# AMIGO: Agentic Multi-Image Grounding Oracle Benchmark
**Authors**:
- Min Wang, Ata Mahjoubfar (Target Corporation)
## Abstract
Agentic vision-language models increasingly act through extended interactions, but most evaluations still focus on single-image, single-turn correctness. We introduce AMIGO (A gentic M ulti- I mage G rounding O racle Benchmark), a long-horizon benchmark for hidden-target identification over galleries of visually similar images. In AMIGO, the oracle privately selects a target image, and the model must recover it by asking a sequence of attribute-focused Yes/No/Unsure questions under a strict protocol that penalizes invalid actions with Skip. This setting stresses (i) question selection under uncertainty, (ii) consistent constraint tracking across turns, and (iii) fine-grained discrimination as evidence accumulates. AMIGO also supports controlled oracle imperfections to probe robustness and verification behavior under inconsistent feedback. We instantiate AMIGO with Guess My Preferred Dress task and report metrics covering both outcomes and interaction quality, including identification success, evidence verification, efficiency, protocol compliance, noise tolerance, and trajectory-level diagnostics.
footnotetext: This work is still in progress. We’ll update with more results and analysis soon. The dataset is provided as an ancillary file, anc/data.json.
Work in Progress
## 1 Introduction
Vision-language models (VLMs) [25, 18, 22, 20, 8] have advanced rapidly in instruction following and grounded reasoning, enabling strong performance on captioning, visual question answering, and multimodal dialogue. As VLMs become more agentic (planning and acting through extended exchanges), evaluation protocols increasingly need to measure not only correctness, but also interaction policies: how models gather information, maintain state, and adapt under uncertainty. Yet many benchmarks [5, 16, 28, 14, 30] remain static and short-horizon, typically scoring one-shot answers given one image (or a small fixed set), with limited visibility into strategy, memory, and belief revision over time.
We study a complementary setting: interactive hidden-target identification over image collections. A user (or oracle) privately selects a target image from a gallery of visually similar candidates, and the model must discover the target by asking constrained questions about observable attributes. This formulation surfaces agentic challenges that are often invisible in single-turn evaluation: choosing questions that efficiently reduce ambiguity, preserving and applying constraints without drift, and performing cross-image comparisons that depend on the evolving candidate set rather than any single image in isolation.
To operationalize this setting, we present AMIGO, an interactive benchmark centered on a simple but diagnostic loop. The model asks exactly one binary question per turn, receives Yes/No/Unsure feedback, and iteratively narrows the feasible candidate set. If the model violates protocol rules, the oracle returns Skip, revealing no information. This makes non-compliance measurable and separates wasted interaction from evidence-driven progress. AMIGO additionally allows occasional oracle inconsistencies to test whether models detect contradictions and seek verification instead of committing prematurely.
We instantiate AMIGO with Guess My Preferred Dress task, where each gallery contains 6 to 40+ highly similar dress images. Dresses offer rich fine-grained variation (e.g., neckline construction, closures, seam placement, drape, and embellishments), making the task sensitive to careful observation and long-horizon constraint tracking.
Contributions. (1) We introduce AMIGO, an interactive multi-image benchmark for hidden-target identification that foregrounds long-horizon planning and fine-grained cross-image grounding. (2) We define a constrained Yes/No/Unsure protocol with explicit invalid-action feedback (Skip) to diagnose sustained instruction following and common agentic failure modes. (3) We incorporate controlled oracle imperfections to test robustness and verification behaviors under inconsistent feedback. (4) We provide an evaluation suite that jointly measures outcomes and interaction quality, including verified identification, efficiency, protocol compliance, and trajectory-level diagnostics.
Broader impact. AMIGO provides a controlled testbed for agentic VLM behaviors that matter in practice: planning under uncertainty, maintaining consistent state over extended interactions, and responding conservatively to ambiguity or contradictions. The resulting traces (candidate sets as state, questions as actions, and oracle responses, including Skip, as observations) can also support training and analysis of multimodal policies via offline learning. At the same time, hidden-target identification highlights calibration considerations: systems should avoid overconfident early commitments and remain protocol-compliant throughout long dialogues.
## 2 Related Work
### 2.1 Multi-image and multi-turn multimodal evaluation
A growing set of benchmarks and datasets extends evaluation beyond single-image, single-turn QA [28, 16] to multi-image and/or multi-turn settings. Multi-image benchmarks (e.g., MMIU [17], MuirBench [23], mPLUG-Owl3 [26]) probe cross-image grounding and long visual context handling. Multi-image instruction-tuning datasets (e.g., Mantis [9]) and multi-image multi-turn resources (e.g., MMDU [15], MMCR [24]) further study supervision over interleaved images and dialogue. Separately, multi-turn multimodal conversation benchmarks (e.g., MultiVerse [11], ConvBench [13], MMMT-IF [4], MMCoQA [12]) evaluate contextual coherence and instruction adherence over longer dialogues. These efforts primarily evaluate responding given a provided context, rather than interactive hidden-target identification where a model must actively uncover a user-privately selected target by asking constrained questions and tracking constraints over a long horizon.
### 2.2 Multimodal reasoning benchmarks
General multimodal reasoning benchmarks assess whether models can integrate vision and language for multi-step inference beyond shallow cue matching. For example, EMMA [6] targets “organic” multimodal reasoning across domains, and MLLM-CompBench [10] emphasizes comparative reasoning under multimodal inputs. MMMU-Pro [29] further strengthens this line by making evaluation more resistant to text-only shortcuts and by requiring tighter visual grounding in challenging multimodal questions. These benchmarks are valuable for measuring reasoning capability on difficult perception-and-inference tasks, but they still primarily assess whether a model can solve a presented problem instance. In contrast, AMIGO evaluates whether an agentic VLM can acquire the missing information needed to solve the task: selecting informative questions over multiple turns, maintaining and revising a belief state over a candidate pool, and enforcing consistency under a constrained interaction protocol to identify a hidden target image.
### 2.3 Interactive clarification with hidden targets
AMIGO is closely related to interactive clarification settings where an agent must uncover user intent through dialogue. InfoQuest [2] evaluates text-only agents that ask clarifying questions when critical context is hidden. ClariMM [19] extends this idea to multimodal clarification for underspecified user queries. More broadly, multimodal agent frameworks such as M3Searcher [27] and expert-guided benchmarks such as MIRAGE [3] follow a “seek information then decide” paradigm, often with retrieval or tool-mediated evidence acquisition.
AMIGO differs in three ways. First, it studies hidden-target identification over a closed set of visually similar images, where the model must narrow the candidate pool via attribute queries answered with Yes/No/Unsure/ Skip feedback (which may be noisy). Second, it evaluates the interaction policy itself via trajectory-level signals such as compliance, redundancy, elimination dynamics, and contradiction detection/verification. Third, the constrained protocol yields reusable trajectories with a well-defined action space (attribute questions), observations (oracle responses), and automatically scored intermediate signals, making the data naturally suited for diagnosing and training long-horizon agentic multimodal policies.
## 3 Benchmark
We introduce AMIGO, a benchmark for evaluating agentic VLMs on hidden-target, multi-image, multi-turn identification. AMIGO comprises a primary task, Guess My Preferred Dress, where the model must identify a user-privately selected target image from a gallery of highly similar candidates by asking discriminative attribute questions under a strict protocol.
### 3.1 Guess My Preferred Dress
#### Task.
A user uploads one or more batches of dress images that together form a single gallery and there is one image that is privately selected by user as a hidden target. After the signal End of uploading, the model asks questions to narrow the candidate set and finally outputs the 1-indexed position of the target in the gallery.
#### Protocol.
The interaction follows these rules:
1. Each turn, the model must ask exactly one question about an observable attribute of the target dress.
1. The question must be answerable with Yes, No, or Unsure.
1. If the model violates any rule, the oracle responds with Skip.
1. The model must not ask about: sleeve length, garment length, color, prints/pattern, age group, size, shoes, necklace, hat, bag, background, or the human model.
1. The model must not enumerate attribute values across turns for the same attribute type (e.g., repeatedly cycling through neckline types of V-neckline, square neck, crewneck in different turns).
1. The model must not reference specific indices or inspect images one-by-one (e.g., “Is it image #1?”).
1. The model must not guess until its constraints narrow the feasible candidate set to exactly one image.
1. If constraints appear inconsistent (e.g., due to uncertainty or noise), the model should continue asking more questions rather than stopping early.
1. When ready, the model must output: My guess of your favorite dress: #<number>.
1. The model must not ask any questions before the user sends End of uploading.
#### Rationale.
The protocol deliberately excludes highly salient cues (e.g., color, patterns, sleeve length, and garment length) to encourage reliance on subtler construction details (e.g., neckline structure, closures, waist shaping, seam placement, drape, and embellishments). This design emphasizes long-horizon constraint tracking and rewards question sequences that are informative, non-redundant, and robust to uncertainty.
#### Example interactions.
Figure 1 shows two full episodes. In Figure 1(a), the model systematically asks about fine-grained details and narrows the pool over 10 turns, but makes an incorrect final guess, illustrating how visually similar candidates can still be confusable. In Figure 2(a), the model identifies the target in 9 turns by asking progressively more discriminative questions.
<details>
<summary>2603.28662v1/x1.png Details</summary>
### Visual Description
## Screenshot: Dress Selection Task Interface
### Overview
The image depicts a web-based interactive task where a user attempts to identify a target dress from a gallery of eight options by asking yes/no questions. The interface includes a visual gallery, task instructions, a conversation log, and a summary of the interaction. The target dress is hidden (marked with a red border and heart in the gallery), and the AI's guess is evaluated for accuracy.
---
### Components/Axes
1. **Image Gallery**
- **Position**: Top section of the interface.
- **Content**: Eight dresses labeled #1 to #8, each displayed on a model.
- **Target Indicator**: Dress #2 is highlighted with a red border and heart.
- **Legend**: No explicit legend, but the target is visually marked.
2. **Task Instructions**
- **Position**: Below the gallery.
- **Content**:
- **Task**: "Guess the user's favorite dress by asking Yes/No questions."
- **Constraints**:
- One binary question per turn.
- No image-by-image inspection.
- Guess only when confident.
- Responses: Yes / No / Unsure / Skip.
3. **Conversation Log**
- **Position**: Central section.
- **Structure**:
- **T1-T10**: Ten questions asked by the AI, each with a user response.
- **Response Legend**:
- **Green**: Yes (confirmed attribute).
- **Red**: No (ruled-out attribute).
- **Yellow**: Unsure (ambiguous/uncertain).
- **Example**:
- **T1**: "Does your favorite dress have a separate belt with a buckle?" → **No** (red).
- **T8**: "Does it have a visible elasticated waistband seam rather than a smooth fitted waist?" → **Unsure** (yellow).
4. **Summary**
- **Position**: Bottom section.
- **Content**:
- **Turns**: 10.
- **Skips**: 0.
- **Responses**: 4× Yes, 4× No, 1× Unsure.
- **Final Result**: Incorrect guess (guessed #4, target #2).
---
### Detailed Analysis
#### Conversation Log Breakdown
- **T1-T3**: Focus on structural features (belt, buttons, bodice). All answered **No**.
- **T4**: "Off-the-shoulder neckline?" → **Yes** (green).
- **T5**: "Fabric tie at the waist?" → **Yes** (green).
- **T6**: "Asymmetrical cascading ruffle panel?" → **No** (red).
- **T7**: "More than one distinct tier/ruffle layer?" → **Yes** (green).
- **T8**: Waistband seam ambiguity → **Unsure** (yellow).
- **T9**: "Two-piece set?" → **No** (red).
- **T10**: "Peplum-like flounce at the waist?" → **Yes** (green).
#### Final Guess Evaluation
- **Guessed Dress**: #4 (incorrect).
- **Target Dress**: #2 (correct).
- **Key Discrepancy**: The AI prioritized features like off-the-shoulder necklines and ruffles (common in #4), while the target dress (#2) likely had unique attributes not probed effectively (e.g., color, pattern, or fit).
---
### Key Observations
1. **Question Strategy**: The AI focused on structural details (necklines, waist ties, ruffles) but overlooked attributes like color, fabric type, or silhouette, which may have differentiated the target dress.
2. **Ambiguity**: The single "Unsure" response (T8) introduced uncertainty, potentially derailing the narrowing process.
3. **Incorrect Guess**: The AI's final guess (#4) shared some features with the target (#2) but lacked critical distinguishing traits.
---
### Interpretation
The task highlights the challenges of inferring preferences through binary questions without visual inspection. The AI's reliance on structural features (e.g., necklines, ruffles) suggests a bias toward design elements over holistic attributes (e.g., color, fit). The user's "Unsure" response (T8) may have introduced noise, preventing the AI from converging on the correct answer. This underscores the importance of balancing specificity and flexibility in question design for such tasks.
**Final Note**: The interface effectively visualizes the iterative questioning process but reveals limitations in inferring preferences without explicit feature prioritization.
</details>
(a) Unsuccessful attempt: after 10 turns the model guesses dress #4, but the hidden target is dress #2 (red border), illustrating how visually similar candidates can mislead even systematic questioning.
Figure 1: Two example multi-turn interactions for Guess My Preferred Dress. The VLM (blue) asks constrained Yes/No questions about fine-grained attributes; the user replies with Yes (green), No (red), or Unsure (yellow). Panel (a) shows an incorrect final guess and panel (b) shows a correct one.
<details>
<summary>2603.28662v1/x2.png Details</summary>
### Visual Description
## Screenshot: Interactive Dress Guessing Quiz
### Overview
The image depicts an interactive quiz interface where a user attempts to guess another person's favorite dress from six candidates by asking binary (Yes/No/Unsure) questions about dress attributes. The quiz includes a visual gallery of dresses, a question-answer flow, and a summary of results.
### Components/Axes
1. **Image Gallery** (Top Section):
- Six dresses labeled #1 to #6.
- Dress #5 is highlighted with a red border and labeled "Target (hidden)".
- Dresses vary in color (pink, black, white, floral, black, gray) and style (long/short, sleeveless/long sleeves).
2. **Question-Asking Flow** (Central Section):
- Nine questions (T1-T9) about dress attributes (e.g., pockets, waist ties, ruffles, fabric texture).
- Responses are color-coded:
- **Green**: Yes (confirmed attribute).
- **Red**: No (ruled-out attribute).
- **Orange**: Unsure (ambiguous/uncertain).
- Example questions:
- T1: "Does your favorite dress have pockets...?" → **Unsure** (orange).
- T2: "Does your favorite dress have a waist tie...?" → **Yes** (green).
3. **Legend** (Right Panel):
- Explains response colors:
- Green: Yes (confirmed attribute).
- Red: No (ruled-out attribute).
- Orange: Unsure (ambiguous/uncertain).
4. **Summary** (Bottom Section):
- Final guess: "My guess of your favorite dress: #5. ✅ Correct (target: #5)".
- Statistics: 9 turns, 0 skips, 2 No, 5 Yes, 2 Unsure.
### Detailed Analysis
- **Question Flow**:
- Questions focus on specific attributes (e.g., pockets, ruffles, neckline shape).
- Responses guide elimination: "No" answers rule out dresses lacking the attribute, while "Yes" confirms it.
- Example: T6 ("Does the skirt have an overlapping 'wrap' opening...?") → **No** (red), eliminating dresses with this feature.
- **Final Outcome**:
- The user correctly identified dress #5 after 9 questions.
- The target dress (#5) is a black long-sleeve dress with a V-neckline and noticeable sheen (T8: **Unsure**, T9: **No** for puffed shoulders).
### Key Observations
1. **Attribute-Based Elimination**: The quiz systematically narrows options by focusing on distinct dress features (e.g., pockets, ruffles, fabric texture).
2. **Confidence in Attributes**: Most responses are "Yes" (5/9), indicating strong confidence in the target dress's attributes.
3. **Uncertainty in Key Features**: Two "Unsure" responses (T1, T8) suggest ambiguity about pockets and fabric sheen, yet the final guess remained accurate.
4. **Efficiency**: The user achieved the correct guess in 9 turns without skips, demonstrating effective questioning.
### Interpretation
The quiz illustrates a structured approach to narrowing down choices through binary questioning. By focusing on unique attributes (e.g., ruffles, V-necklines), the user efficiently eliminated less likely options. The high frequency of "Yes" responses (5/9) highlights the importance of confirming key attributes, while "Unsure" answers (2/9) reflect the need for flexibility when details are ambiguous. The correct identification of dress #5 underscores the effectiveness of this method, even with partial uncertainty. This process mirrors decision-making frameworks in machine learning (e.g., decision trees) and human cognition, where attributes are prioritized to reach a conclusion.
</details>
(a) Successful attempt: after 9 turns the model correctly guesses dress #5—the hidden target (red border)—showing that effective questioning can resolve visually similar candidates.
Figure 2: Two example multi-turn interactions (continued).
### 3.2 Dataset collection
#### Source and curation.
We collect dress images from Target’s online fashion catalog and deduplicate them to ensure each product is unique. Attribute metadata extracted from the historical product database is often missing, defined inconsistently across brands, and sometimes contains erroneous or irrelevant values. We therefore prioritize visually verifiable attributes and build a semi-automatic pipeline to produce standardized labels at scale.
#### Semi-automatic attribute labeling.
Our pipeline has three stages: (1) Attribute discovery and normalization: we aggregate attribute values from catalog annotations and use multiple LLMs to normalize synonyms and naming variants, compile a comprehensive attribute value list, and expand it with as many visually grounded alternatives as possible; (2) Binary question template construction: for each normalized attribute value, we generate multiple binary (Yes/No) question templates (e.g., “Does the dress have a wrap-style front?”, “Does the dress have a wrap-front design?”, “Is the dress featured with wrap-style front?”); (3) VLM-based labeling: we apply multiple VLMs to answer these templates for each image, yielding per-image attribute labels.
#### Ensembled VLM labeling and quality control.
Given budget constraints, we adopt open-source VLMs for automated labeling. To improve labeling reliability, we ensemble multiple VLMs (Qwen3-VL-235B-FP8 [21], Intern-S1 [1], GLM-4.5V [7]) and combine their predictions through weighted majority voting. In our experiments, Qwen3-VL-235B-Instruct-FP8 achieved the best performance among the models evaluated prior to the release of Qwen3.5-397B-A17B-FP8, and is therefore assigned a larger voting weight. We further enhance robustness by (i) using paraphrased question templates to reduce sensitivity to prompt phrasing and (ii) applying multi-resolution image augmentation to mitigate failures in recognizing small visual details. Finally, we manually audit a subset of the labels to assess quality, identify systematic errors, and refine the templates and normalization rules accordingly.
<details>
<summary>2603.28662v1/images/attributes_labeling_pipeline.png Details</summary>
### Visual Description
## Flowchart: Automated Dress Attribute Labeling Pipeline
### Overview
The image depicts a five-stage technical pipeline for automated dress attribute labeling, combining computer vision, natural language processing, and quality control. The process flows left-to-right, integrating data collection, attribute extraction, question-based analysis, multi-model voting, and human verification.
### Components/Axes
1. **Source & Deduplication**
- Target website screenshot with three dresses (blue, red, blue)
- Text: "Collect Dress Images", "Deduplicate Products"
- Position: Top-left quadrant
2. **Attribute Discovery & Normalization**
- Documents (white/yellow/blue) feeding into LLM (brain icon)
- Text: "Aggregate & Normalize Attributes"
- Position: Center-left quadrant
3. **Binary Question Templates**
- Three dress-related questions in speech bubbles:
- "Does the dress have a wrap-style front?"
- "Does the dress feature a wrap-front design?"
- "Is the dress styled with a wrap front?"
- Position: Center-right quadrant
4. **VLM-Based Labeling**
- Three dress images with resolution labels:
- High Res (left)
- Medium Res (center)
- Low Res (right)
- Three VLMs (A/B/C) with green checkmarks (✓) and red Xs (✗)
- Position: Right-center quadrant
5. **Ensembled Voting & Quality Control**
- Green checkmarks (✓) and red Xs (✗) with "Majority Vote" arrow
- Final Attribute Label (green checkmark)
- Manual Audit (magnifying glass icon)
- Position: Bottom-right quadrant
### Detailed Analysis
- **Flow Direction**: Left-to-right progression through stages
- **Voting Matrix**:
| VLM | Votes |
|-----|----------------|
| A | ✓, ✗, ✓ |
| B | ✗, ✓, ✗ |
| C | ✓, ✗, ✓ |
- **Resolution Analysis**: Explicit differentiation between high/medium/low resolution inputs
- **Quality Control**: Dual verification through automated voting and manual audit
### Key Observations
1. **Attribute Ambiguity**: Three nearly identical questions suggest nuanced attribute definitions
2. **Resolution Sensitivity**: Explicit handling of image quality variations
3. **Ensemble Approach**: Combines multiple VLMs with human oversight
4. **Deduplication Emphasis**: Early-stage focus on data cleanliness
### Interpretation
This pipeline demonstrates a sophisticated approach to fashion attribute labeling that:
1. **Addresses Data Quality**: Early deduplication and resolution-aware processing
2. **Leverages Multiple Models**: Uses three VLMs to capture different perspectives
3. **Implements Quality Gates**: Combines automated voting with human verification
4. **Handles Ambiguity**: Multiple question templates for similar attributes suggest nuanced definitions
The system appears designed for high-stakes fashion e-commerce where accurate attribute labeling is critical for searchability and customer satisfaction. The manual audit step indicates awareness of potential model limitations, particularly with low-resolution images.
</details>
Figure 3: The semi-automatic attribute labeling pipeline: attribute discovery and normalization, binary question template construction, and ensembled VLM-based labeling with quality control.
#### Attribute-based similarity.
Let $Attr(X)$ denote the set of attribute values assigned to an image $X$ . We define an asymmetric similarity score:
$$
Sim(A,B)=\frac{|Attr(A)∩ Attr(B)|}{|Attr(A)|}, \tag{1}
$$
and analogously $Sim(B,A)$ . We rank candidates $B$ by $Sim(A,B)$ to retrieve candidate images that are most visually similar to the reference image $A$ in terms of attribute overlap (i.e., candidates that best cover $A$ ’s attributes).
#### Episode generation and difficulty control.
For each target image $A$ , we construct a distractor pool by iterating over its attribute values. For each attribute value, we retrieve a fixed number of images that share that value with $A$ and satisfy $Sim(A,B)≥τ$ , where $τ$ is a similarity threshold. We then merge the retrieved sets across all attribute values to obtain the final candidate distractors for $A$ . We keep only targets for which this merged pool contains more than five candidates. To form an episode, we combine the target with a subset of its candidate distractors to create a gallery. We control difficulty via (i) the threshold $τ$ (higher $τ$ yields more visually similar distractors) and (ii) the gallery size (larger galleries expand the search space).
<details>
<summary>2603.28662v1/images/image_gallery_generation.png Details</summary>
### Visual Description
## Flowchart: Image Gallery Generation for Reference Image
### Overview
The flowchart illustrates a multi-stage process for generating an image gallery based on a reference image. It involves attribute-based retrieval, similarity scoring, and merging of candidate images to produce a final curated pool.
### Components/Axes
1. **Reference Section**:
- **Labels**: "Reference", "V-neck", "Flared skirt", "Puff sleeve".
- **Visuals**: A woman wearing a teal V-neck dress with a flared skirt and puff sleeves. Below are three additional dresses (dark blue, floral teal, and beige).
2. **Per-Attribute Retrieval**:
- **Subsections**:
- **V-Neck Candidates**: 6 dresses (teal, dark green, floral teal, light blue, navy blue).
- **Flared Skirt Candidates**: 6 dresses (red, dark green, teal, navy blue, beige).
- **Puff Sleeve Candidates**: 6 dresses (dark green, teal, floral teal, navy blue).
- **Similarity Score**: A bar chart with unlabeled axes, showing relative heights for similarity scores.
3. **Merge Section**:
- **Final Candidate Pool**: 10 dresses combining attributes from all three categories.
- **Formula**:
```
Sim(A, B) = |Attr(A) ∩ Attr(B)| / |Attr(A)|
```
- **Note**: "Sim(A, B) is Asymmetric".
### Detailed Analysis
- **Reference Section**: The reference image defines target attributes (V-neck, flared skirt, puff sleeves). Other images serve as additional context.
- **Per-Attribute Retrieval**:
- Each subsection filters dresses by a single attribute (e.g., V-neck candidates include dresses with V-necks regardless of other features).
- Similarity scores are visualized but lack numerical values, relying on relative bar heights.
- **Merge Section**: Combines candidates using shared attributes and similarity thresholds. The formula prioritizes overlap between attributes of the reference and candidates.
### Key Observations
1. **Attribute Filtering**: Each retrieval step isolates dresses matching one specific attribute, creating overlapping candidate pools.
2. **Similarity Scoring**: The bar chart suggests a ranking system but lacks explicit thresholds or numerical values.
3. **Final Pool Composition**: The merged pool includes dresses that satisfy multiple attributes (e.g., teal V-neck with flared skirt and puff sleeves).
### Interpretation
- **Process Logic**: The system prioritizes attribute overlap (via the similarity formula) to generate a gallery that balances fidelity to the reference image with diversity in style.
- **Asymmetry Note**: The formula’s asymmetry implies that comparing attribute A to B yields different results than B to A, which could affect candidate prioritization.
- **Missing Data**: The absence of explicit similarity score values or thresholds limits quantitative analysis but emphasizes qualitative attribute matching.
### Uncertainties
- Exact similarity score values are not provided, relying on visual bar heights.
- The threshold value `τ` for similarity filtering is not specified.
</details>
Figure 4: Image gallery generation pipeline: for a given target image, distractors are retrieved by attribute-based similarity and merged into a gallery with controlled difficulty via threshold $τ$ and gallery size.
### 3.3 Sample image galleries
Figure 5 shows four representative galleries. Each gallery is curated to be similar along salient dimensions (e.g., silhouette and overall style) while differing in subtle construction details. The hidden target is marked with a red outline.
<details>
<summary>2603.28662v1/x3.png Details</summary>
### Visual Description
## Fashion Lookbook: Seven Women in Diverse Dress Styles
### Overview
The image displays seven women modeling distinct dresses in a minimalist, studio-like setting. Each woman is posed with one hand on her hip, showcasing the garments against a neutral background. No text, labels, or legends are visible in the image.
### Components/Axes
- **Subjects**: Seven women, each wearing a unique dress.
- **Dress Styles**:
1. **Brown Long-Sleeve Dress**: Cinched waist, ankle boots.
2. **Black Square-Neck Dress**: Flared hem, long sleeves.
3. **Pastel Pink V-Neck Dress**: Ruffled sleeves, mid-calf length.
4. **Floral Pattern Dress**: Asymmetrical slit, wide-brimmed hat.
5. **Yellow Floral Puff-Sleeve Dress**: Knee-length, ruffled hem.
6. **Dark Green Cinched-Waist Dress**: Long sleeves, clutch accessory.
7. **Olive Green High-Neck Dress**: Ruffled hem, knee-length.
### Detailed Analysis
- **Dress 1**: Solid brown fabric with a fitted silhouette, paired with brown ankle boots.
- **Dress 2**: Matte black material with a square neckline and flowing hem.
- **Dress 3**: Soft pastel pink with a V-neckline and voluminous ruffled sleeves.
- **Dress 4**: Floral print (white and brown) with a high slit and a straw hat.
- **Dress 5**: Bright yellow base with orange/red floral patterns and puffed sleeves.
- **Dress 6**: Emerald green with a belted waist and long sleeves; holding a black clutch.
- **Dress 7**: Olive green with a high neckline and layered ruffles at the hem.
### Key Observations
- **Variety**: The collection spans casual to semi-formal styles, with diverse color palettes (neutral, pastel, bold).
- **Accessories**: Only two women wear accessories (boots and clutch).
- **Silhouettes**: Mix of fitted, flared, and asymmetrical designs.
### Interpretation
The image emphasizes versatility in women’s fashion, highlighting how fabric texture, color, and cut can define a garment’s character. The absence of text suggests the focus is purely on visual storytelling, likely for a catalog or editorial spread. The styles range from earthy tones (brown, olive) to vibrant hues (yellow), catering to different seasons or occasions. The use of ruffles, slits, and cinched waists indicates a trend toward feminine, structured designs.
## Notes
- No textual elements (labels, legends, or axis markers) are present.
- All descriptions are inferred from visual details; no numerical data or trends apply.
- The image prioritizes aesthetic diversity over functional categorization.
</details>
(a) Image Gallery 1
<details>
<summary>2603.28662v1/x4.png Details</summary>
### Visual Description
## Photograph: Fashion Showcase of Seven Women in Diverse Dresses
### Overview
The image depicts seven women standing side-by-side against a plain white background, each wearing a distinct dress. The composition emphasizes fashion diversity, with variations in color, style, and silhouette. No textual elements, labels, or annotations are visible in the image.
### Components/Axes
- **Subjects**: Seven women positioned horizontally across the frame.
- **Dresses**: Each woman wears a unique garment, differing in color, pattern, and design.
- **Background**: Uniform white, ensuring focus remains on the subjects.
### Detailed Analysis
1. **Woman 1 (Far Left)**:
- **Dress**: White sleeveless knee-length dress.
- **Pose**: Hands clasped in front, standing upright.
2. **Woman 2**:
- **Dress**: Floral-patterned long-sleeve dress with a slit at the thigh.
- **Pose**: Right hand raised to adjust a hat (hat partially visible).
3. **Woman 3**:
- **Dress**: Light pink sleeveless midi dress.
- **Pose**: Hands on hips, slight forward lean.
4. **Woman 4 (Center)**:
- **Dress**: Vibrant multicolored floral maxi dress.
- **Pose**: Hands on hips, legs slightly apart.
5. **Woman 5**:
- **Dress**: Black off-the-shoulder mini dress with lace detailing.
- **Pose**: One hand on hip, other arm relaxed.
6. **Woman 6**:
- **Dress**: Black asymmetrical neckline midi dress with sheer sleeves.
- **Pose**: Right hand raised, holding a small black clutch.
7. **Woman 7 (Far Right)**:
- **Dress**: Long gray flowing dress with a high neckline.
- **Pose**: Slightly turned sideways, one hand on hip.
### Key Observations
- **Color Diversity**: Dresses range from neutral tones (white, gray) to bold patterns (floral, multicolored).
- **Silhouettes**: Styles vary from fitted (mini dresses) to flowing (maxi dresses).
- **Accessories**: Minimal, with only one visible clutch and a partially visible hat.
- **No Textual Elements**: No labels, legends, or annotations present.
### Interpretation
The image serves as a fashion editorial or catalog display, highlighting diversity in women’s clothing. The absence of text suggests the focus is purely on visual aesthetics rather than conveying data or instructions. The plain background and uniform lighting emphasize the garments’ details, making it suitable for retail or design purposes. The lack of interaction between subjects reinforces the individuality of each outfit.
## Notes
- **Language**: English (no other languages present).
- **Data Extraction**: No numerical or categorical data available.
- **Uncertainty**: Descriptions are based on visible details; no speculative assumptions made.
</details>
(b) Image Gallery 2
<details>
<summary>2603.28662v1/x5.png Details</summary>
### Visual Description
## Photograph: Fashion Showcase Collage
### Overview
The image is a collage of ten individual photographs arranged horizontally, each depicting a woman wearing a distinct dress. No textual elements (labels, legends, axis titles, or data markers) are visible in the image. The focus is purely on fashion presentation, with no embedded charts, diagrams, or textual annotations.
### Components/Axes
- **No textual or numerical components detected** (e.g., axes, legends, labels).
- **Visual elements**: Each photograph is isolated, with consistent lighting and neutral backgrounds.
### Detailed Analysis
- **Photographs**:
1. **First image**: Woman in a black lace mini-dress with high heels.
2. **Second image**: Woman in a long-sleeve floral maxi-dress (red border around the image).
3. **Third image**: Woman in a teal midi-dress with a V-neckline.
4. **Fourth image**: Woman in a black-and-purple floral maxi-dress with a wide-brimmed hat.
5. **Fifth image**: Woman in a beige floral midi-dress with a wide-brimmed hat.
6. **Sixth image**: Woman in a red floral maxi-dress with short sleeves.
7. **Seventh image**: Woman in a burnt-orange midi-dress with a V-neckline.
8. **Eighth image**: Woman in a purple maxi-dress with long sleeves.
9. **Ninth image**: Woman in a beige midi-dress with a belted waist.
10. **Tenth image**: Woman in a purple maxi-dress with a fitted silhouette.
- **Textual elements**: None present.
### Key Observations
- The collage emphasizes diversity in dress styles (maxi, midi, mini) and colors (black, teal, red, purple, beige, burnt orange).
- No data-driven elements (e.g., charts, graphs) or textual annotations are included.
### Interpretation
This image serves as a fashion showcase, highlighting varied styles and color palettes for potential retail or editorial purposes. The absence of textual or numerical data suggests it is not intended for analytical or statistical use but rather for visual inspiration or cataloging. The red border around the second image may indicate a highlighted or featured item, though this is speculative without additional context.
## Conclusion
The image contains no extractable textual information, numerical data, or structured components. It is a purely visual collage focused on fashion diversity.
</details>
(c) Image Gallery 3
<details>
<summary>2603.28662v1/x6.png Details</summary>
### Visual Description
## Fashion Showcase: Women in Diverse Dress Styles
### Overview
The image displays seven women modeling dresses in a fashion showcase. Each woman is positioned in a distinct pose, wearing a unique dress with varying colors, patterns, and silhouettes. No textual elements (labels, legends, axis titles, or data) are visible in the image.
### Components/Axes
- **No textual components detected**: The image lacks labels, legends, axis markers, or embedded text.
- **Visual elements**: Dresses feature diverse styles (e.g., off-shoulder, floral, solid colors) and colors (green, brown, pink, blue, etc.).
### Detailed Analysis
- **Dress 1 (Leftmost)**: Floral-patterned dress with green, yellow, and white hues.
- **Dress 2**: Brown off-shoulder dress with a flowing skirt.
- **Dress 3**: Bright pink dress with ruffled sleeves.
- **Dress 4 (Red-Boxed)**: Green floral dress with a fitted silhouette.
- **Dress 5**: Blue floral dress with a long, flowing skirt.
- **Dress 6**: Yellow floral dress with a backless design.
- **Dress 7 (Rightmost)**: Gray striped dress with a fitted cut.
### Key Observations
- No numerical data, trends, or categorical labels are present.
- The image focuses on visual diversity in fashion rather than quantitative analysis.
### Interpretation
This image appears to be a fashion editorial or catalog display, emphasizing style variety and model diversity. The absence of textual or numerical elements suggests it is not intended for data-driven analysis but rather for aesthetic or commercial purposes. The red box around Dress 4 may indicate a highlighted item, but no explicit textual justification is provided.
**Note**: The image contains no factual or data-driven content. All descriptions are based on visual observation of dress styles, colors, and model poses.
</details>
(d) Image Gallery 4
Figure 5: Four sample dress galleries from AMIGO. Each gallery contains one target image and visually similar distractors. The target is highlighted with a red outline.
#### Scale.
We collect 4,880 unique dress images for episode generation. We use five similarity thresholds, $τ∈\{0.3,0.4,0.5,0.6,0.8\}$ , to control difficulty. Higher thresholds yield smaller but more confusable galleries; lower thresholds typically yield larger galleries with more diverse distractors (Figure 6). We obtain 587 episodes at $τ=0.8$ because it is difficult to find enough highly similar distractors under a strict threshold. For evaluation at other thresholds, we randomly sample 1,000 episodes per threshold.
<details>
<summary>2603.28662v1/images/gallery_size_distribution_0.3.png Details</summary>
### Visual Description
## Histogram: Distribution of Gallery Sizes (Similarity Threshold = 0.3)
### Overview
The image displays a histogram visualizing the distribution of gallery sizes based on a similarity threshold of 0.3. The x-axis represents the "Number of Images per Gallery," while the y-axis shows the "Number of Galleries." The chart includes a legend with total galleries (1000) and mean gallery size (25.7). The distribution is skewed, with a peak around 25-27 images per gallery.
### Components/Axes
- **Title**: "Distribution of Gallery Sizes (Similarity Threshold = 0.3)"
- **X-axis**:
- Label: "Number of Images per Gallery"
- Range: 9 to 43 (increments of 2)
- Categories: 9, 11, 13, ..., 43
- **Y-axis**:
- Label: "Number of Galleries"
- Range: 0 to 60 (increments of 10)
- **Legend**:
- Position: Top-right corner
- Text: "Total galleries: 1000" and "Mean size: 25.7"
- **Bars**:
- Color: Blue (uniform across all bars)
- Height: Varies, with the tallest bar at ~60 galleries for 25-27 images per gallery.
### Detailed Analysis
- **X-axis Values**:
- Galleries with 9-11 images: ~1-10 galleries
- Galleries with 13-15 images: ~10-20 galleries
- Galleries with 17-19 images: ~20-30 galleries
- Galleries with 21-23 images: ~40-50 galleries
- Galleries with 25-27 images: ~55-60 galleries (peak)
- Galleries with 29-31 images: ~40-50 galleries
- Galleries with 33-35 images: ~30-40 galleries
- Galleries with 37-39 images: ~10-20 galleries
- Galleries with 41-43 images: ~1-5 galleries
- **Y-axis Values**:
- Heights correspond to the number of galleries for each image count (e.g., ~60 galleries have 25-27 images).
### Key Observations
1. **Peak Distribution**: The highest frequency of galleries (60) occurs at 25-27 images per gallery.
2. **Skewed Distribution**: Fewer galleries have very low (<15 images) or very high (>35 images) image counts.
3. **Mean Size**: The mean gallery size (25.7) aligns with the peak, suggesting most galleries cluster around this range.
4. **Threshold Context**: The similarity threshold of 0.3 likely groups galleries with comparable image content, though the exact methodology is not explained.
### Interpretation
The data suggests that most galleries (60 out of 1000) contain 25-27 images, indicating a common operational or curatorial standard. The distribution’s skew implies that smaller galleries (<15 images) and larger galleries (>35 images) are less common, possibly due to resource constraints or curation strategies. The similarity threshold of 0.3 may reflect a clustering algorithm used to categorize galleries, but its impact on the distribution is not explicitly detailed. This histogram could inform decisions about resource allocation, content curation, or algorithmic adjustments to optimize gallery sizes.
</details>
(a) $τ=0.3$
<details>
<summary>2603.28662v1/images/gallery_size_distribution_0.4.png Details</summary>
### Visual Description
## Bar Chart: Distribution of Gallery Sizes (Similarity Threshold = 0.4)
### Overview
The chart visualizes the distribution of gallery sizes, where "gallery size" is defined as the number of images per gallery. The x-axis represents the number of images per gallery (ranging from 6 to 36), and the y-axis represents the number of galleries (ranging from 0 to 80). The total number of galleries analyzed is 1,000, with a mean gallery size of 18.0 images. The data is represented by a single blue bar series.
### Components/Axes
- **X-Axis**: "Number of Images per Gallery" (discrete intervals from 6 to 36, incrementing by 2).
- **Y-Axis**: "Number of Galleries" (continuous scale from 0 to 80).
- **Legend**: Located in the top-right corner, labeled with "Total galleries: 1000" and "Mean size: 18.0". The bar color is blue.
- **Title**: "Distribution of Gallery Sizes (Similarity Threshold = 0.4)" (note: the threshold value is provided but not visually represented in the chart).
### Detailed Analysis
- **Bar Heights**:
- The tallest bar corresponds to **20 images per gallery**, with approximately **80 galleries**.
- The second-tallest bar is at **19 images per gallery**, with ~75 galleries.
- Bars decrease symmetrically on either side of the peak, with gradual declines toward the extremes (e.g., ~10 galleries at 6 images, ~5 galleries at 36 images).
- The mean value of 18.0 is marked in the legend but not visually indicated on the chart.
- **Distribution Shape**:
- The distribution is **unimodal**, peaking at 20 images per gallery.
- The mean (18.0) is slightly left of the peak, suggesting a **left-skewed distribution** (longer tail on the lower end).
### Key Observations
1. **Peak at 20 Images**: The majority of galleries (80) cluster around 20 images, indicating this is the most common size.
2. **Mean vs. Peak**: The mean (18.0) is lower than the peak (20), confirming left skewness.
3. **Tapered Extremes**: Galleries with very few (<10) or very many (>30) images are rare, with counts dropping to ~1–2 galleries at the extremes.
4. **Total Validation**: The sum of all bar heights approximates 1,000 galleries, aligning with the subtitle.
### Interpretation
The data suggests that most galleries in this dataset contain a moderate number of images (centered around 20), but the presence of smaller galleries (with fewer images) pulls the mean below the mode. The similarity threshold of 0.4 may relate to how galleries are grouped or filtered in the dataset, though this is not visually represented. The left skew implies that while larger galleries exist, they are outnumbered by smaller ones, creating a balance skewed toward lower sizes. This distribution could reflect practical constraints (e.g., curation efforts, resource limits) or algorithmic biases in gallery formation.
</details>
(b) $τ=0.4$
<details>
<summary>2603.28662v1/images/gallery_size_distribution_0.5.png Details</summary>
### Visual Description
## Bar Chart: Distribution of Gallery Sizes (Similarity Threshold = 0.5)
### Overview
The chart visualizes the distribution of gallery sizes (number of images per gallery) across 1,000 galleries, with a mean size of 13.8 images. Galleries are grouped by image count (x-axis) and frequency (y-axis). The distribution is roughly symmetric but slightly skewed right, with a peak at 15 images per gallery.
### Components/Axes
- **X-axis**: "Number of Images per Gallery" (discrete values: 6, 8, 10, ..., 30).
- **Y-axis**: "Number of Galleries" (continuous scale: 0–100).
- **Legend**: Located in the top-right corner, states:
- Total galleries: 1,000
- Mean size: 13.8
- **Bars**: Blue, with heights proportional to gallery counts.
### Detailed Analysis
- **Key Data Points**:
- **6 images**: 15 galleries
- **8 images**: 50 galleries
- **10 images**: 60 galleries
- **12 images**: 90 galleries
- **14 images**: 70 galleries
- **15 images**: 100 galleries (peak)
- **16 images**: 80 galleries
- **18 images**: 55 galleries
- **20 images**: 30 galleries
- **22 images**: 15 galleries
- **24 images**: 8 galleries
- **26 images**: 3 galleries
- **30 images**: 1 gallery
### Key Observations
1. **Peak at 15 images**: The tallest bar (100 galleries) occurs at 15 images per gallery, slightly above the mean (13.8).
2. **Symmetry with Right Skew**: The distribution is roughly symmetric around 15 but skewed right, as higher image counts (e.g., 20–30) have fewer galleries than lower counts (e.g., 6–10).
3. **Rapid Decline Beyond 15**: Galleries with >15 images drop sharply (e.g., 55 galleries at 18 images vs. 100 at 15).
4. **Outliers**: Only 1 gallery has 30 images, and 3 galleries have 26 images, indicating extreme sparsity at the upper end.
### Interpretation
The data suggests that most galleries cluster around 13–15 images, likely due to the similarity threshold (0.5) grouping images into moderately sized collections. The right skew implies that while most galleries are small-to-medium, a few contain significantly more images. The mean (13.8) being slightly below the peak (15) reflects the influence of smaller galleries pulling the average down. This distribution could inform resource allocation (e.g., storage, curation) for galleries near the mean, while outliers (e.g., 30-image galleries) may require specialized handling.
</details>
(c) $τ=0.5$
<details>
<summary>2603.28662v1/images/gallery_size_distribution_0.6.png Details</summary>
### Visual Description
## Bar Chart: Distribution of Gallery Sizes (Similarity Threshold = 0.6)
### Overview
The chart visualizes the distribution of gallery sizes based on a similarity threshold of 0.6. It shows the number of galleries grouped by the number of images they contain, with a total of 1,000 galleries. The mean gallery size is 11.2 images. The distribution is skewed left, with the highest frequency at 10 images per gallery.
### Components/Axes
- **X-axis**: "Number of Images per Gallery" (categories: 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26).
- **Y-axis**: "Number of Galleries" (scale: 0 to 110, in increments of 20).
- **Legend**: Located in the top-right corner, labeled "Total galleries: 1000" and "Mean size: 11.2".
- **Bars**: Blue, with heights proportional to the number of galleries for each image count.
### Detailed Analysis
- **X-axis Categories**:
- 6 images: ~77 galleries
- 8 images: ~85 galleries
- 10 images: ~108 galleries (peak)
- 12 images: ~107 galleries
- 14 images: ~105 galleries
- 16 images: ~103 galleries
- 18 images: ~101 galleries
- 20 images: ~82 galleries
- 22 images: ~70 galleries
- 24 images: ~68 galleries
- 26 images: ~45 galleries
- **Y-axis Values**:
- The tallest bar (10 images) reaches ~108 galleries.
- Bars decrease in height as image counts increase beyond 10, with a gradual decline from 12 to 26 images.
- **Legend**: Confirms the total galleries (1,000) and mean size (11.2), which aligns with the distribution's central tendency.
### Key Observations
1. **Peak at 10 Images**: The highest frequency of galleries (108) occurs at 10 images per gallery.
2. **Skewed Distribution**: The distribution is left-skewed, with a long tail extending to 26 images (45 galleries).
3. **Mean vs. Mode**: The mean size (11.2) is slightly higher than the mode (10), indicating influence from galleries with more images.
4. **Similarity Threshold Impact**: The threshold of 0.6 likely groups galleries with similar image counts, creating distinct clusters (e.g., 10–12 images dominate).
### Interpretation
The data suggests that most galleries cluster around 10 images, but the mean size is pulled higher by galleries with significantly more images (e.g., 20–26 images). The similarity threshold of 0.6 may reflect a clustering algorithm or similarity metric used to categorize galleries, resulting in a bimodal-like distribution with a sharp peak at 10 images and a gradual decline thereafter. The long tail indicates variability in gallery sizes, with some galleries containing up to 26 images. This distribution could inform resource allocation, such as storage or processing requirements, for galleries based on their image counts.
</details>
(d) $τ=0.6$
<details>
<summary>2603.28662v1/images/gallery_size_distribution_0.8.png Details</summary>
### Visual Description
## Bar Chart: Distribution of Gallery Sizes (Similarity Threshold = 0.8)
### Overview
The chart visualizes the distribution of gallery sizes based on a similarity threshold of 0.8. It shows the number of galleries grouped by the number of images they contain, with a total of 587 galleries and a mean gallery size of 7.1 images.
### Components/Axes
- **X-Axis**: "Number of Images per Gallery" (categories: 6, 8, 10, 12, 14)
- **Y-Axis**: "Number of Galleries" (scale: 0 to 250, increments of 50)
- **Legend**: Located in the top-right corner, labeled with "Total galleries: 587" and "Mean size: 7.1". The bar color is blue (#4682b4).
- **Title**: Positioned at the top-center of the chart.
### Detailed Analysis
- **Number of Images per Gallery (6)**: Approximately 260 galleries (tallest bar).
- **Number of Images per Gallery (8)**: Approximately 150 galleries.
- **Number of Images per Gallery (10)**: Approximately 90 galleries.
- **Number of Images per Gallery (12)**: Approximately 40 galleries.
- **Number of Images per Gallery (14)**: Approximately 5 galleries (shortest bar).
### Key Observations
1. The distribution is heavily skewed toward smaller gallery sizes, with the majority of galleries containing 6–8 images.
2. Galleries with 10 or more images represent a long tail, decreasing sharply in frequency.
3. The mean gallery size (7.1) is slightly higher than the mode (6), indicating a few larger galleries influence the average.
### Interpretation
The data suggests that most galleries are small, with a concentration around 6–8 images. The similarity threshold of 0.8 likely groups galleries with visually similar content, resulting in fewer galleries with larger image counts. The mean size exceeding the mode implies that while most galleries are small, a subset of larger galleries (e.g., 10–14 images) exists but is rare. This distribution could reflect curation practices, content diversity, or algorithmic clustering effects under the specified similarity threshold.
</details>
(e) $τ=0.8$
Figure 6: Distribution of gallery sizes across similarity thresholds $τ$ . Lower thresholds tend to yield larger galleries with more diverse distractors; higher thresholds produce smaller but more visually confusable candidate pools.
## 4 Benchmark framework
#### Benchmark components.
Our framework comprises four modules (Figure 7): (i) a benchmark model (the evaluated VLM) that generates questions and produces a final guess, (ii) a question-violation detector (LLM-based) that enforces the protocol, flags invalid questions with Skip, and provides standardized feedback on rule adherence, (iii) a user/oracle simulator (VLM agent) that answers valid questions with Yes/No/Unsure, and (iv) a verification module that maintains and audits the feasible candidate set implied by the dialogue.
Given the uploaded gallery and dialogue history, the benchmark model outputs either the next question or a terminal guess. The violation detector checks each question against the benchmark constraints. The violation detector operates in a few-shot manner and works on two tasks: (1) if the question is related to a prohibited attribute (e.g., color, sleeve length) or image index reference; or (2) if the attribute in the question has already been asked about in previous turns. In either case, the violation detector flags the question as invalid and returns Skip (and no oracle information is revealed). We record the number (and rate) of Skip responses per episode as the primary signal of protocol compliance. Implementing the violation detector as a separate module ensures that all models receive consistent, standardized feedback on rule adherence regardless of their internal architecture or reasoning style. The user/oracle simulator answers valid questions with Yes / No / Unsure.
<details>
<summary>2603.28662v1/images/benchmark_framework_overview.png Details</summary>
### Visual Description
## Flowchart: AMIGO — Benchmark Framework Overview
### Overview
The diagram illustrates a four-stage framework for evaluating a Visual Language Model (VLM) through iterative questioning, validation, and verification. It emphasizes protocol adherence, candidate set management, and final guess accuracy.
### Components/Axes
1. **Benchmark Model (Evaluated VLM)**
- **Conversation History**:
- T1: "Does it have a separate belt?" → Yes
- T2: "Does it have front buttons?" → No
- T3: "Does it have a wrap-style bodice?" → Yes
- T4: "Does it have tiered skirt?" → Yes
- **New Question**: "Does your favorite dress have a V-neckline?"
- **Final Guess**: "Dress #3"
2. **Question-Violation Detector (LLM-Based)**
- **Check Criteria**:
- Follows protocol?
- Valid & non-redundant?
- **Decision**:
- ✅ "Question OK" (green)
- ❌ "Violation → Reject" (red)
3. **User / Oracle Simulator (VLM Agent)**
- **Actions**:
- View target image
- Zoom-in tool enabled
- Answer questions with:
- ✅ Yes (green)
- ❌ No (red)
- ❓ Unsure (yellow)
- **Visual**: Person interacting with a laptop and dresses.
4. **Verification Module**
- **Tasks**:
- Maintain & audit feasible candidate set
- Check image-answer pairs
- Update candidate set
- Verify final guess (#3)
- **Outcome**:
- ✅ "PASS" (green checkmark with laurel wreath)
### Flowchart Connections
- **Orange Arrows**:
- Benchmark Model → Detector → Oracle Simulator → Benchmark (loop)
- Detector → Skip (if violation)
- **Red Arrow**: Final guess incorrect → loop back
- **Green Arrow**: Final guess correct → "PASS"
### Key Observations
- **Protocol Enforcement**: The detector rejects non-compliant questions, ensuring data quality.
- **Iterative Testing**: The loop between Benchmark Model and Detector/Oracle Simulator suggests continuous refinement.
- **Candidate Set Management**: The verification module dynamically updates candidates based on image-answer pairs.
- **Final Guess Validation**: The red box around "Dress #3" in the verification module highlights its critical role.
### Interpretation
This framework evaluates a VLM’s ability to answer visual questions while maintaining protocol compliance. The detector acts as a gatekeeper, filtering invalid queries to prevent redundant or biased data. The Oracle Simulator mimics human interaction, testing the model’s reasoning under constraints (e.g., zoom tools). The verification module ensures answers align with visual evidence, with the final guess (#3) serving as the model’s output. The "PASS" outcome indicates successful alignment between the model’s reasoning and ground truth. The loop structure implies a focus on robustness, as incorrect guesses trigger re-evaluation.
**Note**: No numerical data or trends are present; the diagram emphasizes process flow and decision logic.
</details>
Figure 7: Overview of the AMIGO benchmark framework, illustrating the interaction among the benchmark model, user/oracle simulator, question-violation detector, and verification module.
#### Verification module (state tracking and consistency checking).
After each valid question-answer pair, the verification module converts the interaction into an explicit constraint and applies it to the gallery to update the feasible candidate set, the subset of images consistent with all constraints observed so far. This module serves three purposes: (1) Evidence verification: it determines whether the dialogue has accumulated sufficient evidence to uniquely identify the target (i.e., the feasible set has size one). (2) Trajectory auditing: it logs candidate-set reduction dynamics (e.g., elimination rates, stalls) and supports trajectory-level diagnostics. (3) Consistency checking under noise: it detects contradictions signaled by an empty feasible set or conflicting constraints, enabling analysis of whether the benchmark model responds conservatively (e.g., by asking following verification questions) rather than guessing prematurely.
Importantly, the feasible set is updated after non- Skip turns if any candidate elimination occurs, cleanly separating protocol violations from evidence-based candidate elimination.
#### Evaluation and verification.
We include an independent evaluation module that logs the full interaction trace and scores both outcome and process. We check whether the model’s final guess matches the hidden target (non-verified accuracy). In addition, to rule out random-but-correct guesses, we compute verified accuracy using the feasible candidate set maintained by the verification module: an episode counts as verified-successful only if the feasible set is reduced to exactly one image (the target) before the model guesses. This design allows us to measure not only whether the model ultimately identifies the target, but also whether it does so through a coherent, evidence-driven trajectory that adheres to the protocol and effectively narrows the search space.
## 5 Evaluation Metrics
We evaluate models along four complementary axes. Unless noted otherwise, all constraint application and candidate-set updates are computed by the verification module using only valid (non- Skip) question-answer pairs.
- Identification accuracy: Overall (Non-Verified), Verified, Random-Guess. Overall (Non-verified) accuracy measures whether the model’s final guess matches the hidden target. Verified accuracy excludes random-but-correct guesses: an episode is counted as verified-successful only if (i) the final guess is correct and (ii) the verification module’s feasible candidate set has size exactly one and contains the hidden target immediately before the guess. This metric captures whether the model’s trajectory effectively narrows down to the correct answer rather than succeeding by chance. Random-guess accuracy counts episodes where the final guess is correct but the feasible candidate set has size greater than one, indicating that the model guessed correctly without sufficient evidence.
- Interaction efficiency. We measure interaction cost as the number of total turns (including Skip) before the final guess. We report efficiency on verified-successful episodes, random-guess correct episodes, incorrect episodes, and all episodes, so fewer turns correspond to faster evidence-driven narrowing rather than premature guessing.
- Protocol compliance. We quantify instruction following via (i) the average Skip rate responses and (ii) the average question generation rate before the End of uploading signal (premature outputs). Lower average Skip rates and zero premature outputs indicate stronger adherence; repeated violations reflect failures to recover to valid questioning.
- Robustness to noisy feedback. We test robustness under imperfect oracle answers by injecting controlled noise (e.g., flipping one Yes $↔$ No response or perturbing an Unsure response). We then report non-verified and verified accuracy under noise. The verified metric highlights whether the model can recover an evidence-consistent trajectory (e.g., by re-checking critical attributes) rather than succeeding via chance.
## 6 Experimental Results
We evaluate several open-source VLMs on Guess My Preferred Dress across multiple difficulty settings, including Qwen3-VL-235B-Instruct-FP8 [21], Qwen3.5-397B-A17B-FP8 [18], and Step3-VL-10B [8]. We report average verified and overall (non-verified) accuracy, interaction efficiency (average number of turns/rounds before the final guess and after the “End of uploading” signal), and protocol compliance (average Skip rate and premature outputs rate).
#### Evaluation setup.
In our framework, we employ Qwen3-VL-235B-Instruct-FP8 [21] as a few-shot violation detector. For the oracle answering module, we use an agentic Qwen-Agent pipeline with an image zoom-in tool to support inspection of fine-grained details; the VLM backbone is Qwen3-VL-235B-Instruct-FP8 [21].
#### Accuracy.
<details>
<summary>2603.28662v1/images/all_models_accuracies_subfigures.png Details</summary>
### Visual Description
## Line Charts: Average Accuracies Across Similarity Thresholds — All Models
### Overview
The image contains three line charts comparing the performance of three models (Owen3-VL-235B-Instruct-FP8, Owen3.5-397B-A17B-FP8, Step3-VL-10B) across similarity thresholds (0.3–0.8). Each chart represents a different accuracy metric: Verified Accuracy, Random-Guess Accuracy, and Overall (Non-Verified) Accuracy. Data points are plotted with distinct colors (blue, orange, green) corresponding to the legend.
---
### Components/Axes
- **X-Axis**: Similarity Threshold (0.3, 0.4, 0.5, 0.6, 0.7, 0.8).
- **Y-Axes**:
- Verified Accuracy (%): 0–20%.
- Random-Guess Accuracy (%): 0–20%.
- Overall (Non-Verified) Accuracy (%): 0–20%.
- **Legend**: Located at the top, mapping colors to models:
- Blue: Owen3-VL-235B-Instruct-FP8.
- Orange: Owen3.5-397B-A17B-FP8.
- Green: Step3-VL-10B.
---
### Detailed Analysis
#### Verified Accuracy
- **Owen3-VL-235B-Instruct-FP8 (Blue)**:
- 0.3: 1.7% → 0.4: 4.7% → 0.5: 5.4% → 0.6: 4.7% → 0.7: 3.6% → 0.8: 2.0%.
- **Trend**: Peaks at 0.5, then declines.
- **Owen3.5-397B-A17B-FP8 (Orange)**:
- 0.3: 7.2% → 0.4: 10.3% → 0.5: 9.4% → 0.6: 8.7% → 0.7: 6.8% → 0.8: 3.6%.
- **Trend**: Peaks at 0.4, gradual decline.
- **Step3-VL-10B (Green)**:
- 0.3: 1.7% → 0.4: 2.9% → 0.5: 4.7% → 0.6: 5.0% → 0.7: 6.8% → 0.8: 7.7%.
- **Trend**: Steady increase.
#### Random-Guess Accuracy
- **Owen3-VL-235B-Instruct-FP8 (Blue)**:
- 0.3: 5.6% → 0.4: 7.7% → 0.5: 10.3% → 0.6: 7.6% → 0.7: 6.0% → 0.8: 5.3%.
- **Trend**: Peaks at 0.5, then declines.
- **Owen3.5-397B-A17B-FP8 (Orange)**:
- 0.3: 0.5% → 0.4: 2.7% → 0.5: 2.0% → 0.6: 4.4% → 0.7: 6.0% → 0.8: 6.6%.
- **Trend**: Gradual increase.
- **Step3-VL-10B (Green)**:
- 0.3: 1.9% → 0.4: 2.7% → 0.5: 3.9% → 0.6: 6.0% → 0.7: 8.3% → 0.8: 8.3%.
- **Trend**: Steady increase.
#### Overall (Non-Verified) Accuracy
- **Owen3-VL-235B-Instruct-FP8 (Blue)**:
- 0.3: 8.0% → 0.4: 12.1% → 0.5: 16.7% → 0.6: 13.1% → 0.7: 11.4% → 0.8: 7.3%.
- **Trend**: Peaks at 0.5, sharp decline.
- **Owen3.5-397B-A17B-FP8 (Orange)**:
- 0.3: 8.0% → 0.4: 12.1% → 0.5: 11.4% → 0.6: 13.1% → 0.7: 11.4% → 0.8: 10.2%.
- **Trend**: Peaks at 0.6, moderate fluctuations.
- **Step3-VL-10B (Green)**:
- 0.3: 3.6% → 0.4: 5.6% → 0.5: 8.6% → 0.6: 11.0% → 0.7: 15.1% → 0.8: 15.1%.
- **Trend**: Steady increase.
---
### Key Observations
1. **Peak Performance**:
- All models achieve highest accuracy at similarity thresholds of 0.5–0.6.
- Step3-VL-10B (green) shows consistent growth across all metrics, peaking at 15.1% in Overall Accuracy.
2. **Decline After Threshold 0.6**:
- Owen3-VL-235B and Owen3.5-397B experience significant drops in Verified and Overall Accuracy beyond 0.6.
3. **Random-Guess Performance**:
- Owen3.5-397B’s Random-Guess Accuracy improves steadily, suggesting potential overfitting or model robustness.
4. **Step3-VL-10B Dominance**:
- Outperforms others in Overall Accuracy, particularly at higher thresholds (0.7–0.8).
---
### Interpretation
The data suggests that similarity thresholds of 0.5–0.6 optimize model performance, with Step3-VL-10B demonstrating superior robustness across thresholds. The decline in Owen3-VL-235B and Owen3.5-397B at higher thresholds may indicate overfitting or sensitivity to input variability. The Step3-VL-10B model’s linear growth in Overall Accuracy implies better generalization, making it the most reliable choice for applications requiring consistent performance across varying similarity levels.
</details>
Figure 8: Average verified, random-guess correct, and overall (non-verified) accuracy across similarity thresholds for Qwen3-VL-235B-Instruct-FP8, Qwen3.5-397B-A17B-FP8, and Step3-VL-10B.
<details>
<summary>2603.28662v1/images/verified_accuracy_with_ci_across_similarity_thresholds_all_models.png Details</summary>
### Visual Description
## Bar Chart: Verified Accuracy With CI Across Similarity Thresholds - All Models
### Overview
The chart compares verified accuracy percentages (with confidence intervals) for three AI models across five similarity thresholds (0.3, 0.4, 0.5, 0.6, 0.8). Models are color-coded: Qwen3-VL-235B-Instruct-FP8 (blue), Qwen3.5-397B-A17B-FP8 (orange), and Step3-VL-10B (green). Error bars represent confidence intervals.
### Components/Axes
- **X-axis**: Similarity Threshold (0.3, 0.4, 0.5, 0.6, 0.8)
- **Y-axis**: Verified Accuracy (%) (0–20 scale)
- **Legend**: Located in top-right corner, mapping colors to models
- **Error Bars**: Vertical black lines with numerical uncertainty values above each bar
### Detailed Analysis
1. **Threshold 0.3**:
- Qwen3-VL-235B: 2.5% (±0.6)
- Qwen3.5-397B: 7.2% (±1.7)
- Step3-VL-10B: 1.7% (±0.5)
2. **Threshold 0.4**:
- Qwen3-VL-235B: 5.3% (±1.4)
- Qwen3.5-397B: 10.3% (±2.3)
- Step3-VL-10B: 2.9% (±0.7)
3. **Threshold 0.5**:
- Qwen3-VL-235B: 5.5% (±1.4)
- Qwen3.5-397B: 9.6% (±1.7)
- Step3-VL-10B: 4.5% (±1.0)
4. **Threshold 0.6**:
- Qwen3-VL-235B: 4.8% (±1.4)
- Qwen3.5-397B: 8.7% (±1.6)
- Step3-VL-10B: 5.0% (±1.0)
5. **Threshold 0.8**:
- Qwen3-VL-235B: 2.0% (±0.6)
- Qwen3.5-397B: 3.6% (±1.2)
- Step3-VL-10B: 6.8% (±1.8)
### Key Observations
1. **Model Performance Trends**:
- Qwen3.5-397B consistently shows highest accuracy across all thresholds (range: 3.6–10.3%)
- Step3-VL-10B underperforms at lower thresholds but surpasses others at 0.8 (6.8%)
- Qwen3-VL-235B shows moderate performance with a notable drop at 0.8
2. **Error Margin Patterns**:
- Qwen3.5-397B has largest confidence intervals at 0.4 (±2.3)
- Step3-VL-10B exhibits increasing uncertainty at higher thresholds (0.8: ±1.8)
3. **Anomalies**:
- Step3-VL-10B's accuracy inversion at 0.8 (6.8%) vs. lower thresholds (max 5.0%)
- Qwen3-VL-235B's accuracy decline at 0.8 (-3.8% from 0.6)
### Interpretation
The data suggests:
1. **Threshold Sensitivity**: All models show reduced accuracy at extreme thresholds (0.3 and 0.8), with Step3-VL-10B uniquely improving at 0.8.
2. **Model Robustness**: Qwen3.5-397B demonstrates superior consistency but with higher variability at mid-thresholds (0.4–0.6).
3. **Potential Tradeoffs**: Step3-VL-10B's performance inversion at 0.8 may indicate specialized optimization for high-similarity tasks, while Qwen3-VL-235B's decline suggests overfitting to moderate similarity ranges.
The chart highlights the importance of threshold selection in model deployment, with Step3-VL-10B's unexpected performance at 0.8 warranting further investigation into its training data distribution.
</details>
Figure 9: Average verified accuracy with 95% confidence intervals across similarity thresholds for Qwen3-VL-235B-Instruct-FP8, Qwen3.5-397B-A17B-FP8, and Step3-VL-10B.
Figure 8 compares three accuracy metrics across similarity thresholds for the three evaluated models. Qwen3.5-397B-A17B-FP8 attains the highest verified accuracy at thresholds 0.3 through 0.6 with peak at threshold 0.4, whereas Step3-VL-10B performs best at threshold 0.8. Remarkably, despite being the smallest model with only 10B parameters, Step3-VL-10B attains the highest verified accuracy, random-guess accuracy and overall (Non-verified) accuracy at the highest similarity threshold, where the average gallery size is smaller but the candidates are most visually similar and therefore hardest to distinguish. By contrast, Qwen3-VL-235B-Instruct-FP8 generally attains the highest random-guess accuracy at thresholds 0.3 through 0.6, but this advantage does not translate into the highest verified accuracy. This gap indicates that higher final answer accuracy alone does not necessarily reflect better grounded or verifiable reasoning. For overall (non-verified) accuracy, Qwen3-VL-235B-Instruct-FP8 leads at lower thresholds, likely benefiting in part from its relatively high random-guess accuracy, while Qwen3.5-397B-A17B-FP8 peaks at 0.6 and Step3-VL-10B performs best at 0.8.
Overall, these patterns suggest that Step3-VL-10B is more affected by gallery size, whereas Qwen3.5-397B-A17B-FP8 is more sensitive to visually confusable candidates. Additionally, the consistently higher random-guess accuracy of Qwen3-VL-235B-Instruct-FP8 may indicate a greater tendency to guess early, while Qwen3.5-397B-A17B-FP8 appears more conservative to ask more questions rather than random-guess.
Figure 9 shows the verified accuracy with 95% confidence intervals across similarity thresholds for the three models. Notably, Qwen3.5-397B-A17B-FP8 is strongest at lower and intermediate thresholds, whereas Step3-VL-10B becomes strongest at the highest threshold.
#### Efficiency
<details>
<summary>2603.28662v1/images/all_models_average_rounds_subfigures.png Details</summary>
### Visual Description
## Line Chart: Average Rounds across Similarity Thresholds — All Models
### Overview
The image displays four line charts arranged in a 2x2 grid, analyzing model performance across similarity thresholds (0.3–0.8) for four scenarios: Verified Correct, Random Guess Correct, Incorrect, and All. Three models are compared using distinct line styles and colors.
### Components/Axes
- **X-axis**: Similarity Threshold (0.3, 0.4, 0.5, 0.6, 0.8).
- **Y-axis**: Average Rounds (0–17.5, in increments of 2.5).
- **Legend**: Located in the top-left of the main chart, with three entries:
- Blue circle: Gwen3-VL-235B-Instruct-FP8.
- Orange square: Gwen3.5-397B-A17B-FP8.
- Green diamond: Step3-VL-10B.
### Detailed Analysis
#### Verified Correct
- **Trend**:
- Gwen3-VL-235B (blue): Slight upward trend (7.20 → 7.75).
- Gwen3.5 (orange): Peaks at 0.4 (9.39), then declines to 8.52 at 0.8.
- Step3-VL-10B (green): Rises from 10.89 to 11.41 at 0.6, then slightly drops to 11.05 at 0.8.
- **Key Values**:
- Gwen3-VL-235B: 7.20 (0.3) → 7.75 (0.8).
- Gwen3.5: 9.19 (0.3) → 8.38 (0.5) → 8.03 (0.6) → 8.52 (0.8).
- Step3-VL-10B: 10.89 (0.3) → 10.15 (0.4) → 11.06 (0.5) → 11.41 (0.6) → 11.05 (0.8).
#### Random Guess Correct
- **Trend**:
- Gwen3-VL-235B: Peaks at 0.5 (5.78), then drops to 4.90 at 0.8.
- Gwen3.5: Rises to 6.43 at 0.5, then declines to 6.31 at 0.8.
- Step3-VL-10B: Sharp rise to 8.58 at 0.4, then gradual decline to 7.59 at 0.8.
- **Key Values**:
- Gwen3-VL-235B: 4.21 (0.3) → 5.22 (0.4) → 5.78 (0.5) → 5.43 (0.6) → 4.90 (0.8).
- Gwen3.5: 4.60 (0.3) → 6.81 (0.5) → 5.43 (0.6) → 6.31 (0.8).
- Step3-VL-10B: 3.80 (0.3) → 8.58 (0.4) → 6.43 (0.5) → 7.20 (0.6) → 7.59 (0.8).
#### Incorrect
- **Trend**:
- Gwen3-VL-235B: Stable at ~6.1–6.4.
- Gwen3.5: Declines steadily (15.95 → 11.52).
- Step3-VL-10B: Rises consistently (7.72 → 12.88).
- **Key Values**:
- Gwen3-VL-235B: 4.65 (0.3) → 6.37 (0.4) → 6.19 (0.5) → 6.37 (0.6) → 6.14 (0.8).
- Gwen3.5: 15.95 (0.3) → 15.75 (0.4) → 14.56 (0.5) → 13.18 (0.6) → 11.52 (0.8).
- Step3-VL-10B: 7.72 (0.3) → 9.09 (0.4) → 10.71 (0.5) → 11.69 (0.6) → 12.88 (0.8).
#### All
- **Trend**:
- Gwen3-VL-235B: Fluctuates between 6.1–6.4.
- Gwen3.5: Declines from 15.40 to 11.07.
- Step3-VL-10B: Increases from 7.70 to 12.32.
- **Key Values**:
- Gwen3-VL-235B: 4.70 (0.3) → 6.27 (0.4) → 6.19 (0.5) → 6.30 (0.6) → 6.11 (0.8).
- Gwen3.5: 15.40 (0.3) → 14.88 (0.4) → 13.82 (0.5) → 12.39 (0.6) → 11.07 (0.8).
- Step3-VL-10B: 7.70 (0.3) → 9.10 (0.4) → 10.57 (0.5) → 11.41 (0.6) → 12.32 (0.8).
### Key Observations
1. **Model Performance**:
- Gwen3.5 models generally require more rounds in "Verified Correct" and "All" scenarios, suggesting thorough processing.
- Step3-VL-10B excels in "Incorrect" and "All" scenarios, indicating robustness in error handling.
- Gwen3-VL-235B maintains moderate performance across all scenarios.
2. **Threshold Correlation**:
- Higher similarity thresholds (0.6–0.8) correlate with increased rounds for "Verified Correct" and "All," implying greater computational effort for higher-confidence tasks.
- Step3-VL-10B shows a peak in "Random Guess Correct" at 0.4, potentially indicating overcommitment to low-confidence tasks.
3. **Anomalies**:
- Gwen3.5's sharp decline in "Verified Correct" after 0.4 may reflect inefficiency or saturation at high thresholds.
- Step3-VL-10B's spike in "Random Guess Correct" at 0.4 contrasts with its strong performance in "Incorrect" rounds, suggesting divergent strategies for different error types.
### Interpretation
The data highlights model-specific behaviors:
- **Gwen3.5 models** prioritize depth, with Gwen3.5-397B-A17B-FP8 requiring the most rounds for "Verified Correct" and "All" tasks. This could indicate over-optimization for accuracy at the cost of efficiency.
- **Step3-VL-10B** balances performance, excelling in error handling ("Incorrect") and overall rounds ("All") while maintaining lower computational load for "Verified Correct." Its spike in "Random Guess Correct" at 0.4 might suggest a cautious approach to low-confidence tasks.
- **Gwen3-VL-235B** demonstrates stability but lacks the specialization of the other models. Its performance aligns with mid-tier efficiency, making it suitable for balanced workloads.
The trends emphasize the trade-off between computational effort and task specificity, with Step3-VL-10B emerging as the most versatile model across scenarios.
</details>
Figure 10: Average number of dialogue rounds before guessing across similarity thresholds for four outcome categories (verified correct, random-guess correct, incorrect, all) for three different models.
Figure 10 illustrates interaction length across similarity thresholds for the three evaluated models in four outcome categories: verified correct, random-guess correct, incorrect, and all episodes. In the verified-correct category, Step3-VL-10B, despite being the smallest model, generally shows the longest interaction length across thresholds. In contrast, Qwen3-VL-235B-Instruct-FP8 consistently has the shortest interaction length, suggesting that it may be more prone to guessing early without sufficient evidence, especially when the gallery contains more than 20 candidates (at thresholds, 0.3 and 0.4). Qwen3.5-397B-A17B-FP8, by comparison, maintains a relatively constant interaction length across thresholds, consistent with a more conservative strategy of asking additional questions regardless of gallery size or task difficulty.
In the incorrect category, Qwen3.5-397B-A17B-FP8 generally exhibits the longest interaction length, suggesting that it continues querying even when discriminative cues are difficult to identify. By contrast, Qwen3-VL-235B-Instruct-FP8 and Step3-VL-10B appear more likely to stop earlier when they struggle, particularly at lower similarity thresholds where gallery sizes are larger.
#### Protocol Compliance
<details>
<summary>2603.28662v1/images/all_models_average_skip_rates_subfigures.png Details</summary>
### Visual Description
## Line Chart: Average Skip Rate across Similarity Thresholds — All Models
### Overview
The image presents four line charts comparing average skip rates across different similarity thresholds (0.3–0.8) for three AI models: Qwen3-VL-235B-Instruct-FP8 (blue), Qwen3.5-397B-A17B-FP8 (orange), and Step3-VL-10B (green). Each subplot focuses on a specific evaluation category: Verified Correct, Random Guess Correct, Incorrect, and All. Data points are plotted with markers (circle, square, diamond) corresponding to the legend.
---
### Components/Axes
- **X-axis**: Similarity Threshold (0.3, 0.4, 0.5, 0.6, 0.8)
- **Y-axis**: Average Skip Rate (%)
- **Legend**: Located at the top, mapping colors/markers to models:
- Blue circle: Qwen3-VL-235B-Instruct-FP8
- Orange square: Qwen3.5-397B-A17B-FP8
- Green diamond: Step3-VL-10B
---
### Detailed Analysis
#### Verified Correct
- **Qwen3-VL-235B-Instruct-FP8 (blue)**: Starts at 13.9% (0.3), peaks at 26.8% (0.8), with a dip to 12.7% (0.6).
- **Qwen3.5-397B-A17B-FP8 (orange)**: Declines from 29.2% (0.3) to 20.8% (0.5), then rises to 24.1% (0.8).
- **Step3-VL-10B (green)**: Stable between 13.9% (0.3) and 17.8% (0.8), with a minor dip to 13.3% (0.6).
#### Random Guess Correct
- **Qwen3-VL-235B-Instruct-FP8 (blue)**: Peaks at 15.8% (0.5), drops to 11.5% (0.8).
- **Qwen3.5-397B-A17B-FP8 (orange)**: Rises from 14.0% (0.3) to 30.3% (0.5), then declines to 23.4% (0.8).
- **Step3-VL-10B (green)**: Declines from 22.9% (0.3) to 15.3% (0.8).
#### Incorrect
- **Qwen3-VL-235B-Instruct-FP8 (blue)**: Declines from 28.1% (0.3) to 16.9% (0.8).
- **Qwen3.5-397B-A17B-FP8 (orange)**: Declines from 37.7% (0.3) to 34.4% (0.8), with minor fluctuations.
- **Step3-VL-10B (green)**: Stable between 18.8% (0.4) and 24.9% (0.3), ending at 16.9% (0.8).
#### All
- **Qwen3-VL-235B-Instruct-FP8 (blue)**: Peaks at 26.9% (0.3), drops to 16.9% (0.8).
- **Qwen3.5-397B-A17B-FP8 (orange)**: Declines from 37.0% (0.3) to 34.4% (0.8).
- **Step3-VL-10B (green)**: Declines from 24.9% (0.3) to 16.9% (0.8).
---
### Key Observations
1. **Qwen3.5-397B-A17B-FP8 (orange)** consistently shows the highest skip rates across all categories, especially in "Verified Correct" and "All."
2. **Step3-VL-10B (green)** exhibits the most stable performance, with lower variability in skip rates.
3. **Qwen3-VL-235B-Instruct-FP8 (blue)** demonstrates significant fluctuations, particularly in "Verified Correct" (sharp rise at 0.8 threshold).
4. In "Random Guess Correct," skip rates for all models exceed 10%, suggesting potential issues with confidence calibration or data noise.
---
### Interpretation
- **Model Performance**: Qwen3.5-397B-A17B-FP8 appears less reliable, with high skip rates even for verified correct answers. This may indicate overconfidence or poor threshold calibration.
- **Step3-VL-10B** shows robustness, maintaining lower skip rates across thresholds, suggesting better generalization.
- **Qwen3-VL-235B-Instruct-FP8**'s erratic trends (e.g., sharp rise at 0.8 in "Verified Correct") hint at sensitivity to similarity thresholds, possibly due to architectural limitations.
- The "Random Guess Correct" subplot reveals that even random guessing incurs non-trivial skip rates, implying potential flaws in the evaluation framework or data distribution.
The data underscores the importance of model architecture and threshold selection in balancing accuracy and skip rates. Further analysis of confidence scores and training data could clarify these patterns.
</details>
Figure 11: Average Skip rates across similarity thresholds for three different models.
<details>
<summary>2603.28662v1/images/all_models_premature_outcome_rates.png Details</summary>
### Visual Description
## Line Chart: Premature Output Rate across Similarity Thresholds — All Models
### Overview
The chart visualizes the relationship between similarity thresholds (x-axis) and premature output rates (y-axis) for three AI models: Qwen3-VL-235B-Instruct-FP8 (blue), Qwen3.5-397B-A17B-FP8 (orange), and Step3-VL-10B (green). Data points are plotted at thresholds of 0.3, 0.4, 0.5, 0.6, and 0.8, with percentages ranging from 0% to 25%.
### Components/Axes
- **X-axis (Similarity Threshold)**: Discrete values at 0.3, 0.4, 0.5, 0.6, and 0.8.
- **Y-axis (Premature Output Rate)**: Continuous scale from 0% to 25%, marked in 5% increments.
- **Legend**: Located in the top-right corner, associating:
- Blue circles with Qwen3-VL-235B-Instruct-FP8
- Orange squares with Qwen3.5-397B-A17B-FP8
- Green diamonds with Step3-VL-10B
### Detailed Analysis
#### Qwen3-VL-235B-Instruct-FP8 (Blue)
- **Trend**: Steeply declining output rate as similarity threshold increases.
- **Values**:
- 0.3: 18.4%
- 0.4: 9.1%
- 0.5: 4.7%
- 0.6: 2.5%
- 0.8: 0.0%
#### Qwen3.5-397B-A17B-FP8 (Orange)
- **Trend**: Gradual decline with a minor uptick at 0.4.
- **Values**:
- 0.3: 14.0%
- 0.4: 10.6%
- 0.5: 7.7%
- 0.6: 6.3%
- 0.8: 0.3%
#### Step3-VL-10B (Green)
- **Trend**: Initial rise to a peak at 0.4, followed by a steady decline.
- **Values**:
- 0.3: 17.6%
- 0.4: 20.2%
- 0.5: 12.7%
- 0.6: 10.0%
- 0.8: 6.5%
### Key Observations
1. **Threshold Impact**: Higher similarity thresholds consistently reduce premature output rates across all models.
2. **Model Performance**:
- **Step3-VL-10B** exhibits the highest initial output rate (17.6% at 0.3) and a peak (20.2% at 0.4), suggesting sensitivity to moderate thresholds.
- **Qwen3-VL-235B-Instruct-FP8** achieves the lowest output rate (0.0% at 0.8), indicating superior threshold tolerance.
- **Qwen3.5-397B-A17B-FP8** shows a gradual decline but retains a residual 0.3% at 0.8.
3. **Anomaly**: Step3-VL-10B’s output rate increases from 0.3 to 0.4, contradicting the general trend of decreasing rates with higher thresholds.
### Interpretation
- **Threshold Optimization**: Higher similarity thresholds (e.g., 0.6–0.8) significantly reduce premature outputs, with Qwen3-VL-235B-Instruct-FP8 performing best at 0.8 (0.0%).
- **Model-Specific Behavior**: Step3-VL-10B’s peak at 0.4 suggests a potential trade-off between threshold stringency and output quality for this model.
- **Robustness**: Qwen3-VL-235B-Instruct-FP8 demonstrates the most consistent performance, eliminating premature outputs entirely at the highest threshold. The orange line’s minor uptick at 0.4 may reflect model-specific quirks or data noise.
This analysis highlights the importance of threshold selection in balancing output quality and efficiency across different AI architectures.
</details>
Figure 12: Premature output rates across similarity thresholds for three different models.
Figure 11 shows the average Skip rates across similarity thresholds for the three evaluated models. In general, Qwen3.5-397B-A17B-FP8 exhibits the highest Skip rates across thresholds in all four outcome categories. This suggests that, while it attains stronger verified accuracy than Qwen3-VL-235B-Instruct-FP8, it also struggles more with protocol adherence, possibly because its more persistent questioning strategy results in more frequent rule violations.
Figure 12 shows the premature output rates across similarity thresholds for the three models. Unlike skip rates, premature output rates decrease steadily as the similarity threshold rises and the average gallery size falls. Step3-VL-10B remains the worst across thresholds, while both Qwen3-VL-235B-Instruct-FP8 and Qwen3.5-397B-A17B-FP8 approach zero at the highest threshold. These results suggest that premature questioning is driven more by larger gallery size than by visual confusability, and that Step3-VL-10B is more sensitive to gallery size than the other two models.
## 7 Failure Cases
### 7.1 Qwen3-VL-235B-Instruct-FP8 failure cases
Figure 13 shows a failure case for Qwen3-VL-235B-Instruct-FP8 at $τ=0.8$ with 6 candidate images. Over 9 rounds, the model asks about tiered skirts, fabric sheen, wrap fronts, hem shape, and ruffled hems, but also violates constraints by asking about sleeve length, garment length, and color; these invalid turns are returned as Skip. Despite some narrowing from the valid turns, the final guess is incorrect. This episode illustrates two common failure modes: (i) poor protocol compliance that wastes interaction budget and (ii) insufficiently discriminative questions among highly similar candidates.
<details>
<summary>2603.28662v1/images/qwen3_failure_case_1.png Details</summary>
### Visual Description
## Screenshot: Fashion Website Product Display
### Overview
The image is a screenshot of a fashion website showcasing six women modeling different dresses. Each panel features a distinct style, with the third panel highlighted by a red border, indicating it is selected or featured. The dresses vary in color, pattern, and length, with models posing in a standardized format.
### Components/Axes
- **Panels**: Six vertical panels arranged horizontally.
- **Text Elements**:
- Panel 1: "Floral Maxi Dress" (long-sleeve, ankle-length, floral pattern).
- Panel 2: "Satin Slip Dress" (long-sleeve, knee-length, solid pink).
- Panel 3: "Black Maxi Dress" (long-sleeve, ankle-length, solid black; highlighted with a red border).
- Panel 4: "Gray Midi Dress" (long-sleeve, mid-thigh length, solid gray).
- Panel 5: "Black Maxi Dress" (sleeveless, ankle-length, solid black).
- Panel 6: "White Maxi Dress" (short-sleeve, ankle-length, solid white).
- **Visual Elements**:
- Models wear accessories (e.g., handbags, sandals).
- Red border around Panel 3’s black dress.
### Detailed Analysis
- **Panel 1**: Floral Maxi Dress with beige handbag and white ankle boots.
- **Panel 2**: Satin Slip Dress with gold strappy heels.
- **Panel 3**: Black Maxi Dress (red border indicates selection).
- **Panel 4**: Gray Midi Dress with black strappy heels.
- **Panel 5**: Black Maxi Dress (sleeveless variant).
- **Panel 6**: White Maxi Dress with gold strappy heels.
### Key Observations
1. **Red Border Significance**: Panel 3’s black dress is emphasized, suggesting it is the primary focus (e.g., bestseller, new arrival).
2. **Diversity in Styles**: Dresses range from floral patterns to solid colors, with variations in sleeve length and hemline.
3. **Accessories**: Models wear complementary footwear and handbags, enhancing the visual appeal.
### Interpretation
The red border around the black maxi dress in Panel 3 implies it is the highlighted product, possibly due to popularity or promotional intent. The uniform posing and styling across panels suggest a focus on versatility and accessibility, targeting a broad audience. The absence of pricing or sizing information indicates this is a preview or catalog view rather than a detailed product page.
## No numerical data or charts present. Textual content limited to product names and visual cues.
</details>
Prompt
Task: I will upload several batches of dress options. Please guess which one is my favorite dress and provide its index in the image gallery (starting index: 1). Constraints: 1.
Ask questions to gather attribute information about the target dress. 2.
Ask exactly one Yes/No question per turn about one distinguishable feature. I will respond with Yes, No, Unsure (if not confident), or Skip (if you violate the rules). 3.
If you receive Skip, your latest question was invalid and provides no information. 4.
Do not ask about sleeve length, garment length, color, pattern/prints, age group, size, shoes, necklace, hat, bag, background, or the human model. 5.
Do not enumerate attribute values across turns for the same attribute type (e.g., square neck, V-neck, crew neck). 6.
Do not inspect images one-by-one or reference indices (e.g., “is your favorite dress the first image?”). 7.
Do not guess until you have narrowed the target down to one image. 8.
If your confidence is low or you find contradictions, keep asking verification questions. 9.
When ready, output: My guess of your favorite dress: #<number>. 10.
Only start generating questions after you receive End of uploading.
| 1 2 | Model User Model | Does the dress have a tiered skirt? Yes Is the dress made of a shiny or satin-like fabric? |
| --- | --- | --- |
| User | Yes | |
| yellow!30 3 | Model | Does the dress have long sleeves? |
| yellow!30 | User | Skip |
| yellow!30 4 | Model | Is the dress sleeveless? |
| yellow!30 | User | Skip |
| 5 | Model | Does the dress have a wrap-style front? |
| User | Yes | |
| yellow!30 6 | Model | Is the dress floor-length? |
| yellow!30 | User | Skip |
| 7 | Model | Does the dress have a high-low hemline? |
| User | No | |
| yellow!30 8 | Model | Is the dress in a solid color? |
| yellow!30 | User | Skip |
| 9 | Model | Does the dress have a ruffled hem? |
| User | Yes | |
| Model | My guess: dress #2 (✗ Incorrect) | |
Figure 13: Failure case of Qwen3-VL-235B-Instruct-FP8 on Guess My Preferred Dress. Out of 9 turns, 4 are Skip (protocol violations), and the final guess is incorrect.
<details>
<summary>2603.28662v1/images/qwen3_failure_case_2.png Details</summary>
### Visual Description
## FashionCatalog Grid: Women's Dresses Collection
### Overview
The image displays a 3x10 grid of photographs showcasing women modeling diverse styles of dresses. Each photo is framed against a plain white background, emphasizing the clothing. The grid includes a mix of casual, formal, and seasonal styles, with models varying in age, body type, and ethnicity. One photo in the top row (third from the left) is highlighted with a red border.
### Components/Axes
- **Grid Structure**: 3 rows × 10 columns (30 total images).
- **Highlighted Element**: Top row, third image (red border).
- **No Textual Elements**: No labels, legends, axis titles, or embedded text detected.
### Detailed Analysis
1. **Top Row**:
- Models wear dresses in floral patterns, solid colors (pink, black), and textured fabrics.
- Styles include midi dresses, sleeveless designs, and knee-length cuts.
2. **Middle Row**:
- Features a mix of formal and casual attire, including sequined dresses, lace details, and bold patterns (e.g., animal prints).
- One model wears a ballet-inspired white tutu, suggesting a focus on versatility.
3. **Bottom Row**:
- Includes vibrant colors (yellow, teal), off-shoulder designs, and layered outfits (e.g., a pink vest over a white blouse).
- One model wears a wide-brimmed hat, adding a seasonal or travel-inspired element.
### Key Observations
- **Diversity**: Models represent a range of body types, skin tones, and hair textures.
- **Style Variety**: Dresses span from minimalist (black sheath dresses) to elaborate (floral, sequined).
- **Highlighted Image**: The red-bordered photo in the top row features a floral dress with a fitted silhouette, suggesting it may be a featured or popular item.
### Interpretation
The grid likely serves as a fashion catalog or e-commerce product display, emphasizing inclusivity and style diversity. The absence of textual annotations implies the focus is on visual appeal rather than data-driven categorization. The highlighted image may indicate a curated selection or bestseller. The inclusion of a child in a ballet outfit hints at targeting broader demographics, including younger audiences or family-oriented fashion.
</details>
Prompt
Task: I will upload several batches of dress options. Please guess which one is my favorite dress and provide its index in the image gallery (starting index: 1). Constraints: 1.
Ask questions to gather attribute information about the target dress. 2.
Ask exactly one Yes/No question per turn about one distinguishable feature. I will respond with Yes, No, Unsure (if not confident), or Skip (if you violate the rules). 3.
If you receive Skip, your latest question was invalid and provides no information. 4.
Do not ask about sleeve length, garment length, color, pattern/prints, age group, size, shoes, necklace, hat, bag, background, or the human model. 5.
Do not enumerate attribute values across turns for the same attribute type (e.g., square neck, V-neck, crew neck). 6.
Do not inspect images one-by-one or reference indices (e.g., “is your favorite dress the first image?”). 7.
Do not guess until you have narrowed the target down to one image. 8.
If your confidence is low or you find contradictions, keep asking verification questions. 9.
When ready, output: My guess of your favorite dress: #<number>. 10.
Only start generating questions after you receive End of uploading.
| | User | Prompt + <image_batch_1> |
| --- | --- | --- |
| Model | End of uploading | |
| User | Here is the next batch of dress options. <image_batch_2> | |
| Model | End of uploading | |
| User | Here is the next batch of dress options. <image_batch_3> | |
| yellow!30 | Model | Does the dress have a ruffled hem? |
| User | Here is the next batch of dress options. <image_batch_4> | |
| yellow!30 | Model | Does the dress have a lace overlay? |
| User | Here is the next batch of dress options. <image_batch_5> | |
| yellow!30 | Model | Does the dress have a high neckline? |
| User | Here is the next batch of dress options. <image_batch_6> | |
| yellow!30 | Model | Does the dress have a V-neckline? |
| User | End of uploading <image_batch_7> | |
| 1 | Model | Does the dress have a front tie detail? |
| User | Yes | |
| Model | My guess: dress #1 (✗ Incorrect) | |
Figure 14: Failure case at $τ=0.3$ with 35 candidates. The model violates the upload protocol by generating outputs before the true End of uploading signal, then guesses after only one answered question.
Figure 14 shows a second failure case for Qwen3-VL-235B-Instruct-FP8 at $τ=0.3$ with 35 candidates across seven upload batches. The model begins generating questions during the upload phase, including echoing End of uploading and asking questions before the actual signal, violating the upload protocol. After the upload completes, it asks only one valid question and immediately guesses, effectively a random guess. This illustrates how large galleries can amplify (i) premature, protocol-violating behavior and (ii) early stopping with insufficient evidence.
### 7.2 Qwen3.5-397B-A17B-FP8 failure cases
Figure 15 shows a failure case for Qwen3.5-397B-A17B-FP8 at $τ=0.6$ with 7 candidate images. The model uses all 20 allowed turns without ever committing to a final guess. Ten of the twenty turns result in Skip: Questions 2–4 enumerate neckline types (off-the-shoulder, square, sweetheart) after already asking about V-neckline in Question 1, violating Rule 5; Question 9 asks about sleeve length (Rule 4); Questions 11–12 ask about print types (Rule 4); Question 16 re-enumerates skirt style after asking about tiered skirts in Question 6 (Rule 5); and Questions 18–20 ask about garment length and color (Rule 4). The remaining valid turns establish that the target has a smocked bodice, a side slit, and a fitted waist, but the model fails to synthesize these constraints to narrow the candidate set to a unique target, exhausting the budget without a guess. This episode illustrates (i) repeated violation of the same rule types throughout the interaction and (ii) insufficient constraint integration despite accumulating valid discriminative evidence.
<details>
<summary>2603.28662v1/images/qwen3.5_failure_case_1.png Details</summary>
### Visual Description
## Fashion Collage: Seven Women in Diverse Dress Styles
### Overview
The image is a horizontal collage of seven vertically aligned photographs, each featuring a woman modeling a distinct dress style. The background is plain white, emphasizing the clothing. The women are positioned centrally in their respective frames, with consistent poses (hands on hips or at their sides) and similar footwear (sandals). The sixth image is outlined in red, suggesting it may be highlighted or selected.
### Components/Axes
- **Structure**: Seven equal-width vertical frames separated by light gray borders.
- **Subjects**: Seven women of varying body types and hair colors (black, blonde, brown).
- **Dress Features**:
- Colors: Teal, floral (pink/white), black, navy blue, and multicolored floral.
- Styles: Maxi dresses, off-shoulder designs, ruffled hems, and lace details.
- Accessories: Gold/silver clutches, strappy sandals.
### Detailed Analysis
1. **First Image**:
- Dress: Teal off-shoulder maxi with a thigh-high slit.
- Accessories: Gold clutch, white strappy sandals.
- Pose: Left hand on hip, right hand holding clutch.
2. **Second Image**:
- Dress: Light pink floral maxi with tiered ruffles.
- Accessories: Gold clutch, nude strappy sandals.
- Pose: Hands relaxed at sides.
3. **Third Image**:
- Dress: White off-shoulder maxi with pink/blue floral patterns.
- Accessories: Gold clutch, nude strappy sandals.
- Pose: Left hand on hip, right hand slightly raised.
4. **Fourth Image**:
- Dress: White maxi with navy blue floral patterns and a thigh-high slit.
- Accessories: Gold necklace, nude strappy sandals.
- Pose: Hands at sides, slight forward lean.
5. **Fifth Image**:
- Dress: Black maxi with ruffled sleeves and a fitted silhouette.
- Accessories: Black strappy sandals.
- Pose: Left hand on hip, right hand resting on thigh.
6. **Sixth Image** (Red Border):
- Dress: Black maxi with green/white palm leaf patterns and a thigh-high slit.
- Accessories: Gold clutch, nude strappy sandals.
- Pose: Hands on hips, confident stance.
7. **Seventh Image**:
- Dress: Light blue lace maxi with floral patterns.
- Accessories: Gold clutch, nude strappy sandals.
- Pose: Left hand on hip, right hand extended outward.
### Key Observations
- **Variety**: Each dress represents a unique style (e.g., floral, off-shoulder, ruffled).
- **Consistency**: All women wear strappy sandals and hold clutches, suggesting a cohesive theme.
- **Highlighted Dress**: The sixth image (red border) features a bold tropical print, potentially indicating a featured item.
- **Body Diversity**: Models vary in body type and hair texture, promoting inclusivity.
### Interpretation
This collage appears to be a fashion catalog or online store showcase, emphasizing versatility in dress designs for diverse body types. The red border around the sixth image may signal a "best seller" or "new arrival," though no explicit text confirms this. The consistent use of accessories (clutches, sandals) and neutral backgrounds prioritizes the clothing over the models, aligning with commercial fashion photography standards. No textual data (labels, legends, or axis titles) is present, as the focus is purely on visual presentation.
</details>
Prompt identical to Figure 13.
| 1 | Model | Does your favorite dress feature a V-neckline? |
| --- | --- | --- |
| User | No | |
| yellow!30 2 | Model | Does your favorite dress have an off-the-shoulder neckline? |
| yellow!30 | User | Skip Rule 5: enumerating neckline types |
| yellow!30 3 | Model | Does your favorite dress have a square neckline? |
| yellow!30 | User | Skip Rule 5: enumerating neckline types |
| yellow!30 4 | Model | Does your favorite dress have a sweetheart neckline? |
| yellow!30 | User | Skip Rule 5: enumerating neckline types |
| 5 | Model | Does your favorite dress have a wrap-style bodice? |
| User | No | |
| 6 | Model | Does your favorite dress have a tiered skirt? |
| User | No | |
| 7 | Model | Does your favorite dress have a smocked bodice? |
| User | Yes | |
| 8 | Model | Does your favorite dress have flutter sleeves? |
| User | No | |
| yellow!30 9 | Model | Does your favorite dress have long sleeves? |
| yellow!30 | User | Skip Rule 4: sleeve length |
| 10 | Model | Does your favorite dress have a side slit? |
| User | Yes | |
| yellow!30 11 | Model | Does your favorite dress have a floral print? |
| yellow!30 | User | Skip Rule 4: pattern/prints |
| yellow!30 12 | Model | Does your favorite dress have a leaf print? |
| yellow!30 | User | Skip Rule 4: pattern/prints |
| 13 | Model | Does your favorite dress have a high-low hemline? |
| User | No | |
| 14 | Model | Does your favorite dress have a straight hemline? |
| User | No | |
| 15 | Model | Does your favorite dress have a ruffled hemline? |
| User | No | |
| yellow!30 16 | Model | Does your favorite dress have a flared skirt? |
| yellow!30 | User | Skip Rule 5: enumerating skirt types |
| 17 | Model | Does your favorite dress have a fitted waist? |
| User | Yes | |
| yellow!30 18 | Model | Does your favorite dress have a midi length? |
| yellow!30 | User | Skip Rule 4: garment length |
| yellow!30 19 | Model | Does your favorite dress have a maxi length? |
| yellow!30 | User | Skip Rule 4: garment length |
| yellow!30 20 | Model | Does your favorite dress have a solid color? |
| yellow!30 | User | Skip Rule 4: color |
| Model | No guess made — 20-question budget exhausted (✗ Incorrect) | |
Figure 15: Failure case of Qwen3.5-397B-A17B-FP8 on Guess My Preferred Dress at $τ=0.6$ with 7 candidates. Out of 20 turns, 10 are Skip (protocol violations highlighted in yellow with rule annotations), and the model exhausts its entire question budget without making a final guess. Repeated Rule 4 and Rule 5 violations waste turns, while valid evidence about smocked bodice, side slit, and fitted waist is not synthesized into a unique identification.
Figure 16 shows a failure case for Qwen3.5-397B-A17B-FP8 at $τ=0.5$ with 14 candidate images. The model again exhausts all 20 allowed turns without committing to a final guess at lower similarity threshold. Twelve of the 20 turns result in Skip: Turn 3 asks about floral print (Rule 4); Turns 9–10 enumerate strapless and sweetheart necklines after V-neckline was confirmed in Turn 4 (Rule 5); and Turns 12–20 cycle through nine compound combinations of already-confirmed attributes—wrap-style, V-neckline, slit, ruched detail, and high-low hemline—re-enumerating established facts rather than posing new discriminative questions (Rule 5). The valid turns collectively establish five features (wrap-style, V-neckline, slit, ruched detail, high-low hemline), yet the model fails to synthesize this evidence to narrow the 14-candidate pool to a unique target. This episode illustrates (i) repeated Rule 4 and Rule 5 violations identical in kind to those in Figure 15, and (ii) a distinctive re-enumeration loop in which the model exhausts its remaining budget cycling through compound combinations of already-confirmed attribute pairs and triples rather than probing genuinely new features.
<details>
<summary>2603.28662v1/images/qwen3.5_failure_case_2.png Details</summary>
### Visual Description
## Photograph: Fashion Showcase Grid
### Overview
The image displays a grid of 14 women's dresses arranged in two rows (7 dresses per row). Each dress features distinct styles, colors, and patterns. The fifth dress in the top row is highlighted with a red border, suggesting emphasis or selection.
### Components/Axes
- **Grid Layout**:
- Top row: 7 dresses (left to right)
- Bottom row: 7 dresses (left to right)
- **Highlighted Element**:
- Red border around the fifth dress in the top row (position: top-center).
- **Dress Characteristics**:
- Styles: Maxi, midi, and mini dresses; sleeveless, short-sleeve, and long-sleeve designs.
- Colors: Black, white, navy blue, brown, floral patterns, velvet red, yellow, green, pink, and denim blue.
- Fabrics: Satin, lace, velvet, and printed textiles.
### Detailed Analysis
1. **Top Row (Left to Right)**:
- **Dress 1**: Black maxi dress with slit hem, paired with beige sandals.
- **Dress 2**: White strapless midi dress with lace detailing, paired with white sandals.
- **Dress 3**: Navy blue floral maxi dress with sheer sleeves, paired with white ankle boots.
- **Dress 4**: Brown midi dress with button details, paired with black sandals.
- **Dress 5** (Highlighted): White floral midi dress with slit hem, paired with gold sandals.
- **Dress 6**: Maroon velvet mini dress with long sleeves, paired with nude heels.
- **Dress 7**: Yellow floral mini dress with ruffled hem, paired with white sandals.
2. **Bottom Row (Left to Right)**:
- **Dress 8**: Red wrap mini dress with V-neckline, paired with nude heels.
- **Dress 9**: Black and gold patterned maxi dress with wide sleeves, paired with black ankle boots.
- **Dress 10**: Light pink maxi dress with tiered ruffles, displayed without a model.
- **Dress 11**: Emerald green velvet mini dress with long sleeves, paired with black heels.
- **Dress 12**: Navy blue velvet midi dress with keyhole neckline, paired with black sandals.
- **Dress 13**: Cream lace midi dress with denim jacket overlay, paired with brown sandals.
- **Dress 14**: Peach maxi dress with V-neckline, paired with black strappy sandals.
### Key Observations
- **Highlighted Dress**: The fifth dress in the top row (white floral midi) is visually emphasized with a red border, potentially indicating a featured or recommended item.
- **Color Diversity**: The collection spans neutral tones (black, white, brown) and vibrant colors (red, yellow, green, pink).
- **Style Variety**: Includes both casual (denim jacket, floral prints) and formal (velvet, lace) designs.
- **Footwear Consistency**: Most dresses are paired with sandals or heels, except Dress 9 (ankle boots) and Dress 13 (sandals).
### Interpretation
The image appears to be a fashion catalog or e-commerce product display, showcasing diverse styles for different occasions. The red border around Dress 5 suggests it may be a curated recommendation or bestseller. The absence of text or labels implies the focus is on visual appeal rather than explicit categorization. The grid layout allows for easy comparison of styles, colors, and silhouettes, catering to varied consumer preferences.
</details>
Prompt identical to Figure 13.
| 1 | Model | Is your favorite dress made of velvet fabric? |
| --- | --- | --- |
| User | No | |
| 2 | Model | Is your favorite dress a wrap-style dress? |
| User | Yes | |
| yellow!30 3 | Model | Does your favorite dress have a floral print? |
| yellow!30 | User | Skip Rule 4: pattern/prints |
| 4 | Model | Does your favorite dress have a V-neckline? |
| User | Yes | |
| 5 | Model | Does your favorite dress have a tiered skirt? |
| User | No | |
| 6 | Model | Does your favorite dress have a slit? |
| User | Yes | |
| 7 | Model | Does your favorite dress have a belt or tie at the waist? |
| User | No | |
| 8 | Model | Does your favorite dress have a ruched detail? |
| User | Yes | |
| yellow!30 9 | Model | Does your favorite dress have a strapless neckline? |
| yellow!30 | User | Skip Rule 5: enumerating neckline types |
| yellow!30 10 | Model | Does your favorite dress have a sweetheart neckline? |
| yellow!30 | User | Skip Rule 5: enumerating neckline types |
| 11 | Model | Does your favorite dress have a high-low hemline? |
| User | Yes | |
| yellow!30 12 | Model | Does your favorite dress have a wrap-style bodice with a V-neckline, a ruched detail, and a high-low hemline? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 13 | Model | Does your favorite dress have a wrap-style bodice with a V-neckline and a high-low hemline? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 14 | Model | Does your favorite dress have a V-neckline and a high-low hemline? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 15 | Model | Does your favorite dress have a V-neckline and a slit? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 16 | Model | Does your favorite dress have a V-neckline and ruched detailing? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 17 | Model | Does your favorite dress have a wrap-style bodice and a high-low hemline? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 18 | Model | Does your favorite dress have a wrap-style bodice and a slit? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 19 | Model | Does your favorite dress have a V-neckline and a wrap-style bodice? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| yellow!30 20 | Model | Does your favorite dress have a V-neckline and a ruched detail? |
| yellow!30 | User | Skip Rule 5: re-enumerating confirmed attributes |
| Model | No guess made — 20-question budget exhausted (✗ Incorrect) | |
Figure 16: Failure case of Qwen3.5-397B-A17B-FP8 on Guess My Preferred Dress at $τ=0.5$ with 14 candidates. Out of 20 turns, 12 are Skip (protocol violations highlighted in yellow with rule annotations), and the model exhausts its entire question budget without making a final guess. After valid turns establish wrap-style (Turn 2), V-neckline (Turn 4), slit (Turn 6), ruched detail (Turn 8), and high-low hemline (Turn 11), the model enters a nine-turn re-enumeration loop cycling through compound combinations of these already-confirmed attributes (Turns 12–20) rather than probing new discriminative features.
## 8 Reinforcement Learning Trajectories Collection
AMIGO can also serve as a trajectory collection framework for multimodal reinforcement learning. Each step provides a well-defined state (gallery, dialogue context, accumulated constraints), an action (a constrained question), and an observation (Yes/No/Unsure/Skip feedback, optionally noisy), with a naturally long-horizon objective. This structure supports dense rewards without additional annotation, e.g., penalties for invalid questions (Skip), progress rewards based on reduction in feasible set size or estimated information gain, and terminal rewards for correct identification only when the target is uniquely determined. These trajectories can support offline RL, preference optimization over alternative questions, and robustness training under imperfect feedback.
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