model even demonstrates deeper understanding by identifying the potential manipulation tactic of pairing
Mooe experiment results detailed in Appendix A.4

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Published as a conference paper at ICLR 2026
plausible image with sensational text. Finally, by synthesizing these diverse reasoning paths, model confidently reaches the correct conclusion: Fake. Through this sample, we illustrate how social self-distillation equips MLLMs with diverse analytical perspectives comprehensive human-like thinking, thus effectively detecting complex multimodal misinformation.
5 CONCLUSION
In this work, we introduce a multi-social-age self-distillation framework, resolving the critical trade-off between the limited perspective single-agent models and inefficiency of multi-agent systems for MMD task. In our framework, we first generate Social Chain-of-Thought (SCoT) data to distill diverse social perspectives into high-quality training examples. Based on SCoT data, propose the Social Correction Value-Driven Preference Optimization (SCPO) algorithm, which uniquely leverages the degree social misjudgement as a verifiable signal to adjust training. Our extensive experiments demonstrate the effectiveness, with our 7B model surpassing larger open- source models, dedicated multi-agent frameworks and even powerful proprietary models on chal- lenging misinformation benchmarks. Ultimately, work provides insights for distilling collective social intelligence into foundation models, offering robust and efficient path to thinking as society.
6 ACKNOWLEDGMMENTS
This work is supported by the National Natural Science Foundation of China under Grants 62425307, 62472303, and 62402335.
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Reviews:
[{"review_id": "UcpXibuWf1", "reviewer": "Reviewer_2dcz", "summary": "The paper proposes a novel framework called Multi-Social-Agent Self-Distillation (MSA-SD) to enhance the reasoning capability and social robustness of Multimodal Misinformation Detection (MMD). Based on the Qwen2-VL-7B model, the authors simulate feedback from multiple social roles to generate Social Chain-of-Thought (SCoT) data and introduce a new preference alignment algorithm, Social Correction Value-Driven Preference Optimization (SCPO), which dynamically focuses on samples with large social cognition divergences. Experiments conducted on two multimodal fact-checking benchmarks, MFC-Bench and MMFakeBench, demonstrate that the proposed framework significantly outperforms multi-agent methods as well as various open-source and closed-source MLLM models.", "questions": "1.Insufficient ablation and error analysis. Although multiple optimization methods are compared, the paper does not quantify how the quality of SCoT data affects performance, nor analyze the independent contributions of each module (e.g., coordinator/summarizer). In Sec. 4.2 (Table 1), results are reported without standard deviations or significance tests. The paper also fails to present performance differences across samples with varying difficulty levels (high/low sc(x)).\n\n2.Missing experimental details. The concept of “thinking as society” lacks a concrete definition or illustrative examples. Hardware environment, training duration, and batch size should be explicitly reported. Key results in tables should include ± standard deviation or t-test significance. The differences between open and closed prompts should be analyzed, along with a discussion of model generalization.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Innovative framework design. The paper proposes a multi-social-agent self-distillation mechanism that integrates multi-perspective social reasoning into a single model, demonstrating strong novelty.\n\n2.Introduction of the social correction value sc(x). The dynamic weighting of samples through sc(x) provides a new, verifiable optimization signal. The “thinking as society” paradigm holds potential for broad conceptual influence.\n\n3.Comprehensive and significant experimental validation. Based on Qwen2-VL, SCPO achieves a +9.9% accuracy improvement, significantly surpassing InternVL3 and Qwen2.5-VL. Using GPT-4 to evaluate reasoning quality, SCPO achieves the best performance across four dimensions—misleadingness, informativeness, logicality, and readability.", "weaknesses": "1.Insufficient ablation and error analysis. Although multiple optimization methods are compared, the paper does not quantify how the quality of SCoT data affects performance, nor analyze the independent contributions of each module (e.g., coordinator/summarizer). In Sec. 4.2 (Table 1), results are reported without standard deviations or significance tests. The paper also fails to present performance differences across samples with varying difficulty levels (high/low sc(x)).\n\n2.Missing experimental details. The concept of “thinking as society” lacks a concrete definition or illustrative examples. Hardware environment, training duration, and batch size should be explicitly reported. Key results in tables should include ± standard deviation or t-test significance. The differences between open and closed prompts should be analyzed, along with a discussion of model generalization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9TZPpsx6kS", "reviewer": "Reviewer_FGcP", "summary": "This paper proposes a novel Multi-Social-Agent Self-Distillation framework for Multimodal Misinformation Detection (MMD), addressing the critical trade-off between the limited perspective of single-agent methods and the high computational cost of multi-agent systems. The core idea is to simulate a diverse society of MLLM agents to generate multi-perspective judgments on multimodal misinformation, synthesizing their collective feedback into high-quality Social Chain-of-Thought (SCoT) data. To effectively utilize this data, the authors introduce Social Correction Value-Driven Preference Optimization (SCPO), a new alignment algorithm. Extensive experiments on the MFC-Bench and MMFakeBench benchmarks demonstrate its effectiveness.", "questions": "1 Could the authors provide a more in-depth analysis or qualitative examples demonstrating the actual cognitive diversity of the reasoning generated by the different user agents? How can we be assured that the profile-based prompting doesn't lead to a homogenized or biased \"society\" that limits the effectiveness of the collective reasoning?\n\n2 To better understand the individual contributions, could the authors include an ablation study comparing the performance when the high-quality SCoT data is trained using standard DPO or SFT, versus the proposed SCPO algorithm? \n\n3 Could the authors provide an analysis of the failure cases on the benchmark datasets?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1 The central concept of \"thinking as society\" is highly innovative and well-motivated. The framework effectively addresses a genuine dilemma in the field by internalizing the benefits of multi-agent reasoning into a single, efficient model through self-distillation.\n\n2 The paper introduces two key novel components: the generation of Social Chain-of-Thought (SCoT) data through simulated social feedback and the SCPO algorithm. \n\n3 The experimental evaluation is thorough, covering two relevant benchmarks (MFC-Bench, MMFakeBench) and a wide range of baselines, including state-of-the-art open-source and proprietary models. \n\n4 The paper is generally well-written and logically organized. The figures are effective in illustrating the core concepts and the data generation pipeline.", "weaknesses": "1 The quality of the final model is heavily dependent on the quality and diversity of the simulated social agents. The paper relies on profile-based prompting to create diverse agents, but the actual diversity and realism of the generated reasoning are not deeply analyzed or validated. There is a risk that the simulated \"society\" could exhibit unforeseen biases or lack true cognitive diversity, limiting the benefit of the approach.\n\n2 The paper presents the full SCPO framework but lacks a crucial ablation study. It's unclear how much of the performance gain comes from the SCoT data itself versus the specific SCPO optimization algorithm. \n\n3 The paper focuses on the impressive results but provides little analysis of where the proposed model still fails. Understanding the types of multimodal misinformation that remain challenging would provide deeper insight into the method's limitations and guide future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LriPOZK0eH", "reviewer": "Reviewer_7qYS", "summary": "This paper presents a Multi-Social-Agent Self-Distillation framework for multimodal misinformation detection (MMD) leveraging Large Multimodal Language Models (MLLMs). The proposed approach first simulates diverse social agents to produce multiview feedback on misinformation samples, synthesizing this into Social Chain-of-Thought (SCoT) data. Building on this, the Social Correction Value-Driven Preference Optimization (SCPO) algorithm is introduced, which dynamically scales the alignment loss by the observed social misjudgment. Experiments on MFC-Bench and MMFakeBench show that this self-distilled, socially-aware Qwen2-VL-based model outperforms a variety of open-source and proprietary baselines and matches or exceeds complex multi-agent or larger-scale systems on key multimodal reasoning tasks.", "questions": "1. How well do the simulated social agents reflect real human social cognition? Specifically, is there any quantitative or qualitative validation comparing agent-generated perspectives with real crowd or expert responses?\n\n2. Is the linear weighting $1+ωsc(x)$ sufficient and justified? Can the authors provide evidence that this formulation meaningfully differentiates sample difficulty, especially given the limited gains over ORPO in Table 3?\n\n3. Which models are used for user agents, the coordinator, and the summarizer, and why? Do different context lengths (number of user numbers) affect the model selection for coordinator and summarizer agents?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper tackles an important and timely challenge in MMD, seeking to integrate multi-perspective social reasoning into a single, efficient agent, thereby addressing the real-world complexity of misinformation and limitations of both single- and multi-agent systems.\n2. The methodology is clearly articulated: simulating social context via user-profiled MLLMs, aggregating their reasoning, and synthesizing collective chains of thought. The data pipeline is well-detailed, and the synthesized dataset design is a practical advance.\n3. SCPO introduces an interpretable, mathematically explicit mechanism to adjust model alignment based on task difficulty, which stands out against prior, less verifiable margin-scaling approaches.\n4. The paper is competently written and mostly clear, with methodological details in both equations and pipeline diagrams.", "weaknesses": "1. While the diversity of simulated agents is claimed, the realism of their social perspectives is not externally validated. It remains unclear to what extent these agent-generated viewpoints transfer to true human social cognition. The manuscript omits ablation or validation against authentic crowd or expert responses, leaving the practical value of the “social” aspect somewhat speculative.\n2. Although the authors proved the range of values ​​for $sc(x)$ in the appendix, I still have doubts about its simple form as $1+ωsc(x)$ in the social correction value: Is this linear weighting sufficient to distinguish samples of different difficulties? For example, for a completely correct simple sample, its social correction value is still 1, no different from the standard ORPO. Furthermore, the results in Table 3 show that the improvement of SCPO compared to ORPO is not significant, further raising concerns about the rationality of this weighting form.\n3. During data synthesis, different large models naturally produce different responses. However, the paper does not specify which models are used for the user agents, coordinator agent, or summarizer agent, nor the rationale behind these choices. Moreover, model differences in handling long contexts (e.g., varying user_number) can substantially affect the outputs generated by the coordinator and summarizer agents, potentially introducing additional variability into the synthesized data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p8m9guEsYp", "reviewer": "Reviewer_NiZd", "summary": "This paper addresses multimodal misinformation detection (MMD) under realistic social media conditions. The authors argue that existing single-agent LMM-based detectors lack perspective diversity and are thus easily misled, while multi-agent systems provide multiple viewpoints but are inefficient and hard to optimize end-to-end. To resolve this trade-off, the paper proposes a “Multi-Social-Agent Self-Distillation” framework that aims to compress multi-agent social reasoning into a single model. Their SCPO-tuned 7B model achieves superior or comparable performance to larger open-source, multi-agent, and even proprietary models like GPT-4o and Claude 3.5-Sonnet on MFC-Bench and MMFakeBench under both open- and closed-prompt evaluations.", "questions": "6. The paper lacks clarity regarding human supervision and quality control, including the number of annotators involved, their expertise, and the specific labeling guidelines used to ensure annotation reliability.\n7. The paper should report the proportion of samples assigned to each feedback category (correct, partially correct, incorrect), since these distributions directly influence the SCPO weighting and training balance.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This paper attempts to model socially diverse reasoning and then distill it into a single deployable model is conceptually compelling and practically relevant for scalable moderation systems.\n2. By coupling Social Chain-of-Thought (SCoT) data synthesis with the Social Correction Value-Driven Preference Optimization (SCPO) algorithm, the framework achieves robust reasoning performance.", "weaknesses": "1. The paper should be more precise here: is there any real agent interaction, disagreement resolution protocol, or iterative critique loop beyond single-shot persona prompting and post-hoc summarization? Otherwise, “multi-social-agent” risks overselling standard multi-persona sampling plus LLM judging.\n2. Need for overlap analysis between source data and evaluation benchmarks.\n3. The paper should clearly states: What is the size of the original pooled dataset (before filtering)? How large is the filtered “validation set”? How many instances survive as “High-quality Training Data” after selection?\n4. The “Coordinator Agent” and “Summarizer Agent” are described as specialized components, yet their concrete implementation remains vague. Are these modules merely prompt-based LLM refiners, and if so, how do they differ functionally from standard LLM data post-processing?\n5. Table 3 shows only marginal improvement of SCPO over ORPO (67.15 vs 66.30 accuracy; 66.83 vs 66.01 F1), suggesting that performance bottlenecks may stem more from the availability and quality of CoT-style supervision data than from the SCPO weighting mechanism itself", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)