MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution

April 18, 2026 · Grace Period · + Add venue

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Authors Dao Sy Duy Minh, Tran Chi Nguyen, Trung Kiet Huynh, Pham Phu Hoa, Nguyen Lam Phu Quy, Vu Nguyen arXiv ID 2604.16941 Category cs.SE: Software Engineering Cross-listed cs.AI Citations 0
Abstract
We present MEMRES, an agentic system for Python dependency resolution that introduces a multi-level confidence cascade where the LLM serves as the last resort. Our system combines: (1) a Self-Evolving Memory that accumulates reusable resolution patterns via tips and shortcuts; (2) an Error Pattern Knowledge Base with 200+ curated import-to-package mappings; (3) a Semantic Import Analyzer; and (4) a Python 2 heuristic detector resolving the largest failure category. On HG2.9K using Gemma-2 9B (10 GB VRAM). MEMRES resolves 2503 of 2890 (86.6%, 10-run average) snippets, combining intra-session memory with our confidence cascade for the remainder. This already exceeds PLLM's 54.7% overall success rate by a wide margin.
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