arXiv AI
Jul 10
GitLake: Git-for-data for the agentic lakehouse
arXiv:2607. 08319v1 Announce Type: cross Abstract: We present GitLake, a Git-for-data design for an agent-first lakehouse.
Git4Data: Database-Native Version Control for AI Agents
Git4Data introduces a database-native version‑control layer that treats a database as a repository and each table as a versioned object, exposing Git‑style operations—snapshot/tag, branch, diff, and merge—through SQL extensions. Implemented in MatrixOne, it leverages immutable object storage and MVCC so that operation costs depend on the size of the change rather than the entire dataset. In agentic branching workloads, Git4Data outperforms DoltDB by up to an order of magnitude, demonstrating efficient versioning for AI agents.
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arXiv AI
Jun 2
"Skill issues'': data-centric optimization of lakehouse agents
arXiv:2606. 01185v1 Announce Type: new Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system.
arXiv AI
Jul 8
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
arXiv AI
Jun 11
Exploration Structure in LLM Agents for Multi-File Change Localization
arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.
arXiv AI
Jul 17
"Skill Issues'': Data-Centric Optimization of Lakehouse Agents
arXiv:2606. 01185v2 Announce Type: replace Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system.
arXiv AI
Aug 19
StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents
StagedWorkspace is a versioned workspace designed for knowledge‑work AI agents, ensuring that every parsed view, native file edit, and review diff is explicitly tied to a specific version of the workspace state. By binding parsed records and review diffs to content hashes of native files, the system improves performance on tasks such as OfficeQA and APEX‑Agents, achieving higher pass rates and rubric scores compared to single‑view approaches. The study demonstrates that providing dual parsed/native access and visible diffs enhances agent performance, highlighting workspace state as a key experimental variable for future benchmarks.
arXiv AI
Jul 9
Effective Strategies for Asynchronous Software Engineering Agents
arXiv:2603. 21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github.
arXiv AI
Jul 17
Why Git Is the Memory Solution for the Agentic Development Lifecycle
arXiv:2607. 14390v1 Announce Type: cross Abstract: Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session.
Hugging Face Trending Papers
Jun 10
Exploration Structure in LLM Agents for Multi-File Change Localization
Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step.
arXiv AI
3d ago
AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent
arXiv:2608.29204v1 Announce Type: cross Abstract: Generative AI-based software engineering agents are becoming routine contributors to real-world software projects. On GitHub, developers can assign t...
arXiv AI
Jun 29
LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
arXiv:2508. 12232v5 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challenging task.
arXiv AI
Jun 18
Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents
arXiv:2606. 19319v1 Announce Type: cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively discover, structure, and query enterprise data.