DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning
arXiv:2509.20912v4 Announce Type: replace Abstract: Recent advances in multimodal language models (MLLMs) have made thinking with images a dominant paradigm for multimodal reasoning. However, existing methods still fail to ensure evidence-answer consistency, where correct answers must be supported by correct visual evidence. To address this issue, we propose DeFacto, a counterfactual reasoning framework that explicitly aligns visual evidence with final answers. Our approach integrates three complementary training paradigms: positive, counterfactual, and random-masking. We further develop a lan
The rapid advancement and integration of multimodal language models necessitate more robust methods to ensure the reliability and factual grounding of their outputs.
This research is critical for developing more trustworthy and less 'hallucinatory' AI systems, which is a major barrier to broader adoption and deployment in sensitive applications.
The focus on counterfactual thinking and explicit visual evidence alignment introduces a new paradigm for enhancing reasoning and mitigating inconsistencies in MLLMs.
- · AI developers
- · Enterprises deploying MLLMs
- · Academic researchers in AI
- · Platforms reliant on unverified AI outputs
- · Less robust MLLM architectures
DeFacto directly improves the explainability and faithfulness of multimodal AI reasoning by aligning answers with visual evidence.
This improved reliability could accelerate the adoption of MLLMs in high-stakes fields like medicine or legal analysis, where evidence-based reasoning is paramount.
More trustworthy AI agents, leveraging such reasoning, could fundamentally change white-collar workflows by providing demonstrably sound analysis and decision support.
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Read at arXiv cs.AI