Mind Duel: Offline AI for Structured Debates
Bibliographic record
Abstract
In this paper, we introduce AI Debater - an interactive chatbot hosted locally that is intended to engage in debates on issues specified by the user. The architecture is based on the DeepSeek-R1 14B large language model, which runs with the Ollama framework. Since all computations are carried out offline, privacy for the data is assured as well as latency is extremely low. Users can choose a personal tone for the debate level and degree of creativity for each AI speaker so that the tool can be used for learning, argumentation practice, or enjoyment. Streamlit is the framework employed to create the application's UI so that it is interactive and responsive. The design of the prompt guarantees the arguments advanced in debates are coherent, rational, and engaging. This paper illustrates the potential of instruction-conditioned language models for dialog systems as it opens up the possibility to AI-assisted tools for powerful reasoning and eloquent speech that will boost further development in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".