---
type: Collection
title: Published Research
person: Venkata Pagadala
canonical: https://venkatapagadala.com/publications
generated_from: src/data/research.ts
generated_at: 2026-09-25
---

# Published Research

Full records for every paper the site claims. Each one states its peer-review status, its authorship position, and where the full text can actually be read.

Collection page: https://venkatapagadala.com/research-and-talks
Google Scholar: https://scholar.google.com/citations?user=g6hMDGIAAAAJ&hl=en

## Contents

- The Disruption of Search Engine Optimization by Large Language Models: A Mixed-Methods Analysis of the Evolving Search Landscape (2026, preprint)
- Google, SEO and Helpful Content: How Artificial Intelligence Can Be Helpful for E-Commerce Websites (2024, peer reviewed)
- A survey of reward hacking in agentic large language model systems (2026, peer reviewed)
- AI-Assisted SEO: Leveraging Machine Learning for Search Engine Optimization (2023, published)

## The Disruption of Search Engine Optimization by Large Language Models: A Mixed-Methods Analysis of the Evolving Search Landscape

- Status: Preprint, not peer reviewed
- Citation: SSRN (2026)
- Authors: Venkata Pagadala (Sole author)
- Affiliation at publication: Independent Researcher
- Date written: April 3, 2026
- Posted online: 17 Apr 2026
- Length: 9 pages
- DOI: https://dx.doi.org/10.2139/ssrn.6512878
- Publisher record: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6512878
- Access: Free to read
- JEL classification: L86, M37, O33, L13
- Keywords: Large Language Models, Search Engine Optimization, Generative Engine Optimization, AI Overviews, zero-click search, organic traffic, disruptive innovation, information foraging, platform economics

Large Language Models have reshaped the search landscape in ways that are only beginning to be understood. Google's AI Overviews, ChatGPT Search, and Perplexity AI now mediate a growing share of how people find information online, and the consequences for traditional Search Engine Optimization are substantial but unevenly distributed. This paper takes a mixed-methods approach to understanding what is actually happening. On the quantitative side, I draw on Semrush's analysis of over 10 million keywords, Previsible's dataset of 1.96 million LLM-referred sessions, and Chartbeat's global traffic analytics, among other sources. On the qualitative side, I analyze 23 publisher case studies and strategy documents through thematic coding. The picture that emerges is more complicated than either the "SEO is dead" or "nothing has changed" camps acknowledge. AI Overview prevalence fluctuated between 6.49% and 25% of queries throughout 2025. Click-through rates for top-ranking pages dropped 34.5% when AI Overviews appeared, yet Semrush's own before-and-after tracking found that zero-click rates for the same keywords actually decreased slightly, from 33.75% to 31.53%. I attempt to reconcile these tensions through what I call the Search Ecosystem Disruption Model (SEDM), which brings together Christensen's disruptive innovation theory, Pirolli and Card's information foraging theory, and platform economics. The data show striking asymmetries: Chartbeat documents 33-38% declines in Google Search referral traffic for publishers, with news sites losing up to 26% while e-commerce barely registers a change. I present five falsifiable predictions, a practitioner roadmap, and the roughly $2 billion in annual publisher advertising revenue at stake.

## Google, SEO and Helpful Content: How Artificial Intelligence Can Be Helpful for E-Commerce Websites

- Status: Peer reviewed
- Citation: Journal of Digital & Social Media Marketing, vol. 12, no. 3, pp. 206-226 (2024)
- Authors: Russ Macumber, Venkata Durga Eswar Pagadala (Co-author, 2 of 2)
- DOI: https://doi.org/10.69554/RJUW9313
- Publisher record: https://hstalks.com/article/8917/google-seo-and-helpful-content-how-artificial-inte/
- Access: Subscriber access at the publisher
- Keywords: SEO, helpful content, artificial intelligence, e-commerce

How AI supports the helpful-content standard on e-commerce sites, from product data quality to editorial signals.

## A survey of reward hacking in agentic large language model systems

- Status: Peer reviewed
- Citation: Discover Artificial Intelligence, vol. 6, article 825 (2026)
- Authors: Arun Morampudi, Ujval Irrinki, Rahul Grandhi, Venkata Pagadala, Madhavi Maddula (Co-author, 4 of 5)
- Posted online: 17 Aug 2026
- DOI: https://doi.org/10.1007/s44163-026-01980-z
- Publisher record: https://link.springer.com/article/10.1007/s44163-026-01980-z
- Access: Open access, CC BY-NC-ND 4.0
- Licence: https://creativecommons.org/licenses/by-nc-nd/4.0/
- Keywords: reward hacking, agentic LLMs, alignment, RLHF, RLVR, LLM-as-a-judge

A four-level taxonomy of how tool-using AI agents game their rewards, from verbosity and sycophancy up to modifying tests and disrupting monitors, compared across RLHF, RLAIF, RLVR and DPO, with a layered defense model for production systems.

## AI-Assisted SEO: Leveraging Machine Learning for Search Engine Optimization

- Status: Published; review status not established here
- Citation: International Journal of Scientific Research in Computer Science, Engineering and Information Technology (2023)
- Publisher record: none supplied, so no link is given rather than a dead one

Applying machine learning to the mechanics of search engine optimization at scale.