CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
Jinjie Shen, Yaxiong Wang, Yujiao Wu, Lechao Cheng, Tianrui Hui, Nan Pu, Zhihui Li, Zhun Zhong
Abstract
The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability. Existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, resulting in poor generalization to emerging manipulation types. We observed that the essence of manipulated misinformation lies in its intrinsic conflicts, \textbf{i.e.,} semantic or physical inconsistencies either across modalities or with common world knowledge. Inspired by this observation, we propose \textbf{C}onflict-\textbf{O}riented \textbf{RE}asoning (\textbf{CORE}) framework, an effective paradigm that learns to endows multimodal large language models (MLLMs) with explicit conflict-capturing capability. To this end, CORE first constructs the Conflict Attribution Corpus (CAC) with fine-grained annotations of conflict factors and sources, providing essential data support for subsequent conflict perception training. By performing conflict-oriented representation enhancement and reasoning based on CAC, CORE achieves robust and generalizable conflict detection, effectively and rapidly adapting to unseen manipulation types with a few samples or in even zero-shot settings. Extensive experiments demonstrate that CORE surpasses state-of-the-art models. The dataset and code are publicly available at https://github.com/shen8424/CORE.
AI Impact Assessments
(1 models)Scientific Impact Assessment: CORE – Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
1. Core Contribution
CORE addresses a critical limitation of existing multimodal misinformation detection methods: their reliance on manipulation-specific model designs and large-scale labeled data, which leads to poor generalization to novel manipulation types. The key insight is that manipulated misinformation fundamentally contains intrinsic conflicts—semantic or physical inconsistencies across modalities or with world knowledge. Rather than designing detection mechanisms for specific forgery traces, CORE endows MLLMs with explicit conflict-capturing capability through three stages: (1) constructing the Conflict Attribution Corpus (CAC) with 14k samples annotated with fine-grained conflict factors and sources, (2) Modality Bridging Pre-Training (MBPT) using a cross-modal aligner, and (3) Conflict Perception Training (CPT) that reshapes conceptual boundaries in the feature space via conflict-aware contrastive learning.
This represents a genuine paradigm shift from "detect specific artifacts" to "detect fundamental inconsistencies," which is conceptually appealing and practically motivated.
2. Methodological Rigor
Strengths in experimental design:
Potential concerns:
3. Potential Impact
Practical applications are significant: the framework directly addresses the real-world "arms race" between forgery creation and detection. The rapid adaptation capability (achieving strong performance with 100 samples) is particularly valuable for deploying detection systems against emerging manipulation techniques.
Broader influence:
Limitations on impact:
4. Timeliness & Relevance
This paper is highly timely. The proliferation of generative AI tools (DALL-E, Midjourney, Sora, GPT-4o image generation) has dramatically lowered the barrier to creating convincing multimodal misinformation. The field desperately needs detection paradigms that generalize beyond specific manipulation signatures. CORE directly addresses this bottleneck by shifting focus from manipulation-specific artifacts to fundamental logical inconsistencies—a principle that should remain relevant as generation techniques evolve.
The positioning within the MLLM ecosystem is also well-timed, leveraging the rich world knowledge of foundation models while addressing their specific weaknesses in conflict perception.
5. Strengths & Limitations
Key Strengths:
Notable Weaknesses:
Overall Assessment
CORE makes a meaningful conceptual and practical contribution to multimodal misinformation detection. The shift from manipulation-specific to conflict-oriented detection is well-motivated, rigorously validated, and timely. The framework demonstrates strong generalization capabilities, though questions remain about false positive rates, scalability, and robustness to adversarial attacks designed to minimize detectable conflicts.
Generated Jun 3, 2026
Comparison History (23)
Paper 2 likely has higher scientific impact: it targets a timely, high-stakes real-world problem (multimodal misinformation) with broad societal relevance and clear deployment pathways. It introduces a generalizable paradigm (conflict-oriented reasoning) plus a new annotated resource (CAC) and claims strong few/zero-shot adaptation—features that typically drive adoption and follow-on work. The public dataset/code further increases reuse. Paper 1 is conceptually novel and important for LLM evaluation/selection, but its impact may be narrower (primarily methodological for Best-of-N metrics) and less directly tied to immediate applications.
Paper 2 is more novel and potentially higher-impact: it proposes a general bilevel meta-optimization architecture where an outer loop improves an inner LLM-based research loop by generating and injecting new search mechanisms at runtime. If robust, this could influence agentic AI, automated science, optimization, and software engineering broadly. Paper 1 addresses an important, timely application (multimodal manipulation detection) with a solid conflict-based framing and dataset, but it is more domain-specific and likely to have narrower cross-field impact. Paper 2’s methodological risk is higher, but upside is larger.
Paper 2 (CORE) likely has higher impact due to broader scope and transferability: it targets general multimodal misinformation/manipulation, emphasizes a manipulation-agnostic principle (conflict/inconsistency) and demonstrates few-shot/zero-shot adaptation to unseen types, which is timely and widely applicable across security, HCI, social media integrity, and multimodal AI. Its publicly released dataset/code also boosts adoption. Paper 1 is highly valuable and rigorous for healthcare safety, but is more domain-specific and its benchmark/model may generalize less beyond medical imaging.
Paper 1 (CORE) presents a concrete, well-validated framework with extensive experiments, a publicly available dataset and code, addressing the timely and critical problem of multimodal misinformation detection. It introduces novel technical contributions (Conflict Attribution Corpus, conflict-oriented reasoning for MLLMs) with demonstrated generalization to unseen manipulation types. Paper 2 proposes a conceptual governance framework for agentic AI that, while addressing an important problem, lacks rigorous empirical validation and reads more as a position/design paper. CORE's methodological rigor, reproducibility, and direct applicability give it higher near-term scientific impact.
Paper 2 addresses the urgent, widespread threat of AI-generated multimodal misinformation. By focusing on intrinsic conflict reasoning rather than artifact-based detection, it offers a highly generalizable, zero-shot capable solution for emerging manipulation types. While Paper 1 provides valuable advancements in clinical EHR prediction, Paper 2's broader applicability across domains, extreme timeliness regarding generative AI societal risks, and the introduction of a novel fine-grained attribution corpus give it a higher potential for widespread scientific and societal impact.
Paper 2 (CORE) addresses the critical and timely problem of multimodal fake news detection with a novel framework that offers practical generalization to unseen manipulation types via conflict-oriented reasoning. It provides a new dataset (CAC), open-source code, and demonstrates state-of-the-art results, enabling broad real-world impact on misinformation detection. Paper 1 identifies an important vulnerability in LLM-as-judge systems and proposes ERS, but its scope is narrower—focused on evaluation robustness—and its practical remediation pathways are less developed. Paper 2's broader societal relevance and methodological contributions give it higher estimated impact.
Paper 2 likely has higher scientific impact: it proposes a new, generalizable framework (CORE) plus a new annotated dataset (CAC) and open code, enabling broad reuse and benchmarking. Its focus on multimodal manipulation detection is timely and has clear real-world applications in platform integrity, journalism, and security. The methodological contribution (conflict-oriented representation + reasoning, few/zero-shot generalization, extensive experiments) suggests stronger rigor and scalability. Paper 1 is insightful and novel as a rare audit of covert LLM persuasion, but it is largely retrospective/observational and constrained by a single discontinued experiment, limiting generalizability and downstream adoption.
CORE addresses the broadly impactful problem of multimodal misinformation detection with a novel conflict-oriented reasoning framework that generalizes to unseen manipulation types. Its contributions—a new annotated corpus, a generalizable detection paradigm using MLLMs, and strong zero/few-shot performance—have wide applicability across misinformation research, social media, and AI safety. Paper 2 (AuditFlow) tackles a valuable but narrower domain (financial audit verification) with a well-designed multi-agent system, but its impact is more domain-specific. CORE's broader societal relevance and cross-field applicability give it higher estimated impact.
Paper 2 addresses a critical and universal bottleneck in modern AI: inference-time scaling and compute budget allocation. By applying economic principles to optimize LLM reasoning under resource constraints, it offers massive potential for real-world cost savings and efficiency gains across all LLM deployments. While Paper 1 tackles an important societal issue (fake news), Paper 2's methodology has a broader, immediate impact on the foundational mechanics of deploying large-scale AI systems.
Paper 1 addresses a fundamental and broadly relevant problem in LRM reliability—harmful overthinking—with a novel evaluation protocol that reveals surprising findings (up to 21% accuracy improvement by early stopping). Its insights generalize across multimodal and language-only benchmarks, impacting the entire reasoning model ecosystem. The work has broad implications for model design, efficiency, and trustworthiness. Paper 2, while addressing an important misinformation detection problem, is more narrowly focused on a specific application domain with incremental methodological contributions (applying MLLMs to conflict detection).
Paper 1 addresses a critical and highly timely societal issue (multimodal fake news and generative AI manipulation) with a novel approach focusing on intrinsic semantic and physical conflicts. Its ability to generalize to unseen manipulation types in zero-shot settings offers broader, more immediate real-world impact compared to Paper 2, which presents a valuable but somewhat more incremental improvement (3.7% success rate increase) in LLM agent instruction clarification.
Paper 1 addresses a critical and urgent societal challenge—multimodal fake news and generative AI manipulation. By introducing a novel, generalizable 'conflict-oriented reasoning' paradigm for MLLMs, it overcomes the severe limitations of existing domain-specific models. Its zero-shot adaptation capabilities provide immediate real-world value for AI safety, security, and trust. While Paper 2 offers a valuable benchmark for GUI agents, Paper 1's methodological innovation and its profound, widespread implications across security and information integrity give it a higher potential for broad scientific and societal impact.
Paper 2 likely has higher scientific impact due to a novel, generalizable detection paradigm (conflict-oriented reasoning) that targets the core weakness of manipulation detectors—poor transfer to unseen manipulations—while leveraging MLLMs for few/zero-shot adaptation. Its methodological contribution spans a new framework plus a fine-grained annotated corpus (CAC) and is immediately relevant to urgent real-world misinformation threats, with broad applicability across multimodal AI safety, media forensics, and NLP/Vision. Paper 1 is valuable as a dataset/benchmark, but its scope is narrower (embodied-agent conversations/forensics) and may have more limited cross-domain uptake.
Paper 2 addresses the broadly impactful problem of multimodal fake news detection, which has significant societal relevance. Its conflict-oriented reasoning framework offers a generalizable paradigm applicable to unseen manipulation types, with zero-shot and few-shot capabilities. It introduces both a new dataset (CAC) and a reusable framework, increasing community adoption potential. Paper 1, while technically interesting, targets a narrower niche—closed-loop steering of VLA models—with impact limited primarily to the embodied AI community. Paper 2's broader applicability across misinformation research, content moderation, and MLLM reasoning gives it higher potential impact.
CORE addresses the urgent, broadly impactful problem of multimodal fake news detection with a generalizable framework that works in zero-shot/few-shot settings on unseen manipulation types. It offers a concrete, reproducible contribution (public dataset + code), strong experimental validation against SOTA, and high practical relevance given the proliferation of AI-generated misinformation. Paper 1 (TBS) introduces a novel multi-agent simulation framework grounded in social psychology, but its impact is more niche—focused on opinion dynamics simulation—and its evaluation is limited to a single policy scenario. CORE's broader applicability and timeliness give it higher potential impact.
Paper 2 addresses an urgent and highly critical societal issue—multimodal fake news generated by AI. Its approach to detecting emerging, unseen manipulations through zero-shot conflict reasoning offers immediate, broad real-world impact across cybersecurity, journalism, and social media. While Paper 1 presents strong advancements in embodied AI and human-robot interaction, the immediate societal threat of generative AI misinformation and the generalizability of Paper 2's framework give it a higher potential for widespread scientific and practical impact.
Paper 1 introduces a novel and highly practical paradigm ('handoff debt') that challenges the current single-run standard for evaluating AI coding agents. By formalizing and measuring the cost of interrupting and resuming tasks, it addresses a crucial gap in real-world multi-agent software engineering workflows. While Paper 2 tackles the critical societal issue of multimodal fake news, its approach of finding inconsistencies is a more incremental conceptual advance. Paper 1's fundamental shift in evaluation metrics and rigorous methodology will likely redefine how future autonomous coding agents are benchmarked and deployed.
Paper 2 addresses the highly timely and broadly impactful problem of AI-generated misinformation detection, proposing a novel conflict-oriented reasoning framework (CORE) that generalizes to unseen manipulation types—a significant advance over existing methods. It introduces both a new dataset and methodology with zero-shot/few-shot capabilities, has broad societal relevance, and provides public code/data. Paper 1, while solid, presents a relatively incremental architectural comparison (Transformer vs. LSTM) for hydrology with a somewhat expected finding that LSTMs outperform Transformers on sequential hydrologic tasks, limiting its novelty and breadth of impact.
Paper 1 presents a novel framework (CORE) with a new dataset and methodology for detecting multimodal manipulation, addressing a critical and growing problem in AI-generated misinformation. It offers a generalizable, zero-shot capable approach with strong experimental results and publicly available code/data, enabling broad adoption. Paper 2 provides important safety evaluations of LLMs for eating disorder queries but is more narrowly scoped as an evaluation study without proposing new technical solutions. Paper 1's methodological contributions, broader applicability across manipulation types, and reproducible artifacts give it higher potential impact.
Paper 1 is more methodologically and conceptually innovative: it reframes autonomous LLM RL from static “recipe search” to co-evolution of policy and training harness/diagnostics with backtesting and skill accumulation, a generally applicable training paradigm. Its scope spans math, coding, and long-horizon agentic software engineering, suggesting broad impact across RL, automated alignment, and agent training infrastructure. Paper 2 is timely and valuable (misinformation detection + dataset), but is more application-specific and likely to see narrower cross-field methodological uptake despite strong real-world relevance.