Wi-Fi Diagnosis: The AI Myth Exposed

Diagnosing Wi-Fi packet captures has long been the territory of seasoned experts. It's slow, inconsistent, and difficult to scale. Enter the LLM-based approaches, hailed as the future but fundamentally flawed. They fabricate events, like some kind of digital magician pulling rabbits out of non-existent hats. The confidence scores they produce? Uncalibrated and misleading.

Introducing PROBE

Meet PROBE, a tool determined to fix the mess. It’s a multi-stage pipeline designed to tackle the three major failings of LLMs in this area. Here's the crux: PROBE uses a deterministic approach to translate packet captures into text, allowing frame-level verification. It doesn’t stop there. It employs multi-run, multi-candidate ensembles and even seeks a second opinion from other models, obfuscating progressively to ensure reliability.

The real kicker? It offers a composite reliability score that's based on real evidence, not the AI’s self-assessment. This isn’t just theory, PROBE was tested on 87 enterprise Wi-Fi captures. The results? While single-pass LLMs edge the expert baseline F1 score of 0.871 to 0.912, they still miss critical frames a whopping 35% of the time.

False Positives and Misleading Confidence

Naive ensemble voting, touted as the genius solution by some, falls flat. It drops below the expert baseline, amplifying conservative verdicts that misclassify half of the confirmed failures. Let’s be blunt: relying on LLM self-reported confidence is a fool's errand. Over 71% of these models report a confidence score of exactly 0.95, regardless of the task's difficulty, making it about as informative as a magic 8-ball.

The Real Solution

So, what's the answer? PROBE’s evidence-grounded reconciliation pushes the F1 to 0.957 with an auto-accept rate of 96%. It even holds a worst-case floor above 0.70. That’s a breakthrough. Instead of falling for AI’s false bravado, the industry should pivot to model-agnostic evaluation frameworks. Why trust a model's golden references when they’re co-produced by the same flawed system?

Are we ready to admit that AI, for all its promises, is still far from infallible? The data already knows it. In the race to automate, we can't let the shiny allure of AI blind us to its shortcomings. Everyone has a plan until liquidation hits, and in this scenario, liquidation looks a lot like failure masked by AI-generated hopium.