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版本更新 · v2arXiv:2605.26599

从算法视角重新审视仅求特征值的分治三对角特征值求解器

Revisiting Eigenvalue-Only Divide-and-Conquer Tridiagonal Eigensolver from Algorithmic Perspective

Ruiyi Zhan, Dajun Huang, Lu Shi, Shaoshuai Zhang

AI 速读 基于摘要

作者提出仅用每个合并节点的首末两行边界行构成递推来携带分治状态，将持久内存从二次降为线性，并在 CPU 与 GPU 上按去膨胀后的有效秩做细粒度调度。

cs.DC · math.NA

arXiv ↗PDF ↗
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Divide-and-conquer (D&C) is the natural parallel strategy for symmetric tridiagonal eigenvalue decomposition, yet eigenvalue-only solvers fall back to serial QR algorithm because the quadratic transform state each merge carries exhausts memory at scale. In this paper, we demonstrate that each merge stage in D&C needs only two boundary rows, the first and last rows of a node's eigenvector block, and these close into a recurrence whose parent rows follow from its children's and local merge data alone, so the state is carried up the tree with nothing replayed and persistent memory drops from quadratic to linear. Because this state keeps no replay workspace, adapting the schedule to each merge's post-deflation active rank is a scheduling choice, so both CPU and GPU spend finer-grained parallelism only where substantial secular work survives. We evaluate optimized CPU and GPU implementations, with complexity and correctness analysis, against QR, bisection, and D&C routines in standard libraries.

版本更新 · v2arXiv:2601.22230

JURIST: Training Small Data-Reweighted Reasoning Judges for Test-Time Scaling in Code Generation

Peijia Qin, Ruiyi Zhang, Qi Cao, Pengtao Xie

原文摘要

Best-of-N selection for code generation is only as good as the judge that picks among the candidates, and the judges accurate enough for this role are costly frontier LLMs. A small judge can be trained on abundant earlier data labeled by unit tests, but it is deployed on newer problems: the earlier problems are easier and teach it little, their tasks differ, and their candidates come from weaker policies, while data close to deployment are scarce. We present JURIST, a 1.7B reasoning judge that uses a small meta set close to deployment to learn how much each earlier training sample should count. In our experiments, we find that training on the meta set alone, or adding it to the training data, does not help, while using it to weight the earlier data through a bi-level objective adds 3.9 Hard points over adding it to the training data. On LiveCodeBench, JURIST reaches 84.7% Overall and matches Claude Sonnet 4.6 on Hard problems (73.8%) at 463x lower cost; on BigCodeBench, it surpasses all test-time scaling baselines (35.9%). Abundant earlier data, guided by a small deployment-aligned meta set, can thus train a compact judge that selects accurately at low cost.

cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1613ACL Anthology · ACL 2026

Lizard: An Efficient Linearization Framework for Large Language Models

Chien Van Nguyen University of Oregon, Huy Huu Nguyen, Ruiyi Zhang, Hanieh Deilamsalehy Adobe Systems, Puneet Mathur Adobe Systems, Viet Dac Lai Adobe Systems, Haoliang Wang Adobe Research, Jayakumar Subramanian Adobe Systems 等 13 位作者

原文摘要

We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe computational and memory bottlenecks with long sequences due to the quadratic complexity of softmax attention and the growing Key-Value (KV) cache that makes inference memory-bound by context length. Lizard addresses these limitations by introducing a subquadratic attention mechanism that closely approximates softmax attention while preserving model quality. Unlike prior linearization methods constrained by fixed, non-adaptive structures, Lizard augments the architecture with compact, learnable modules that enable adaptive memory control and robust length generalization. Moreover, we introduce a hardware-aware algorithm that solves numerical instability in gated attention to accelerate training. Extensive experiments show that Lizard achieves near-lossless recovery of its teacher model’s performance, significantly outperforming previous methods by up to 9.4 - 24.5 points on the 5-shot MMLU benchmark and demonstrating superior associative recall.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2138ACL Anthology · ACL 2026

BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models

Xin Gao, Ruiyi Zhang University of California, San Diego, Meixi Du, Peijia Qin, Pengtao Xie University of California, San Diego

原文摘要

Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensively in daily workflows. While recent general-domain tool-calling datasets have substantially improved the capabilities of LLM agents, existing efforts in the biomedical domain largely rely on in-context learning and restrict models to a small set of tools. To address this gap, we introduce BioTool, a comprehensive biomedical tool-calling dataset designed for fine-tuning LLMs. BioTool comprises 34 frequently used tools collected from the NCBI, Ensembl, and UniProt databases, along with 7,040 high-quality, human-verified query–API call pairs spanning variation, genomics, proteomics, evolution, and general biology. Fine-tuning a 4-billion-parameter LLM on BioTool yields substantial improvements in biomedical tool-calling performance, outperforming cutting-edge commercial LLMs such as GPT-5.1. Furthermore, human expert evaluations demonstrate that integrating a BioTool-fine-tuned tool caller significantly improves downstream answer quality compared to the same LLM without tool usage, highlighting the effectiveness of BioTool in enhancing the biomedical capabilities of LLMs. The full dataset and evaluation code are available at https://github.com/gxx27/BioTool.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.70ACL Anthology · ACL 2026

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing

Rui Wang Adobe Systems, Junda Wu University of California, San Diego, Yu Xia University of California, San Diego, Tong Yu Adobe Research, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra Adobe Systems, Lina Yao University of New South Wales 等 9 位作者

原文摘要

Large Language Models (LLMs) are susceptible to indirect prompt injection attack, where the model inadvertently responds to instructions injected into the prompt context. This vulnerability stems from LLMs’ inability to distinguish between data and instructions within a prompt. We propose CachePrune that defends against this attack by identifying and pruning neurons associated with instruction-following, during KV cache encoding of the prompt context. The pruning steers the LLM toward interpreting the context purely as data rather than as instructions to follow. To identify these neurons, we introduce a neural attribution mechanism guided by a preferential attribution loss, and theoretically connect this loss to an upper bound of the Direct Preference Optimization (DPO) objective. Further, we improve on the fidelity of neural attribution by leveraging an observed triggering effect in instruction-following. Our approach does not interfere with prompt formatting or incur test-time overhead in response generation. Experiments show that CachePrune significantly reduces the attack success rate while preserving the LLM’s ability to follow user instructions.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

版本更新 · v2arXiv:2506.15924Google Research

SNPeek：面向机密虚拟机隐私应用的侧信道分析

SNPeek: Side-Channel Analysis for Privacy Applications on Confidential VMs

Ruiyi Zhang, Albert Cheu, Adria Gascon, Daniel Moghimi, Phillipp Schoppmann, Michael Schwarz, Octavian Suciu

AI 速读 基于摘要

作者提出开源工具包 SNPeek，在生产级 AMD SEV-SNP 硬件上提供可配置的侧信道追踪原语，并结合统计与机器学习分析流程，自动估计隐私应用在机密虚拟机中的泄漏。

cs.CR

arXiv ↗PDF ↗
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Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not generic or too slow for practical use, and developers currently lack a systematic, efficient way to measure and compare leakage across real-world deployments. In this paper, we present SNPeek, an open-source toolkit that offers configurable side-channel tracing primitives on production AMD SEV-SNP hardware and couples them with statistical and machine-learning-based analysis pipelines for automated leakage estimation. We apply SNPeek to three representative workloads that are deployed on CVMs to enhance user privacy-private information retrieval, private heavy hitters, and Wasm user-defined functions-and uncover previously unnoticed leaks, including a covert channel that exfiltrates data at 497 kbit/s. The results show that SNPeek pinpoints vulnerabilities and guides low-overhead mitigations based on oblivious memory and differential privacy, giving practitioners a practical path to deploy CVMs with meaningful confidentiality guarantees.