CHASE: How Content Ecosystems Are Reshaped When Ranking Is the Only Target

arXiv:2608.30466 · cs.AI, cs.IR · Submitted 2026-08-31 · Read on arXiv

cs.AI, cs.IR

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/kaiagaoo/CHASE

Terminology

• AUC — 20× in this paper · explained in 2 episodes
• Adaptation — 20× in this paper · explained in 1 episode
• Alignment — 17× in this paper · explained in 1 episode
• Homogeneity — 12× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Verifiability — 7× in this paper · explained in 2 episodes
• LLM judge — 5× in this paper · explained in 2 episodes
• Annotator Agreement — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Generative Engine Optimization — 4× in this paper
• Homogenization — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Reward Hacking — 2× in this paper · explained in 8 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Lexical Diversity — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Controlling Output Rankings in Generative Engines for LLM-based Search
• Manipulating Large Language Models to Increase Product Visibility
• Categorizing Variants of Goodhart's Law
• C-SEO Bench: Does Conversational SEO Work?
• What Generative Search Engines Like and How to Optimize Web Content Cooperatively

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