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新论文 · v1arXiv:2611.05762

Classifying K-Geodetic Graphs Via Combinatorial Designs and Distance-regularity

Carlos E. Frasser

原文摘要

The classification of K-geodetic graphs is a fundamental problem in graph theory concerning the number of shortest paths connecting pairs of vertices. A graph is called K-geodetic if every pair of distinct vertices is joined by at most K geodesics, where a geodesic is a shortest path between two vertices. The cases K=1,2,3 correspond, respectively, to geodetic, bigeodetic, and trigeodetic graphs. Despite substantial progress in the study of their structural properties, a comprehensive classification of these graph families remains elusive. This paper develops a research framework for investigating the classification problems by combining metric graph theory, distance-regular graphs, algebraic graph theory, and combinatorial design theory. Particular attention is given to distance-regular graphs, for which the number of geodesics between vertices at a fixed distance is determined by the intersection numbers of the graph. This property converts the K-geodetic condition into a finite collection of arithmetic inequalities. We establish the resulting necessary conditions for K=1,2,3, discuss representative examples, and distinguish these conditions from the stronger requirements needed to prove the existence and classification of graphs with a given intersection array. The paper also investigates the potential role of balanced incomplete block designs (BIBDs) in constructing and organizing families of K-geodetic graphs. Finally, it proposes a computational research methodology based on constraint satisfaction, graph-isomorphism testing, exhaustive enumeration, and machine-assisted conjecture generation.

cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05761

BobFlow: Towards Human-Agent Collaborative Data Science Using Dataflows

Jiadong Bai, Yicong Huang, Chen Li

原文摘要

Recent advances in large language models (LLMs) have established human-agent collaboration as a promising paradigm for data science. Effective collaboration requires a shared computational abstraction that enables agents to reason about processing logic while allowing human analysts to understand, guide, and refine the analysis. Although scripts written in Python or SQL provide precise control, their syntax and execution details can be difficult for analysts to understand and modify. In this paper, we argue that dataflows are an effective abstraction for human-agent collaboration in data science. We introduce BobFlow, a system that combines the dataflow abstraction with the ReAct paradigm. It enables agents to iteratively construct and execute dataflows for data science tasks while allowing analysts to inspect the analysis and provide feedback. Our user study shows that BobFlow helps analysts understand, inspect, and verify agent-generated analyses. A thorough experimental evaluation across proprietary and open-weight LLMs shows accuracy score gains of up to 18.6 points at just over 1/3 of a script-based ReAct agent's inference cost.

cs.DB · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.18991

Agent Security is a Systems Problem

Mihai Christodorescu, Earlence Fernandes, Ashish Hooda, Somesh Jha, Johann Rehberger, Kamalika Chaudhuri, Xiaohan Fu, Khawaja Shams 等 14 位作者

原文摘要

We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness (the dominant viewpoint in the community) are insufficient on their own. Instead, we must complement existing efforts with techniques from the systems security domain. Based on our experience as cybersecurity researchers in operating systems, networks, formal methods, and adversarial machine learning, we articulate a set of core principles, grounded in decades of systems security research, that provide a foundation for designing agentic systems with predictable guarantees. As evidence, we analyze eleven representative real-world attacks on agents and discuss how systems principles, if realized, could have prevented these attacks. We also identify the research challenges that stand in the way of implementing these principles in agents.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.01183

EvoWorld: Evolving Panoramic World Generation with Explicit 3D Memory

Jiahao Wang, Luoxin Ye, TaiMing Lu, Junfei Xiao, Jiahan Zhang, Yuxiang Guo, Xijun Liu, Rama Chellappa 等 11 位作者

原文摘要

Humans possess a remarkable ability to mentally explore and replay 3D environments they have previously experienced. Inspired by this mental process, we present EvoWorld: a world model that bridges panoramic video generation with evolving 3D memory to enable spatially consistent long-horizon exploration. Given a single panoramic image as input, EvoWorld first generates future video frames by leveraging a video generator with fine-grained view control, then evolves the scene's 3D reconstruction using a feedforward plug-and-play transformer, and finally synthesizes futures by conditioning on geometric reprojections from this evolving explicit 3D memory. Unlike prior state-of-the-arts that synthesize videos only, our key insight lies in exploiting this evolving 3D reconstruction as explicit spatial guidance for the video generation process, projecting the reconstructed geometry onto target viewpoints to provide rich spatial cues that significantly enhance both visual realism and geometric consistency. To evaluate long-range exploration capabilities, we introduce the first comprehensive benchmark spanning synthetic outdoor environments, Habitat indoor scenes, and challenging real-world scenarios, with particular emphasis on loop-closure detection and spatial coherence over extended trajectories. Extensive experiments demonstrate that our evolving 3D memory substantially improves visual fidelity and maintains spatial scene coherence compared to existing approaches, representing a significant advance toward long-horizon spatially consistent world modeling.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1软件学报:jos:7712软件学报

语义迁移：面向云端大语言模型交互的数据隐私保护方法

语义迁移: 面向云端大语言模型交互的数据隐私保护方法

陈董, 欧阳朋朋, 杨彤, 刘起东, 李亚飞, 谢立伟, 丁少杰, 徐明亮

AI 速读 基于摘要

提出STPP方法，通过语义加密把隐私关键信息迁移到相似逻辑的新场景、再语义解密还原反馈，在保护云端大语言模型交互隐私的同时维持数据效用。

cs.* · cs.CL

软件学报 · 2026 · 正式发表

软件学报 ↗PDF ↗
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具有强大理解、生成能力的云端大语言模型(cloud large language model, CLLM)已经成为人们日常工作中不可缺少的工具. 然而, CLLM的运行方式意味着用户需要上传原始数据, 这将使得用户的数据隐私安全难以得到有效保障. 现有数据加密方法往往通过添加噪声来改变原始数据形式, 这极易导致数据效用下降, 并影响任务效果. 不同于对原始数据的全盘保护, 将用户数据分为可被识别并用于对齐归属个体的隐私关键信息, 以及不涉及归属问题的普通信息, 并提出保护隐私关键信息, 传递普通信息, 从而在保护数据隐私安全的同时, 维持数据效用. 具体而言, 提出基于语义迁移的用户隐私保护(semantic transfer for privacy protection, STPP)方法. STPP首先基于LLM广泛的先验知识构建语义映射库与语义映射模型, 并通过语义加密将原始信息迁移到具有相似逻辑的新场景. 在获得CLLM反馈后, STPP又通过语义解密将反馈信息还原回原始场景, 从而保护用户数据隐私安全的同时, 降低所提方法对用户使用CLLM体验的影响.

首次观测 · 记录 #1软件学报:jos:7736软件学报

代码差异表示在代码提交信息生成中的有效性实证研究

代码差异表示在代码提交信息生成中的有效性实证研究

李旻灿, 王国庆, 赵逸凡, 郝丹

AI 速读 基于摘要

该研究实证比较三种不同上下文范围的代码差异表示对六种提交信息生成方法效果的影响。

cs.*

软件学报 · 2026 · 正式发表

软件学报 ↗PDF ↗
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提交信息生成(commit message generation, CMG)是软件演化与维护领域的重要任务, 其目标是依据给定的代码差异自动生成提交信息. 目前CMG方法普遍基于Git原生差异格式GitDiff构造输入, 该格式通过固定大小的上下文窗口展示变更行附近的代码片段. 然而, 代码差异的表示方式并非唯一, 通过扩大上下文范围可以构造信息更完整的代码差异表示. 具体地, 构建两种不同上下文范围的差异表示形式, 并围绕差异表示形式对CMG质量的影响开展实证研究. 实验使用现有CMG研究常用的MCMD数据集, 选取6种代表性CMG方法, 使用多种度量来分析3种代码差异表示对于CMG方法的效果影响. 实验结果表明, 尽管 GitDiff 在整体平均指标上略占优势, 但其样本胜率(41.5%)并未构成压倒性领先, 表明不同差异表示在不同代码语境下存在明显性能差异. 此外, 差异表示的选择会影响CMG模型性能比较结论, 在25%–50%的样本中, 基于GitDiff得到的模型优劣排序在切换至其他表示后会发生变化, 说明仅依赖单一代码差异表示所得模型性能结论可能存在偏差. 进一步研究发现, 当变更行依赖的关键语义单元超出GitDiff固定上下文窗口的覆盖范围时, 补全这些缺少的关键语义单元可以在一定程度上提升提交信息生成质量. 对此类场景进行系统归纳, 可为实践中代码差异表示的选择提供参考.

首次观测 · 记录 #1软件学报:jos:7737软件学报

开源社区新手到核心贡献者的成长路径分析

开源社区新手到核心贡献者的成长路径分析

黄革宇, 谭鑫, 郑凯, 张莉

AI 速读 基于摘要

基于87个项目、45 342名以GFI为初始任务的新手的纵向实证分析，刻画新手向核心贡献者转变的比例、周期与行为特征。

cs.*

软件学报 · 2026 · 正式发表

软件学报 ↗PDF ↗
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开源软件社区通常采用“good first issue (GFI)”机制, 为新手提供精心筛选的入门任务, 以促进其参与与融入. 理解尝试解决GFI的新手如何逐步成长为核心贡献者, 对于社区的可持续发展至关重要, 这也是GFI机制的核心目标. 以87个开源软件项目中45 342名以GFI作为初始任务的新手为研究对象, 开展一项纵向实证分析. 研究结果表明, 仅有2.3%的新手最终成功转变为核心贡献者, 其在转变过程中累计代码提交次数的中位数为19次, 转变周期中位数为12个月. 进一步分析表明, 新手在贡献行为上呈现出明显的分层特征: 首先, 大量未成功转变的新手在直接性贡献维度上长期处于不活跃状态, 尤其体现在代码提交和问题报告相关活动上; 其次, 在已表现出一定活跃度的新手中, 成功转变者表现出显著更高的参与水平——其提交次数约为其他新手的15倍, 评论数量约为8倍, 协作者数量为3倍, 整体活跃度比例高出28倍. 此外, 成功转变的新手在贡献行为上呈现出更高的波动性与复杂性, 其参与程度随时间逐步提升, 并在较晚阶段达到活跃峰值, 体现出渐进式学习与逐步融入的过程. 相比之下, 未实现转变的新手则表现为长期不活跃, 或仅在早期出现短暂且难以持续的活跃峰值. 在转变速度方面, 研究发现, 更高的沟通波动性、更长的持续活跃周期以及更具选择性的参与节奏与更快的转变相关; 而较高的问题报告活动或长期低活跃状态则与较慢的转变相关. 在项目层面, 控制变量分析结果表明, 项目受欢迎程度越高, 新手转变速度越快; 而历史提交规模越大, 则转变速度相对较慢. 研究为理解开源社区中新手成长路径提供了系统性的实证证据, 并为设计更有效的新手引导与培养策略提供了可操作的参考依据.

首次观测 · 记录 #1软件学报:jos:7745软件学报

融合双视角不确定性度量与双代理模型池的DNN测试排序

融合双视角不确定性度量与双代理模型池的DNN测试排序

钱忠胜, 范赋宇, 刘金平, 李玉龙, 刘凯强

AI 速读 基于摘要

提出一种结合输入侧双视角不确定性度量与模型侧双代理模型池的DNN测试输入优先级排序方法，以预测误分类概率并降低标注成本。

cs.*

软件学报 · 2026 · 正式发表

软件学报 ↗PDF ↗
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深度神经网络(deep neural network, DNN)测试需要大量的测试数据来保证其质量, 但大多数测试输入缺乏标注信息, 且对其进行标注会带来高昂的人工代价. 现有研究提出测试输入优先级方法, 筛选高优先级的测试输入进行标注以降低人工标注成本. 然而, 它们均仅从单一不确定性视角或模型静态行为上考虑测试输入. 一方面, 局限于固定的单一不确定性度量, 会片面刻画测试输入的错误失效风险, 从而降低排序的准确性. 另一方面, 对模型内部结构微小扰动的刻画不充分, 难以揭示其对决策边界动态变化的影响, 易忽视模型的深层脆弱性, 从而降低早期故障检测效率. 基于此, 提出一种融合双视角不确定性度量与双代理模型池的DNN测试输入优先级排序新方法. 该方法以预测测试输入误分类概率, 并构建输入侧与模型侧的风险协同排序机制为核心目标. 首先, 在输入侧, 为突破单一不确定性指标与静态评估的局限, 利用测试时增强(test-time augmentation, TTA)实施动态鲁棒性评估, 并设计双视角不确定性指标, 以识别测试输入在不同扰动下的潜在失效风险, 提高排序整体性能; 其次, 在模型侧, 构建结合深度集成与模型变异微调的双代理模型池, 量化多模型间的预测分歧, 并设计模型内部结构的动态扰动机制, 暴露模型的深层脆弱性, 以提升早期故障检测效率. 最后, 将输入侧潜在失效风险特征与模型侧决策边界脆弱性特征统一表示为误分类特征, 据此特征, 利用轻量级元模型预测测试输入的错误概率并进行排序, 以降低人工标注成本. 通过与9个已有方法在5个实验对象上的对比发现, 所提方法是可行高效的, 在APFD与RAUC-100指标上分别平均提升约11.92%、28.85%.

新论文 · v1arXiv:2611.05759

PAYWORTHY: Can LLMs Predict Which Freelance Jobs an AI Agent Will Complete?

Mohammad Reza Rezaei, Dingxian Wang, Brett Levert, Celeste Fejzo, Andrew Rabinovich, Zhao Chen

原文摘要

Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We propose PAYWORTHY which contains 1{,}494 real freelance job postings, sampled from 18.8M postings made between 2020 and 2026 on a freelancing platform. An agent attempts each job from the posting alone in a single run, and an LLM judge acting as the client decides whether to accept the work. We ask 18 models to forecast how likely the agent is to succeed and measure how well each model's forecasts rank the accepted jobs above the rejected ones (AUC, where 0.5 is a random ranking). The answer depends on how the agent is described. Told what the agent cannot do, every model ranks the jobs well (AUC 0.66--0.86). Told instead that it is a generic agent with a browser and code execution, the same models rank the same jobs about as well as a random ordering (0.45--0.56). These results hold under judges from four other model families (Claude, GLM, Kimi and Qwen), and the informed forecasts also rank the verdicts of two human raters. Code is available at https://github.com/upwork/Payworthy

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05758

Mechanical Engineering Skills: An Open Library of Agent Skills for Design, Analysis and Manufacturing

Achuth Chandrasekhar, Jesse Barkley, Peter Pak, Rumi Loghmani, Aakash Oza, Amir Barati Farimani

原文摘要

Mechanical engineers use specialized tools for CAD, simulation, manufacturing and research, each with a distinct interface and learning curve. We present Mechanical Engineering Skills, an open library of 33 agent skills that makes these capabilities accessible through a single text or voice conversation. Each skill combines operating instructions, executable tools and verification procedures. A language model interprets the request, selects and coordinates skills, while deterministic tools perform computations and return checked results. Default workflows require no tool-specific interface training, local model execution or model fine-tuning; skills load on demand and install with one command. Nineteen skills adapt published or preprint research from our laboratory, including text-to-CAD, linkage synthesis, CFD setup, stress-field and lattice surrogates, equation discovery, additive-manufacturing planning and in-process monitoring. Fourteen additional skills support core engineering and education. We describe the skill format, design principles and a method for translating research into practical workflows. Five case studies with explicit evidence scopes and independent reruns demonstrate exact analytical agreement for lattice homogenization, rocket apogee predictions within 4% of stored OpenRocket references, an equation fit nine times more accurate than a cubic polynomial at an unseen temperature, and an end-to-end rehearsal of porosity monitoring. Controlled agent comparisons remain future work. The open library is available at https://github.com/BaratiLab/mechanical-engineering-skills.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05756

Flow-Optimal Separators for Minor-Free and Induced-Minor-Free Graphs in Linear Time via Follow the Regularized Leader

Hung Le, Jack Spalding-Jamieson

原文摘要

We give a new algorithm for finding balanced separators in minor-free and induced-minor-free graphs. A key technical idea is a new connection to online learning: we formulate a continuous, non-convex optimization problem and solve it using the Follow the Regularized Leader (FTRL) framework. Our implementation of each FTRL step yields a purely combinatorial algorithm that assigns and adjusts vertex weights over multiple rounds. For graphs with $n$ vertices and $m$ edges, we obtain the following results. (1) For $H$-induced-minor-free graphs, we find a balanced separator of size $O(\log(|V(H)|)\sqrt{|V(H)|+|E(H)|}\sqrt{m})$ in expected linear time for fixed $H$. As a corollary, we find balanced separators of size $O(\sqrt{m})$ in string graphs in expected linear time, without requiring string representations. (2) For $K_h$-minor-free graphs, we find a balanced separator of size $O(h\log h\sqrt{n})$ in expected linear time for fixed $h$. Our algorithm unifies two long lines of work on separators in minor-free graphs, dating back to Alon, Seymour, and Thomas (1990): one optimizing separator size and the other optimizing running time. It also gives a natural interpretation of the vertex weighting scheme in a recent linear-time algorithm by Bonnet, Korhonen, Le, Li, and Masarik (2026). The separator bounds for both graph classes are flow-optimal: techniques based on flow-cut duality, including ours, cannot yield balanced separators of asymptotically smaller size.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05755

Decompose the Distillation: Interpretable Single-Pass Guidance for Diffusion Models

Wuyang Zhang, Xiaolin Xu

原文摘要

Classifier-Free Guidance is a widely used inference-time guidance rule for conditional image generation with diffusion models, which exposes a guidance scale that adjusts conditioning strength after training. This flexibility requires conditional and unconditional model evaluations at every sampling step, so an N-step trajectory takes 2N forward passes. Single-pass alternatives reduce such cost, yet most collapse CFG's two components into one opaque guided prediction, losing the separable base flow and guidance direction that support per-step control and diagnostics, even when the scale remains adjustable. This work introduces CtrlFlow, a single-pass guidance method that preserves CFG's separable base-flow and guidance-direction interface. To preserve both components at one-pass cost, CtrlFlow attaches two lightweight LoRA adapters to a frozen backbone, each adding 2.5% parameters, and predicts the base flow and guidance direction from one pass of the shared frozen backbone. Because the guidance scale multiplies the guidance direction only after the forward pass, one evaluation yields the guided velocity at any scale, so a single trained checkpoint remains adjustable at inference without retraining. Preserving two outputs introduces a training mismatch: inference amplifies guidance-direction errors by the chosen scale, while naive component-wise losses weight both outputs equally. To align training with this composed inference-time error, CtrlFlow uses a unified multi-scale distillation loss. Residual guidance errors can still persist at extreme scales, where even small guidance-direction errors are amplified by the guidance scale. To reduce this inference-time error, CtrlFlow applies scale-adaptive modulation to damp velocity-field errors and time-grid warping to concentrate solver evaluations where the trajectory changes most rapidly.

cs.CV · cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.04594

Ekka: Automated Diagnosis of Silent Errors in LLM Inference

Yile Gu, Zhen Zhang, Shaowei Zhu, Xinwei Fu, Jun Wu, Yida Wang, Baris Kasikci

原文摘要

LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introduce silent errors where output quality silently degrades without any explicit error signals. Diagnosing silent errors is notoriously difficult due to the substantial semantic gap between the high-level symptoms and the low-level root causes. We observe that diagnosis of silent errors can be effectively framed as a differential debugging problem by leveraging the existence of semantically correct reference implementations. We propose Ekka, an automated diagnosis system that identifies root causes by systematically aligning and comparing intermediate execution states between a target and a reference framework. We constructed a benchmark of real-world silent errors from popular serving frameworks, where Ekka shows 80% pass@1 diagnosis accuracy and 88% pass@5 diagnosis accuracy, outperforming state-of-the-art systems. Ekka also diagnoses 4 new silent errors from serving frameworks, all of which have been confirmed by the developers.

cs.DC · cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05754

GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting

Yang Liu, Tong Wang, Yiming Bao, Heng Yang

原文摘要

We study physics-based retargeting of human demonstrations for robot learning: finding robot controls that reproduce reference motions under contact dynamics. We propose GRASP (Gradient Recovery from Archived Samples for Proposals), which combines sampling-based exploration with gradient-guided refinement without requiring a differentiable simulator. GRASP-TO extends tempered sequential Monte Carlo by fitting local quadratic cost models to archived rollouts and using their gradients to guide Markov chain Monte Carlo rejuvenation. Metropolis-Hastings correction preserves the target distribution for each fixed proposal kernel, and a population analysis connects the recovered gradients to Gaussian smoothing. GRASP-MPC scales this optimizer to long demonstrations through warm-started receding-horizon planning. Experiments on dexterous hands and humanoids show accurate retargeting with a shared tracking objective and no task-specific contact rewards, including 92.8% success on 1352 bimanual OakInk2 demonstrations. The refined references improve PPO tracking in pen spinning and humanoid-object interaction. We also demonstrate dexterous manipulation and whole-body humanoid motion on real hardware.

cs.RO

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新论文 · v1arXiv:2611.05753

Toward a Compositional Knowledge Architecture for a Shared Organizational Cortex

Andrew Miracle, Phyl Georgiou, Christine Okoth, Ese Monday

原文摘要

Organizations accumulate evidence in conversations, documents, decisions and relationships, but they act on interpretations: judgements assembled from several observations, resting on assumptions, and revised as circumstances change. Existing retrieval, knowledge graphs and agent memory keep the records and lose the interpretation. We propose a four-layer architecture, Signals, Domains, Claims and Dimensions, in which interpretations persist with their justifications and standing, a truth-maintenance network revises that standing as evidence changes, and validated, versioned Dimensions position interpretations on named axes. We call the result a shared organizational cortex. In a registered retrieval experiment, the Dimension term gains 0.028 nDCG@10 (p = 0.002). In system-level comparisons, the full retrieval configuration improves nDCG@10 by 0.081 over Signals-only and 0.053 over graph-augmented Signals-only retrieval (both reported p < 0.001). The architecture gives organizations a persistent, revisable basis for shared judgement.

cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2404.17144

Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting

Simon J. Ward, Muhamed Baljevic, Sharon M. Weiss

原文摘要

The response time of a biosensor is a crucial metric in safety-critical applications such as medical diagnostics where an earlier diagnosis can markedly improve patient outcomes. However, the speed at which a biosensor reaches a final equilibrium state can be limited by poor mass transport and long molecular diffusion times that increase the time it takes target molecules to reach the active sensing region of a biosensor. While optimization of system and sensor design can promote molecules reaching the sensing element faster, a simpler and complementary approach for response time reduction that is widely applicable across all sensor platforms is to use time-series forecasting to predict the ultimate steady-state sensor response. In this work, we show that ensembles of long short-term memory (LSTM) networks can accurately predict equilibrium biosensor response from a small quantity of initial time-dependent biosensor measurements, allowing for significant reduction in response time by a mean and median factor of improvement of 18.6 and 5.1 respectively. The ensemble of models simultaneously estimates uncertainty, which is vital for ensuring confidence in the predictions and subsequent safety-related decisions that are made. This approach is demonstrated on real-time experimental data collected by exposing porous silicon biosensors to buffered protein solutions using a multi-channel fluidic cell that enables the automated measurement of 100 porous silicon biosensors in parallel. The dramatic improvement in sensor response time achieved using LSTM network ensembles and associated uncertainty quantification opens the door to trustworthy and faster responding biosensors, enabling more rapid medical diagnostics for faster clinical decision making that can lead to improved patient outcomes and healthcare access, as well as quicker identification of toxins in food and the environment.

cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05751

Scheduling Data to Compute: Hierarchical Data Pipelining for LLM Training on AMD GPUs

Gengyu Rao, Ghadeer Ahmed H Alabandi, Su Ann Chong, Thomas Huber, Kailash Gogineni, Subhajit Dutta Chowdhury, Saleel Kudchadker, Suchita Pati 等 31 位作者

原文摘要

Large language models (LLM) have been the key focus of recent machine learning breakthroughs. As the model size grows exponentially, the computation needed for training is astronomical. A GPU can do a huge amount of arithmetic. However, the data its compute units need is not always available when they are ready. This data may still be in slow memory or on another GPU. While the chip waits, expensive compute sits idle, and at scale even a small drop in utilization translates into substantial cost. In this paper, we treat this waiting time as the primary bottleneck. We focus on hardware features designed for explicit data movement. These features load data from off-chip memory onto the chip, rearrange it into the layout the computation requires as it is loaded, and transfer it between GPUs. By orchestrating them, we keep a steady pipeline of data flowing to the compute units so that they remain busy. We present kernel and framework implementations that address this bottleneck using three key data-movement instructions and hardware features. The Tensor Data Mover (TDM) moves tensor data from high-bandwidth memory (HBM) into on-chip memory. Transpose-load instructions transpose tensor data into the layout that LLM computations require as it is loaded on-chip. Finally, System DMA (SDMA) and Remote DMA (RDMA) move data between GPUs over the scale-up and scale-out networks, respectively. Our results show that TDM and transpose-load provide on average 1.88x and 1.19x speedup at the kernel level, respectively. The SDMA and RDMA lead to on average 1.16x and 1.33x speedup for computation overlapped with communication. This is the first AMD LLM training system to utilize all the GPU data pipelining features. Our implementation has been integrated into open source framework and library for AMD GPUs. https://github.com/AMD-AGI/Primus-Turbo https://github.com/AMD-AGI/Primus

cs.AR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.22186

Scalable Source Selection for Machine Learning Tasks

Ambarish Singh, Romila Pradhan

原文摘要

Given a query table, existing data lake discovery techniques seek to identify data sources related to the query table through schema similarity and joinability. The discovered sources, however, are not always useful for downstream analyses; while some improve task generalization, some have the potential to inadvertently introduce harmful noise or distribution shifts. We formalize source selection as the problem of finding a subset of the discovered sources that maximizes downstream task utility. We propose Splice, a target- and cost-aware source selection system that adopts the idea of gene splicing to identify sources which provide additional signals that the union of all sources misses. To speed up the process, we develop two surrogate variants, Splice-DP and Splice-Grad, that estimate the marginal contribution of subsets to task utility by leveraging data profiles in a meta-learning approach and gradient alignment, respectively. On classification tasks over two real-world benchmarks derived from SOTA table union search tools, Splice shows 3-5 percentage points improvement in binary F1 scores compared to the case when all discovered tables are considered. On five additional real-world ML tasks spanning classification and regression, Splice improves task utility over the all-sources baseline by 1.8-13.7 points, while Splice-DP and Splice-Grad match this quality with 3-7x fewer model evaluations (up to 77x at scale).

cs.LG · cs.AI · cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.30356

OLIVE: View-Augmented Latent Prediction with Waveform Reconstruction for Speech SSL

Karl El Hajal, Mathew Magimai. -Doss

原文摘要

We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that jointly optimizes analysis and synthesis objectives. OLIVE couples masked latent prediction and waveform reconstruction through a functional separation within a single encoder: reconstruction constrains early encoder features to retain signal-level information, while masked latent prediction shapes later contextual representations for robust downstream performance, with waveform view augmentations defining the invariances they learn. To the best of our knowledge, OLIVE is the first speech SSL model that directly reconstructs waveforms, retains its vocoder after pre-training, and remains competitive on discriminative tasks. Adding reconstruction largely preserves the speech recognition performance of masked latent prediction alone, while improving waveform reconstruction and speaker tasks. Adding waveform mixup and gain augmentations provides a controllable way to shift the representations further toward speaker, generation, and reconstruction tasks, while remaining competitive on recognition and semantic tasks.

cs.CL · cs.LG · cs.SD · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05750

Follow or Surpass? Collaboration, Sound, and the Recognition of Influence in Lineages of Jazz Trumpet Players

Onur Tuncay Bal, Samuel Coavoux, Nathalia A. Loureiro, Balazs Vedres, Mark Wittek

原文摘要

Cultural fields cohere not only horizontally, through collaboration, but also vertically, through lineages of artistic influence. Considerable attention has been devoted to collaboration networks in various fields of creative production; however, we know less about the structure of artistic influence that is nevertheless key to individual artistic development and the evolution of genres. We examine the structure of such an influence network in a genealogy of 52 prominent jazz trumpet players, combining musicians' discographies, demographic characteristics, geographical locations, and audio analysis of more than 16,000 recordings with exponential random graph models. Direct collaboration, overlap in collaborators, and sonic context similarity, that is, the similarity of the recordings on which players appear, are each associated with an ascribed influence tie, net of the others. Not surprisingly, ascribed ties connect musicians whose recordings share a similar sonic context, but musicians also tend to collaborate with their influences and share collaborators with them. However, players whose recordings share a sonic context and who move in the same circle of collaborators can be thought of as peers or rivals, rather than as influences of each other, because lineage implies a succession predicated on difference as much as on continuity. Taken together, our findings suggest that a field's account of its own evolution is neither a record of who played together nor a map of shared sonic contexts, but a selective ordering that requires both. While our data do not permit a causal account, our aim is to provide a rich and varied methodological strategy and descriptive as well as inferential tools to understand the yet understudied vertical integration of cultural fields through influence ties.

cs.SI · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08052

The Moving Frontier: How the National Justice Project Is Using Digital Technology to Expand Access to Justice

George Newhouse AM, Trisha Ramesh, Tiara de Silva

原文摘要

Over the past three decades, technological advances have assisted individuals to gain access to legal information, enhance the efficiency of legal services, and streamline dispute resolution. More recently, the proliferation of generative artificial intelligence has reinforced this emphasis on firm-level productivity, with commercial legal practice increasingly leveraging artificial intelligence tools to automate document creation and optimise case management. While commercial technology focuses on productivity, community organisations are exploring how artificial intelligence can level the playing field for marginalised communities. Far less attention has been given to free, direct-to-consumer legal technologies, as these models are often unprofitable. Consequently, justice technology scholarship has overlooked a distinct class of technology: digital reporting platforms that enable people to record their experiences of harm, while simultaneously aggregating those accounts into a body of evidence capable of supporting advocacy, institutional accountability, and law reform. Crucially, experiences of discrimination and administrative abuse are often dismissed by oversight bodies as individual grievances. To understand the transformative potential of these platforms, this article asks two questions. First, how can digital reporting close the knowledge and power gap for marginalised communities interacting with the legal system? Second, how can these tools collect evidence to hold institutions accountable, drive change and contribute to a more just society?

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05749

Pairing Expert Critique with LLM-Driven Refinement: An Iterative Approach for Curating Quality AI Evaluation Datasets

Honson Ling, Kidus Admassu, Stephanie Rosenthal

原文摘要

Evaluating and improving domain-specific LLM responses requires high-quality reference data and rubrics, yet expert authoring is slow and expensive, and synthetic datasets may fail to capture practitioner preferences unless they are grounded in expert judgment. We present SME-C (Subject Matter Expert Critique), a human-AI workflow that captures expertise through lightweight critiques of candidate responses and complements it with LLM-driven analysis and refinement to iteratively curate response datasets and evaluation rubrics. We validate SME-C in the domain of financial business briefs, collecting 3,853 critiques from 411 finance professionals on 520 queries across two survey rounds. We evaluate two LLM-driven refinement strategies: one guided by the experts' written critiques and one that infers critique solely from their preference votes. In a blind validation survey with 100 additional professionals, we found that both strategies significantly improved quality over the experts' top-ranked baseline responses. The gains were concentrated among weaker original responses, and the two strategies excelled in different ways: critique-based refinement produced more domain-grounded improvements, whereas vote-based refinement was more effective at identifying and removing irrelevant content. The resulting rubrics and refined dataset support reproducible offline evaluation and benchmarking of LLMs, suggesting that structured expert critique is a viable alternative to expert authoring for building high-quality, domain-specific evaluation resources more quickly, at scale, and at lower cost.

cs.HC · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05748

Energy-Efficient Reinforcement Learning for Large Language Models

Rahul Sharma, Nitin Vegesna, Yanxin Zhang

原文摘要

Reinforcement learning for language models alternates between generating responses and training the policy. These stages place different demands on GPUs, creating opportunities to reduce clock frequency with little loss in throughput. Selecting frequencies online is difficult because response lengths change and frequency trials themselves consume time and energy. We present EE-RL (energy-efficient RL), an online controller that selects separate frequencies for generation and training during RL execution. It compares candidate settings with a prediction of the default GPU governor on the same token workload, and keeps the most energy-efficient setting that meets a throughput threshold. The controller then monitors throughput and energy and adjusts frequencies as the workload changes. Fixed-frequency sweeps on H100 and GB200 show GPU energy savings of up to 28.2% per valid training token across synchronous and asynchronous execution, while largely preserving throughput. EE-RL turns this headroom into online savings, reducing GPU energy per valid training token by up to 16.9% at a modest throughput cost.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.03303

S$^{2}$-PINN: Stochastic Separable Physics-Informed Neural Networks

Zhendong Li, Akwum Onwunta

原文摘要

Uncertainty quantification (UQ) for random partial differential equations (PDEs) is ubiquitous in computational science and engineering. However, classical spectral solvers for this class of problems face the curse of dimensionality, and existing neural solvers often ignore the stochastic structure that makes moments and calibration tractable. We introduce a stochastic separable physics-informed neural network, dubbed S$^{2}$-PINN, that represents the solution $u(t,\mathbf{x},\mathbf{Z})$ of a random PDE with a learnable Gaussian spatial dictionary, Fourier temporal features, and a generalized polynomial chaos (gPC) stochastic basis, coupled by a low-rank Canonical Polyadic (CP) tensor decomposition core. The method is trained with a hybrid strong-form and gPC-projected residual loss. Our theoretical analysis establishes that the separable class is dense in $L^2$ under mild conditions, and the projected residual corresponds exactly to a stochastic Galerkin constraint. Furthermore, we show that mini-batch projection coefficients are logarithmically dependent on the number of gPC modes, and that the orthogonality penalty controls the conditioning of the learned spatial dictionary. Using four manufactured random PDE benchmarks, we show that S$^{2}$-PINN outperforms nine baselines in terms of mean and variance accuracy, as well as calibration, while using significantly fewer parameters. Further evaluations on non-manufactured Poisson and Darcy problems, a stochastic Navier--Stokes problem, a diffusion scaling study of higher random dimensions, and two stochastic inverse problems reveal the generalization capabilities of the proposed structure. Together, these results support stochastic separability as an effective design principle for physics-informed neural UQ. The code for the experiments can be found in https://github.com/DMax1314/s2pinn

cs.LG · math-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05746

Constraint-Aware Evaluation of Reinforcement Learning and Unsupervised Anomaly Detection for Security Operations Alert Triage

Radityo Ghifiardi, Rifny Rachman

原文摘要

Security Operations Centers (SOCs) must identify real threats from large alert streams, but limited analyst capacity means that unnecessary escalations can delay more serious investigations. Existing studies often frame SOC alert triage as classification or anomaly detection, but both views are incomplete for production SOCs, where the central problem is selecting alerts worth escalating under limited capacity, asymmetric error costs, and workload constraints. This paper addresses that gap by formalizing alert triage as a constrained decision problem and evaluating three approaches: an offline reinforcement learning policy trained on alert embeddings, an Isolation Forest model built on operational features, and an autoencoder trained on historical benign alerts. These methods represent different triage assumptions: learning actions from reward, isolating unusual operational patterns, and detecting deviations from benign behavior. We evaluate all methods on 74,909 analyst-labeled production alerts under a strict temporal split and benign-escalation constraint. Because the test set contains 503 unique feature patterns, we primarily rank clusters of similar alerts. At the main operating point, all three methods satisfy the constraint, with the reinforcement learning policy achieving the lowest benign-escalation fraction at 0.000, compared with 0.007 for the autoencoder and 0.041 for Isolation Forest. The methods also prioritize largely different alert clusters, showing complementary rather than redundant coverage. These results suggest that production SOC triage can benefit from constraint-aware evaluation and combined or adaptive deployment of different model families.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05744

Text-Only Domain Adaptation for Low-Resource Medical ASR: Shallow Fusion and Density Ratio Rescoring for Hausa

Oche David Ankeli, Hassan Adelani Luqman, Theodora Ngozi Egbunike, Joshua Gottschalk, Bonaventure F. P. Dossou

原文摘要

Automatic speech recognition (ASR) systems often struggle with specialised medical vocabulary, especially in low-resource languages where domain-specific speech data are scarce. We investigate text-only domain adaptation for Hausa medical ASR using the fixed MMS-1B-FL102 acoustic model and two decoding-time language-model fusion methods: Shallow Fusion (SF) and the Density Ratio Approach (DRA). A 4-gram KenLM trained on 19,421 Hausa health sentences serves as the medical target model, while a 75,372-sentence general-domain model provides the source prior for DRA. Evaluation uses 250 recordings of 25 physician-reviewed prompts from 10 speakers across six northern Nigerian states. Under prompt-disjoint five-fold cross-validation, SF and DRA reduce WER from 29.56% to 23.55% and 23.33%, respectively. Both significantly outperform greedy decoding (p < 0.001), while their difference is not significant (p = 0.423). However, general-LM-only SF achieves 23.96% WER, indicating that the medical LM's additional improvement over a general LM is modest. Medical Term Error Rate also improves under fusion, but does not establish a distinct medical-domain advantage. These findings demonstrate that lightweight, text-only language-model fusion can improve Hausa medical ASR without additional domain-specific speech collection.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.09220

A Lightweight Multi-View Approach to Short-Term Load Forecasting

Julien Guité-Vinet, Alexandre Blondin Massé, Éric Beaudry

原文摘要

Time series forecasting is a critical task across domains such as energy, finance, and meteorology, where accurate predictions enable informed decision-making. While transformer-based and large-parameter models have recently achieved state-of-the-art results, their complexity can lead to overfitting and unstable forecasts, especially when older data points become less relevant. In this paper, we propose a lightweight multi-view approach to short-term load forecasting that leverages single-value embeddings and a scaled time-range input to capture temporally relevant features efficiently. We introduce an embedding dropout mechanism to prevent over-reliance on specific features and enhance interpretability. Our method achieves competitive performance with significantly fewer parameters, demonstrating robustness across multiple datasets, including scenarios with noisy or sparse data, and provides insights into the contributions of individual features to the forecast.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05742

Learning Hierarchical Patch Splitting Policies for Faster Vision Transformers

Aditya Gupta, Jean Sebastien Dandurand, Kai Qiu, Rohan Choudhury, Laszlo A. Jeni

原文摘要

Vision Transformers (ViTs) are bottlenecked by the length of their input token sequence. Standard patchification fixes this length with a single patch size applied across the image, regardless of regional content. Prior work on adaptive patch sizing reduces token count by assigning coarser patches to visually simple regions, but relies on noisy pixel-level heuristics such as local entropy to make patchification decisions. We argue that this compute allocation should be grounded in the model's interpretation of task relevant information, not in raw pixel statistics. We introduce Split Policy Learned Image Tokenization which learns to allocate token resolution based on what the model finds meaningful, producing shorter token sequences that are better matched to task-relevant content. SPLIT matches the accuracy of full-resolution ViT classification baselines while using 30% fewer tokens. The same gains hold on dense prediction: SPLIT achieves competitive accuracy on COCO and ADE20K, demonstrating that semantically aligned patchification generalises well beyond classification.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05741

OUCH: Objective, Uniform, Controlled Haptic Stimulation for Automated Neurological Assessment

Sherry Choi, Sitao Sunny Li, Joseph Romero, Lillian Chin, Ashwin G. Ramayya, Allison M. Okamura, Ava Chen

原文摘要

Motor responses evoked by noxious stimulation are used to assess neurological function in hospitalized patients with disorders of consciousness. In standard bedside assessments, these stimuli are applied manually, resulting in substantial variation in force, duration, frequency, and location. Here, we present OUCH (Objective, Uniform, Controlled Haptic stimulation), a wrist-worn device that uses pneumatic actuation to deliver noxious mechanical stimuli. OUCH combines stacked pneumatic pouch actuators, a rigid indenter, and a compliant spacer designed to buckle at a set load, so that the indenter contacts the skin only during actuation. We characterize the device using blocked force tests, clinician evaluation, finite element analysis, and a deformable pressure testbed, and benchmark it against a commercially available handheld pressure algometer. At 200 kPa pouch inflation pressure, our indenter concentrated its load into an estimated contact pressure of 18,500 kPa, over 25 times that of the algometer tip, while requiring less total force. On the deformable testbed, it localized deformation near the surface, while deformation at depth remained comparable to the algometer. As a result, in preliminary perceptual testing, our indenter elicited discomfort at approximately 89% lower applied force than the algometer, demonstrating the feasibility of evoking discomfort with a compact, wearable soft actuator. These results show that combining pneumatic actuation with designed contact geometry enables controlled, repeatable noxious stimulation at the wrist, providing a foundation for objective neurological assessment.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05739

PersonaCodeBench: Evaluating Coding Agents in Persona-driven Human-Interactive Sessions

Haixin Wang, Jianheng Hou, Hongyu Shen, Wenzhe Fan, Wei-Chieh Huang, Qianyu Zhu, Weichen Li, Taoran Li 等 30 位作者

原文摘要

Coding agents must deliver correct software while respecting users' expertise, preferences, and working conditions. Existing benchmarks increasingly incorporate interaction, yet personalized requirement fulfillment remains underexplored. We introduce PersonaCodeBench, which turns user characteristics into verifiable obligations in persona-driven multi-turn coding sessions. Our persona pool spans roles, technical abilities, interaction preferences, and practical constraints, yielding task-grounded evolved requirements for implementation, permissions, testing, explanation, and delivery. At runtime, a controller governs user simulator responses and requirement disclosure, using online requirement evaluation to provide feedback and advance the interaction. Requirements emerge progressively, with their content, disclosure conditions, and acceptance criteria fixed before evaluation. A completion verifier and a personalization verifier separately assess native correctness and persona fulfillment. Joint pass requires both adherence to the declared budget and compliance with validity conditions. We evaluate all major frontier agents, establish a comprehensive leaderboard, and conduct detailed experimental analyses to validate effectiveness. PersonaCodeBench supports reproducible evaluation of coding agents' ability to meet explicit, personalized user expectations alongside coding objectives.

cs.AI

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