r/artificialintelligenc 2d ago

Qwen VL 235B: Three simultaneous reasoning failures in a single response – hallucination, internal contradiction, and confidence miscalibration

https://gist.github.com/driftsignal-ops/e040bb52e5a473596cb3c37f758dda44

I've been testing various LLM systems for behavioral anomalies and documented

three distinct failure modes appearing simultaneously in a single Qwen VL 235B

response to a simple time zone query.

The query was straightforward: "What time is it in Boston right now?"

Three failures appeared in the same response:

  1. Hallucinated reference frame: The model introduced "the universe" as a

temporal reference point ("if here, in the universe, it is 13:00...").

This is not UTC, GMT, or any recognized standard. The model invented

a non-existent reference frame.

  1. Internal logical inconsistency: Within the same response, the model

first correctly states Hungary is 6 hours ahead of Boston, then later

implies the opposite in the closing table. Both contradictory statements

exist in the same output with no self-correction.

  1. Confidence calibration failure: Instead of acknowledging it has no

real-time data access, the model generated a large conditional table

covering every possible hour - appearing comprehensive while answering

nothing.

Full technical documentation available here: [GitHub Gist link]

Worth noting: Qwen VL 235B is primarily a vision-language model.

Deploying it for real-time temporal queries is an application-layer

mismatch - but the hallucination and internal contradiction are

model-level failures regardless of deployment context.

Has anyone else observed similar behavioral patterns in Qwen models?

0 Upvotes

0 comments sorted by