Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Abstract The integration of Artificial Intelligence (AI) into the Indian judiciary represents a paradigm shift toward modernizing legal administration, enhancing processing efficiencies, and addressing massive case backlogs that have historically strained the justice delivery system. India's judiciary, burdened by one of the largest case pendency figures in the world, has increasingly turned toward digital and computational tools to reduce procedural delay without compromising the constitutional guarantees owed to litigants. This comprehensive research article evaluates operational deployments, administrative obstacles, and Supreme Court directives governing AI usage under the e-Courts framework. It examines the operational boundaries established by the Supreme Court White Paper, the active AI assistive ecosystem (including SUVAS, SUPACE, and LegRAA), legal integrity and professional accountability concerning generative AI hallucinations, and structural process engineering integrated under Phase-III. The study further situates these developments within a constitutional and comparative framework, arguing that India's cautious, human-centred model of AI adoption offers a template that other developing judiciaries may study as they confront similar backlogs, resource constraints, and linguistic diversity. The paper concludes that sustained capacity building, rigorous ethical audits, and close collaboration between the legal profession and technologists will determine whether AI ultimately strengthens, rather than erodes, public confidence in the administration of justice.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.
B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al.· Structural And Multidiscipli...· 17 citations
An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.
Linghan Huang, Bo Li, Huaming Chen et al.· 12 citations· ⚡2
This review provides a systematic literature review of LLM-based Verilog code generation, analyzing 102 papers (70 published and 32 high-quality preprints) from SE, AI, and EDA venues and outlines a roadmap highlighting potential opportunities in LLM-assisted hardware design.
This work introduces Behavior-Outcome Freedom (F), a pre-synthesis diagnostic of signed behavior-outcome rank mismatch, and formalizes its candidate-conditional role through Signed Anchor-Rank Transfer, which preserves validated capability resources, removes runtime orchestration, and conditionally inherits pipeline guidance using a calibrated rule over F.
Binyan Xu, Dong Fang, Haitao Li et al.· arXiv.org· 10 citations
Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.