arXiv Machine Learning
Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer
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arXiv AI
Aug 14
Humans are Missing from AI Coding Agent Research
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.
arXiv AI
Oct 16
Spec-Driven Development as Prescribed and as Practiced
Spec-Driven Development (SDD) is a practice where developers and AI coding agents write specifications to guide software implementation, aiming to preserve the intent behind natural‑language instructions. This study analyzes SDD through Reddit discussions, interviews with 21 practitioners (including eight tool makers), and a large corpus of 470,795 spec files from 73,030 GitHub repositories. Findings reveal that while specs often include implementation details that aid agents, they also risk decay; tool-specific structures influence perception, and benefits vary between individuals and teams, with SDD mainly used for new features and involving higher planning and review costs.
arXiv AI
Jun 8
Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review
arXiv:2605. 17548v2 Announce Type: replace-cross Abstract: Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual and cognitively demanding process.
arXiv AI
Oct 16
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Kolab is a new dataset comprising over 115 hours of screen recordings from 50 professionals across 40 occupations who collaborate with AI agents on realistic, long‑horizon tasks. The dataset includes participants’ judgments of collaboration quality and output, revealing varied coordination patterns and the need for agents to anticipate how experts will use or modify their contributions. Analysis shows current state‑of‑the‑art models overestimate collaboration success and struggle to differentiate high‑ from low‑quality interactions, indicating a gap in understanding long‑horizon human‑agent teamwork.
arXiv AI
5d ago
Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic Models
Co-Design Bench is a new, reference‑free benchmark comprising 273 multimodal coding tasks derived from 26,314 real user sessions. It features two tracks—Multimodal Editing and Repository Editing—that test agents on localized edits with optional image, audio, video, or document inputs and coordinated repository changes across three capability dimensions: Grounded Understanding, UI Transformation, and Functional Implementation. The benchmark uses a judge‑then‑refute protocol with hidden expert‑authored acceptance criteria, and the best of ten model endpoints achieves an average of 69.9% criterion completion but fully satisfies only 26.4% of tasks, especially struggling with repository edits involving multiple change types.
arXiv AI
Jun 30
DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows
arXiv:2606. 29727v1 Announce Type: new Abstract: Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents.