Leverage video generation models as unified multimodal reasoning engines that bridge text and vision by embedding reasoning as dynamic visual processes, enabling models to tackle both spatial puzzles and mathematical problems within a single temporal framework.
Leverage video generation models as unified multimodal reasoning engines that bridge text and vision by embedding reasoning as dynamic visual processes, enabling models to tackle both spatial puzzles and mathematical problems within a single temporal framework.
Title: Use Video Generation as a Universal Reasoning Modality
Text-centric reasoning (thinking with text) excels at logic but struggles with spatial problems. Image-centric reasoning (thinking with images) handles space but lacks temporal dynamics. Video reasoning bridges both: generate videos showing problem-solving steps, with text embedded in frames. This unifies multimodal understanding within a single continuous temporal process.
The key insight is that video naturally represents processes—drawing, moving, transforming—that neither text nor static images capture well.
Core Concept
Video as Unified Reasoning Substrate:
Dynamic Representation: Video captures continuous transformations over time
Text Embedding: Embed reasoning text within video frames (e.g., solving equations step-by-step)
Spatial Reasoning: Show spatial manipulations explicitly (moving, rotating, drawing)
Temporal Reasoning: Natural representation of sequences and processes
Single Model: One video generation model handles diverse reasoning tasks
Architecture Overview
Video Generation Model: Pre-trained (Sora-2 or similar) for high-fidelity generation
Task-Specific Prompting: Frame problems as video generation tasks
Text-in-Video Encoding: Embed reasoning steps as video content
Evaluation: Both spatial (puzzle accuracy) and mathematical (answer correctness)
Implementation Steps
1. Formulate Problem as Video Generation Task
Convert different problem types into video generation prompts.
classVideoReasoningPromptFormatting:
@staticmethoddefspatial_puzzle_to_video(puzzle_description):
"""Convert spatial puzzle to video generation prompt"""
prompt = f"""Generate a video showing the solution to this puzzle:
{puzzle_description}
The video should:
1. Start with the puzzle state
2. Show step-by-step manipulations
3. End with the solution
4. Include visual indicators (arrows, highlights) showing the transformations
"""
prompt
():
prompt =
prompt
():
prompt =
prompt
return
@staticmethod
def
math_problem_to_video
problem_text
"""Convert math problem to video generation prompt"""
f"""Generate a video showing the step-by-step solution to this math problem:
{problem_text}
The video should:
1. Display the problem at the beginning
2. Show each reasoning step on screen
3. Include handwritten/typeset equations
4. Highlight key transitions between steps
5. Display the final answer prominently
"""
return
@staticmethod
def
eyeballing_puzzle_to_video
puzzle_image
"""Convert visual puzzle to video generation prompt"""
f"""Generate a video showing how to solve this eyeballing puzzle:
[Image shown]
The video should:
1. Show the original puzzle
2. Demonstrate measurements or comparisons
3. Show the discovered relationship
4. End with the answer
"""
return
2. Generate Videos and Extract Answers
Use video generation model as reasoning engine.
classVideoReasoningEngine:
def__init__(self, video_model, answer_extractor):
self.video_model = video_model # Sora-2 or similarself.answer_extractor = answer_extractor
defsolve_spatial_problem(self, puzzle_description):
# Convert problem to video prompt
prompt = VideoReasoningPromptFormatting.spatial_puzzle_to_video(puzzle_description)
# Generate video showing solution
video = self.video_model.generate(
prompt=prompt,
resolution="1280x720",
num_frames=24, # ~1 second at 24fps
duration=1.0
)
# Extract answer from final frame
final_frame = video.frames[-1]
answer = self.answer_extractor.extract_from_image(final_frame)
return answer, video
defsolve_math_problem(self, problem_text):
# Convert problem to video prompt
prompt = VideoReasoningPromptFormatting.math_problem_to_video(problem_text)
# Generate video showing step-by-step solution
video = self.video_model.generate(
prompt=prompt,
resolution="1280x720",
num_frames=120, # ~5 seconds for more complex problems
duration=5.0
)
# Extract reasoning and answer from video frames# Parse text that appears in frames
reasoning_steps = self.extract_reasoning_from_frames(video.frames)
final_answer = reasoning_steps[-1]
return final_answer, video, reasoning_steps
defextract_reasoning_from_frames(self, frames):
# Use OCR to extract text from frames
steps = []
for frame in frames:
text = pytesseract.image_to_string(frame)
if text.strip():
steps.append(text)
return steps
3. Implement Answer Extraction
Parse answers from generated video frames.
classVideoAnswerExtractor:
def__init__(self):
self.ocr_engine = pytesseract # Or cloud-based OCRself.object_detector = ObjectDetector() # For spatial answersdefextract_from_image(self, frame):
# Try multiple extraction strategies# Strategy 1: OCR
text = self.ocr_engine.image_to_string(frame)
if text andany(c.isdigit() for c in text):
return text
# Strategy 2: Object detection for spatial problems
objects = self.object_detector.detect(frame)
if objects:
# Extract spatial relationshipreturnself.describe_spatial_relationship(objects)
# Strategy 3: Color/shape analysisreturnself.analyze_visual_properties(frame)
defextract_answer_from_video(self, video, problem_type):
"""Extract answer from entire video sequence"""if problem_type == 'math':
# Parse all text frames for equations
frames_with_text = []
for frame in video.frames:
text = self.ocr_engine.image_to_string(frame)
if text:
frames_with_text.append(text)
final_line = frames_with_text[-1] if frames_with_text else""# Extract numeric answerreturnself.extract_number(final_line)
elif problem_type == 'spatial':
# Use final frame(s) showing solution
final_frame = video.frames[-1]
returnself.extract_from_image(final_frame)
defextract_number(self, text):
# Extract numeric answer from textimport re
numbers = re.findall(r'-?\d+\.?\d*', text)
return numbers[-1] if numbers elseNone
4. Benchmark on VideoThinkBench
Evaluate reasoning across different problem types.
Reasoning tasks combining spatial and textual elements
Problem-solving that benefits from visual/temporal explanation
Applications where reasoning process visibility is valuable
Educational contexts (video explanations)
Hyperparameters:
num_frames: 24-120 (more frames for complex reasoning)
duration: 1-5 seconds (longer for detailed steps)
resolution: 1280x720 (balance quality and inference cost)
When NOT to Use:
Real-time reasoning (video generation is slow)
Closed-book contexts (answer visible in generated video violates some setups)
High-frequency reasoning (too expensive per problem)
Pitfalls:
Answer leakage: Generated video may show answer too early; control frame ordering
OCR errors: Extracting text from video frames is fragile; use robust OCR
Incoherent videos: Video models can generate unrealistic visualizations; validate plausibility
Integration Strategy: Use as auxiliary reasoning modality alongside text. For hybrid tasks, generate both text reasoning and video reasoning, then ensemble answers.
