arXiv subjects

Jiahuan He

Publications and source records attributed to Jiahuan He.

5 recordsLinked to original sources

arXiv2025

ESS: An Offload-Centric Latent-Cache Management Architecture for DeepSeek-V3.2-Exp

DeepSeek-V3.2-Exp introduces a sparse attention mechanism that significantly reduces inference latency in long-context scenarios. Although the overall throughput has improved greatly, the Decode-stage of PD disaggregation remains to be a major bottleneck. This bottleneck primarily stems from the conflict between linear growth of Latent-Cache with sequence length and the limited GPU memory capacity, which constrains the feasible batch-size and thereby suppresses Decode-stage throughput. To address this challenge, we propose ESS (Extended Sparse Server), an offload-centric system design tailored for DeepSeek-V3.2-Exp. ESS selectively offloads Latent-Cache to CPU memory while preserving latency-critical components on GPU. By freeing up GPU memory, ESS effectively decoupling batch-size scaling from GPU memory constraints. This design significantly improves Decode-stage throughput, thereby reducing deployment costs in real-world settings. Our high-fidelity simulations show that ESS delivers 69.4\% throughput improvement at 32K context length and up to 123\% throughput improvement at 128K, demonstrating its effectiveness for large-context inference workloads. These results highlight ESS as a practical and scalable solution for long-context LLM serving.

cs.DC↗

arXiv2025

Understanding Down Syndrome Stereotypes in LLM-Based Personas

We present a case study of Persona-L, a system that leverages large language models (LLMs) and retrieval-augmented generation (RAG) to model personas of people with Down syndrome. Existing approaches to persona creation can often lead to oversimplified or stereotypical profiles of people with Down Syndrome. To that end, we built stereotype detection capabilities into Persona-L. Through interviews with caregivers and healthcare professionals (N=10), we examine how Down Syndrome stereotypes could manifest in both, content and delivery of LLMs, and interface design. Our findings show the challenges in stereotypes definition, and reveal the potential stereotype emergence from the training data, interface design, and the tone of LLM output. This highlights the need for participatory methods that capture the heterogeneity of lived experiences of people with Down Syndrome.

cs.HC↗

arXiv2024

Twice Epi-Differentiability of Orthogonally Invariant Matrix Functions and Application

In this paper, our focus lies on the study of the second-order variational analysis of orthogonally invariant matrix functions. It is well-known that an orthogonally invariant matrix function is an extended-real-value function defined on ${\mathbb M}_{m,n}\,(n \leqslant m)$ of the form $f \circ \sigma$ for an absolutely symmetric function $f \colon \R^n \rightarrow [-\infty,+\infty]$ and the singular values $\sigma \colon {\mathbb M}_{m,n} \rightarrow \R^{n}$. We establish several second-order properties of orthogonally invariant matrix functions, such as parabolic epi-differentiability, parabolic regularity, and twice epi-differentiability when their associated absolutely symmetric functions enjoy some properties. Specifically, we show that the nuclear norm of a real $m \times n$ matrix is twice epi-differentiable and we derive an explicit expression of its second-order epi-derivative. Moreover, for a convex orthogonally invariant matrix function, we calculate its second subderivative and present sufficient conditions for twice epi-differentiability. This enables us to establish second-order optimality conditions for a class of matrix optimization problems.

math.OC↗

arXiv2024

On Solution Uniqueness and Robust Recovery for Sparse Regularization with a Gauge: from Dual Point of View

In this paper, we focus on the exploration of solution uniqueness, sharpness, and robust recovery in sparse regularization with a gauge $J$. Based on the criteria for the uniqueness of Lagrange multipliers in the dual problem, we give a characterization of the unique solution via the so-called radial cone. We establish characterizations of the isolated calmness (i.e., uniqueness of solutions combined with the calmness properties) of two types of the solution mappings and prove that they are equivalent under the assumption of metric subregularity of the subdifferentials for regularizers. Furthermore, we present sufficient and necessary conditions for a sharp solution, and show that these conditions guarantee robust recovery with a linear rate and imply local upper Lipschitzian properties of the solution mapping. Some applications of the polyhedral regularizer case such as sparse analysis regularization, weighted sorted $\ell_1$-norm sparse regularization, and non-polyhedral regularizer cases such as nuclear norm optimization, conic gauge optimization, and semidefinite conic gauge optimization are given.

math.OC↗

arXiv2020

Broadband high-resolution terahertz single-pixel imaging

We report a simple single-pixel imaging system with a low mean squared error in the entire terahertz frequency region (3-13 THz) that employs a thin metallic ring with a series of directly perforated random masks and a subpixel mask digitization technique. This imaging system produces high pixel resolution reconstructed images, up to 1200 x 1200 pixels, and imaging area of 32 mm x 32 mm. It can be extended to develop advanced imaging systems in the near-ultraviolet to terahertz region.

physics.optics↗