惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Jina AI
Jina AI
S
SegmentFault 最新的问题
D
DataBreaches.Net
H
Help Net Security
有赞技术团队
有赞技术团队
M
MIT News - Artificial intelligence
Martin Fowler
Martin Fowler
IT之家
IT之家
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
腾讯CDC
罗磊的独立博客
Y
Y Combinator Blog
阮一峰的网络日志
阮一峰的网络日志
云风的 BLOG
云风的 BLOG
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
WordPress大学
WordPress大学
Microsoft Security Blog
Microsoft Security Blog
J
Java Code Geeks
Vercel News
Vercel News
Hugging Face - Blog
Hugging Face - Blog
aimingoo的专栏
aimingoo的专栏
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
博客园 - 三生石上(FineUI控件)

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
The Athena Startup Kit
W. T. L. P. Lavrijsen · 2003-06-14 · via cs.SE updates on arXiv.org

The Athena Startup Kit (ASK), is an interactive front-end to the Atlas software framework (ATHENA). Written in python, a very effective "glue" language, it is build on top of the, in principle unrelated, code repository, build, configuration, debug, binding, and analysis tools. ASK automates many error-prone tasks that are otherwise left to the end-user, thereby pre-empting a whole category of potential problems. Through the existing tools, which ASK will setup for the user if and as needed, it locates available resources, maintains job coherency, manages the run-time environment, allows for interactivity and debugging, and provides standalone execution scripts. An end-user who wants to run her own analysis algorithms within the standard environment can let ASK generate the appropriate skeleton package, the needed dependencies and run-time, as well as a default job options script. For new and casual users, ASK comes with a graphical user interface; for advanced users, ASK has a scriptable command line interface. Both are built on top of the same set of libraries. ASK does not need to be, and isn't, experiment neutral. Thus it has built-in workarounds for known gotcha's, that would otherwise be a major time-sink for each and every new user. ASK minimizes the overhead for those physicists in Atlas who just want to write and run their analysis code.