[Paper] VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice

Source: arXiv - 2601.05175v1

Overview

The paper VideoAuto‑R1 investigates when large multimodal models really need to “think out loud” (chain‑of‑thought) for video understanding. The authors find that, for many tasks, a direct answer is just as good—and far cheaper—than a full reasoning trace. Building on this insight, they propose a “think‑once, answer‑twice” framework that decides on‑the‑fly whether to invoke explicit reasoning, delivering state‑of‑the‑art accuracy while cutting inference cost by more than three times.

Key Contributions

• Empirical study of CoT vs. direct answering for reinforcement‑learning‑trained video models, showing that CoT often offers no accuracy gain despite higher compute.
• VideoAuto‑R1 framework that:
  • Generates an initial answer,
  • Optionally performs a reasoning pass,
  • Produces a reviewed final answer.
• Dual‑reward supervision: both the first and second answers are trained with verifiable reward signals, encouraging the model to self‑assess confidence.
• Dynamic inference control: the model uses the confidence of the initial answer to decide whether to trigger the reasoning stage, effectively “thinking only when needed.”
• Efficiency gains: average response length drops from ~149 tokens to ~44 tokens (≈3.3× reduction) while achieving new SOTA on several video QA and grounding benchmarks.
• Task‑aware activation patterns: low reasoning activation on perception‑heavy tasks, high activation on reasoning‑intensive queries, confirming the adaptive nature of the approach.

Methodology

Training Phase – “Thinking Once, Answering Twice”

1. Initial Answer – The model processes the video and question, emitting a concise answer token sequence.
2. Reasoning Pass – Conditioned on the initial answer, the model generates a chain‑of‑thought explanation (e.g., “I saw a red ball, then it rolled …”).
3. Reviewed Answer – Using both the video context and the generated reasoning, the model outputs a final answer.
4. Supervision – Both the initial and final answers receive reward‑based supervision (e.g., correctness, alignment with ground‑truth), while the reasoning trace is trained implicitly through the final answer’s reward.

Inference Phase – Confidence‑Driven Skipping

• The model computes a confidence score (e.g., softmax probability or calibrated uncertainty) for the initial answer.
• If confidence exceeds a learned threshold, the answer is returned immediately (no reasoning).
• Otherwise, the model proceeds to generate the reasoning and the reviewed answer.

Implementation Details

• Built on top of a reinforcement‑learning‑trained video‑language backbone (e.g., Video‑BERT + RL fine‑tuning).
• Rewards are derived from task‑specific metrics (QA accuracy, grounding IoU).
• Token‑level efficiency measured by average output length; compute savings are proportional to reduced token generation.

Results & Findings

• Accuracy: Consistently improves or matches the best published numbers across both QA and grounding tasks.
• Efficiency: Average output length shrinks from ~149 tokens (full CoT) to ~44 tokens, translating to ~3× faster inference on GPU/CPU.
• Reasoning Activation: Only ~12 % of perception‑oriented queries trigger the reasoning stage, while ~68 % of reasoning‑heavy queries do, confirming the model’s ability to self‑regulate.

Practical Implications

• Cost‑Effective Deployments – Video‑centric AI services (e.g., video assistants, content moderation, interactive tutoring) can now afford richer multimodal reasoning without a proportional increase in latency or cloud compute bills.
• Dynamic Resource Allocation – The confidence‑driven gating mechanism can be integrated into existing pipelines to automatically balance speed vs. interpretability on a per‑request basis.
• Explainability on Demand – Developers can expose the reasoning trace only for low‑confidence or high‑risk queries, giving end‑users transparent explanations when needed while keeping routine answers lightweight.
• Framework‑Agnostic – The “think‑once, answer‑twice” paradigm can be retro‑fitted onto any video‑language model that already supports token‑level generation, making it a low‑effort upgrade for existing products.
• Better User Experience – Faster responses for the majority of queries (perception‑heavy) while still providing deep reasoning for complex questions improves overall interaction quality.

Limitations & Future Work

• Reward Design Dependency – The dual‑reward setup relies on well‑calibrated, task‑specific reward signals; poorly designed rewards could misguide the confidence estimator.
• Scalability to Longer Videos – Experiments focus on clips up to ~10 seconds; handling hour‑long videos may require additional temporal abstraction mechanisms.
• Reasoning Quality Evaluation – The paper treats the reasoning trace as an intermediate step; systematic human evaluation of explanation fidelity is left for future studies.
• Cross‑Modal Generalization – Extending the approach to audio‑only or multimodal (audio‑visual‑text) tasks remains an open question.
• Adaptive Threshold Learning – Current confidence thresholds are static; future work could explore meta‑learning or reinforcement strategies to adapt thresholds per user or domain.

Authors

• Shuming Liu
• Mingchen Zhuge
• Changsheng Zhao
• Jun Chen
• Lemeng Wu
• Zechun Liu
• Chenchen Zhu
• Zhipeng Cai
• Chong Zhou
• Haozhe Liu
• Ernie Chang
• Saksham Suri
• Hongyu Xu
• Qi Qian
• Wei Wen
• Balakrishnan Varadarajan
• Zhuang Liu
• Hu Xu
• Florian Bordes
• Raghuraman Krishnamoorthi
• Bernard Ghanem
• Vikas Chandra
• Yunyang Xiong

Paper Information

• arXiv ID: 2601.05175v1
• Categories: cs.CV
• Published: January 8, 2026
• PDF: Download PDF