arXiv subjects

Shaodi Feng

Publications and source records attributed to Shaodi Feng.

3 recordsLinked to original sources

arXiv2026

Aligning LLMs with Graph Neural Solvers for Combinatorial Optimization

Recent research has demonstrated the effectiveness of large language models (LLMs) in solving combinatorial optimization problems (COPs) by representing tasks and instances in natural language. However, purely language-based approaches struggle to accurately capture complex relational structures inherent in many COPs, rendering them less effective at addressing medium-sized or larger instances. To address these limitations, we propose AlignOPT, a novel approach that aligns LLMs with graph neural solvers to learn a more generalizable neural COP heuristic. Specifically, AlignOPT leverages the semantic understanding capabilities of LLMs to encode textual descriptions of COPs and their instances, while concurrently exploiting graph neural solvers to explicitly model the underlying graph structures of COP instances. Our approach facilitates a robust integration and alignment between linguistic semantics and structural representations, enabling more accurate and scalable COP solutions. Experimental results demonstrate that AlignOPT achieves state-of-the-art results across diverse COPs, underscoring its effectiveness in aligning semantic and structural representations. In particular, AlignOPT demonstrates strong generalization, effectively extending to previously unseen COP instances.

cs.AI↗

arXiv2025

Do Reviews Matter for Recommendations in the Era of Large Language Models?

With the advent of large language models (LLMs), the landscape of recommender systems is undergoing a significant transformation. Traditionally, user reviews have served as a critical source of rich, contextual information for enhancing recommendation quality. However, as LLMs demonstrate an unprecedented ability to understand and generate human-like text, this raises the question of whether explicit user reviews remain essential in the era of LLMs. In this paper, we provide a systematic investigation of the evolving role of text reviews in recommendation by comparing deep learning methods and LLM approaches. Particularly, we conduct extensive experiments on eight public datasets with LLMs and evaluate their performance in zero-shot, few-shot, and fine-tuning scenarios. We further introduce a benchmarking evaluation framework for review-aware recommender systems, RAREval, to comprehensively assess the contribution of textual reviews to the recommendation performance of review-aware recommender systems. Our framework examines various scenarios, including the removal of some or all textual reviews, random distortion, as well as recommendation performance in data sparsity and cold-start user settings. Our findings demonstrate that LLMs are capable of functioning as effective review-aware recommendation engines, generally outperforming traditional deep learning approaches, particularly in scenarios characterized by data sparsity and cold-start conditions. In addition, the removal of some or all textual reviews and random distortion does not necessarily lead to declines in recommendation accuracy. These findings motivate a rethinking of how user preference from text reviews can be more effectively leveraged. All code and supplementary materials are available at: https://github.com/zhytk/RAREval-data-processing.

cs.IR↗

arXiv2025

Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems

Deep learning has been extensively explored to solve vehicle routing problems (VRPs), which yields a range of data-driven neural solvers with promising outcomes. However, most neural solvers are trained to tackle VRP instances in a relatively monotonous context, e.g., simplifying VRPs by using Euclidean distance between nodes and adhering to a single problem size, which harms their off-the-shelf application in different scenarios. To enhance their versatility, this paper presents a novel lifelong learning framework that incrementally trains a neural solver to manage VRPs in distinct contexts. Specifically, we propose a lifelong learner (LL), exploiting a Transformer network as the backbone, to solve a series of VRPs. The inter-context self-attention mechanism is proposed within LL to transfer the knowledge obtained from solving preceding VRPs into the succeeding ones. On top of that, we develop a dynamic context scheduler (DCS), employing the cross-context experience replay to further facilitate LL looking back on the attained policies of solving preceding VRPs. Extensive results on synthetic and benchmark instances (problem sizes up to 18k) show that our LL is capable of discovering effective policies for tackling generic VRPs in varying contexts, which outperforms other neural solvers and achieves the best performance for most VRPs.

cs.LG↗