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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
Exploring Engagement in Hybrid Meetings
Daniela Grassi, Fabio Calefato, Darja Smite, Nicole Novielli, Fi · 2025-09-25 · via cs.SE updates on arXiv.org

Background. The widespread adoption of hybrid work following the COVID-19 pandemic has fundamentally transformed software development practices, introducing new challenges in communication and collaboration as organizations transition from traditional office-based structures to flexible working arrangements. This shift has established a new organizational norm where even traditionally office-first companies now embrace hybrid team structures. While remote participation in meetings has become commonplace in this new environment, it may lead to isolation, alienation, and decreased engagement among remote team members. Aims. This study aims to identify and characterize engagement patterns in hybrid meetings through objective measurements, focusing on the differences between co-located and remote participants. Method. We studied professionals from three software companies over several weeks, employing a multimodal approach to measure engagement. Data were collected through self-reported questionnaires and physiological measurements using biometric devices during hybrid meetings to understand engagement dynamics. Results. The regression analyses revealed comparable engagement levels between onsite and remote participants, though remote participants show lower engagement in long meetings regardless of participation mode. Active roles positively correlate with higher engagement, while larger meetings and afternoon sessions are associated with lower engagement. Conclusions. Our results offer insights into factors associated with engagement and disengagement in hybrid meetings, as well as potential meeting improvement recommendations. These insights are potentially relevant not only for software teams but also for knowledge-intensive organizations across various sectors facing similar hybrid collaboration challenges.