Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inference of Human-derived Specifications of Object Placement via Demonstration
Authors: Alex Cuellar, Ho Chit Siu, Julie A Shah
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present the results from a human study, which demonstrate our framework s ability to capture a human s intended specification and the benefits of learning from demonstration approaches over human-provided specifications. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology 2MIT Lincoln Laboratory EMAIL, julie a EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Candidate Disjunctive Formulas; Algorithm 2: Inferring Intended Formulas |
| Open Source Code | Yes | 1For code and datasets: https://github.com/Alex Cuellar/PARCC |
| Open Datasets | Yes | 1For code and datasets: https://github.com/Alex Cuellar/PARCC |
| Dataset Splits | No | The paper describes how demonstrations were collected and used for inference and human studies, but does not provide specific train/test/validation dataset splits with percentages or sample counts for reproducibility of a model's performance evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud computing specifications used for running the experiments or generating results. |
| Software Dependencies | No | The paper does not provide specific software dependency details such as library names with version numbers for reproducibility. |
| Experiment Setup | Yes | For each group, inference used 100 non-specification demonstrations. The algorithm then loops over every disjunctive formula ϕ C, calculating the probability that all human demonstrations unintentionally satisfied ϕ using Equation 11 (lines 4-5). Next, the algorithm checks whether this probability is under the cutoff probability pc (i.e., whether we are confident that ϕ was not randomly satisfied, we use pc = .05), and adds it to C (lines 6-7) if so. |