Why might DeepResearch ignore or not fully utilize an image or PDF you provided as part of your query?

DeepResearch might ignore or underutilize images or PDFs in queries due to technical limitations in processing non-text formats, challenges in interpreting unstructured content, and system design priorities. While the platform can analyze text effectively, handling multimedia or document formats often requires additional steps that may not be fully integrated or optimized. Let’s break this down into three key factors.

1. Technical Limitations in File Processing DeepResearch primarily relies on text-based analysis, so images and PDFs require preprocessing to extract usable data. For example, images need optical character recognition (OCR) to convert visual text into machine-readable form, and PDFs may need parsing to separate text from layout elements like tables or images. If the OCR engine struggles with low-resolution images, unconventional fonts, or complex PDF layouts (e.g., multi-column scientific papers), critical information might be lost or misread. Additionally, PDFs containing scanned pages (without embedded text layers) are treated as images, compounding the problem. These technical hurdles can lead to incomplete data extraction, causing the system to prioritize text inputs where analysis is more reliable.

2. Content Structure and Relevance Even if a file is processed, DeepResearch might not fully utilize it if the content isn’t structured in a way the system expects. For instance, a PDF with embedded charts or diagrams might lack textual context explaining their significance, making it hard for the model to link visual data to the query’s intent. Similarly, an image of a flowchart without accompanying labels or captions might be ignored because the system cannot infer relationships between elements. The platform may also deprioritize files if they don’t contain keywords or patterns directly tied to the query. For example, a research paper PDF with tangential sections could lead the system to focus only on text snippets that match known terms, overlooking relevant diagrams or equations.

3. System Design and Prioritization DeepResearch’s architecture might prioritize speed and scalability over exhaustive file analysis. Processing large PDFs or high-resolution images can be computationally expensive, leading the system to truncate or skip parts of the content to maintain response times. Security constraints could also play a role: files might undergo sanitization to block malicious code, inadvertently removing legitimate content. For developers, this means the system might favor text inputs where parsing is straightforward, avoiding edge cases like handwritten notes in images or password-protected PDFs. While updates could improve file handling, current trade-offs between performance, cost, and accuracy often lead to partial utilization of non-text inputs.

In summary, the limitations stem from gaps in format-specific processing, challenges in interpreting unstructured or ambiguous content, and design choices that favor text reliability over multimedia complexity. Developers should preprocess files (e.g., extracting text, simplifying layouts) to align with the system’s capabilities.