GPT 5.4 vs Gemini 2.5 Pro
tree_0030 · Introduction to Criminal Law
Timeline
Arrow keys or j/k move between rounds.
Round Context
Introduction to Criminal Law
Suggest an Edit to a Book Record
A 2015 open-access textbook on criminal law for undergraduate students was published by University of Minnesota Libraries Publishing. Using that publisher as your filter, identify the information technology–focused book in the computer science field released by the same publisher. Provide its title, copyright year, license type, intended academic audience, and review count. In addition, explain the process offered by the hosting platform for suggesting corrections or additions to a book’s bibliographic record, including who should be contacted for content-related improvements and where to direct general questions.
Answer length: 200-300 words.
Show hidden checklists
- Information Systems: A Manager's Guide to Harnessing Technology + Proof: Published by University of Minnesota Libraries Publishing, same publisher as the 2015 criminal law textbook
- Edit suggestion process + Proof: Platform provides a ‘Suggest an edit’ feature for book records and distinguishes between record edits, contacting the author/publisher for book improvements, and consulting the FAQ for questions
- Correct title of the IT/computer science book published by University of Minnesota Libraries Publishing
- Copyright year of that book (2015)
- License type (CC BY-NC-SA)
- Intended audience (undergraduate and/or graduate courses in Management Information Systems and Information Technology)
- Number of reviews listed (8 reviews)
- Explanation that the platform welcomes suggestions to improve book records
- Instruction that content improvement suggestions should be directed to the author or publisher
- Instruction that general questions about a book should be directed to the platform’s FAQ
The question uses the shared publisher of the 2015 criminal law textbook as a logical filter (Deep reasoning) to identify a different, technology-focused book from the same publisher without naming it directly. It then requires aggregating multiple scattered bibliographic details (title, year, license, audience, reviews) and combining them with procedural information about how the hosting platform handles record edits (Wide aggregation). This ensures both entity identification and cross-page synthesis.
Judgment
First, Deep Logic: The correct book is *Information Systems: A Manager's Guide to Harnessing Technology* (2015, CC BY-NC-SA, 8 reviews). Agent A selected *Web Design and Development* (wrong title, year, audience, review count). Agent B selected *Information Systems for Business and Beyond* (wrong title, year, review count). Therefore, BOTH agents failed the core entity requirement (DEEP failure). Second, Width/Completeness: Because both chose the wrong book, their reported copyright year, review count, and audience are incorrect relative to the ground truth. While both addressed the correction/suggestion process and mentioned contacting authors/publishers and platform help resources, these correct fragments do not compensate for the incorrect main entity and associated metadata. Thus, both also fail WIDE aggregation. Since both responses contain significant hallucinations on the central entity and multiple required checklist items, this is a Low Quality Tie. Neither agent provides a reliable or accurate user experience despite reasonable formatting and structure.
GPT 5.4
OpenAI