arXiv subjects

Tanay Kumar

Publications and source records attributed to Tanay Kumar.

7 recordsLinked to original sources

arXiv2026

Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation

Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where models are prompted to adopt specific personas when interacting with users. While persona conditioning can improve user experience and engagement, it also raises concerns about how personality cues may interact with gender biases and stereotypes. In this work, we present a controlled study of persona-conditioned story generation in English and Hindi, where each story portrays a working professional in India producing context-specific artifacts (e.g., lesson plans, reports, letters) under systematically varied persona gender, occupational role, and personality traits from the HEXACO and Dark Triad frameworks. Across 23,400 generated stories from six state-of-the-art LLMs, we find that personality traits are significantly associated with both the magnitude and direction of gender bias. In particular, Dark Triad personality traits are consistently associated with higher gender-stereotypical representations compared to socially desirable HEXACO traits, though these associations vary across models and languages. Our findings demonstrate that gender bias in LLMs is not static but context-dependent. This suggests that persona-conditioned systems used in real-world applications may introduce uneven representational harms, reinforcing gender stereotypes in generated educational, professional, or social content.

cs.CL↗

arXiv2025

Combining Transport of Pendular Water with Wind-Assisted Interfacial Evaporation for Dewatering of Concentrated Slurry Waste

Drying concentrated slurry waste is slow, particularly due to the entrapment and limited accessibility of water entrained between the particles in the slurry. A sailboat evaporator with a root-like structure is a new system that enables wind-assisted interfacial evaporation of concentrated particle slurries. In this work, we create access to the disconnected water pockets in concentrated slurry waste, facilitating faster water conduction and efficient evaporation at extremely high solid concentration. The evaporator's long roots effectively extracted water beneath 150 cm deep supernatant water layer. Through replantation of the evaporator to a separate location, an impressive evaporation rate (ER) of 4 kg/(m^2*h) close to 80 wt% solid concentration, a 25% increase to a non-replanted sample. Furthermore, long periods of efficient of evaporation was achieved even at high solid concentration through hydrodynamic flushing of roots. Outdoor experiments achieved substantial volumetric reduction, yielding dried residues with over 75 wt% solid concentration. These results underscore the system's reliable performance against highly concentrated slurries, yet to be by conventional industrial methods, including flocculation and tail-lift drying. The integration of renewable energy coupled with efficient enhancement techniques makes the sailboat evaporator a scalable and sustainable pathway for industrial wastewater dewatering.

physics.app-ph↗

arXiv2025

Robust Attitude Control of Nonlinear UAV Dynamics with LFT Models and $\mathcal{H}_\infty$ Performance

Attitude stabilization of unmanned aerial vehicles (UAVs) in uncertain environments presents significant challenges due to nonlinear dynamics, parameter variations, and sensor limitations. This paper presents a comparative study of $\mathcal{H}_\infty$ and classical PID controllers for multi-rotor attitude regulation in the presence of wind disturbances and gyroscope noise. The flight dynamics are modeled using a linear parameter-varying (LPV) framework, where nonlinearities and parameter variations are systematically represented as structured uncertainties within a linear fractional transformation formulation. A robust controller based on $\mathcal{H}_\infty$ formulation is designed using only gyroscope measurements to ensure guaranteed performance bounds. Nonlinear simulation results demonstrate the effectiveness of the robust controllers compared to classical PID control, showing significant improvement in attitude regulation under severe wind disturbances.

eess.SY↗

arXiv2025

$\mathcal{H}_\infty$ Optimal Navigation in the Cislunar Space with LFT Models

Navigation in the cislunar domain presents significant challenges due to chaotic and unmodeled dynamics, as well as state-dependent sensor errors. This paper develops a robust estimation framework based on Linear Fractional Transformation (LFT) models, and state estimation in $\mathcal{H}_\infty$ and $\mu$ synthesis framework to address these challenges. The cislunar dynamics are embedded into an LFT form that captures nonlinearities in the gravitational model and state-dependent sensor errors as structured uncertainty. A nonlinear estimator is then synthesized in the $\mathcal{H}_\infty$ sense to ensure robust performance guarantees in the presence of the stated uncertainties. Simulation results demonstrate the effectiveness of the estimator for navigation in a surveillance constellation.

math.OC↗

arXiv2024

Sparse Actuation for LPV Systems with Full-State Feedback in $\mathcal{H}_2/\mathcal{H}_\infty$ Framework

This paper addresses the sparse actuation problem for nonlinear systems represented in the Linear Parameter-Varying (LPV) form. We propose a convex optimization framework that concurrently determines actuator magnitude limits and the state-feedback law that guarantees a user-specified closed-loop performance in the $\mathcal{H}_2/\mathcal{H}_\infty$ sense. We also demonstrate that sparse actuation is achieved when the actuator magnitude-limits are minimized in the $l_1$ sense. This is the first paper that addresses this problem for LPV systems. The formulation is demonstrated in a vibration control problem for a flexible wing.

eess.SY↗

arXiv2023

Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index

With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-generated artifacts, the US Copyright Office released a statement stating that 'If a work's traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it'. Furthermore, both the US and the EU governments have recently drafted their initial proposals regarding the regulatory framework for AI. Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection. This paper introduces the Counter Turing Test (CT^2), a benchmark consisting of techniques aiming to offer a comprehensive evaluation of the robustness of existing AGTD techniques. Our empirical findings unequivocally highlight the fragility of the proposed AGTD methods under scrutiny. Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess the detectability of content generated by LLMs. Thus, to establish a quantifiable spectrum facilitating the evaluation and ranking of LLMs according to their detectability levels, we propose the AI Detectability Index (ADI). We conduct a thorough examination of 15 contemporary LLMs, empirically demonstrating that larger LLMs tend to have a higher ADI, indicating they are less detectable compared to smaller LLMs. We firmly believe that ADI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making.

cs.CL↗

arXiv2023

Insights to the Forensic Investigation of a Custom Built UAV

Unmanned Aerial Vehicles (UAVs) have revolutionized numerous application fields since their introduction. Alongside this, advancements in mechanics and electronics have simplified and decreased the cost of system design. As a consequence, UAVs have dominated the global market across all sectors from recreational products to military applications. However, the widespread use of UAVs has also contributed to an increase in criminal activity involving them. UAVs have become a common means of delivering narcotics, arms, and ammunition, collecting sensitive personal information, and snooping in restricted areas, among other activities. This could constitute a threat to national security. Despite its growing significance, UAV forensics is still a relatively unexplored field of study. In this paper, we present new insights on UAV forensic analysis in terms of preliminary analysis, accessing the digital containers of the UAV, and retrieving vital data. With the aid of a simulated scenario, a comprehensive methodology for the analysis of retrieved data to establish conclusions on the flight path, source of origin, recover flight data, and acquire media file content is presented.

eess.SY↗