From the 1 of 6 linked papers with an AI index.
6 papers
Winning by Peeking: Unenforced Budgets and Test-Set Selection Inflate Short-Budget AutoML Comparisons
Guilin Zhang, Kai Zhao
Comparisons between AutoML systems at short time budgets -- tens of seconds rather than hours -- are common in tool READMEs and workshop papers, and they are easy to get wrong. We…
Robust Explanations for User Trust in Enterprise NLP Systems
Guilin Zhang, Kai Zhao, Jeffrey Friedman +3
The paper introduces a black‑box framework to evaluate the robustness of token‑level explanations for enterprise NLP models, measuring how often top explanatory tokens change under…
Deployment-Time Memorization in Foundation-Model Agents
Lei, Chen, Guilin Zhang +8
Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time function rather than solely a p…
Adaptive Memory Admission Control for LLM Agents
Guilin Zhang, Wei Jiang, Xiejiashan Wang +5
LLM-based agents increasingly rely on long-term memory to support multi-session reasoning and interaction, yet current systems provide little control over what information is retai…
LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems
Guilin Zhang, Kai Zhao, Jeffrey Friedman +1
Recommendation systems must optimize multiple objectives while satisfying hard business constraints such as fairness and coverage. For example, an e-commerce platform may require e…
RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendation
Guilin Zhang, Kai Zhao, Jeffrey Friedman +1
Large Language Models (LLMs) are increasingly used to generate natural-language explanations in recommender systems, acting as explanation agents that reason over user behavior his…