collaborators

5 papers

cs.AI2026

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…

cs.CY2026

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…

cs.IR2026

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…

cs.IR2026

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…

cs.HC2025

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…