Evolving Minds: Logic-Informed Inference from Temporal Action Patterns
Authors: Chao Yang, Shuting Cui, Yang Yang, Shuang Li
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets show that our method outperforms existing approaches in accurately inferring mental states and predicting actions, demonstrating its effectiveness in modeling human cognitive processes. (Abstract) ... 5. Experiments ... 5.1. Experimental Setup ... 5.2. Experiments on Synthetic Dataset ... 5.3. Experiments on Real-World Dataset |
| Researcher Affiliation | Academia | 1School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China. Correspondence to: Shuang Li <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Predicting Actions with Backtracking ... Alg. 2 in Appendix. A.1, using inverse transform sampling method. ... The algorithm for the column generation is presented in Alg. 3, Appendix. A.2. ... algorithm shown in Alg. 4, Appendix. A.3. ... In Alg. 5, we provide the pseudocodes for the backtracking sampling mechanism |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Hand Me-That (Wan et al., 2022)... Car-following (Li et al., 2023)... Multi THUMOS (Yeung et al., 2018)... EPIC-Kitchen-100 (Damen et al., 2018) |
| Dataset Splits | Yes | For all the datasets, we split each dataset into 80%, 10%, 10% train/dev/test by the total sequences. |
| Hardware Specification | Yes | All synthetic data experiments and real-world data experiments, including the comparison experiments with baselines, are performed on Ubuntu 20.04.3 LTS system with Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz, 227 Gigabyte memory. |
| Software Dependencies | No | The paper mentions the operating system (Ubuntu 20.04.3 LTS) but does not provide specific version numbers for any programming languages, libraries, or other software dependencies critical for replication. |
| Experiment Setup | Yes | We present the selected hyper-parameters on both synthetic datasets and real-world datasets in Tab. 19. (Section E.1: Hyper-Parameter Selection) ... Table 19. Descriptions and values of hyper-parameters used for models trained on both synthetic dataset and real-world datasets. |