Structure-based graph rewiring, a graph augmentation approach, modifies the graph structure for enhancing the graph connectivity while keeping the graph intrinsic semantics, improving node embeddings in heterogeneous graphs. To augment the graph connectivity while avoiding introducing more noise into the graph, this paper proposes a novel model EulerRewire for heterogeneous graph by identifying all pairs of nodes with odd degree and adding edges between these node pairs while the graph spectral gap is utilized to guide edge selection, adding edges as few and enhancing the graph connectivity as much as possible. Then, a dual-view encoder based on GCN and Transformer to jointly learn node embeddings in the rewired Eulerian graph due to their abilities in capturing local structure and high-order dependency, in which a circuit sequence is generated for each node in the rewired Eulerian graph, better capturing high-order dependency between nodes. Extensive experiments for three downstream tasks including node classification, node clustering and link prediction demonstrate the proposed EulerRewire is superior to state-of-the-art baselines in most datasets.
Real-time agentic bidders are autonomous systems that participate in programmatic advertising auctions on behalf of advertisers, making instantaneous decisions on how much to bid on ad impressions while optimizing advertiser-defined objectives such as cost-per-acquisition (CPA). These agents operate under strict budget constraints in a dynamic, multi-agent environment where auctions repeat with constantly evolving bidding dynamics. In this paper, we propose a novel budget-constrained, CPA-aware agentic bidder that learns an adaptive bidding strategy to maximize the acquisition of high-quality users while ensuring effective budget utilization under sparse and delayed feedback. At the core of our approach is a linear-time approximation algorithm that formulates bid selection under a budget constraint as a greedy knapsack optimization problem. The algorithm scales to billions of auctions per day and provides a theoretical bound on the optimality gap, yielding provable near-optimal performance. To maintain robustness in production environments, the agent incorporates real-time feedback to pace budget spend throughout the day and adapt to shifting market conditions. Without explicitly modeling competing agents, the system learns a pragmatic near-optimal bidding strategy that balances campaign performance and budget utilization. Empirical evaluation on a large-scale demand-side platform (DSP) demonstrates that the proposed agent achieves approximately 92% of the theoretical optimum in acquisition efficiency under budget constraints. Our contributions include: (1) a scalable, near-optimal knapsack-based approximation algorithm with provable bounds; (2) a real-time adaptive bidding agent that jointly addresses budget pacing and CPA optimization under sparse and delayed feedback; and (3) extensive large-scale production validation in repeated auction environments without dominant bidding strategies.
We benchmark large language models for entity matching (EM) using the Compound Entity Matching (ComEM) framework, evaluating 46 model configurations across 8 datasets under a fixed protocol. Beyond accuracy, we quantify API-equivalent token cost and analyze the impact of configurable reasoning/thinking modes. We find that open-weight models can rival proprietary APIs: GPT-OSS:120b attains 92.10 F1 while costing ∼ 20 × less than GPT-5(high). Reasoning controls have opposite effects across families—low is optimal for GPT-5, whereas high improves open-weight models—and Claude shows the largest sensitivity (Sonnet 4.5: +10.13 F1 with thinking; Opus 4.5: slight degradation at 2.6 × cost). Finally, several ultra-budget configurations under 0.10 reach up to 89 F1, enabling inexpensive deployment. Our study offers practical model-selection guidance for EM across diverse budget and infrastructure constraints.
Sequential recommendation aims to model the dynamic preferences of users from historical behavior sequences for accurate item prediction. State space models (SSM) such as Mamba have demonstrated strong capability in long-sequence processing with linear complexity. However, in sequential recommendation, Mamba exhibits limited sensitivity to high frequency and transient local dynamics, suffers from insufficient nonlinear expressivity, and fails to capture the oscillatory nature present in complex user preference evolution. To address this limitation, we introduce Osc-Mamba, an effective framework that enhances Mamba for recommendation with a chaotic oscillatory mechanism (Lee-oscillator). The oscillator introduces frequency-sensitive nonlinear responses that adaptively regulate hidden-state transitions, improving the model’s feature representation ability. We design three strategies to integrate oscillatory dynamics with Mamba. Experimental results demonstrate that our approach outperforms competitive baselines in three sequential recommendation datasets.
The rapid progress of Large Language Models (LLMs) and Knowledge Graphs (KGs) has created new opportunities for building intelligent systems that are simultaneously expressive, grounded, and capable of complex reasoning. However, each paradigm has distinct limitations: LLMs struggle with factual consistency, long term memory, and controllability, while KGs lack the generative fluency and adaptive reasoning required for open ended tasks. This survey provides the first comprehensive study of how these two paradigms are increasingly unified through the emerging paradigm of agentic AI systems composed of autonomous agents. The survey introduces agentic AI as a conceptual and architectural bridge that unifies symbolic and neural intelligence, charting a path toward more adaptive LLM–KG agentic ecosystems. To the best of our knowledge, this is the first survey to explore the synergy between knowledge graphs and LLMs from an Agentic AI perspective.
