# Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning

arXiv: https://arxiv.org/abs/2501.05069 · HTML: https://mlsanity.com/paper/2501.05069/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [VideoAgent: Long-form Video Understanding with Large Language Model as Agent](https://mlsanity.com/paper/2403.10517/) (2024)
- [VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos](https://mlsanity.com/paper/2405.19209/) (2024)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)
- [Explaining Answers with Entailment Trees](https://mlsanity.com/paper/2104.08661/) (2021)
- [Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://mlsanity.com/paper/2210.12217/) (2022)
- [How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs](https://mlsanity.com/paper/2405.03690/) (2024)
- [MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning](https://mlsanity.com/paper/2409.17647/) (2024)
- [NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning](https://mlsanity.com/paper/2209.07662/) (2022)
- [Language Models with Rationality](https://mlsanity.com/paper/2305.14250/) (2023)
- [TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning](https://mlsanity.com/paper/2402.19467/) (2024)
- [Few-shot Action Recognition via Intra- and Inter-Video Information Maximization](https://mlsanity.com/paper/2305.06114/) (2023)

## Built on by (most cited first, 12 of 12)

- [Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)](https://mlsanity.com/paper/2504.03151/) (2025)
- [VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues](https://mlsanity.com/paper/2506.21742/) (2025)
- [CogStream: Context-guided Streaming Video Question Answering](https://mlsanity.com/paper/2506.10516/) (2025)
- [Instance-Aligned Captions for Explainable Video Anomaly Detection](https://mlsanity.com/paper/2601.08155/) (2026)
- [Reinforce to Learn, Elect to Reason: A Dual Paradigm for Video Reasoning](https://mlsanity.com/paper/2604.04379/) (2026)
- [POVQA: Preference-Optimized Video Question Answering with Rationales for Data Efficiency](https://mlsanity.com/paper/2510.01009/) (2025)
- [VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs](https://mlsanity.com/paper/2609.16722/) (2026)
- [VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering](https://mlsanity.com/paper/2508.03039/) (2025)
- [Counterfactual Reasoning for Fine-Grained Evidence Disentanglement in VideoQA](https://mlsanity.com/paper/2606.09181/) (2026)
- [GRACE: Boosting Video MLLMs with Grounded Action-Centric Evidence for Viewer Sentiment Prediction](https://mlsanity.com/paper/2606.16198/) (2026)
- [Agentic Video Understanding: A Survey](https://mlsanity.com/paper/2609.31713/) (2026)
- [EventLens: Event-Structure Reinforcement Learning for Video Understanding](https://mlsanity.com/paper/2610.16172/) (2026)

## Abstract (arXiv)

This paper proposes the first video-grounded entailment tree reasoning method for commonsense video question answering (VQA). Despite the remarkable progress of large visual-language models (VLMs), there are growing concerns that they learn spurious correlations between videos and likely answers, reinforced by their black-box nature and remaining benchmarking biases. Our method explicitly grounds VQA tasks to video fragments in four steps: entailment tree construction, video-language entailment verification, tree reasoning, and dynamic tree expansion. A vital benefit of the method is its generalizability to current video and image-based VLMs across reasoning types. To support fair evaluation, we devise a de-biasing procedure based on large-language models that rewrites VQA benchmark answer sets to enforce model reasoning. Systematic experiments on existing and de-biased benchmarks highlight the impact of our method components across benchmarks, VLMs, and reasoning types.