Peeyush Agarwal

Avinash Agarwal, Peeyush Agarwal, Manisha J. Nene

As Artificial Intelligence becomes increasingly embedded in critical telecommunications infrastructure, existing legal frameworks remain ill-equipped to address the distinct risks this development introduces. This paper proposes the AI Regulatory Readiness Index (ARRI), a reproducible instrument for doctrinally assessing the legal preparedness of national frameworks to govern AI in critical digital infrastructure, and applies it across ten jurisdictions spanning five continents. ARRI comprises seven indicators across three dimensions: substantive AI-specific obligations, operational safeguards, and governance coordination, scored on a four-point ordinal scale and aggregated to a normalised 0-100 index. Legal instruments in force as of 28 February 2026 are assessed across telecommunications, cybersecurity, data protection, and AI governance domains. The study finds that global AI regulatory readiness in telecommunications remains concentrated in the lower range, with a mean ARRI score of 34 and a median of 26.5. AI incident reporting and risk classification emerge as the most acute and near-universal gaps, with binding legal definitions of AI-specific incidents largely absent across the legal frameworks applicable to telecommunications in the jurisdictions studied. ARRI scores diverge systematically from existing composite indices. For example, Indonesia achieves ITU Global Cybersecurity Index Tier 1 status yet scores 19 under ARRI, demonstrating that cybersecurity readiness and AI regulatory readiness are legally distinct conditions that existing frameworks conflate. The ten jurisdictions are classified into five regulatory archetypes, and a normative minimum standards framework is proposed, anchoring baseline AI governance readiness at an ARRI score of 67. ARRI is designed to be sector-portable and applicable beyond telecommunications to energy, healthcare, and transport infrastructure.

Akshat Rana, Peeyush Agarwal, K. P. S. Rana et al.

Ambiguity poses a major challenge to large language models (LLMs) used as robotic planners. In this letter, we present Scene Graph-Chain-of-Thought (SG-CoT), a two-stage framework where LLMs iteratively query a scene graph representation of the environment to detect and clarify ambiguities. First, a structured scene graph representation of the environment is constructed from input observations, capturing objects, their attributes, and relationships with other objects. Second, the LLM is equipped with retrieval functions to query portions of the scene graph that are relevant to the provided instruction. This grounds the reasoning process of the LLM in the observation, increasing the reliability of robotic planners under ambiguous situations. SG-CoT also allows the LLM to identify the source of ambiguity and pose a relevant disambiguation question to the user or another robot. Extensive experimentation demonstrates that SG-CoT consistently outperforms prior methods, with a minimum of 10% improvement in question accuracy and a minimum success rate increase of 4% in single-agent and 15% in multi-agent environments, validating its effectiveness for more generalizable robot planning.

Peeyush Agarwal, Harsh Agarwal, Akshat Rana

Purpose: The rapid integration of artificial intelligence (AI) systems like ChatGPT, Claude AI, etc., has a deep impact on how work is done. Predicting how AI will reshape work requires understanding not just its capabilities, but how it is actually being adopted. This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems. Methodology: This study utilizes the Anthropic Economic Index dataset of four million Claude AI interactions mapped to O*NET tasks. We systematically scored each task across seven key dimensions: Routine, Cognitive, Social Intelligence, Creativity, Domain Knowledge, Complexity, and Decision Making using 35 parameters. We then employed multivariate techniques to identify latent task archetypes and analyzed their relationship with AI usage. Findings: Tasks requiring high creativity, complexity, and cognitive demand, but low routineness, attracted the most AI engagement. Furthermore, we identified three task archetypes: Dynamic Problem Solving, Procedural & Analytical Work, and Standardized Operational Tasks, demonstrating that AI applicability is best predicted by a combination of task characteristics, over individual factors. Our analysis revealed highly concentrated AI usage patterns, with just 5% of tasks accounting for 59% of all interactions. Originality: This research provides the first systematic evidence linking real-world generative AI usage to a comprehensive, multi-dimensional framework of intrinsic task characteristics. It introduces a data-driven classification of work archetypes that offers a new framework for analyzing the emerging human-AI division of labor.