videoauto-r1-adaptive-video-reasoning

name	videoauto-r1-adaptive-video-reasoning
title	VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.05175
keywords	["Video Understanding","Adaptive Reasoning","Efficiency","Multi-modal LLMs"]
description	Reduce inference latency and token cost in video question-answering by selectively invoking chain-of-thought reasoning. VideoAuto-R1 uses confidence-based early-exit to determine when explicit reasoning is necessary, achieving 3.3× response length reduction while maintaining state-of-the-art accuracy.

When to Use This Skill

• Video understanding tasks with mixed perception and reasoning needs
• Applications requiring efficient inference (reduced token generation)
• Scenarios where reasoning quality matters more than speed
• Tasks combining perception (MVBench) and reasoning (VideoMMMU)
• Real-time video QA with computational constraints

When NOT to Use This Skill

• All tasks equally require step-by-step reasoning
• Output length minimization is less important than accuracy
• Applications needing guaranteed reasoning traces for auditability
• Domains where skipping reasoning increases error rates significantly

Problem Summary

Chain-of-thought reasoning in video understanding produces detailed step-by-step analyses, but often underperforms or merely matches direct answering while incurring 2-3× computational overhead. This creates a fundamental inefficiency: perception-heavy tasks (detecting objects, describing scenes) benefit minimally from reasoning, while reasoning-intensive tasks (causal inference, counting across frames) require detailed intermediate steps. Existing approaches treat reasoning as always-on, wasting computation on tasks that don't need it.

Solution: Think When Necessary Framework

Train models to generate initial answers, evaluate confidence, and only invoke reasoning when necessary.

class VideoAutoR1:
    def __init__(self, base_model):
        self.model = base_model

    def forward_with_adaptive_reasoning(self, video, question):
        """Generate initial answer, then decide on reasoning"""

        # Step 1: Generate initial answer
        initial_logits = self.model.generate_initial_answer(video, question)
        initial_answer = sample_from_logits(initial_logits)

        # Step 2: Compute confidence from initial response
        initial_logprobs = get_token_logprobs(initial_logits)
        # Use length-normalized mean log probability
        confidence_score = initial_logprobs.mean()
        confidence_normalized = confidence_score / sqrt(len(initial_answer))

        # Step 3: Decide on reasoning via threshold
        threshold =   
         confidence_normalized > threshold:
            
             initial_answer, reasoning_trace=
        :
            
            reasoning_trace = .model.generate_reasoning(
                video, question, initial_answer
            )
            
            refined_answer = .model.generate_refined_answer(
                video, question, reasoning_trace
            )
             refined_answer, reasoning_trace=reasoning_trace

     ():
        
         batch  dataset:
            video, question, gold_answer = batch

            
            initial_answer = .model.generate_initial_answer(video, question)
            reasoning_trace = .model.generate_reasoning(
                video, question, initial_answer
            )
            refined_answer = .model.generate_refined_answer(
                video, question, reasoning_trace
            )

            
            initial_correctness = (initial_answer == gold_answer)
            refined_correctness = (refined_answer == gold_answer)

            
            reward_initial = initial_correctness * 
            reward_refined = refined_correctness *  + fallback_bonus

            
            loss = compute_policy_gradient_loss(
                [reward_initial, reward_refined],
                weights=[, ]
            )
            loss.backward()