From the 2 of 13 linked papers with an AI index.
13 papers
Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression
Haolin Tian, Yuzhe Liu, Tonghan Wang
As large language models (LLMs) process increasingly long contexts, KV cache storage and repeated access have become a major bottleneck. Existing KV cache compression methods rely…
Evaluating and Pricing Advertisements in AI-Generated Responses
John L. Turner-Smith, Zimeng Huang, Yuhan Fu +2
The paper introduces a psychologically grounded agent simulation to create supervision for predicting click‑through intent of ads embedded in LLM‑generated responses, builds a ligh…
PILA: Plug-and-Play Insertion for LLM-native Advertising
Zhaowei Zhang, Yuhan Fu, Yihang Zhang +6
The paper introduces PILA, a plug‑and‑play sidecar that rewrites LLM responses to insert sponsored content, allowing ad placement without modifying the underlying language model.
Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning
Yanchen Jiang, David C. Parkes, Tonghan Wang
Characterizing revenue-optimal auctions for multi-item, multi-bidder settings remains a fundamental open problem, with no known closed-form solution existing beyond restrictive bin…
NaiAD: Initiate Data-Driven Research for LLM Advertising
Yihang Zhang, Zimeng Huang, Ren Zhai +2
Reconciling platform revenue with user experience in LLM advertising motivates a data-centric foundation. We introduce NaiAD, the first comprehensive dataset for LLM-native adverti…
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
Xiangkun Sun, Lingkai Kong, Aoqi Zhang +2
Language models can be persuaded to abandon factual knowledge. This vulnerability is central to AI safety, but its internal mechanism remains poorly understood. We uncover a compac…