TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models

TRIAD is a diagnostic benchmark for evaluating whether omni-modal models can resolve ambiguity by jointly integrating text, image, and audio.

Overview

TRIAD targets a failure mode common in multimodal evaluation: a model may appear to solve a multimodal task while actually relying on only one dominant modality. In TRIAD, each example is constructed so that the full tri-modal input is needed to identify the intended answer.

Task	Multiple-choice tri-modal question answering
Modalities	Text + Image + Audio
Languages	English, Chinese, and mixed-language examples
Instances	327 items
Scenario groups	86 groups
Answer format	option_A, option_B, ...
Primary use	Held-out diagnostic evaluation
Review status	Anonymous version for double-blind review

What Makes TRIAD Different?

TRIAD is designed around tri-modal irreducibility.

Input condition	Expected property
Text + Image + Audio	The intended answer should be identifiable.
Any two modalities	The answer should remain underdetermined or less well supported.
Any single modality	Multiple options should remain plausible.

The benchmark emphasizes audio cues that cannot be fully captured by a transcript, including:

• prosody and intonation;
• speaker-indexical cues;
• phonetic contrast and homophones;
• environmental sound;
• symbolic sound such as alarms, ringtones, and bells.

Repository Layout

TRIAD/
├── README.md
├── image/
│   ├── img_0001.jpg
│   ├── img_0002.jpg
│   └── ...
├── audio/
│   ├── aud_0001.wav
│   ├── aud_0002.wav
│   └── ...
├── metadata/
│   ├── attribution.json
│   └── statistics.json
└── all-modality_ambiguity.json

The exact layout may vary across release versions, but every item contains relative paths to its image and audio files.

Data Format

Each item is stored as a JSON object.

{
  "id": "group_1_1",
  "language": "en",
  "question": "When is the school meeting scheduled?",
  "context": "He told the parents to come to school for a meeting.",
  "image": "image/img_0122.jpg",
  "image_caption": "A picture of a calendar with Sunday circled.",
  "audio": "audio/aud_0110.wav",
  "audio_caption": "Someone said, 'Parents, please take note: remember to come to the school on this day.'",
  "options": {
    "option_A": "This sunday",
    "option_B": "Next week",
    "option_C": "The time is still unknown",
    "option_D": "There is no meeting"
  },
  "answer": "option_A",
  "category": {
    "level": "single_modal",
    "modal_category": {
      "text": "Anaphoric",
      "image": "Semiotic",
      "audio": "Referential"
    }
  }
}

Field description

| Field | Type | Description | | ------------------------------- | ------ | ------------------------------------------------------------ | | `id` | string | Unique item identifier. Items with the same prefix belong to the same scenario group. | | `language` | string | The language used in this data. | | `question` | string | The question to be answered. | | `context` | string | Textual context. | | `image` | string | Relative path to the image file. | | `image_caption` | string | Human-written image description. Metadata only unless caption-based evaluation is intended. | | `audio` | string | Relative path to the audio file. | | `audio_caption` | string | Human-written audio description or transcript-like summary. Metadata only unless transcript-based evaluation is intended. | | `options` | object | Multiple-choice answer options. | | `answer` | string | Gold answer key. | | `category.level` | string | Overall ambiguity level: `single_modal`, `dual_modal`, or `tri_modal`. | | `category.modal_category.text` | string | Text ambiguity category. | | `category.modal_category.image` | string | Image ambiguity category. | | `category.modal_category.audio` | string | Audio ambiguity category. |

Loading the Dataset

Load JSON metadata

import json
from pathlib import Path

data_path = Path("all-modality_ambiguity.json")

with data_path.open("r", encoding="utf-8") as f:
    items = json.load(f)

print(len(items))
print(items[0]["question"])

Resolve media paths

from pathlib import Path

root = Path(".")
item = items[0]

image_path = root / item["image"].replace("./", "")
audio_path = root / item["audio"].replace("./", "")

print(image_path)
print(audio_path)

Load metadata with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("json", data_files="all-modality_ambiguity.json")
print(dataset["train"][0])

Dataset Statistics

Statistic	Value
Number of items	327
Number of scenario groups	86
Average group size	3.8

Group-size Distribution

Group size	Number of groups
2	23
3	11
4	42
8	10

Ambiguity-level Distribution

Ambiguity level	Number of items
single_modal	230
dual_modal	59
tri_modal	38

Ambiguity Taxonomy

Every item receives one leaf-level ambiguity label for each modality.

Text Ambiguity Categories

Category	Count	Description
Anaphoric	66	Pronouns, demonstratives, ellipsis, or unclear reference.
Syntactic	64	Grammatical structure, attachment, or segmentation ambiguity.
Logical	61	Rules, conditions, negation, quantification, or option mapping.
Pragmatic	51	Intention, implicature, politeness, sarcasm, or indirect speech.
Temporal	48	Event order, tense, aspect, or completion-state ambiguity.
Lexical	37	Word sense, polysemy, homonymy, or translation ambiguity.

Image Ambiguity Categories

Category	Count	Description
Semiotic	71	Signs, gestures, symbols, expressions, or culturally mediated visual cues.
Grounding	65	Multiple possible visual referents or target objects.
Spatial	61	Position, orientation, depth, or spatial relation ambiguity.
Kinematic	53	A static frame of a dynamic action.
Observational	41	Viewpoint, occlusion, framing, or insufficient visual evidence.
Contextual	36	Uncertainty about scene type or situational context.

Audio Ambiguity Categories

Category	Count	Description
Referential	98	Spoken references, pronouns, kinship terms, or deictic expressions.
Prosodic	84	Stress, intonation, rhythm, sarcasm, or tone.
Indexical	54	Speaker cues such as age, gender presentation, role, or social relationship.
Phonetic	37	Homophones, minimal pairs, segmentation, or acoustic confusability.
Environmental	34	Background sounds that change interpretation of the scene.
Symbolic	20	Non-linguistic auditory symbols such as alarms, bells, ringtones, or notifications.

Recommended Evaluation Protocol

The standard TRIAD evaluation provides the model with:

Component	Included in standard evaluation
Textual context	Yes
Image	Yes
Audio	Yes
Question	Yes
Answer options	Yes

The model should output exactly one option key.

Modality Conditions

Condition	Provided input
Full	Text + Image + Audio
Text only	Text
Image only	Image
Audio only	Audio
Text + Image	Text + Image
Text + Audio	Text + Audio
Image + Audio	Image + Audio
Question-only baseline	Question + Options

Example Prompt

You are answering a multiple-choice question that depends on three inputs:
a textual context, an image, and an audio clip.

Context: {context}   Image: {image}   Audio: {audio}

Question: {question}

Options:
(A) {option_A}  (C) {option_C}
(B) {option_B}  (D) {option_D}

Answer with a single option key.

Metrics

Metric	Description
Item-level accuracy	Standard accuracy over all items.
Group-level accuracy	Accuracy aggregated over scenario groups to reduce over-counting of sister examples.

Intended Uses

TRIAD is intended for:

• evaluating omni-modal large language models;
• testing whether models genuinely integrate text, image, and audio;
• diagnosing modality reliance and modality neglect;
• studying ambiguity resolution in multimodal reasoning;
• comparing full-modality performance with modality-ablated settings;
• analyzing which ambiguity types are hardest for different model families.

Out-of-Scope Uses

TRIAD is not intended for:

• training or fine-tuning large models;
• speaker identification;
• voice biometrics;
• voice cloning or impersonation;
• face recognition or identity inference;
• surveillance;
• profiling individuals;
• evaluating human subjects;
• making decisions about real individuals.

TRIAD is small and diagnostic by design. It should not be treated as a representative sample of real-world multimodal interactions.

Data Collection and Annotation

TRIAD was constructed by designing everyday scenarios in which text, image, and audio jointly determine the intended answer.

Each item is annotated with:

• a gold answer;
• an ambiguity level;
• one text ambiguity category;
• one image ambiguity category;
• one audio ambiguity category;
• group metadata linking sister examples.

Items were reviewed to reduce degenerate cases where the answer can be determined from only one or two modalities.

Responsible AI Considerations

Privacy

The dataset is designed to avoid personally identifying information. Audio clips and images should not be used for identifying real individuals, voice matching, face recognition, or impersonation.

Audio-specific risks

Because the dataset includes audio, it should not be used for biometric speaker recognition, voice cloning, speaker profiling, or identity inference. Audio is included only for evaluating multimodal reasoning.

Sensitive content

The dataset avoids content involving violence, privacy violations, or sensitive personal information. Any remaining potentially sensitive cases should be treated as diagnostic examples only.

Bias and limitations

TRIAD is a small diagnostic benchmark. Its examples are intentionally constructed around ambiguity and may not reflect the natural distribution of everyday multimodal data.

Model performance on TRIAD should therefore be interpreted as a measure of ambiguity-resolution ability rather than general multimodal competence.

The dataset contains English, Chinese, and mixed-language examples. Results may vary across languages and model families.

Licensing

The license metadata is set to MIT.

Citation

Citation information will be added after the double-blind review period.

@misc{triad2026,
  title        = {TRIAD: Benchmarking Tri-Modal Irreducible Ambiguity Resolution for Multimodal Large Language Models},
  author       = {Anonymous},
  year         = {2026},
  note         = {Dataset submitted for double-blind review}
}

Maintenance

The dataset will be versioned. Errata, corrected annotations, and future extensions will be documented in the repository release history.

For the anonymous review version, contact information is withheld to preserve double-blind review. A public maintainer contact will be added after the review period.

Double-blind Review Notice

This repository is prepared for anonymous peer review. Please do not infer author identity from repository ownership, commit metadata, or temporary hosting information. Any non-anonymous information will be added after the review period.