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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
Aristotle vs. Ringelmann: On Superlinear Production in Op...
Thomas Maillart, Didier Sornette · 2016-08-12 · via cs.SE updates on arXiv.org

Organizations exist because they provide additional production gains, in comparison to horizontal ways of allocating resources, such as markets, and the open source movement is deemed to be a new kind of peer-production organization somehow in between hierarchically organized firms and markets. However, to strive as a new kind of organization, open source must provide production gains, which in turn should be measurable. The open source movement is particularly interesting to study for this reason. Here, we confront and discuss two contrasting views, which were reported in the literature recently. On the one hand, Sornette et al. uncovered a superlinear production mechanism, which quantifies Aristotle adage: `the whole is more than the sum of its parts'. On the other hand, Scholtes et al. found opposite results, and referred to Maximilien Ringelmann, a French agricultural engineer (1861-1931), who discovered the tendency for individual members of a group to become increasingly less productive as the size of their group increases. Since Ringelmann, the topic of collective intelligence has interested numbers of researchers in social sciences and social psychology, as well as practitioners in management aiming at improving the performance of their team. In most research and practice case studies, the Ringelmann effect has been found to hold, while, in contrast, the superlinear effect found by Sornette et al.is novel and may challenge common wisdom. Here, we compare these two theories, weigh their strengths and weaknesses, and discuss how they have been tested with empirical data. We find that they may not contradict each other as much as was claimed by Scholtes et al.