arXiv subjects

Guiquan Sun

Publications and source records attributed to Guiquan Sun.

3 recordsLinked to original sources

arXiv2026

Judging the Justification: Decision Certificate Supervision for Generative Reward Models

Generative reward models (GenRMs) generate natural language rationales to justify preference judgments, yet a correct verdict does not guarantee a sound justification. Existing approaches often rely on reference explanations to supervise rationales, but obtaining such references can be costly and does not necessarily establish whether generated rationales support the resulting verdict. To address these limitations, we propose DeCert, a framework that evaluates the validity and verdict consistency of generated rationales without requiring reference explanations. Crucially, DeCert translates these assessments into preference supervision that accounts for both verdict correctness and justification quality. In particular, when multiple sampled judgments share the same correct verdict, differences in certificate quality enable DeCert to construct rationale preference pairs that distinguish stronger from weaker justifications. Extensive empirical studies across two model families and multiple in- and out-of-distribution benchmarks demonstrate that DeCert consistently outperforms strong generative judge baselines while improving rationale quality. Moreover, DeCert effectively mitigates positional bias, improving position-consistent accuracy by up to 14.8 points. These results highlight the importance of supervising not only which verdict is correct, but also how that verdict is justified.

cs.CL↗

arXiv2026

HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning

Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data. However, most existing studies assume static graphs, whereas real-world graph data are often continuously updated. Continual learning in this setting is particularly challenging because historical knowledge is encoded not only in target-node representations, but also in their heterogeneous structural contexts and relation-dependent semantic responses. To this end, we introduce HERO, a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to incoming tasks. We analyze experience replay as an approximation to the unavailable historical gradient and decompose its error into target-selection and context-reconstruction terms. This analysis shows that replaying target nodes alone is generally insufficient for relation-sensitive HGNNs, as removing typed neighborhood context can alter both representations and historical gradients. Motivated by this, HERO employs DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts through relation-aware multi-type neighbor expansion. To further preserve knowledge that cannot be captured by replayed subgraphs alone, HERO aligns task-conditioned predictions and heterogeneous semantic responses through multi-level knowledge distillation. A lightweight look-ahead adaptation step is additionally used to improve plasticity on incoming tasks. Experiments on four datasets with three HGNN backbones show that HERO achieves the best or tied-best average performance among state-of-the-art baselines, while maintaining competitive forgetting. Our code is available at https://github.com/gqBond/HCGL.

cs.LG↗

arXiv2026

DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts

Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formulation, where the data stream is partitioned into a sequence of discrete tasks with pre-defined boundaries. However, such assumptions rarely hold in real-world environments, where data distributions evolve continuously and task identity is often unavailable. To better reflect realistic non-stationary environments, we revisit continual graph learning from a task-free perspective. We propose a unified formulation that models the data stream as a time-varying mixture of latent task distributions, enabling continuous modeling of distribution drift. Based on this formulation, we construct \emph{DRIFT}, a benchmark that spans a spectrum of transition dynamics ranging from hard task switches to smooth distributional drift through a Gaussian parameterization. We evaluate representative continual learning methods under this task-free setting and observe substantial performance degradation compared to traditional task-based protocols. Our findings indicate that many existing approaches implicitly rely on task boundary information and struggle under realistic task-free graph streams. This work highlights the importance of studying continual graph learning under realistic non-stationary conditions and provides a benchmark for future research in this direction. Our code is available at https://github.com/UConn-DSIS/DRIFT.

cs.LG↗