4 citations · 7 across the 6 of their papers we have counts for
6 papers
Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
Will Jack, Noah Lehman, Keller Maloney +1
The same prompt -- "best CRM software" -- reaches AI assistants from buyers in widely different contexts: a solo founder, an enterprise VP, a UK SMB owner. We audit how strongly th…
Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation
Will Jack, Noah Lehman, Keller Maloney +1
A brand whose customers use both ChatGPT and Claude for product recommendations faces a strategic choice: a single optimization playbook, or one per provider? Across 215 commercial…
Paraphrase Brittleness in Production Retrieval-Augmented Commercial Recommendation: Reproducibility Below the Rerun-Stability Baseline
Will Jack, Noah Lehman, Keller Maloney +1
Small changes to how a buyer phrases a question -- "best CRM" vs "top CRM" vs "best CRM for a SaaS startup" -- produce substantially different brand recommendations from AI assista…
Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit
Will Jack, Noah Lehman, Keller Maloney +1
AI assistants like ChatGPT and Claude are recommendation engines, not search engines: they answer commercial queries by directly nominating brands rather than returning a list of l…
Perfectly to a Tee: Understanding User Perceptions of Personalized LLM-Enhanced Narrative Interventions
Ananya Bhattacharjee, Sarah Yi Xu, Pranav Rao +8
Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' exper…
Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination
Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu +9
Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense…