Publications

Sparse autoencoders (SAEs) are widely used for LLM interpretability, but existing variants often impose non-negativity that prevents single features from representing bidirectional concepts. We derive SAE variants from unrolled proximal gradient updates, identify this structural limitation, and propose AbsTopK SAE with magnitude-based hard thresholding. Across four LLMs and seven probing/steering tasks, AbsTopK improves reconstruction and interpretability while enabling single features to encode contrasting concepts.

We study the emergence and control of self-reflection in large language models. Our probing method reveals that pretrained models already contain a latent capacity for reflection, which can be amplified without additional training. By identifying and manipulating a “self-reflection vector” in activation space, we achieve bidirectional control over reflective behavior, improving reasoning accuracy or reducing computation as needed. This work deepens understanding of self-reflection and demonstrates how model internals can enable precise behavioral modulation.

We introduce FCDS, a document-level relation extraction model that fuses constituency and dependency syntax. By combining sentence-level aggregation from constituency trees with dependency-based graph reasoning, FCDS better captures cross-sentence relations between entities. Experiments across multiple domains show significant performance gains, highlighting the effectiveness of integrating both syntactic views.

We address the oversmoothing problem in link prediction caused by repetitive high-degree nodes across subgraphs. Our method introduces a coarse-graining strategy that merges strongly correlated nodes, yielding more diverse receptive fields and reducing subgraph size. This not only mitigates oversmoothing but also improves scalability and efficiency of GNN-based link prediction.