arXiv subjects

Huiyi Zhang

Publications and source records attributed to Huiyi Zhang.

5 recordsLinked to original sources

arXiv2026

ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization

Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transfer across disparate model families, researchers increasingly explore cross-tokenizer distillation. However, cross-tokenizer distillation remains challenging due to vocabulary and sequence misalignment, while approximate vocabulary alignment can introduce additional noise into distillation. To address these challenges, we propose Anchor-Based Cross-Tokenizer Distillation with Residual Regularization (ACTD). ACTD bridges structural heterogeneity through vocabulary and sequence alignment, while mitigating alignment noise via a novel anchor loss with residual regularization. We further extend this framework to a multi-teacher setting. Evaluated across five reasoning benchmarks with three distinct teacher models, ACTD achieves state-of-the-art performance. Moreover, its multi-teacher extension outperforms the strongest single-teacher and multi-teacher baselines, further demonstrating the robustness of our method.

cs.CL↗

arXiv2026

Local (Anti-)Superderivations on Nilpotent Lie Superalgebras

In this paper, we study local (anti-)superderivations on finite-dimensional nilpotent Lie superalgebras. Firstly, we prove that every finite-dimensional 2-step nilpotent Lie superalgebra over a field $\mathbb{F}$ with $\operatorname{char}\mathbb{F}\neq2$ admits pure local (anti-)superderivations (namely, local (anti-)superderivations that are not (anti-)superderivations). Then for $n$-step nilpotent Lie superalgebras over arbitrary fields with n greater than 2, we provide a sufficient criterion to guarantee the existence of pure local (anti-)superderivations. Furthermore, we show that 3-step nilpotent Lie superalgebras admit pure localsuperderivations.

math.RA↗

arXiv2025

One for All: Update Parameterized Knowledge Across Multiple Models

Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternative to retraining, enabling targeted modifications by updating specific model parameters. However, existing methods primarily focus on individual models, posing challenges in efficiently updating multiple models and adapting to new models. To address this, we propose OnceEdit, a novel ensemble-based approach that employs a plug-in model as the editing module, enabling stable knowledge updates across multiple models. Building on the model ensemble, OnceEdit introduces two key mechanisms to enhance its effectiveness. First, we introduce a dynamic weight mechanism through a \weight token for distinguishing between edit-related and non-edit-related instances, ensuring the appropriate utilization of knowledge from integrated models. Second, we incorporate an ensemble enhancement mechanism to mitigate the excessive reliance on the central model inherent in the model ensemble technique, making it more suitable for knowledge editing. Extensive experiments on diverse LLMs demonstrate that OnceEdit consistently outperforms existing methods while achieving superior editing efficiency. Further analysis confirms its adaptability and stability in multi-model editing scenarios. Our code will be available.

cs.CL↗

arXiv2024

Implementation of a scalable universal two-qubit quantum processor with electron and nuclear spins in a trapped ion

Increasing the quantum information processing power with limited number of hosts is vital for achieving quantum advantage. Here we propose a novel scheme that achieves a scalable n-ion-2n-qubit quantum processor utilizing four internal levels of each ion, and experimentally implement a 1-ion-2-qubit universal processor using the valence electron spin and nuclear spin of a single 171Yb+ ion. Fidelities of single-qubit and two-qubit gates are around 0.98 obtained by quantum process tomography. Additionally, the Grover's algorithm is implemented with a successful rate exceeding 0.99. We provide explicit scaling-up protocols based on standard laser-less and laser-based frameworks, and further demonstrate that the electron/nuclear-spin scheme allows less demanding two-qubit entangling gates between different ions. The replacement of some inter-atomic gates by intra-atomic gates could increase the fidelity of some quantum circuits. Our work paves the way towards achieving 2n-times increase in the size of quantum computational Hilbert space with n ions.

quant-ph↗

arXiv2021

Analyzing scientific data sharing patterns for in-network data caching

The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.

cs.NI↗