# See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding

arXiv: https://arxiv.org/abs/2606.09064 · HTML: https://mlsanity.com/paper/2606.09064/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 31 of its 64 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

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

## Builds on (most cited first, 30 of 31)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs](https://mlsanity.com/paper/2406.07476/) (2024)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [Long Context Transfer from Language to Vision](https://mlsanity.com/paper/2406.16852/) (2024)
- [LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding](https://mlsanity.com/paper/2410.17434/) (2024)
- [VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling](https://mlsanity.com/paper/2501.00574/) (2024)
- [Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning](https://mlsanity.com/paper/2508.04416/) (2025)
- [R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning](https://mlsanity.com/paper/2505.02835/) (2025)
- [LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling](https://mlsanity.com/paper/2511.20785/) (2025)
- [Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding](https://mlsanity.com/paper/2503.18478/) (2025)
- [VLM-R3: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought](https://mlsanity.com/paper/2505.16192/) (2025)
- [Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy](https://mlsanity.com/paper/2502.05177/) (2025)
- [Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation](https://mlsanity.com/paper/2504.02438/) (2025)
- [Video-Thinker: Sparking "Thinking with Videos" via Reinforcement Learning](https://mlsanity.com/paper/2510.23473/) (2025)
- [Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding](https://mlsanity.com/paper/2508.20478/) (2025)
- [LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in Mechanism via Multi-Step Reasoning](https://mlsanity.com/paper/2509.24786/) (2025)
- [LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://mlsanity.com/paper/2512.20618/) (2025)
- [Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence](https://mlsanity.com/paper/2510.20470/) (2025)
- [REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding](https://mlsanity.com/paper/2511.13026/) (2025)
- [VR-Thinker: Boosting Video Reward Models through Thinking-with-Image Reasoning](https://mlsanity.com/paper/2510.10518/) (2025)
- [Video-R2: Reinforcing Consistent and Grounded Reasoning in Multimodal Language Models](https://mlsanity.com/paper/2511.23478/) (2025)
- [Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding](https://mlsanity.com/paper/2603.04977/) (2026)
- [VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification](https://mlsanity.com/paper/2604.01569/) (2026)
- [SeViCES: Unifying Semantic-Visual Evidence Consensus for Long Video Understanding](https://mlsanity.com/paper/2510.20622/) (2025)
- [See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection](https://mlsanity.com/paper/2604.24339/) (2026)
- [VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding](https://mlsanity.com/paper/2603.22285/) (2026)

## Built on by (most cited first, 2 of 2)

- [TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models](https://mlsanity.com/paper/2609.33414/) (2026)
- [Meta-Video: Efficient Standardized Evaluation for Video Understanding](https://mlsanity.com/paper/2610.18535/) (2026)

## Abstract (arXiv)

Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks. However, existing methods still face two key limitations: evidence acquisition often relies on a single search intent, and answer generation lacks an effective visual feedback mechanism. To address these limitations, we propose CoVER, a Comprehensive Visual Evidence and Reflection framework for long-video understanding. CoVER enables Video-LLMs to See More by dynamically gathering query-expanded visual evidence, and Think Deeper by verifying draft answers with effective answer-specific visual feedback. Together, these mechanisms shift long-video understanding from answer-centric generation to evidence-centric and visually verifiable reasoning. Experimental results show that CoVER-7B substantially outperforms models with the same parameter scale and even surpasses state-of-the-art closed-source models on certain metrics.