New framework evaluates generative engine optimization in multi-turn AI interactions

A new research paper introduces "Conversational Capture," a framework for evaluating generative engine optimization (GEO) in multi-turn human-agent interactions. The authors argue that current single-turn evaluations are insufficient, as an agent's response influences the user's subsequent questions and thus the information retrieved. The proposed framework formalizes this closed-loop system, introducing trajectory-level constructs to measure cumulative visibility, direct and feedback gains, and capture coefficients. The research demonstrates that feedback effects can significantly outweigh direct gains in longer conversations, leading to superlinear payoff growth and weak agreement between single-turn and trajectory-based rankings. AI

IMPACT This research could lead to more effective AI agents by improving how their optimization is measured and developed for multi-turn conversations.

RANK_REASON The cluster contains a new academic paper detailing a novel framework for evaluating AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

• Conversational Capture
• Generative Engine Optimization
• Human-Agent Interaction
• large language models

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →