UpstreamQA: A Modular New Framework for Video Question Answering Empowered by Explicit Reasoning

The research team proposes the UpstreamQA framework to address the limitations of implicit reasoning in Video Question Answering (VideoQA) tasks. By combining the explicit reasoning capabilities of Large Reasoning Models (LRMs) with the video understanding capabilities of Multimodal Models (LMMs), this framework achieves dual improvements in performance and interpretability. This article will introduce it from aspects such as background, methodology, experiments, advantages, and limitations.

Challenges of Video Question Answering

Video question answering requires simultaneous understanding of visual spatial information, temporal dynamics, and linguistic semantics. Current mainstream LMMs use end-to-end implicit reasoning, which has two major issues:

1. Lack of interpretability: It is difficult to locate the root cause of errors (visual understanding/temporal reasoning/question comprehension bias);
2. Low accuracy in multi-step reasoning: Complex questions require multi-hop reasoning, and implicit methods easily lead to error propagation.

Potential and Dilemmas of Explicit Reasoning

Large reasoning models (such as OpenAI's o-series) improve interpretability and multi-step reasoning accuracy by generating intermediate steps, but they lack native support for the temporal dimension of videos, making it impossible to directly leverage their advantages.

UpstreamQA adopts a modular design, decomposing reasoning into two stages: upstream reasoning and downstream question answering:

• Upstream Reasoning Stage: Multimodal LRMs perform object recognition (key object attributes + temporal tracking) and scene context generation (high-level information such as location/time/events), outputting structured reasoning trajectories (including intermediate judgments and logical chains);
• Downstream Question Answering Stage: LMMs use the upstream reasoning trajectory plus original video information to perform final question answering, without needing to understand from scratch.

The research team evaluated on the OpenEQA and NExTQA datasets, using combinations of LRMs (o4-mini, Gemini 2.5 Pro) and LMMs (GPT-4o, Gemini 2.5 Flash):

• Findings: Explicit reasoning improves performance in most scenarios, and interpretability is significantly enhanced (errors are diagnosed via reasoning trajectories);
• Exceptions: When baseline performance is high, explicit reasoning may lead to performance degradation due to additional complexity or error propagation.

Framework Advantages

1. Diagnostic Transparency: Decompose the reasoning process to precisely locate problem links;
2. Component Replaceability: Each module can be upgraded independently without reconstructing the system;
3. Controllable Reasoning Depth: Adjust the detail level of upstream reasoning according to task complexity.

Applicable Scenarios

Suitable for education/training applications requiring high interpretability, video analysis systems for safety-critical decisions, and content understanding platforms that need manual review.

Limitations

1. Computational Overhead: Explicit reasoning increases time and cost;
2. Risk of Error Propagation: Errors in upstream reasoning directly affect downstream results.

Improvement Directions

• Develop more robust upstream reasoning modules to reduce error propagation;
• Explore adaptive mechanisms to dynamically decide whether to enable explicit reasoning;
• Extend to broader tasks such as video summarization and retrieval.

UpstreamQA provides a new paradigm for the video question answering field, balancing performance and interpretability through explicit decomposition and modular design. This work emphasizes the importance of intermediate representations and structured reasoning, providing references for the design of complex multimodal AI systems, especially having guiding significance in balancing performance and transparency.