Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.


A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.


The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.

The Burden of Interactive Alignment with Inconsistent Preferences

Authors: Ali Shirali

NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 30,473 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,888 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.

Reproducibility Variable Result LLM Response
Research Type Theoretical Justification: This is a theory paper.
Researcher Affiliation Academia Ali Shirali UC Berkeley
Pseudocode No The paper primarily presents mathematical models, theorems, proofs, and theoretical analysis, without including any explicitly labeled pseudocode or algorithm blocks. The methods are described through equations and logical steps in paragraph form.
Open Source Code No Answer: [NA] Justification: This is a theory paper.
Open Datasets No Answer: [NA] Justification: This is a theory paper.
Dataset Splits No Answer: [NA] Justification: This is a theory paper.
Hardware Specification No Answer: [NA] Justification: This is a theory paper.
Software Dependencies No Answer: [NA] Justification: This is a theory paper.
Experiment Setup No Answer: [NA] Justification: This is a theory paper.