Ontology-Grounded Project Memory for Coding Agents
arXiv cs.AIen
arXiv cs.AI
AI Global WirearXiv:2608.13662v1 Announce Type: new Abstract: Coding agents have become the primary means of generating new code in many software projects, and the resulting velocity of changes makes keeping track of the reasons behind those changes challenging. This paper introduces MOOSEDev, a system designed to give coding agents structured, ontology-grounded project memory. The system captures architectural decisions, lessons, constraints, and rationales in a knowledge graph exposed to agents via a Model Context Protocol (MCP) interface. Records carry lifecycle status, provenance, and supersession links, queryable via MOOSE, a proprietary neurosymbolic engine that treats the symbolic layer as the prim
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
- Agenter
Related AI news
- Tekoälypomo antoi potkut ihmistyöntekijälle – 17 myöhästymisen jälkeenTivi · August 17, 2026
- Qwen 3.8 27B shows a 17GB open-weight general purpose model can have long context, effective tool calling, strong vision ability, and competent code generation (Simon Willison/Simon Willison's Weblog)Techmeme · August 17, 2026
- Modular Cognitive Architecture Emerges in Large Language ModelsarXiv cs.AI · August 17, 2026
- No Universal Signal Predicts Sample-Level LLM Regression under Version UpdatesarXiv cs.AI · August 17, 2026
- Cross-Disciplinary Taxonomy and Modeling of Misunderstanding Generation, Amplification, and Detection, from Pragmatics to AI AgentsarXiv cs.AI · August 17, 2026
- Reward Machines for Signal Temporal LogicarXiv cs.AI · August 17, 2026