DeepResearch doesn’t provide a direct parameter to set a specific time limit for processing a query. Instead, the time it spends is influenced indirectly by adjusting parameters that control the depth, scope, or computational effort of its analysis. Here’s how you can approach this:
1. Control Search Depth or Steps
Many research-oriented systems allow tuning parameters like max_steps, depth, or iterations, which determine how extensively the tool explores a topic. For example, reducing max_steps limits the number of reasoning cycles or data retrieval steps, shortening processing time. Conversely, increasing these values allows deeper analysis but takes longer. If you’re using an API, parameters like max_depth=3 might constrain the tool to prioritize speed over exhaustive results. This trade-off works well for simpler queries where approximate answers are acceptable.
2. Adjust Query Specificity The way you frame the query impacts processing time. Broad questions (e.g., “Explain quantum computing”) require more exploration, while targeted queries (e.g., “List three key differences between superconducting and photonic qubits”) reduce ambiguity and focus the search. Including constraints like time periods, domains, or output formats (e.g., “Provide a 2023 summary of LLM training techniques”) can streamline the workflow. This method doesn’t “set a timer” but guides the system to work efficiently within narrower boundaries.
3. Resource Allocation (Self-Hosted Systems)
If you’re running DeepResearch locally or in a controlled environment, you can influence time by allocating computational resources. For instance, limiting CPU/GPU usage or parallel processes (e.g., threads=2) can slow execution, while scaling up resources might speed it up. Some implementations also support timeouts (e.g., timeout=30 seconds) to halt processing after a threshold, though this risks incomplete results. This approach is more technical and depends on the infrastructure setup.
Example: A developer using an API might combine max_steps=5 with a focused query to get faster results for a real-time application. In contrast, a research team might increase max_depth and allocate more GPU resources for comprehensive analysis. The key is balancing speed with the required depth of analysis, as there’s no one-size-fits-all setting. Always test how parameter changes affect both output quality and latency for your specific use case.