We develop a module-based method that having a dictionary of well fitted GPs, one could build ensemble GP models without revisiting any data.

Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and If you ran experiments... (a) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? Please refer to both main text and appendix for experiment details. Did you report error bars (e.g., with respect to the random seed after running experiments multiple All adaptation experiments in Procgen and RLBench are run for 3 seeds. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal As stated in section 2, we use RTX A5000 GPUs each with 24GB memory. C2F-ARM algorithm and training framework are built based on the original author's implementation Did you mention the license of the assets?

The original CLUTRR data generation framework made sure that each testproof is not in the training set in order to test whether a model is able to generalize to unseen proofs. Initial results on the original CLUTRR test sets resulted in strong model performance ( 99%) on levels seen during training (2, 4, 6) but no generalization at all ( 0%) to other levels. The models are given as input " [story] [query] " and asked to generate the proof and answer. Models are trained on levels2,4,6only. In our case, the entity names are important to evaluate systematic generalization.

Visualising AI spending: How does it compare with history's mega projects? World leaders and tech executives are convening in New Delhi for the India-AI Impact Summit 2026, focusing on the role of artificial intelligence in governance, job disruption and global collaboration. However, behind these discussions lies the financial reality. Over the past decade, AI has drawn one of the largest waves of private investment in modern history, totalling trillions of dollars. According to Gartner, a United States-based business and technology insights company, worldwide spending on AI is forecast to total $2.5 trillion in 2026, a 44 percent increase over 2025.

These datasets enable large-scale study of abuse detection for these languages. Anonymized comments: To further address privacy concerns, we anonymize our dataset. We combine thehate and offensivecategories in these datasets for training a binary classification model. We showthepercentage (%)ofemoticons present inourdatasetMACDinTable12. Infuture work,we will investigate in detail about the impact of emoticons on abuse detection. However,duetothe limited scale and diversity of abuse detection datasets in Indic languages, development of these models for Indic languages has been severely impeded.