How does the multi-modal capability (analyzing text, images, and PDFs) affect the time or complexity of DeepResearch's results?

DeepResearch’s multi-modal capability, which processes text, images, and PDFs, increases both the time and complexity of generating results compared to single-mode systems. This is due to the technical challenges of integrating diverse data types, each requiring distinct preprocessing, analysis, and synchronization steps. For example, text extraction from PDFs involves parsing layouts and handling OCR (optical character recognition) errors, while image analysis demands computer vision models to detect objects or interpret charts. Combining these steps introduces dependencies that can slow down processing and amplify computational overhead.

Time is impacted primarily by the sequential and parallel workloads required for multi-modal processing. For instance, a PDF containing text and images must first be split into its components: text might be extracted quickly using libraries like PyPDF, but images within the PDF need resolution checks and preprocessing (e.g., noise reduction) before analysis. Running a vision model on high-resolution images could take seconds per image, while NLP models process text in milliseconds. If the system waits for all modalities to complete before synthesizing results, the slowest component (often image processing) becomes a bottleneck. Parallel processing can mitigate this, but coordinating outputs across models still adds coordination overhead, especially when combining results (e.g., linking a chart in an image to its textual description).

Complexity arises from managing heterogeneous data and ensuring consistent accuracy. For example, a research paper in PDF format might include tables, equations, and diagrams. Extracting tabular data requires layout detection and table recognition algorithms, which can fail if the PDF has non-standard formatting. Similarly, diagrams might need specialized models to interpret flowcharts versus bar graphs. Errors in one modality (e.g., misread text due to poor OCR) can propagate to others, leading to incorrect conclusions. Developers must design fallback mechanisms, like cross-verifying image captions with OCR results, to reduce such risks. Additionally, storing and indexing multi-modal data for efficient retrieval—such as linking text mentions of “Figure 1” to the actual image—adds layers of infrastructure complexity. These factors make multi-modal systems inherently more intricate to build and maintain than single-mode tools.