From the 1 of 6 linked papers with an AI index.
6 papers
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…
EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
Guilin Zhang, Chuanyi Sun, Kai Zhao +3
Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et al. (2023) showed, this proxy d…
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…