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Hongyang Liu

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AAAI Conference 2026 Conference Paper

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation

Hongyang Liu
Zhu Sun
Tianjun Wei
Yan Wang
Jiajie Zhu
Xinghua Qu

Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulators for RSs face two major limitations: (1) static and single-step prompt-based inference that leads to inaccurate and incomplete user profile construction; (2) unrealistic and single-round recommendation-feedback interaction pattern that fails to capture real-world scenarios. To address these limitations, we propose DGDPO (Diagnostic-Guided Dynamic Profile Optimization), a novel framework that constructs user profile through a dynamic and iterative optimization process to enhance the simulation fidelity. Specifically, DGDPO incorporates two core modules within each optimization loop: firstly, a specialized LLM-based diagnostic module, calibrated through our novel training strategy, accurately identifies specific defects in the user profile. Subsequently, a generalized LLM-based treatment module analyzes the diagnosed defect and generates targeted suggestions to refine the profile. Furthermore, unlike existing LLM-based user simulators that are limited to single-round interactions, we are the first to integrate DGDPO with sequential recommenders, enabling a bidirectional evolution where user profiles and recommendation strategies adapt to each other over multi-round interactions. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our proposed framework.

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STOC Conference 2022 Conference Paper

Simple parallel algorithms for single-site dynamics

Hongyang Liu
Yitong Yin

The single-site dynamics are a canonical class of Markov chains for sampling from high-dimensional probability distributions, e.g. the ones represented by graphical models. We give a simple and generic parallel algorithm that can faithfully simulate single-site dynamics. When the chain asymptotically satisfies the ℓ p -Dobrushin’s condition, specifically, when the Dobrushin’s influence matrix has constantly bounded ℓ p -induced operator norm for an arbitrary p ∈[1, ∞], the parallel simulation of N steps of single-site updates succeeds within O ( N / n +log n ) depth of parallel computing using Õ( m ) processors, where n is the number of sites and m is the size of graphical model. Since the Dobrushin’s condition is almost always satisfied asymptotically by mixing chains, this parallel simulation algorithm essentially transforms single-site dynamics with optimal O ( n log n ) mixing time to algorithms for sampling. In particular we obtain samplers, for the Ising models on general graphs in the uniqueness regime, and for satisfying solutions of CNF formulas in a local lemma regime. With non-adaptive simulated annealing, these samplers can be transformed routinely to algorithms for approximate counting. A key step in our parallel simulation algorithm, is a so-called “universal coupling” procedure, which tries to simultaneously couple all distributions over the same sample space. We construct such a universal coupling, that for every pair of distributions the coupled probability is at least their Jaccard similarity. We also prove that this is optimal in the worst case. The universal coupling and its applications are of independent interests.

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