arXiv subjects

Dilip Thakur

Publications and source records attributed to Dilip Thakur.

3 recordsLinked to original sources

arXiv2026

How do flapping avian wings exhibit superior aerodynamic performance?

This work investigates the unsteady aerodynamic performance and vortex dynamics of avian-inspired flapping wings using our in-house sharp-interface immersed-boundary solver, VorteXdyn. A falcon-inspired body-wing model based on NACA 4312 profile is employed to examine aerodynamic force generation and vortex evolution during steady forward flapping flight at Reynolds numbers of 2,500, 5000, and 10000 and Strouhal numbers of 0.18, 0.225, and 0.27. The influence of physiologies is examined using three wing configurations: a simplified wing without distinct feather structures, a wing incorporating feather-like structures (serrations) along the trailing edge, and a geometrically detailed wing incorporating multiple feather layers consisting of primary, secondary, and median feathers over its span. The aerodynamic performance of these configurations is quantified using the temporal profiles and time-averaged characteristics of the lift and drag coefficients and the lift-to-drag ratio. The associated vortex dynamics are characterized through the formation and evolution of multiple leading-edge vortices (LEVs), their spanwise coherence, circulation, characteristic size, and persistence over the wings' surfaces. Particular emphasis is placed on the spanwise development of the LEVs from the root to the wingtip, their interactions with the tip vortices, and the resulting wake evolution during the downstroke. Our results demonstrate that increasing geometric fidelity modifies aerodynamic force production, the formation and evolution of multiple LEV structures, vortex-vortex and votex-wing interactions, and wake topology. These findings provide insight into the aerodynamic role of feather morphology in three-dimensional flapping flight for the aerodynamic design of efficient bio-inspired flapping-wing micro air vehicles.

physics.flu-dyn↗

arXiv2026

SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions

Retrieval-Augmented Generation (RAG) systems are increasingly evolving into agentic architectures where large language models autonomously coordinate multi-step reasoning, dynamic memory management, and iterative retrieval strategies. Despite rapid industrial adoption, current research lacks a systematic understanding of Agentic RAG as a sequential decision-making system, leading to highly fragmented architectures, inconsistent evaluation methodologies, and unresolved reliability risks. This Systematization of Knowledge (SoK) paper provides the first unified framework for understanding these autonomous systems. We formalize agentic retrieval-generation loops as finite-horizon partially observable Markov decision processes, explicitly modeling their control policies and state transitions. Building upon this formalization, we develop a comprehensive taxonomy and modular architectural decomposition that categorizes systems by their planning mechanisms, retrieval orchestration, memory paradigms, and tool-invocation behaviors. We further analyze the critical limitations of traditional static evaluation practices and identify severe systemic risks inherent to autonomous loops, including compounding hallucination propagation, memory poisoning, retrieval misalignment, and cascading tool-execution vulnerabilities. Finally, we outline key doctoral-scale research directions spanning stable adaptive retrieval, cost-aware orchestration, formal trajectory evaluation, and oversight mechanisms, providing a definitive roadmap for building reliable, controllable, and scalable agentic retrieval systems.

cs.AI↗

arXiv2025

Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem

The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools, effectively functioning as the "USB-C for Agentic AI." While this decoupling of context and execution solves critical interoperability challenges, it introduces a profound new threat landscape where the boundary between epistemic errors (hallucinations) and security breaches (unauthorized actions) dissolves. This Systematization of Knowledge (SoK) aims to provide a comprehensive taxonomy of risks in the MCP ecosystem, distinguishing between adversarial security threats (e.g., indirect prompt injection, tool poisoning) and epistemic safety hazards (e.g., alignment failures in distributed tool delegation). We analyze the structural vulnerabilities of MCP primitives, specifically Resources, Prompts, and Tools, and demonstrate how "context" can be weaponized to trigger unauthorized operations in multi-agent environments. Furthermore, we survey state-of-the-art defenses, ranging from cryptographic provenance (ETDI) to runtime intent verification, and conclude with a roadmap for securing the transition from conversational chatbots to autonomous agentic operating systems.

cs.CR↗