# Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning

arXiv: https://arxiv.org/abs/2404.12966 · HTML: https://mlsanity.com/paper/2404.12966/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models](https://mlsanity.com/paper/2311.06607/) (2023)
- [Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2403.16999/) (2024)
- [Scene Graph Reasoning for Visual Question Answering](https://mlsanity.com/paper/2007.01072/) (2020)
- [Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models](https://mlsanity.com/paper/2403.07304/) (2024)

## Built on by (most cited first, 17 of 17)

- [A Survey on Benchmarks of Multimodal Large Language Models](https://mlsanity.com/paper/2408.08632/) (2024)
- [UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding](https://mlsanity.com/paper/2504.04423/) (2025)
- [A Survey on Evaluation of Multimodal Large Language Models](https://mlsanity.com/paper/2408.15769/) (2024)
- [Not All LLM Reasoners Are Created Equal](https://mlsanity.com/paper/2410.01748/) (2024)
- [A Survey on Multimodal Benchmarks: In the Era of Large AI Models](https://mlsanity.com/paper/2409.18142/) (2024)
- [Reasoning Elicitation in Language Models via Counterfactual Feedback](https://mlsanity.com/paper/2410.03767/) (2024)
- [DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning](https://mlsanity.com/paper/2509.20912/) (2025)
- [Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation](https://mlsanity.com/paper/2503.10691/) (2025)
- [XLM for Autonomous Driving Systems: A Comprehensive Review](https://mlsanity.com/paper/2409.10484/) (2024)
- [Seeing vs. Believing: Evaluating the Language Bias of Open-Source MLLMs in Counter-Intuitive Scenes](https://mlsanity.com/paper/2601.07737/) (2026)
- [Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?](https://mlsanity.com/paper/2506.14805/) (2025)
- [CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection](https://mlsanity.com/paper/2507.05302/) (2025)
- [Visual Credit Audit for Multimodal Spatial Reasoning](https://mlsanity.com/paper/2607.27069/) (2026)
- [Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning](https://mlsanity.com/paper/2608.06938/) (2026)
- [CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators](https://mlsanity.com/paper/2605.09079/) (2026)
- [OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training](https://mlsanity.com/paper/2606.25906/) (2026)
- [SocialReasonBench: A Video-QA Benchmark for Social Reasoning with Counterfactual Narrative Videos](https://mlsanity.com/paper/2608.30716/) (2026)

## Abstract (arXiv)

Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first curate a Multimodal Assumptive Reasoning Benchmark (MARS-Bench) in this paper. Interestingly, we find that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question, whereas such presuppositions appear naive to human reasoning. Besides, we also propose a simple yet effective method, Active Deduction (AD), a novel reinforcement learning paradigm to encourage the model to actively perform composite deduction before reaching a final decision. Equipped with the proposed AD method, a MLLM demonstrates significant improvements in assumptive reasoning abilities without compromising its general-purpose question-answering performance. We also provide extensive evaluations of both open-source and private MLLMs on MARS-Bench, along with experimental analyses of the AD method.