The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for LJP, encompassing a total of 7,02,945 preprocessed cases. NyayaAnumana, which combines the words “Nyaya” and “Anumana” that means “judgment” and “inference” respectively for most major Indian languages, includes a wide range of cases from the Supreme Court, High Courts, Tribunal Courts, District Courts, and Daily Orders and, thus, provides unparalleled diversity and coverage. Our dataset surpasses existing datasets like PredEx and ILDC, offering a comprehensive foundation for advanced AI research in the legal domain. In addition to the dataset, we present INLegalLlama, a domain-specific generative large language model (LLM) tailored to the intricacies of the Indian legal system. It is developed through a two-phase training approach over a base LLaMa model. First, Indian legal documents are injected using continual pretraining. Second, task-specific supervised finetuning is done. This method allows the model to achieve a deeper understanding of legal contexts. Our experiments demonstrate that incorporating diverse court data significantly boosts model accuracy, achieving approximately 90% F1-score in prediction tasks. INLegalLlama not only improves prediction accuracy but also offers comprehensible explanations, addressing the need for explainability in AI-assisted legal decisions.

Semantic drift: the gradual change in word meaning over time, poses a critical challenge to natural language understanding in diachronic corpora. Existing methods detect drift via alignment or distance metrics but lack the ability to model its dynamics or forecast future semantic change. We present Koopman Spectral Dynamics, the first framework to model semantic drift as a nonlinear dynamical system in word embedding space, linearly lifted via the Koopman operator. This enables interpretable forecasting, spectral decomposition, and drift attribution using Koopman eigenfunctions and modes. Applied to the HistWords: All English dataset (1800s-1990s), our method outperforms strong baselines: PCA, LSTM, and Pro-crustes, with up to 24.5% lower RMSE and 19.2% lower MAE in word forecasting, and +7.8 % accuracy in semantic coherence classification. We analyze drift trajectories using spectral energy curves, degradation plots, and mode attributions, and introduce causal influence maps via Granger and mutual information matrices. Our model generalizes to multi-word phrases (e.g., gay marriage) and supports a real-time interactive forecasting tool. This work introduces Koopman operator theory to NLP and data mining, offering a principled, spectral foundation for modeling and interpreting meaning evolution over time.

Adaptive steered molecular dynamics (ASMD) is a computational biophysics method in which an external force is applied to a selected set of atoms or a specific reaction coordinate to induce a particular molecular motion. Virtual reality (VR) based methods for protein-ligand docking are beneficial for visualizing on-the-fly interactive molecular dynamics and performing promising docking trajectories. In this paper, we propose a novel method to guide ASMD with optimal trajectories collected from human experiences using interactive molecular dynamics in virtual reality (iMD-VR). We also explain the benefits of using VR as a tool for expediting the process of ligand binding, outlining an experimental protocol that enables iMD-VR users to guide Amprenavir into and out of the binding pockets of HIV-1 protease and recreate their respective crystallographic binding poses within 5 minutes. Later, we discuss our analysis of the results from iMD-VR-assisted ASMD simulation and assess its performance compared to a standard ASMD simulation. From the accuracy point of view, our proposed method calculates higher Potential Mean Force (PMF) values consistently relative to a standard ASMD simulation with an almost twofold increase in all the experiments. Finally, we describe the novelty of the research and discuss results showcasing a faster and more effective convergence of the ligand to the protein's binding site as compared to a standard molecular dynamics simulation, proving the effectiveness of VR in the field of drug discovery. Future work includes the development of an artificial intelligence algorithm capable of predicting optimal binding trajectories for many protein-ligand pairs, as well as the required force needed to steer the ligand to follow the said trajectory.