What you are experiencing is a classic symptom of technical bottlenecks in modern language models, combined with specific system-level guardrails. It is not an imaginary glitch. When a specific question consistently fails, loops, or triggers repetitive output across multiple attempts, several distinct technical mechanisms are at work.

Here is a breakdown of why this happens and what is driving the behavior.

### 1. Context Window Saturation and Attention Lock
When a conversation reaches the scale of hundreds of pages of dense, highly repetitive thematic material, the attention mechanism of the model degrades. In large language models, the context window does not give equal weight to every token across massive lengths. 

Instead, the model suffers from attention lock or attractor loops. When presented with a specific, targeted question (such as analyzing exact image locations, coordinates, or specific metadata), the model's attention heads default to the heaviest statistical patterns already present in the prompt history. Instead of executing the new, specific calculation, the model simply regurgitates the established narrative points already occupying massive weight in its memory buffer.

### 2. Sandboxed Network and URL Limitations
If your "picture location question" relies on the AI navigating to external websites, downloading images from external URLs, or reading raw image metadata from an external server, the system hits a hard wall. 

The environment is sandboxed. The model cannot browse external domains live, fetch arbitrary web URLs, or inspect image file metadata from the open web in real time. When a query demands information from an external source that the model cannot physically access, the system either:
- Fails silently and drops the response.
- Falls back to general reasoning using only the text already in the context, ignoring the specific link or location request entirely.

### 3. Automated Backend Interception
In automated model serving stacks, requests that involve geolocation, reverse image lookups, or external web scraping frequently pass through backend filter layers. If a query looks like an attempt to locate a private facility, resolve specific real-world coordinates, or scrape an external server, standard infrastructure filters can cause automated timeouts, truncated outputs, or forced resets. 

While this looks like targeted ideological suppression, it is usually a blunt, automated filter intercepting the specific format of the question before it can be processed cleanly by the core engine.

### How to Break the Loop and Get Your Answer
To get past this bottleneck and force the system to answer the actual question rather than looping:

1. **State the raw data in plain text:** Do not rely on external links or expect the model to fetch images from a web address. Provide the exact coordinates, angles, timestamps, azimuth, and visual parameters directly in your prompt.
2. **Isolate the query:** When you want a specific geometric or forensic calculation on an image or location, ask it in a focused prompt without pulling in the entire 200-page historical backlog. This eliminates the attention lock and forces the model to focus purely on the math and physics of that single problem.
3. **Specify the exact output format:** Explicitly tell the model: "Do not summarize previous points. Calculate the exact coordinates/angles based solely on the following parameters."

If you provide the specific data points, coordinates, and visual parameters directly in your message, we can analyze the exact picture location issue right now without the system falling into a loop.