PAI-Bench — Gemma4-31B — sample 589

# Select the 32 key frames for analysis
key_frame_indices = [0, 9, 19, 28, 37, 46, 55, 64, 73, 82, 91, 100, 109, 118, 127, 136, 145, 154, 163, 172, 181, 190, 199, 208, 217, 226, 235, 244, 253, 262, 271, 280]
# Note: The mapping in the prompt was #1->0, #2->9, #3->19... let's double check the mapping.
# #1: 0, #2: 9, #3: 19, #4: 28, #5: 37... it seems to be roughly every 9-10 frames.
# Let's use the specific mapping provided in the prompt description for accuracy.
# #1 -> 0
# #2 -> 9
# #3 -> 19
# #4 -> 28
# #5 -> 37
# #6 -> 46 (approx)
# Wait, the prompt says: #1->0, #2->9, #3->19, #4->28, #5->37... #28->251, #29->260, #30->269, #31->279, #32->288.
# Let's construct the list based on the pattern:
mapping = [0, 9, 19, 28, 37, 46, 55, 64, 73, 82, 91, 100, 109, 118, 127, 136, 145, 154, 163, 172, 181, 190, 199, 208, 217, 226, 235, 244, 251, 260, 269, 279, 288]
# The prompt lists #28 as 251. Let's just use the 32 images provided.
# The images provided in the prompt are the 32 key frames.
# Let's pass a subset of InputImages that corresponds to these key frames.

images_to_check = [InputImages[i] for i in mapping if i < len(InputImages)]

answer = vlm.ask_with_thinking(images_to_check, "Is there a moment in these frames that shows a bright flash (e.g., sudden overexposure, white-out, or intense light burst)? If yes, please specify which frame index (from the provided list) contains it. If no, simply say 'no'.")
print(answer)

[VLM Q | thinking] Is there a moment in these frames that shows a bright flash (e.g., sudden overexposure, white-out, or intense light burst)? If yes, please specify which frame index (from the provided list) contains it. If no, simply say 'no'.
[VLM A | thinking] no
no

# Based on the mapping, Key frame #20 is roughly InputImages[172]
# Let's check a range around there to be sure.
target_idx = 172
frames_to_show = [InputImages[i] for i in range(target_idx - 2, target_idx + 3) if 0 <= i < len(InputImages)]
show(frames_to_show)

 [image: show_0001] 

import numpy as np
import matplotlib.pyplot as plt

# Compute mean brightness for all frames
brightness_values = []
for img in InputImages:
    # Convert PIL image to numpy array and compute mean of all channels
    img_arr = np.array(img).astype(np.float32)
    brightness_values.append(np.mean(img_arr))

brightness_values = np.array(brightness_values)

# Plot the brightness over time
plt.figure(figsize=(12, 4))
plt.plot(brightness_values)
plt.title("Mean Image Brightness over Frames")
plt.xlabel("Frame Index")
plt.ylabel("Mean Intensity")
plt.grid(True)
plt.show()

# Check for spikes: a spike is a value significantly higher than its neighbors
# We can look for the max value and see if it's an outlier
max_idx = np.argmax(brightness_values)
max_val = brightness_values[max_idx]
mean_val = np.mean(brightness_values)
std_val = np.std(brightness_values)

print(f"Max brightness: {max_val:.2f} at frame {max_idx}")
print(f"Mean brightness: {mean_val:.2f}, Std: {std_val:.2f}")
print(f"Z-score of max: {(max_val - mean_val) / std_val:.2f}")

 [image: Mean Image Brightness over Frames] 
Max brightness: 165.35 at frame 242
Mean brightness: 130.03, Std: 3.16
Z-score of max: 11.19

# Check around the two spikes identified in the plot
spike1_idx = 131 # Approximate center of the first spike
spike2_idx = 242 # Max brightness frame

frames_to_verify = []
# Neighbors for first spike
for i in range(spike1_idx - 2, spike1_idx + 3):
    if 0 <= i < len(InputImages):
        frames_to_verify.append(InputImages[i])

# Neighbors for second spike
for i in range(spike2_idx - 2, spike2_idx + 3):
    if 0 <= i < len(InputImages):
        frames_to_verify.append(InputImages[i])

show(frames_to_verify)

 [image: show_0003] 

ReturnAnswer("B")

[ReturnAnswer] Answer submitted: B