metadata

license: mit
task_categories:
  - visual-question-answering
  - multiple-choice
language:
  - en
tags:
  - vision-language
  - multimodal
  - benchmark
  - chess
  - chemistry
  - music
  - graph-theory
  - semantic-equivalence
  - VLM
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: task
      dtype: string
    - name: domain
      dtype: string
    - name: index
      dtype: int32
    - name: question_type
      dtype: string
    - name: question
      dtype: string
    - name: notation
      dtype: string
    - name: notation_type
      dtype: string
    - name: option_a
      dtype: string
    - name: option_b
      dtype: string
    - name: option_c
      dtype: string
    - name: option_d
      dtype: string
    - name: correct_answer
      dtype: string
    - name: correct_idx
      dtype: int32
    - name: image
      dtype: image
  splits:
    - name: fork
      num_bytes: 0
      num_examples: 200
    - name: legal
      num_bytes: 0
      num_examples: 200
    - name: puzzle
      num_bytes: 0
      num_examples: 200
    - name: eval
      num_bytes: 0
      num_examples: 200
    - name: carbon
      num_bytes: 0
      num_examples: 200
    - name: hydrogen
      num_bytes: 0
      num_examples: 200
    - name: weight
      num_bytes: 0
      num_examples: 200
    - name: caption
      num_bytes: 0
      num_examples: 200
    - name: notes
      num_bytes: 0
      num_examples: 200
    - name: measures
      num_bytes: 0
      num_examples: 200
    - name: forms
      num_bytes: 0
      num_examples: 200
    - name: rhythm
      num_bytes: 0
      num_examples: 200
    - name: path_counting
      num_bytes: 0
      num_examples: 200
    - name: path_existence
      num_bytes: 0
      num_examples: 200
    - name: shortest_path
      num_bytes: 0
      num_examples: 200
    - name: bfs_traversal
      num_bytes: 0
      num_examples: 200
  download_size: 0
  dataset_size: 0
configs:
  - config_name: default
    data_files:
      - split: fork
        path: data/fork-*
      - split: legal
        path: data/legal-*
      - split: puzzle
        path: data/puzzle-*
      - split: eval
        path: data/eval-*
      - split: carbon
        path: data/carbon-*
      - split: hydrogen
        path: data/hydrogen-*
      - split: weight
        path: data/weight-*
      - split: caption
        path: data/caption-*
      - split: notes
        path: data/notes-*
      - split: measures
        path: data/measures-*
      - split: forms
        path: data/forms-*
      - split: rhythm
        path: data/rhythm-*
      - split: path_counting
        path: data/path_counting-*
      - split: path_existence
        path: data/path_existence-*
      - split: shortest_path
        path: data/shortest_path-*
      - split: bfs_traversal
        path: data/bfs_traversal-*

SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models

CSSLab, Department of Computer Science, University of Toronto
[COLM '25] Second Conference on Language Modeling

Paper: OpenReview
Leaderboard: SEAM Benchmark
Code: GitHub

Abstract

Evaluating whether vision–language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains with existing standardized textual and visual notations. By employing distinct notation systems across modalities, in contrast to OCR-based image-text pairing, SEAM provides a rigorous comparative assessment of the textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21 contemporary models, we observe systematic modality imbalance: vision frequently lags language in overall performance, despite the problems containing semantically equivalent information, and cross-modal agreement is relatively low. Our error analysis reveals two main drivers: textual perception failures from tokenization in domain notations and visual perception failures that induce hallucinations. We also show that our results are largely robust to visual transformations. SEAM establishes a controlled, semantically equivalent setting for measuring and improving modality-agnostic reasoning.

Key Features

4 Domains: Chess, Chemistry, Music, Graph Theory with standardized notations
16 Tasks: 4 tasks per domain (64 total task-modality combinations)
3 Modalities: Language-only (L), Vision-only (V), Vision-Language (VL)
3,200 Base Samples: 200 samples × 16 tasks
9,600 Evaluations: TaskLoader generates 3 modality-specific prompts per base sample
Semantic Equivalence: Same information presented in different representational formats

Domains and Notation Systems

Chess Domain

Tasks: fork, legal, puzzle, eval
Textual: FEN (Forsyth-Edwards Notation)
Visual: Chess board diagrams

Chemistry Domain

Tasks: carbon, hydrogen, weight, caption
Textual: SMILES (Simplified Molecular Input Line Entry System)
Visual: Chemical structure diagrams

Music Domain

Tasks: notes, measures, forms, rhythm
Textual: ABC notation
Visual: Musical staff notation

Graph Theory Domain

Tasks: path_counting, path_existence, shortest_path, bfs_traversal
Textual: Adjacency matrices
Visual: Node-edge diagrams

Dataset Splits

The dataset is organized into 16 task-based splits (600 samples each):

Chess: fork, legal, puzzle, eval
Chemistry: carbon, hydrogen, weight, caption
Music: notes, measures, forms, rhythm
Graph Theory: path_counting, path_existence, shortest_path, bfs_traversal

Each split contains 200 base samples. TaskLoader generates modality-specific prompts (L, V, VL) from these base samples.

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("lilvjosephtang/SEAM-Benchmark")

# Access specific tasks
chess_fork = dataset["fork"]  # Chess fork detection (600 samples)
chemistry_carbon = dataset["carbon"]  # Carbon atom counting (600 samples)

# Each task contains 200 base samples
# TaskLoader generates modality-specific prompts (L/V/VL) from these base samples
print(f"Task {chess_fork[0]['task']} has {len(chess_fork)} base samples")

# Example sample structure
sample = chess_fork[0]
print(f"Task: {sample['task']}")
print(f"Domain: {sample['domain']}")
# No modality field - TaskLoader handles modality generation
print(f"Question: {sample['question']}")
print(f"Options: A) {sample['option_a']}, B) {sample['option_b']}, C) {sample['option_c']}, D) {sample['option_d']}")
print(f"Correct Answer: {sample['correct_answer']}")
print(f"Notation: {sample['notation']}")  # FEN string for chess
# sample['image'] contains the chess board image for Vision/Vision-Language modalities

Sample Structure

Each sample contains:

task: Task identifier (e.g., "fork", "carbon")
domain: Domain category ("chess", "chemistry", "music", "graph")
No modality field (TaskLoader generates modality-specific prompts)
index: Sample index within the task
question: Question text (if applicable)
notation: Domain-specific notation (FEN, SMILES, ABC, adjacency matrix)
notation_type: Type of notation used
option_a, option_b, option_c, option_d: Multiple choice options
correct_answer: The correct answer
correct_idx: Index of the correct option
image: Associated image (PIL Image, None for base storage - TaskLoader handles image loading for V/VL modalities)

Evaluation Protocol

SEAM enables three types of evaluation:

Language: Models receive only textual notation
Vision: Models receive only visual representation
Vision-Language: Models receive both notation and image

The semantic equivalence across modalities allows for direct comparison of reasoning capabilities and cross-modal agreement analysis.

Citation

@inproceedings{
  tang2025seam,
  title={{SEAM}: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models},
  author={Zhenwei Tang and Difan Jiao and Blair Yang and Ashton Anderson},
  booktitle={Second Conference on Language Modeling},
  year={2025},
  url={https://openreview.net/forum?id=lI4LgGv4sX}
}